Assessment and modeling of angular backscattering variation in ALOS ScanSAR images over tropical forest areas

Transcription

1 Assessment and modeling of angular backscattering variation in ALOS ScanSAR images over tropical forest areas Juan Pablo Ardila Lopez February, 2008

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3 Assessment and modeling of angular backscattering variation in ALOS ScanSAR images over tropical forest areas by Juan Pablo Ardila Lopez Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Geoinformatics. Thesis Assessment Board Chairman: Prof.Dr. A. Stein External examiner: Dr. Y. A. Hussin First supervisor: Dr. V.A. Tolpekin Second supervisor: Ms. Dr. Ir. W. Bijker INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

4 Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

5 Abstract The use of radar images provides the most effective and timely alternative to monitor forest changes and map land cover types in tropical forest areas. Radar images, captured in the microwave range of the spectrum are weather and sun illumination independent, two factors which usually inhibit the use of optical satellite data. Likewise, radar acquisition in the low frequencies can be effectively used to study the physical structure of the forest, which is highly relevant for forestry applications. Nevertheless, some radar issues should still be addressed for a successful implementation of radar in those areas. One of these fundamental issues is the backscattering variation in terms of the incident angle. Although this has been investigated before, it has been done to a great extent in simple land covers and in specific conditions. The incident angle variation is a rather complicated phenomenon when studied for tropical forests of complex physical structure and with seasonal floods. Besides, if these areas are investigated under large L band penetration microwaves it is even more difficult to assess. This research focuses on analyzing radar brightness dependency of one of the most promising SAR products for tropical monitoring systems: L band ScanSAR images. In particular, the radar brightness dependency on incident angle is studied under different viewing geometry observations. ScanSAR products, due to wide swath length extension, offer a high variation in range of observed incident angles. Three ScanSAR images of Colombian tropical forest with incident angle variations of 25 degrees are analyzed. The radar backscattering behaviour in terms of incident angle is studied with the support of spatial information such as digital elevation model, optical satellite data, ecosystems map and existing land cover classifications. The study of angular backscattering variation is investigated by image and for different land cover types. The presented results confirm an effect of incident angle in radar backscattering for the studied images. This effect depends on land cover type, moisture content and the physical structure of the reflecting targets. After incident angle effect is measured two approaches are followed for the consideration of the angular backscattering variation. The first one normalizes and corrects radiometrically the backscattering values by means of a cosine correction to the power index n. A second approach seeks to understand and explain backscattering variation by means of a semi empirical backscattering model. The model applied is an extended version of the water cloud model considering second order backscattering interactions. A successful implementation of the model explains fairly well the observed backscattering of flooded forest areas with R 2 larger than 0.78 while it does not provide good predictions for very dense forest class. Keywords: rain forest, incident angle, incidence angle, ALOS PalSAR, backscattering modelling, water cloud model, rain forest. i

6 Acknowledgements The research presented in this document was carried out at the International Institute for Geo- Information Science and Earth Observation (ITC) in the Netherlands, with the support of the Fellowship Programme of the Nuffic Organization. To these institutions and all their staff I am deeply indebted for making this possible. I also want to extend my gratitude to the SIMCI-UNODC Project in Colombia, The Humboldt Institute of Colombia, Alaska ALOS-ASF node, and M. Sousa Jr. from INPE- Brazil for all the information they kindly provided for this research and for their rapid support. I want to express my thanks to the assessment board committee of this thesis for taking the time to read this document and making sense of the ideas and statements proposed here. This research is in good part the result of the efforts and discussions with my two supervisors: Dr. Tolpekin and Dr. Bijker. I express my most sincere appreciation for all the time they dedicated to me during the last six months and for all their advice and orientation when I was doing research. Although some popular beliefs about the Europeans being cold they proved this wrong. I was always overwhelmed by their encouragement and the confidence they granted me. I also thank Dr. Yousif Hussin for his constant enthusiasm and motivation during the Advanced RS module and the first stages of this research. Special thanks to Marcela Quinones through the distance, for all her attention and consideration with me. For all her expert contributions in the radar field and for inspiring me with her love for our ever sacred tropical forests in Colombia and for her very good use of technology to assist me from Wageningen. I hope she recovers her health soon. I extend my most sincere thanks to Dr. Rodolfo Llinas, head of the SIMCI-UNODC project who trust me and encouraged me when I just started applying my knowledge at the beginning of my career. All that I learnt during my time at SIMCI has contributed enormously to this research. Likewise I have to thank all the SIMCI project staff, Orlando for being my second father, Sandra for her messages, Martha Paredes and JP Latorre for their advice, friendship and for encouraging me to do the MSc, Maria Ximena for making me feel home through the telephone, Oscar for his time and friendship, and Leonardo for all his deep thoughts. Silva Lauffer, thanks for being my family in Europe. Looking back in time I am equally grateful to all my colleagues and former colleagues at ITC. For letting me know a little bit about their lives and their countries as well. I met many valuable persons here in ITC that make me belief we are not that different at all. Many thanks to my dear friends: Juan Francisco, Sally, Jorge, Andrei, Du Ye, Arik, Punya, Shango, Chin and Quiuju. Mila, special thanks must go to you for your valuable help with the prepositions and the correct use of the verbs in this report. I use a good time of your PhD project indeed. Without Gonyito my MSc time would have not been so great. Thank you for letting me know you and being with me all this time. Roomy thank you for trying the bicycle and for Candy. I am astonished for your energy to come with me travelling around Europe. I lost the count of how many countries we visited the last year. Thanks also for letting me know how delicious can be the food in your country. Last but not least I want to thank my family. Mother, sorry for all this time you have been alone, and for always understanding and supporting my ideas. I ought you everything. To my grandparents, my sister and all my family go special thanks. Nubia and Arturo: thank you for always being there. ii

7 Table of contents 1. Introduction Motivation and problem statement Research identification Research objectives Research questions Intended innovation Scientific context Thesis structure Radar remote sensing and related work Incident angle Relief distortion Terrain reflectivity Radar backscatter dependency Wavelength and frequency Polarization Surface roughness Dielectric constant Incident Angle Speckle Multilooking Radar calibration The ALOS system PalSAR processing levels PalSAR calibration PalSAR antenna pattern Related work On incident angle dependency On correction methods On backscatter modelling Water cloud model Extended water cloud model Study area, materials and methods Study area iii

8 Land cover types Materials Radar images Satellite images Digital elevation model Ecosystem map Methods Data processing Radar data Satellite images Digital elevation model Ecosystem map Radar backscattering analysis and modelling Radar backscattering statistics Calculation of incident angle Antenna pattern effect Radar backscattering analysis at image level Radar backscattering analysis by reflecting targets Class 1 - Dense wet forest Class 2 - Dense flooded forest Class 3 - Tall and dense wet forest Class 4 - Short forest Class 5 Savannah Correction model for incident angle dependency Correction model Normalization class 2 (flooded forest) Normalization class 5 (Savannah) Backscattering modelling Initial parameter values for model fitting Backscattering modelling of dense wet forest (class 1) Backscattering modelling of flooded forest (class 2) Model assessment Discussion On backscattering analysis by reflecting targets On simplified correction model iv

9 6.3. On backscattering modelling On used materials On applied methods On applications of this study Conclusions and recomendations Conclusions Recommendations Appendix I. Acquisition modes of ALOS system Appendix II. Radar backscattering of a tree trunk modeled as a dihedral corner reflector Appendix III. Land cover photographs in study area Appendix IV. Distribution of land cover types in ALOS image Appendix V. Metadata of PalSAR images Appendix VI. Radar brightness expressions for ALOS PalSAR images Appendix VII. Rain statistics for image acquisition dates Appendix VIII. Observations of Leaf Area Index in tropical forest ecosystems References v

10 List of figures Figure 2-1 Radar pulse interactions with objects on the ground... 5 Figure 2-2 Radar observation geometry... 6 Figure 2-3 Range resolution and azimuth resolution... 7 Figure 2-4 Diagram of incident angle and local incident angle... 7 Figure 2-5 Increment of incident angle as function of radar range... 8 Figure 2-6 Relationship between incident, look and depression angle... 8 Figure 2-7 Schematic diagram of radar foreshortening, radar layover and radar shadow... 9 Figure 2-8 Brightness expressions for radar backscattering Figure 2-9 Roughness surface reflector Figure 2-10 Schematic radar backscattering variation with incident angle Figure 2-11 Radar backscattering variation in terms of surface roughness and incident angle Figure 2-12 Number of looks of a SAR system Figure 2-13 ScanSAR observation mode Figure 2-14 Uncalibrated and calibrated ScanSAR images in Amazon forest Figure 2-15 L band HH polarization backscattering statistics Figure 2-16 Modelled L-HH backscatter from the floodplain forest stand Figure 2-17 Flooded forest backscattering in L band Figure 3-1 Study area Figure 3-2 Distribution of considered land cover classes Figure 3-3 Layout of radar images investigated in the research Figure 3-4 Layout of the satellite images in the study area Figure 3-5 Digital elevation model for study area Figure 3-6 Ecosystem map for study area Figure 3-7 Workflow methodology applied in this research Figure 3-8 Methods implemented in research Figure 3-9 Segmentation output implemented in SegSAR Figure 4-1 Workflow for radar data processing Figure 4-2 Original image and terrain corrected image Figure 4-3 Georreferenced image and geocoded image Figure 4-4 Workflow for optical satellite image processing Figure 4-5 Workflow for DEM processing Figure 4-6 Workflow for processing of ecosystem map Figure 5-1 Forest window for PDF test Figure 5-2 Comparison of intensity theoretical distribution with observation for test region Figure 5-3 Comparison of amplitude theoretical distribution with observation for test region Figure 5-4 Schematic diagram of incident angle values for the three ALOS radar images Figure 5-5 Calculation of incident angle image for ALOS image Figure 5-6 Radiometric azimuth bins product of antenna pattern correction. Image Figure 5-7 Backscattering values along the range. Image 1. Image Figure 5-8 Land cover sampling strategy for analysis of radar backscattering Figure 5-9 Workflow sampling class Figure 5-10 Sampling areas class 1. Image 1. Image Figure 5-11 Sigma values Image 1 and 3 for class 1 along the range vi

11 Figure 5-12 Sigma values Image 2 for class 1 along the range Figure 5-13 Workflow sampling class Figure 5-15 Sigma values Image 1 and 3 for class 2 along the range Figure 5-14 Sampling areas class 1. Image 1. Image Figure 5-16 Sigma values Image 2 for class 2 along the range Figure 5-17 Workflow sampling class Figure 5-18 Sampling areas class 3. Left: Image 1.Right: Image Figure 5-19 Sigma values Image 1 and 3 for class 3 along the range Figure 5-20 Sigma values Image 2 class 3 along the range Figure 5-21 Workflow sampling class Figure 5-22 Sampling areas class 4. Left: Image 1. Right: Image Figure 5-23 Sigma values Image 1 and 3 for class 4 along the range Figure 5-24 Workflow sampling class Figure 5-25 Sampling areas class 5. Left: Image 2. Right: Image Figure 5-26 Sigma values Image 1 and 3 for class 5 along the range Figure 5-27 Sigma values Image 2 for class 5 along the range Figure 5-28 Sigma values corrected for class 1 image Figure 5-29 Sigma values corrected for class 1 image Figure 5-30 Sigma values corrected for class 5 image Figure 5-31 Sigma values corrected for class 5 image Figure 5-32 Sigma values corrected for class 5 image Figure 5-33 Backscattering modelling class 1. Dense wet forest. Individual contributions Figure 5-34 Backscattering modelling class 2. Flooded forest. Individual contributions Figure 5-35 Class 1 Backscattering observations Vs modelled backscattering. Image 1. Image Figure 5-36 Class 2 Backscattering observations Vs modelled backscattering. Image vii

12 List of tables Table 2-1 Standard bands for radar systems... 5 Table 2-2 Radar backscattering terms Table 2-3 Influencing parameters of radar backscattering Table 2-4 Orbital characteristics of PalSAR system Table 2-5 ScanSAR observation mode of PalSAR system (Data from ADEN users for ESA) Table 3-1 Radar image ID used in this document Table 3-2 Satellite images for the study area Table 4-1 Observed sigma values for different land cover types Table 5-1 Statistics for test region Table 5-2 ENL for ALOS images Table 5-3 Antenna pattern regions in radar images Table 5-4 Correlation coefficients class Table 5-5 Correlation coefficients class Table 5-6 Correlation coefficients class Table 5-7 Correlation coefficients class Table 5-8 Correlation coefficients class Table 5-9 Coefficient values for correction backscattering model class Table 5-10 Coefficient values for correction backscattering model class Table 5-11 Initial and range of variation parameters values for model fitting Table 5-12 Fitting value parameters class Table 5-13 Fitting value parameters class viii

13 1. Introduction 1.1. Motivation and problem statement. Radar remote sensing is one of the main tools used for natural resource mapping and monitoring. Its ability to produce images independently of sun illumination and weather conditions, makes it particularly suitable for monitoring tropical forest, where optical systems fail to provide timely and continuous information [1]. It has been shown that radar returns can be used to effectively identify forested areas and to create models of their structural composition. Many studies have indicated the correlation between radar returns and the different land cover types. Correlations were found between backscattering and forest structure, leading to increasing interest in the use of radar systems for estimating above ground biomass [2]. Scientists have found that the interaction between radar backscattering and vegetation depends on the characteristics of the radar systems as well as on the structure and geometry of the target. Determining characteristics of the radar systems are frequency, polarization and viewing geometry. A great number of studies on radar applications were carried out in the nineties, most indicating that the vegetation can be effectively studied by radar using longer wavelengths, where the return is less saturated by the amount of biomass [3]. L band sensors onboard of aerospace platforms with different polarizations seems to be the most promising solution for forest monitoring systems, which heavily rely on remote sensing (RS) as the main data source [4]. Taking this into consideration, new radar imaging systems were deployed in the last years by space agencies in Europe, Canada and Japan. In particular, the operation of the ALOS observation system offers for the first time the possibility of recording land cover information from the space in L band over a wide swath of 350 Km with 100 meters resolution. This information offers the possibility of early detection of changes in forested regions that would be further explored using higher resolution SAR products. It will be feasible to implement monitoring systems at a regional scale, if this information is successfully processed and interpreted. The Amazonian region of Colombia will be covered, for example, by 6 of those images acquired in a space of nearly ten days, according to actual operational schedule of the system. The backscattering properties of the L band in forested areas has been studied and to a great extent explained by several airborne missions and by the availability of spaceborne JERS products [5-7]. What is not yet fully understood, and therefore is one of the main aims of this study, are the implications that the incident angle may have on the results of forest monitoring systems that seek to identify changes in land cover types and forest physical parameters. For instance, with an antenna capturing images at 700 km above the earth over an area of 350 Km, the influence of the varying incident angle on an image backscattering can no longer be ignored. For the ScanSAR product the variation of incident angle is about 25 degrees along the range for a single image. The influence of 1

14 viewing observation is even more prominent if the variations between images captured with different viewing geometry are considered, such as the case of PalSAR data, a system with pointing capabilities. Ultimately the motivation should not be only to study the range radar dependency of the PalSAR ScanSAR products but also to develop models to correct for this geometry effect and models that can explain the radar backscattering in terms of the physical interactions between microwaves and targets. In a broader extent such study has relevance on one hand for the use of the ScanSAR PalSAR products and on the other hand for the potential use of spaceborne imagery that will be provided by new systems in the coming years. Furthermore, what is more important, it will stimulate the use of SAR products for the implementation of monitoring systems of our vital natural resources Research identification Research objectives The main objective of the proposed research is to study the influence of the incident angle on radar backscatter in ScanSAR PalSAR images and to develop a model for its correction in forested regions. The following sub objectives are defined to reach the main objective: Establish the influence of the variation in incident angle on radar backscattering in ScanSAR products. Model the variation of backscattering in terms of incident angle over individual ScanSAR images in different land cover types. Model the variation of backscattering in terms of incident angle over different ScanSAR images in different land cover types. Develop an effective method for the correction of the influence of the incident angle in ScanSAR images over forested areas Research questions This research addresses the following questions: What is the influence in relation to land cover types of the incident angle variation on the radar backscattering of an L band image with a wide swath? Can the effect of varying incident angle be corrected and removed from the image? If there is noticeable influence of incident angle, can radar backscattering be modelled in terms such as range distance, land cover structure, moisture content and/or biomass? Intended innovation ALOS satellite was launched in January 2005, after some months of calibration radar images were available for the research community and for commercial users [8]. The PalSAR sensor on board ALOS spaceborne system raises many expectations and opens the door to many applications. This is the first time spaceborne observation is possible in the L band over a great extent in one single image. Although JERS system provided L band images until 1998, they covered areas of 70 X 70 km in each scene. Radar attributes of the PalSAR system such as quad polarization, coarse and fine resolution 2

15 modes, and tilting antenna seems very promising for regional vegetation studies. This is one of the first published researches having the opportunity to use ALOS datasets. Few studies have concentrated on the variation effects of incident angle from space borne observations. Most of the research in this area has been done with scatterometer systems and airborne radars [9],[10] and in rather simple targets such as crops [11, 12] and soils [13]. Furthermore, almost all existing studies and applications which consider incident angle variation do so at the image level. That is, a simple and specific correction is applied to counteract for the angular effect in the whole image. In this research it is rather proposed that the angular variation should be considered for every specific land cover type, as structure and composition of the targets in a wide swath image is often complex and heterogeneous. This research implements a specific approach for the sampling of homogeneous backscattering areas. It involves image processing and spatial data modelling on available spatial information of the study area. Although the methods and techniques used during the sampling stage are not new to the geosciences community, they have received limited attention as field survey is often preferred for accuracy verification. Nevertheless, in areas such as the one considered here, with large extensions of dense forest and flooded ecosystems, it will be impractical to implement conventional field verification. Backscattering modelling is implemented here by use of a radiative transfer model. Although these types of models have evolved since the ninety seventies, the use of models has been hardly considered for the correction of factors such as the incident angle. This research seeks to implement a simple backscattering modelling suitable for forested areas which can estimate backscattering variations with incident angle. This is clearly innovative and if implemented successfully will provide a good tool to exploit the angular variation and understand the backscattering behaviour of individual scatterers. Finally, the study of the backscattering range dependency of L band SAR spaceborne images over large areas will facilitate the use of radar technology in regional monitoring systems. Furthermore if a model can be established, which can explain range backscattering dependency, and an algorithm or specific approach is found for the correction of the incident angle variation, the use of ScanSAR PalSAR products as input for remote sensing applications will be facilitated. The approach proposed in this research accounts not only for the consideration of the incident angle in one image but also for images captured from different viewing geometries within a period of a few days Scientific context The effect of backscattering variation in terms of incident angle is known as from the first experiments done with different targets and systems in the early 80 s [14, 15]. It was found that the backscattering is correlated with the geometry between the radar antenna and the target. For distributed targets the radar returns will be stronger in the close range and will decrease progressively as moving towards the far range. The variation is also dependent on the target roughness. In case of specular targets the mentioned decrease in backscattered energy will occur faster than in diffuse targets. In radar observations a target is considered smooth or rough by a criteria relating its size to the as wavelength used for observation [16]. Traditionally the consideration of incident angle effects has been emphasized when using airborne images. Due to the distortion of backscattering found in far ranges several authors have omitted those 3

16 areas of the images for practical applications. Some authors have also investigated the influence of incident angle in airborne images and few models have been proposed for its correction [17-19]. In spaceborne images many studies have neglected the effects of incident angle. This assumption can be mistaken, or due to the nature and application of the study, too small that the range dependency may be ignored. This is true for images captured over a limited extent, such as in observations over areas of 70 km distance in slant range where a variation is about 6 degrees. Some authors have measured the influence of incident angle in spaceborne applications [20] or used multiple incident angles as an advantage for land cover discrimination [21, 22], observing a target from different angles can provide clues about the physical state and conditions of vegetation. The use of radar images from different incident angles is similar to the use of satellite images in multiple spectral bands. More bands or more images provide more information and therefore lead to better discrimination in many cases. Radar images acquired by space borne sensors have been identified as very promising data sources for the implementation of monitoring systems [4, 23, 24]. Currently there is an active research on the use of SAR images for land mapping and forest structure models in tropical forest such as the Colombian Amazon[23]. The study by Quiñones ([25]) considers variations of incident angle for vegetated areas. The author suggests that for a successful implementation of a forest monitoring system using spaceborne images the influence of the incident angle on radar backscattering should be accounted for. Regarding the specific use of PalSAR data for forest monitoring due to the recent launch of the satellite no references to concluded studies can be found to date. Nevertheless it is known of the enthusiasm of the geospatial community for these particular data and some studies are emphasizing on forest mapping and tropical forest monitoring [26, 27]. Numerous on-line sources and proceeding of the Kyoto protocol conferences [28] suggest that as the ALOS system provide important information on land covers, studies which account for calibrations of the system and the impact of the incidence angle in those images should be encouraged Thesis structure This report is divided into seven main chapters. Chapter 1 is a general introduction to the research, and states the main objectives and proposed questions. Chapter 2 explores the radar concepts necessary to undertake the conducted research. Besides it provides the specifications of the ALOS system and a literature review section on related work and studies pertaining to this thesis. Chapter 3 presents the study area as well as the materials and methods involved during the execution of research. Chapter 4 deals with the data processing steps implemented for the preparedness and integration of the spatial data sets needed for the execution of experiments. In Chapter 5 the main results are provided, with a section on radar backscattering analysis by land cover type and two more on radar backscattering normalization and modelling. Chapter 6 presents a discussion of the main findings of this study, analyzing the results in the context of other studies as well as their implications. In the same way the implemented methods and used materials are reviewed. A final chapter is dedicated to the presentation of the main findings of this research and an assessment of the aimed objectives with some recommendations for further work. 4

17 2. Radar remote sensing and related work Remote sensing from the space has been used for more than half a century to map the earth surface and capture important spatial data that let scientist understand phenomena and discover patterns, trends and changes. Optical images recorded in the visible and infrared part of the electromagnetic spectrum have played a central role in the understanding of most of the changes, process and dynamics of the Earth. Although optical remote sensors provide vital data they face serious problems in many areas where atmospheric and weather conditions inhibit the acquisition of timely images. Radar remote sensing is part of the geo observation science that complements and supplies important data where optical images fail to work. Spaceborne radar remote sensing, an active form of observation, uses an emitted pulse of microwave energy by a radar antenna and records the portion of this energy reflected back to the sensor, which orbits the earth on board of a spacecraft vehicle. The energy detected by the antenna, measured as a voltage, is converted by means of electronic systems into a two dimensional digital image. This image, with the recorded returns and their intensities, provides valuable information that can be interpreted to identify and analyze the targets or elements reflecting the energy in the observed area. The portion of the energy reflected back to the antenna is known as backscattered energy. The radar RS process is illustrated in Figure 2-1. Radar antenna Backscattered energy Microwave pulse Targets Figure 2-1 Radar pulse interactions with objects on the ground In the radar remote sensing, active antennas emit energy in specific wavelength or frequency bands. The bands designation comes from the early use of radar in the military field which assigned them different letters. The following table presents the main bands used in radar remote sensing: Table 2-1 Standard bands for radar systems Band designation K X C L P Wavelength (cm) Frequency (MHz) 12,000-40,000 8,000-12,500 4,000-8,000 1,000-2,

18 Most of the current imaging radars are synthetic aperture radars (SAR), which by means of a moving antenna provide resolutions, that depending on the system, may vary from coarse to high (up to 1 meter for civil systems such as Terrasar X). In radar imaging the geometry of observation and antenna design determines the spatial resolution of the system. In this case, the concept of instantaneous field of view (IFOV) for optical sensors does not apply. For SAR systems a resolution along the track and a resolution across the track should be considered. The resolution along the track is known as azimuth resolution and the resolution across the track as range resolution. The basic geometric elements of a SAR system are shown in Figure 2-2. τ pulse duration nadir Azimuth beamwidth Figure 2-2 Radar observation geometry In a radar system (Figure 2-2) a platform carrying the antenna illuminates the earth with microwave energy. The direction of travel of the platform is known as azimuth direction and the direction perpendicular to the platform movement is the range direction [16]. Reflections from scatterers at a given azimuth and range distance are imaged in a two dimensional array that resembles the digital image. Another element of the radar acquisition is the close range and the far range. The portion of the image swath closest to the radar nadir platform is the close range and the portion farthest corresponds to the far range. The spatial resolution of a radar system is determined by its ability to differentiate between two different elements located near each other (Figure 2-3). The smaller the detected distance is, the higher the resolution of the radar system. Typically in a SAR system the range resolution is higher than the azimuth component. The range resolution of radar is directly related to the pulse length of the transmitted signal [14]. The shorter the pulse length is, the finer the range resolution. The pulse length for a radar system can vary between 8 and 210 meters. The main hurdle for a SAR system to increase its range resolution is that a shorter pulse length will be too weak to be recorded back at the antenna [16]. As will be indicated later, the energy reflected by the earth surface, or terrain radar backscatter, depends among other factors on the platform viewing geometry. In a side looking radar the viewing geometry is mainly determined by the angle at which an element is illuminated by the radar antenna and the local slope of the terrain being illuminated. Radar remote sensing literature refers (sometimes confusingly) to incident angle, local incident angle, look angle, and depression angle. 6

19 Vertical ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR 2.1. Incident angle Figure 2-3 Range resolution (left) and azimuth resolution (right) The incident angle (θ і ) is defined as the angle between the radar line of sight and the local vertical with respect to the geoid [16] as shown in Figure 2-4. The incident angle does not consider the local slope. When the terrain is sloping, the contribution of the terrain slope should be added. If this is considered the angle is known as the local incident angle [29]. The incident angle assumes rather a constant slope and it increases with range distance as shown in Figure 2-5. The variation of incident angle is directly related with flying height of the carrying platform. Thus a strong effect is found in airborne radar images where typical variations go from 10 to 70 and is less evident in spaceborne radar systems of moderate range such as the JERS images, where a typical range goes from 32 at near range to 38 at the far range. incident angle Local incident angle Figure 2-4 Diagram of incident angle and local incident angle (Adapted from [13]) The local incident angle accounts for the effect of topography variation. It is formed between the local surface normal and the radar line of sight of an illuminated point, as seen on Figure 2-4. As indicated later in this chapter, the main implication of the local topography in the imaged area is that it affects the radar returns and depending on the degree of variation it can cause strong anomalies in the backscattered signal. To account for the backscattering variation, a digital elevation model (DTM) of the area should be available and involved in the data processing step [30]. 7

20 Near range Far range ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Vector of radar wave front Incident angle Figure 2-5 Increment of incident angle as function of radar range (Modified from [31]) There are other angles considered in radar literature which can be observed in the geometry of the radar system (Figure 2-6). Those are the look angle, depression angle and the squint angle. The look or elevation angle Φ is the angle between the vertical of the antenna to the ground and the radar line of sight. The look angle increases from the near to the far range in a flat earth scenario and it is the complement of the depression angle. On the other hand, the depression angle (β) is defined as the angle between the horizontal line of the antenna and the radar line of sight. When a space imaging radar is considered, the property of complement between look angle and depression angle does not apply due to earth curvature [16]. Meanwhile, the squint angle is involved when the radar antenna is not oriented perpendicular to the flight direction of the platform but within a certain deviation from it. β Depression angle φ Look angle Incident angle θ Ground range Figure 2-6 Relationship between incident, look and depression angle (from [16]) 2.2. Relief distortion The treatment of radar geometry is rather different from that of optical remote sensors. While an aerial image presents a central projection, a radar image is captured in a range projection. In this mode the illuminated elements will be detected in the image as a function of their distance to the antenna, which is equivalent to the pulse travel time. The further the target is from the radar antenna, the longer it will take for it to be detected, and vice versa. Most of the concepts dealing with radar viewing geometry can be found in [29]. When dealing with terrain distortion in radar images three main aspects are considered: foreshortening, layover and shadows. These effects can be observed as a result of the terrain slope facing the radar. In foreshortening distortion (Figure 2-7) the incoming signal of the facing slope will be registered as a 8

21 compressed and shorter element than it actually is in ground projection. Layover is the result of a very steep slope facing the radar; the elements will be imaged upside down as the result of the peak of the mountain (b) being detected earlier than the base. The radar shadow is the strongest relief distortion effect for which no information is received from the back slope of the mountain. Figure 2-7 Schematic diagram of radar foreshortening, radar layover and radar shadow 2.3. Terrain reflectivity While the previous sections regarded radar as an imaging instrument, here it is considered from the measurement capacity that calibrated radars may produce. The proper understanding of the measurements of radar systems is the key for a physical interpretation of the scene. To describe the backscattering properties of the earth s surface measured by a radar system a definition of the relationship between the incoming energy from the radar and the backscattered energy from an illuminated target is usually described in terms of the radar equation [16]: 2-1 In the radar equation the backscatter energy received at the radar antenna is proportional to the transmitted power, the radar cross section over and illuminated area (A), the antenna gain (G) and the wavelength of the system (λ). Meanwhile the total energy registered at the antenna will decrease as a factor of the range distance (R) to the illuminated target. The radar cross section or backscattering coefficient, which is the intrinsic ability of an element to reflect radar signals in the direction of the radar receiver, is a magnitude of the radar equation that results relatively difficult to determine in practice. This difficulty arises from the image speckle or noise like effect of the radar images (see section 2.5). As mentioned in [32], the best way to estimate the radar cross section of an specific target is by averaging multiple L independent observations in intensity. In this way it is possible to obtain the maximum likelihood estimator of while reducing the variance of the measurement by a factor L to become. This is precisely the result of applying multi-looking processing to the radar images. Another method used to obtain estimations of radar cross section when multiple observations are not available [32], is to use adjacent pixels of assumed constant radar backscatter and averaging them in intensity as it is done in this research. With the development of radar systems different terrain reflectivity terms for distributed targets have been formulated. Firstly there is the intrinsic reflectivity of a material (σ ) which can be expressed as the reflectivity per unit surface area of a material, under stipulated conditions. The intrinsic mean 9

22 reflectivity can be considered as the theoretical predicted backscattering a target will exhibit when illuminated by a radar signal [16]. In the radar processing field, a radar reflectivity estimate is usually measured, as it is the closest expression of the true reflectivity that can be observed from radar scenes. The reflectivity estimate can be obtained from radar brightness only if there is a calibration of the radar system and if radiometric corrections are applied to account for terrain slope [16]. Reflectivity estimates are expressed in terms of gamma (γ) and/or sigma (σ) and they differ basically in the way they are normalized. As all these terms expressing radar response get easily confused, the following table tries to explain in a compact and clear way the difference and origins of the just mentioned expressions. Table 2-2 Radar backscattering terms Radar brightness Estimated from calibrated and not calibrated radars. Most of the SAR products come in this format Power or Intensity (I) 2-2 Amplitude (A) 2-3 Log(amplitude) (Log(A)) 2-4 Estimated from calibrated radar systems Reflectivity estimate Sigma (σ) 2-5 Gamma (γ) 2-6 In summary, the strength of the signal returns measured by radar systems can be expressed in intensity or amplitude, being the intensity the square root of the intensity values. Sigma and gamma are trigonometric transformations of radar brightness in logarithmic scale and are favoured for reporting in geo science applications. The term radar backscattering or backscatter is more general and does not restrain to specific measurement units. β γ σ Figure 2-8 Brightness expressions for radar backscattering (Modified from [30]) Figure 2-8 illustrates the three components related to radar backscattering. The most common backscattering term for the geoscientists is sigma (σ) which measures the mean reflectivity of a patch of distributed scatterers per unit area of a horizontal surface, that is it is measure along the ground 10

23 range [14]. The reflectivity of distributed scatterers per unit area of incident wave front is known as gamma (γ) and it has the advantage of maintaining relatively constant reflectivity over a wide range of incident angles for rough surfaces [14]. Strictly speaking, in order to calculate gamma and sigma values an accurate model of the incident angle and information of the slope is needed. As information on local slope is hardly available for many areas, radar distributors release image in the brightness (β ) expression, where the backscatter is in slant range domain and a simple normalization by the cosine of the incident angle is applied [33] Radar backscatter dependency Variations in tone over the image are due to changes in backscattering return from the targets being illuminated by the radar antenna. A stronger backscatter produces a brighter pixel on the image while a weak reflection produces darker tones. Specific radar system parameters that determine the registered power return are given by the illuminating sensor and the properties of the illuminated target. The parameters influencing radar backscattering are shown in the table below [34] : Table 2-3 Influencing parameters on radar backscattering Radar properties Wavelength Polarization Incident angle Squint angle Resolution Surface properties Roughness Slope Complex dielectric constant Inhomogeneity (vertical and horizontal) Wavelength and frequency The microwave portion of the electromagnetic spectrum is divided into different wavelengths as shown in Table 2-1. While it remains true that radar observations are almost independent of atmospheric conditions, the radar return can be affected by strong storms or very adverse climatic conditions. This is particularly true for radar systems working on the shortest wavelengths (K and X bands) in the presence of heavy rain particles or accumulation of large scale droplets [35]. The wavelength of a radar system has a strong influence on the backscattered energy recorded at the receiver. The longer wavelengths have a higher power of penetration as elements which are small compared to the length of the wave will appear transparent to the incoming radiation. Shorter wavelengths, on the other hand, will image those small objects as a result of a limited penetration. Thus different wavelength systems can be used for different aims. It has been shown for example how C band is very suitable for crop monitoring, when the differences in phenological states between adjacent parcels can be directly observed [36, 37]. In the same way, long wavelengths are sometimes preferred for forest observation since major structuring elements can be detected while ignoring small leaves and branches. This is one of the established properties for radar systems: the longer the wavelength the larger the penetration. The rule of thumb for radar penetration states that the penetration is equal to the system wavelength [16]. According to this, for the L band ALOS system penetrations of the order of 30 cm can be expected. Although, dielectric properties of the surface should also be considered since higher soil moisture content will reduce the signal penetration. 11

24 For radar studies on forest, observations in the lower frequencies are commonly preferred. L and P band have been consistently identified as important frequencies to study forest ecosystems and to estimate physical structure parameters from the radar signal. The use of L band SAR images for forest mapping is considered as important part of a tropical monitoring system in [25]. L band HH polarization has been also identified as suitable for observation of forest flooding conditions [38, 39]. Besides, L and P bands have been consistently identified for several authors as the best frequencies for extraction of physical structural parameters of forest areas such as biomass and leaf area index [39-41] Polarization The microwave energy transmitted and received by the radar can be horizontally or vertically polarized. In this way the electromagnetic field can be restricted to a vibration in the vertical or horizontal plane. A radar system can thus work in four different polarizations modes. A radar system can be either HH, VV, VH or HV polarized. The first letter corresponds to the transmitted energy and the second to the received one. The way a target behaves under different polarization settings is studied in radar polarimetry [16]. Although the ALOS system captures information in all the polarizations (quad polarization), the ScanSAR products are only registered in HH polarization. In this respect, studies have shown that HH polarisation is more sensitive to flooding conditions. Pope [42] used SIR-C images of different rainy seasons to detect variations in moisture content finding C and LHH band suitable for detection of significant changes through the seasons. In [43] the full polarimetric C and L bands of the SIR-C system were used for wetland mapping and monitoring finding a higher performance of HH polarisation over VV polarisation for wetland mapping while the L band was found to distinguish better flooded from non flooded forest. Quiñones based on [44] and other studies report that L band with like polarisations (HH) arises problems to detect recently cut areas and states that adding the HV polarisation would solve this problem to a large extent Surface roughness The radar illuminated terrain can be regarded in the macro or micro composition. When considered in the micro scale the size of the soil particles relative to the wavelength determine whether the target will appear rough or smooth in the radar image. Given a particular wavelength and a flat area, a rough terrain will appear brighter on the image, while a smooth target will have darker values or low backscattering levels. In radar remote sensing a surface is rough if the constitutive particles are larger than the incoming wave length. The surface will be smooth for the radar if the constitutive particles are equal or smaller than the wave length [16]. The effect of surface roughness on the radar images is due to specular and diffuse reflectors. A diffuse reflector, associated with a rough target will have higher probability of reflecting the incoming energy in the direction of the radar antenna. Meanwhile, a specular reflector when not viewed from a closenadir position will reflect the energy away from the radar antenna as seen in Figure 2-9. However, specular targets can produce bright effects on the image if a double bounce reflection is registered due to corner reflectors positioned perpendicular to the radar beam. This effect can be clearly seen in urban areas or row patterns crops when they are oriented in the azimuth dimension. 12

25 Incident wave reflected wave diffuse reflector specular reflector corner reflector Figure 2-9 Roughness surface reflector Rayleigh proposed a criterion for surface roughness which depends on the wavelength, the incident angle and the surface irregularities. According to this, a surface will appear smooth if the incoming wavelength and the incident angle are related by the following equation: 2-7 A simpler rule of thumb uses the ratio λ/10 to categorize a surface as either rough or smooth. Therefore for the ALOS system products used in this research, which works with a wavelength of 23 cm, surface roughness of less than 2.3 cm will appear as smooth targets. If equation 2-3 is used, a surface in the centre of the image, where the registered incident angle is 34 will appear smooth if its surface roughness is less than 3.5 cm. In case of varying terrain slope the local incident angle should be used in equation Dielectric constant The electric characteristics of the imaged surface present another element that determines the intensity of the returned radar signal. The dielectric constant is an indicator of the intrinsic ability of a material to store charge and transmit energy [45]. Dry materials (dielectric constant <80) will have low dielectric constants and will appear darker in the radar scene, while a wet surface due to the presence of water, with a high dielectric constant ( 80), will appear as brighter areas on the image for a given reflecting surface [35]. Consequently, if the roughness of a surface remains unchanged, its radar backscattering increases with increasing moisture content [15]. The moisture content will also influence radar penetration. Increasing the amount of moisture in the terrain surfaces reduces the penetration of the radar signal beneath the surface while increases the reflection [16]. The ability of the radar systems to react to different levels of moisture content has been actively used for mapping soil and vegetation moisture content for research and practical applications [1, 13, 46]. An interesting study, is the backscatter simulation done by Wang et al [47] to detect flooding in Amazonian floodplain using C, L and P bands; this study is referenced in section Incident Angle For radar observation, there is a strong backscattering dependency on viewing observation. The incident angle of a radar system at a specific point along the range is a parameter affecting the 13

26 backscattering behaviour. This variation should be considered and modelled for each system configuration and for different sensor viewing geometry. Nevertheless this consideration is often difficult to address due to the factors defining the radar backscattering angular dependency. Determining factors are surface roughness, moisture content and inhomogeneity. For surface scattering, there is a strong reflection at small incident angles, being maximum at the nadir, and decreasing towards the far range [16]. The curves shown in Figure 2-10, modified from [15], presents the range dependency of a random target under different polarizations. They include a quasispecular region near nadir position, a constant and gradual decrease in the plateau region, and a marked drop at larger angles. quasi specular region plateau region shadow region Vertical polarization Horizontal polarization Cross polarization Figure 2-10 Schematic radar backscattering variation with incident angle (from [15]) The radar backscatter variation with incident angle depends also on the surface roughness of the observed target[15]. The curves of Figure 2-11(modified from [48]) show that rougher materials are less dependent and less affected by observation in different incident angles. Meanwhile, a smooth material, due to its specular characteristic, presents a strong backscattering decrease for small increments of incident angle. In that case, as predicted by the Snell reflection law, the energy will be reflected away from the radar antenna. Likewise the backscattering variation of a surface for a given incidence angle is related to its dielectric properties [15]. As shown before, radar backscatter increases with increasing moisture content. A given surface with unchanging roughness will present larger backscattering variations along the range if it is dry. The radar will detect strong returns in the close range and very week in the far for this target. On the contrary, a wet surface with unchanging roughness presents more constant backscattering values along the range as its decrease from close to far range [15]. 14

27 sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR 0 smooth moderately rough -10 rough Incident angle Figure 2-11 Schematic radar backscattering variation in terms of surface roughness and incident angle 2.5. Speckle Radar images are subject to the noise-like effect of speckle. Speckle can be seen in the image as a salt and pepper texture effect that reduces its interpretability. The reason why radar images are affected by speckle can be explained from a physical point of view by the interaction between the microwave energy and the earth s scattering properties. As explained by [32], for a distributed target each resolution cell contain a given number of discrete scatterers. The power registered at the antenna is the result of the coherent wave contribution of all the individual scatterers under the resolution cell. Each scatterer contributes with a change in phase and amplitude, having then the total energy recorded as the summation of the contributions of individual scatterers. The backscattered energy of each individual scatterer can in fact be in phase with that reflected from other elements. If the waves meet at the same point (same phase), this will produce a physical constructive interference that will enhance the signal. If on the contrary, the waves are recorded at the antenna in different phase, a destructive interference will be registered and the wave amplitude will decrease. In radar scenes the noise-like effect of speckle is then the manifestations of those different scatterers located in the resolution cell. For instance, in a forest region, the structuring elements of the trees such as branches, trunks and leaves will be at different range distances and orientations from the radar antenna, producing a strong variation of the total amplitude recorded for different resolution cells [32]. Adjacent pixels of uniform trees may have different backscatter energy in the radar scene. In fact the noise like speckle is a clear phenomenon over forested areas in radar scenes. As it can be seen from the explanation of radar speckle, there is a physical phenomena that explains the noisy appearance of the image. Speckle can be seen as a source of information or as an unwanted effect that prevents from the interpretation of the image. In the radar processing field speckle is often modelled as a multiplicative random noise process which is statistically independent of the scene, although this relatively simple representation is not always appropriate [16]. In this research speckle is treated as a multiplicative effect in the original image which is converted into additive noise after logarithm transformation of the backscattering pixels. This noise is correlated and follows a Rayleigh amplitude PDF. 15

28 Multilooking The effect of speckle in the image can be reduced by averaging a number of independent samples for each resolution cell. In multi-looking processing, independent observations of the radar antenna are combined to estimate the total backscattered energy from a resolution cell. To capture independent observations of the scene, coherent radar backscattering is recorded from slightly different angles for the same point and later multi-looking processing takes place in laboratory. Given a higher number of observations the result is usually a more smooth and easy to interpret image [16]. The reduction in speckle by means of multi-looking processing is done at the expense of spatial resolution. Since SAR systems usually have a larger resolution in azimuth direction the averaging is done in that component. For instance a 4 meters resolution system will degrade the final image to 16 meters when using 8 independent looks. One look image will be the equivalent of a non processed multi-looking observation. This example is shown in Figure Figure 2-12 Number of looks of a SAR system (modified from [14]) Radar calibration In order to relate radar information with physical characteristics of the imaged Earth, radar calibration is needed. In the early years of radar remote sensing, calibration of SAR image data did not have priority. However with the deployment of new radar sensors in the 1990 s and 2000 s it has been intensively sought. According to [49], we have reached a phase in which scientists want to compare data from different sensors, derive geophysical parameters from backscatter measurements using models, carry out multitemporal studies over large areas, and build up a databases of backscatter measurements for different types of terrain/incident angles. This can only be done by using calibrated SAR data products. The calibration of radar remote sensors has been undertaken in particular by the Committee on Earth Observation Satellites (CEOS) group, in an ongoing trans-organizational joint effort to support calibration and standardization of the existing radar remote sensors The ALOS system The Land Observation Satellite (ALOS) launched in January 24, 2006 by the Japanese space agency JAXA has three remote sensors. One of them is the active L band synthetic aperture radar (PalSAR), 16

29 designed for the acquisition of data beneficial to resource exploration, environmental protection and analysis as stated by the PalSAR project [50]. Furthermore the PalSAR system has been promoted since the design stage as a monitoring tool focused on land cover and natural resource analysis. JAXA has put a lot of effort to design a system which can be used for natural resources global observation and as a tool for monitoring systems such as those needed by the Kyoto protocol. As the PalSAR system operates in the microwaves region, making it independent of sun illumination and weather conditions, it captures images in descending and ascending mode day and night. The defining orbital characteristics of the ALOS system relevant for earth observation are presented in Table 2-4. Table 2-4 Orbital characteristics of PalSAR system Orbit Local sun time Altitude Orbit inclination Period Recurrent cycle Sun synchronous sub recurrent 10:30 am ±15 min km on the equator degrees 98.7 min 46 days The PalSAR system operates in the L band (23 cm 1.3 GHz) with pointing capabilities which allow the acquisition of different products by changing the geometry and antenna configuration. As the system is designed for earth observation and environmental monitoring full polarization is provided for first time in spaceborne L band radar. The availability of four polarization modes represents a valuable asset for the scientific community. Likewise the spatial resolution which can vary between 10 and 100 meters allows earth observation at different levels of detail. The resolution and polarization settings change depending on the observation mode. Appendix I presents the characteristics of the ALOS products while Table 2-5 focus in the ScanSAR observation mode, as it is of interest for this research. Table 2-5 ScanSAR observation mode of PalSAR system (Data from ADEN users for ESA) Chirp bandwidth (MHz) 14 Bit quantization (bits) 5 Polarisation HH Data rate (Mbps) 120 Off-nadir angle (deg) Range resolution (m) Incident angle (deg) Azimuth resolution (m) 100 Swath width (Km) Orbit acquisition Asc/Desc The ScanSAR mode image the earth along a very wide swath of 350 Km at 100 meters resolution after processing. In this acquisition mode the radar antenna sweeps the observed area in 3 to 5 scans and composes the image from the union of the swept areas. Figure 2-13 is a diagram of the ALOS system and its ScanSAR acquisition mode. 17

30 nadir track 350 km Off nadir= PalSAR processing levels Figure 2-13 ScanSAR observation mode Although three regional distributors of PalSAR data exist, there are three processing levels which are standard and are commonly used for image distribution [51]. Level 1.0. This level corresponds to raw unprocessed detected data. This product does not involve any radiometric corrections. It is ready to be processed into complex format for which a SAR processor is required on the user or distributor side. It has real and complex component and the phase information is preserved. The data type is 8 bits (I) and 8 bits (Q). Level 1.1. This is the single look complex product. The image has been focused by means of a SAR processor and it is in slant rage geometry preserving the phase information. The data type is 32 bits. Level 1.5. This level is the highest processing level provided. The image has been compressed in azimuth and range and has been subject to multi-look processing. The image geometry is ground range and can be georreferenced or geocoded based on the ellipsoid GRS80 as reference surface. The image is not corrected by relief distortions. The data type is 16 bit unsigned integer PalSAR calibration The first months of operation of ALOS PalSAR system were invested on the calibration and validation phase of the radar antenna. This phase, lasting from May 16, 2003 till November 23, 2006, comprised the study of the antenna response using corner reflectors and observations of surface targets. For the calibration and evaluation of PalSAR data some areas of the Amazon were effectively used as a reference [52]. ALOS calibration has been intensely discussed under the CEOS group, which seeks to formulate standards and recommendations for SAR observation. It is aimed to achieve radiometric errors lower than 1.0 db. Information on radiometric and geometric accuracy for PalSAR products level 1.1 and 1.5 can be found in [53] PalSAR antenna pattern In the field or radar hardware theory and design an important topic is that of the radar antenna. Parameters such as antenna gain, antenna beam width and intensity of side lobes determine the intensity of the recorded energy at the radar antenna. In a SAR system, the antenna is highly directional, meaning that more energy is emitted in certain directions than in others [32]. This will 18

31 Gamma (db) Antenna gain (db) ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR determine for example the antenna gain, a ratio which determines the electrical losses of the antenna along the range directions [54]. An anisotropic radiator, e.g. PalSAR antenna, has an energy field that represents an antenna radiation pattern that is different for observations across the azimuth directions. The hardware dependent phenomena of the antenna pattern should be corrected from SAR images to the end of normalizing single observations along the range and facilitating the quantitative analysis. The antenna pattern for ALOS data products was measured during the initial calibration phase. For the antenna pattern estimation several observations were done in the Amazon forest using corner reflectors as reported in [52]. The Amazon forest has been identified as a uniform and suitable reference target for relative azimuth and range antenna pattern determinations. This is true although variations in order of few decibels in backscattering are found due to seasonal changes. The study on antenna pattern results in the identification of 34.3 and 21.5 off nadir angles as the more convenient orientations of the radar antenna for PalSAR observations. The generation of ScanSAR products from the PalSAR systems is a rather complex and more sophisticated process than that used for generation of polarimetric and fine beam modes. The acquisition of a single ScanSAR image in 5 scans was introduced in section 2.6. The existence of five independent scans in one SAR image results in the application of 5 independent antenna gains revealing patterns that should be corrected. As there is no perfect algorithm for the correction of antenna patterns, it is a rather difficult task to correct for this effect in ScanSAR images. As it was reported by [32], failure to provide sufficient information to allow the correction of antenna effects has been a repeated cause of problems in extracting meaningful information from SAR data. The figure below shows the effect of antenna gain along the range in ScanSAR evaluation image in the Amazon forest, as published in [52]. As observed from the image, five patterns are registered along the range. The uncorrected image presents backscattering variations of the order of 10 db along the range. After antenna pattern correction is implemented, the signal values are more stable, although not completely flat. Uncalibrated Antenna Pattern -6-8 Calibrated Off nadir angle Uncalibrated Calibrated Off nadir angle Off nadir angle Figure 2-14 Left: uncalibrated and calibrated ScanSAR images in Amazon forest. Right: antenna pattern diagrams for ALOS observations and ground measurements on the Amazon forest (Adopted from [52]). 19

32 backscattering coefficient (db) ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR 2.7. Related work This section is a review of previous studies and findings related to the topic of this thesis. Two components have been investigated. The first one deals with surface backscattering dependency of the incident angle and viewing geometry. The second part treats existing methods or solutions that have been proposed addressing the incident angle effect on radar images On incident angle dependency The dependency of radar backscattering on viewing geometry was first studied by Ulaby in the 70 s [12, 55-58]. He reported on the behaviour of different targets under moisture, roughness and incident angles variations by means of scatterometers. In those first studies the angular dependency was shown to be related to moisture content and roughness of the specific target being observed. Drops of radar backscattering along the range were observed for different experiments and land covers. Ulaby conducted research mainly on backscattering of low vegetation targets simple in physical structure and different crop plantations [12, 58-60]. In an effort to make available comprehensive statistics for terrain backscattering, Ulaby [15] published an extensive research work with probability density function of the radar backscattering coefficients extracted from a large number of previous studies and data sources. These probabilities were reported by target category in terms of the incident angle, polarization and frequency. The vegetation category is divided into trees, shrubs, grasses and wetlands. The trend of Ulaby observations registered for vegetation categories are shown in the curves of Figure Trees Shrubs Incident Trees Grass angle (θ) Short vegetation Shrubs Grass Short vegetatin Poly. (Trees) Poly. (Grass) Poly. (Shrubs) Poly. (Short vegetatin ) Figure 2-15 L band HH polarization backscattering statistics In the study by Wang et al [47], on the other hand, a backscattering model was used to predict radar return from flooded and non-flooded forest in the Negro River, Brazilian Amazon. The scattering model was used for C, L and P bands in HH and VV polarizations predictions under different incident angle (20-60 ). In Figure 2-16 the results found for L-HH polarization (same as ALOS-PalSAR configuration) are reported. For the flooded forest stand, canopy volume scattering and double bounce trunk-ground interaction contribute roughly equally to total L-HH backscatter for θ<30. When θ is larger than 30 the trunk ground interaction decreases, and the canopy volume scattering accounts for 20

33 L-HH backscatter (db) L-HH backscatter (db) Ratio of L-HH backscatter (db) ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR almost the total backscattering. There is some scattering from the underlying surface although its contribution to the total is small. For a non flooded scenario, reflection from the ground surface decreases as well as the scattering from the trunk-ground interactions. Canopy volume scattering dominates the total L-HH backscattering. Comparing the two scenarios, the flooded forest area will appear brighter and with higher backscattering moreover at small incident angles while reducing this effect to zero db in the far range at 60, where the flooded and non flooded forest present similar radar returns flooded Angle of incidence θ(deg) non flooded Angle of incidence θ(deg) flooded/nonflooded Angle of incidence θ(deg) t= total backscatter; c=canopy volume scatter; d=trunk-ground interaction; m=canopy-ground interaction; s=surface backscatter Figure 2-16 Modelled L-HH backscatter from the floodplain forest stand (from [47]) On another study, using a series of Radarsat and JERS images, vegetated areas of the Amazon floodplain were analysed [47], paying particular attention to the water level on the ground. For five Radarsat images and 4 JERS images not concluding variation was found in backscattering of upland forest along the range. The study reports on L band wave deep penetration into the tree canopy and interaction with the trunk and the water underneath when forest was observed by 35 JERS images. This double bounce effect was demonstrated by the sigma values in different seasons. The volume scattering and the double bounce effect in flooded areas, as shown schematically in Figure 2-17, have been consistently demonstrated by radar studies on vegetation. Volume scattering L band penetration flooded forest reflection canopy-soil reflection soil interaction Specular scattering flooded forest Figure 2-17 Flooded forest backscattering in L band The effect on backscattering presented in Figure 2-17 is a signal enhancement which occurs primarily in flooded terrain under an open canopy [2]. This phenomenon especially occurs at long wavelength observation systems. According to [16] the L band signal penetrates the canopy, and reflects off the smooth water surface and the vertical trunks of the trees, returning a stronger backscattered signal to the radar antenna. Nevertheless, if the flooded vegetation is low such as grasslands or small shrubs, the signal in the water will bounce away from the radar antenna as these elements will be transparent for the L band signal. 21

34 An study of the University of California [39] on double bounce backscattering effect of forest, considers the interactions canopy to ground in a radar backscattering model for band L under different incident angles. The authors included a term on canopy to ground forward scattering from the experimental evidence pointing to the importance of surface reflection in canopy scattering. The consideration of double bounce effect in the model, effectively explains the observed backscattered values in L band SAR images. Surface response under different radar illumination conditions has been used as a radar characteristic for target discrimination. In an study on spaceborne SAR data[21], RadarSat C band images with different incident angles are used for agricultural application in India. Using four different illumination beams correlation analysis is done on crop parameters, finding sensitivity in the backscattered power for rice crops in wetland areas. In another study [61], nine ERS images with different incident angles are likewise used for crop discrimination finding strong impacts of incident angle on backscattering for the studied images. In an additional study [22], ENVISAT-ASAR C band images taken with different incident angles are used for soil moisture mapping. Apart from the relationship between soils moisture content and radar backscattering it is interesting to mention the normalisation technique applied to compensate for different illumination conditions. Starting from simulations for different surface roughness and moisture content at a given incident angle, the images are corrected or normalised using one of them as the master or reference image. After a library of simulations is available, models for correction of incident angle with different surface roughness and moisture content are generated. A similar study on the use of multiple incident angle images for target discrimination is found in [62], where six Radarsat images with different incident angle are used for land cover mapping. Studies of the incident angle in forested areas can be found in Rauste work [63]. He investigated on the combined effect of variation of incident angle and topography in SeaSat images and its effects on land cover classification. From measurements in different land cover types the effect of local incident angle accounts for 65% of the radar backscattering variation. Likewise differences in slope were found to be significant for land cover discrimination On correction methods The dependence of radar backscattering on incident angle in most cases is an obstacle for the correct interpretation and analysis of the land covers types of the image. However, as it was shown in the previous section, some studies use radar backscattering under different illumination conditions to better identify and discriminate the observed targets. On the other hand, for many applications the interest is on relating the backscattering values to the characteristics of the targets themselves and not to the observation geometry. In consequence, the effect of incident angle on radar images, as well as other system effects such as antenna pattern should be modelled and corrected on the digital values of the SAR image. To counteract for variations of the incident angle effect researches have used different solutions varying in complexity and processing demands. Moreover, the effect of varying incident angle has been considered mainly using airborne data and a very small amount of studies have measured this effect on spaceborne images, where it is usually ignored given the short span of incident angle degrees. Proposed methods for incident angle correction can be divided into three groups as they vary in their degree of complexity. 22

35 The most simplistic approach on incident angle consideration is to disregard parts of the images where the incident angle is too large and its effect is strong, this is to say, in the far range. The sub utilization of far range extents of SAR images can be found in studies such as [64-66], where part of the images has been disregarded due to a large effect of the incident angle in the farther ranges. This approach has been taken mainly on airborne image applications. A method based on radar statistics along the range can be applied to normalize for incident angle dependence. In this empirical approach after the relationship between radar backscatter and incident angle is found for a specific image, a linear or second order regression is applied to normalize the observed backscattered values in the range dimension and the incident angle extent. This approach can be applied to whole area of the image or can be applied to particular land cover types present in the image by means of layer masks. The disadvantage of this correction method is its dependency on particular radar datasets and the impossibility to replicate an identical model in other images. An effort to radiometrically correct the influence of the incident angle in airborne radar images can be found in [10, 17]. The proposed method uses a land cover classification of the study area and measures the similarity of land cover along lines of constant incident angle, so to say azimuth lines. Considering seven thematic classes, binary masks are retrieved for each land cover class and a correction is applied to each one of them considering the frequency distribution of digital values for each azimuth line. The author states as the main advantage of application that it requires little field knowledge and corrects the effects on most land cover types under the assumption that the target is to a large extent homogeneous and presents reduced variability from one azimuth line to another. Another application with a statistical approach can be found in [67], for the generation of mosaics for the Global Rain Forest Mapping project. In this work normalization was done using the measured backscatter on overlapping areas between adjacent JERS images. The corrected backscattering values are calculated by least square estimation. A second approach to backscattering correction is to develop a simple physical model that explains the specific angular dependence in terms of the incident angle (θi) as independent variable. The model might regard the wavelength and the surface geometry. Ulaby [14], refers to three models which might be applied and relate the radar backscattering as a product of a cosine to the n power law. The power is determined by the target being observed. The three proposed models are: Model from equation 2-8 corresponds to an unrealistic scenario where the scatterers are independent and separated, thus the incoming energy is totally reradiated. Model from equation 2-9 corresponds to densely group scatterers where a correction is applied for the illuminated area. This correction is usually applied in the pre-processing step of SAR images, and corresponds to a gamma brightness term in radar analysis. The model from equation 2-10 is based on the Lambert s law for optics. The radiation pattern for a lambertian surface depends on the cosine term of the incident energy angle and as this variation is also dependent on the observed area, the model follows a cosine square law

36 The physical models aforementioned for radar backscattering have been identified to correspond to rough surfaces such as vegetation and thus have been used for backscattering corrections. Applications of this type can be found in [19], where a lambertian surface model is applied to counteract for angular backscattering dependence in C-SAR images On backscatter modelling A third and more sophisticated approach for correction of incident angle dependence on radar returns is to develop a model for radar backscattering taking into consideration the physical structure of the reflecting targets. The outputs of these models, usually developed to quantify biomass levels on radar images, can be alternative applied to perform simulations under varying incident angle conditions and to correct radar observations. The developing of backscattering models has been a relevant challenge for forest ecosystems since they represent a volume scattering where penetration is different depending on observing wavelength. Most of the proposed models consider the forest as a continuous or semi discontinuous canopy layer, a group of trunk reflectors modelled as cylinders of different thickness, and an underlying surface with different roughness and dielectric constants. Among some of the existing models for volume scattering are the water cloud model [68]; the MIMICS model of the University of Michigan [69] with several modifications and extensions through the time [70]; the Santa Barbara model proposed by Richards et al [71], which considers the case of corner reflectors by trunks in L band forest observations [72]; and the UTARTCAN model, which has been used and validated on tropical forests in Colombia[40] Water cloud model As this research considers the water cloud model for radar backscattering modelling (chapter 3) the principles of this model will be explained here. The water cloud is a semi empirical model first introduce in radar analysis by Attema and Ulaby in 1978 [68], where the authors proposed a model to explain the interaction between microwave energy and a layer of soil covered by vegetation. The model simplified the description of the vegetation presenting it as homogeneous dielectric slab with dielectric constant calculated on a basis of a mixing formula of air and vegetation. The assumptions of the water cloud model are [73]: o o o o Volume scattering is the predominant method of scattering. The dielectric cloud is comprised of identical water particles, uniformly distributed in the medium. Only single scattering is accounted for. The most important variables are the depth and density of the cloud, both of which are a function of water content. The proposed model addresses the backscattering of soil covered by a layer of vegetation. The model is based on radiative transfer interactions of energy as the microwave passes through a continuous medium of vegetation. For the formulation of the model two concepts of energy radiation are considered: the Lambert s cosine law and the exponential law. 24

37 The Lambert s cosine law states that the reflection of an incident flux of electromagnetic energy on a surface depends on the orientation of the surface and the flux [74]. This relationship is given by the cosine of the incident angle as seen on the following equation: Where I is the radian flux density at surface; is the flux density normal to the beam and is the angle between the radian beam and the normal to the surface, referenced here as incident angle [74]. The Beer-Lambert-Bouguer law considers the attenuation of radiation as an incident flux of electromagnetic energy propagates through a medium [74]. It states that the energy will be attenuated in an exponential form as indicated in the following equation: 2-11 Where is the attenuated flux of density, z is the distance the beam travels in the medium and k is the attenuation coefficient (m -1 ) [74]. In the radar scenario, the incident microwave energy is attenuated as it travels through the canopy and structuring elements of the vegetation being observed by the radar antenna. In the water cloud model the total backscattering of a vegetated area is regarded by the incoherent sum of the individual reflections of the vegetation layer ( ) and the soil layer ( ) (equation 2-13) The backscattering of the vegetation and soil introduced in [68] is expressed by the equation: Where is the total backscattering of the vegetation and soil layers. A and B are vegetation parameters C and D = soil parameters Wh= volumetric water content for the vegetation layer Θ= incident angle = soil moisture After its initial introduction to radar remote sensing, the water cloud model has been successfully implemented and has been adapted for different applications and backscattering modelling of diverse land cover types. Particular attention has received the backscattering modelling of crops to estimate their physical structure and to relate radar values to their phonological state [11, 37, 56, 73]. An advantage of the water cloud model and its physical foundations is that it can be adapted and used differently according to the specific case. An important evolution of the model was presented in [75] 25

38 introducing the variables V1 and V2, which can have different meanings and may be related to different structural properties of the vegetation. Another extension of the water cloud model was presented in [39], where an implementation for forest observation under L band was done. This model besides considering first order backscatter from vegetation and underlying soils, considers second order backscattering, such as trunk to ground interaction and canopy to ground interactions. Considering the L band radar penetration this is relevant for the backscattering modelling of complex forest areas Extended water cloud model The model used for backscattering modelling regards the forest backscattering as the incoherent contribution of a vegetation layer, and underlying surface, canopy to ground and trunk reflections. These interactions are illustrated in Figure 2-17 and expressed by the following equation: 2-15 Where = volume scattering from the canopy (db). = surface scattering from an underlying surface. In the case of dense forest it is a short layer of vegetation and soil, assumed in the model as smooth reflector. In the case of flooded forest during the rainy season the underlying surface consist of a layer of water (db). = forward scattering from the foliage to the ground followed by specular reflection back to the sensor (db) [39]. = specular reflection from the trunk onto the ground and back to the sensor (db). A= area (m 2 ) of the resolution cell image. The extended water cloud model presents mathematical expressions for the modelling of each of the just mentioned contributions. These expressions need the calculations of coefficients to obtain the individual backscattering contributions to the total backscattering. Every contribution is defined as follows. Volume and surface contribution The contributions of vegetation and soil are implemented in this research by the water cloud model which is expressed by: 2-16 Where A is the volume scattering coefficient (db). This term expresses the radar backscattering of the forest when observed at the nadir point of a radar system. This is equivalent to the backscattering at 0 incident angle. 26

39 B is the attenuation coefficient (db/m). It expresses the attenuation of the radar signal as it travels through the forest canopy. P is the average leaf area index (LAI) of the trees being observed. The LAI is the ratio of total upper leaf surface of vegetation divided by the surface area of the land on which the vegetation grows [76]. C is the soil scattering coefficient (db). This term expresses the radar backscattering of bare soil when observed at the nadir point of a radar system. It is equivalent to the backscattering at 0 incident angle. is the incident angle (degrees) of the radar beam. is the characteristic angle (degrees) where the returned radar signal is independent of the soil characteristics [14]. Its value depends on the wavelength of the radar system. For L band HH polarization this value is set to 12 as used in Richards study [39]. Volume to surface contribution The model for the energy interactions between the canopy and the underlying surface was introduced by Engheta and Elachi [77] and considered along with the water cloud model by Richards et al [39]. The volume to surface contribution is expressed by the following equation: 2-17 Where Rg is the Fresnel reflection coefficient of the air ground interface. The Fresnel coefficient depends on the incident angle and is calculated here using the following equation for HH polarization [78]: 2-18 Where is the incident angle of the radar beam and ε r is the dielectric constant of the soil. A value of 12 is used for ε r as a typical value for a wet soil [16]. The other variables of the equation 2-17 are identical to those defined in equation 2-16 and their initial and domain values are the same as for the volume surface contributions. Trunk to ground contribution The trunk to ground combination is modelled as a dihedral corner reflector, where the trunk represents the vertical arm of the reflector and the projected shadow its horizontal arm. This consideration is taken into account in the model introduced by Richards et al [39] as shown in the next equation: 27

40 2-19 Where = is the radar cross section of a single tree. = is the Fresnel coefficient reflectivity of the air trunk interface. The Fresnel coefficient is calculated using equation 2-18 with a dielectric constant of 20 as a value for wet wood. Rg= is the Fresnel coefficient reflectivity of the air ground interface as previously defined for the volume to surface contribution. = is the attenuation coefficient of the incident energy as it passes trough the canopy and reaches the trunk. This term is introduced here to counteract the overestimation of reported by Richards et al in [39]. It should be noted that in the original proposed model a different correction is implemented. This is done by means of experimental observations, not possible to implement here due to data limitations. The initial value of used in this research is estimated arbitrarily, considering only a value which results in agreement with the simulations of trunk backscattering contribution done in [39]. = radar cross section of an ideal dihedral corner reflector. An explanation on how to calculate this parameter, given by [39], is provided in Appendix II. 28

41 3. Study area, materials and methods This chapter is divided into three sections which present the study area were the investigation is carried out (3.1), the materials used in the experiments to answer the research questions (3.2), and the methods implemented during the execution of this research (3.3) Study area The study area is located in the eastern part of Colombia in the area of Llanos Orientales or Orinoco region. The area extends approximately from 2 to 5 N latitude and -68 to -72 W longitude, within the borders of Colombia. The studied area is located in an average elevation of 150 meters above sea level and presents small topographic or relief variation. It is mainly flat, with 90% of the area with slopes below 10 and some elevations up to 800 m in the southern part, as a consequence of the Guiana shield geologic formation. The extent of the area, 185,000 square km, is shown in Figure 3-1. Figure 3-1 Study area 29

42 The Orinoco drainage basin has a monomodal precipitation pattern with one rainy season, commonly known as winter, and one dry season, known as summer. The precipitation rate of the area can vary in great extent from one year to another but in general the rainy season starts in June and ends in September with annual rainfall averages varying between 1,500 and 3500 mm. Precipitation patterns are irregular not only between years but also on its spatial distribution on the region. The areas close to the Orinoco and Guaviare rivers present higher values of precipitation and stronger and longer winters. The area is covered by large extensions of tropical dense forest and annually flooded forest along the wide stream rivers running in the region and the Mataven forest. The forest in eastern part, close to the Orinoco River, exhibits different structure due to soil processes formation of the Guyana shield. In the northern territory the presence of savannah areas are associated with short vegetation, grasslands, burnt soils and cultivation areas. The main rivers are the Guaviare, Inirida and Vichada, which transport their waters to the big Orinoco River. During the winter season, the rivers reach a water level peak due to strong showers. This causes overflow that generates floods in large extensions of the area. Floods are the general natural phenomena governing and modifying the ecosystem of the area. This can be seen particularly in the Mataven forest, a protected land within the study area. In this forest, there is a very dynamic pattern in terms of the water levels and floods occurring yearly during the rainy season. In the Mataven forest, areas near the main water streams may be flooded throughout most of the year and this determines the presence of a constant water lamina which depending of the intensity of the rainy season can reach several meters covering trunks and even the trees canopies. Some photographs illustrating the floods during 2007 in Mataven forest are presented in Appendix III Land cover types Land cover types under analysis the analysis of this research are semantically defined and identified on the radar images based on a series of conditions and considerations. For the analysis of classes a visual and digital examination of radar backscattering was done. The visual analysis requires the use of interpretation keys such as tone, texture, brightness and pattern. The digital analysis is based on the identification of non overlapping clusters of digital values that are expected to belong to a non mixed land cover type. The definition of land cover types follow also an analysis based on ancillary data, reports, studies and maps of the area. In short, the spatial data presented in section 3.2 are relevant for this analysis. The ecosystem map, which provides information on geomorphology, land cover, biome, humidity and weather, is of vital importance for the identification of the land cover categories. The digital elevation model is also considered to determine the altitude presence of land cover categories and to avoid sampling areas with strong topography changes, or sloping areas as it is explained later. Finally, as financial means were limited for this research the study area was not visited although expert knowledge and criteria of the author was valuable for the definition of land cover types. With the mentioned considerations, the investigated land cover classes are: Class 1. Dense wet forest: the class includes areas where tropical trees are the dominant life form. They exhibit a closed canopy pattern with a dense presence of trees by area unit, and a medium tree height between 10 and 25 meters. The physical structure of these ecosystems can be very complex with few layers of open and closed canopy. These forests are located in areas of elevation less than 1,100 meters above the sea level. Wet forest present high values of humidity as a result of intense rains 30

43 during most part of the year and low evapotranspiration values. The sampling points for this class lie in areas of flat and gentle gradual slopes. Class 2. Dense flooded forest: this class exhibits identical characteristics as those of dense wet forest areas but with a difference in the humidity level. This forest is characterised by a layer of water below the canopy as a product of intense rains and rivers overflow during large periods of the year. The precipitation in these areas can reach values between 3,000 and 5,000 mm per year and the forest remain for at least eight months a year with a superficial layer of water between 30 and 100 cm. These areas are located along major water streams which modify the vicinity by forming aquatic ecosystems. The trees in this class have average height between 10 and 25 meters. Sampling points for this class lie in areas of flat and gentle gradual slopes. Class 3. Tall and dense wet forest: this class represents those densely formed tropical forest ecosystems with high stationary humidity and tall tree species, having an average height of 25 meters and some emerging species with height between 15 and 20 meters. These areas have high moisture content and they maintain a layer of water between 10 and 30 cm during the rainy season for at least 6 months a year. The average values of precipitation range between 3,000 and 3,500 mm per year. Class 4. Short forest: these forests are located in the geomorphologic formation of the Guiana shield which is characterized by stable and very old soils rich in bio diversity. The short forests in this area are represented by trees less than 10 meters high growing in wet and very wet conditions. These areas are prone to seasonal flooding depending on the intensity of rains. Typically the precipitation ranges between 3,000 and 3,500 mm a year. Many short forests species present an open canopy and understory shrubs. Class 5. Dry savannah: these areas have scarce presence of vegetation and are greatly influenced by mostly infertile and low in nutrients soil types which determine the overlaying vegetation. Vegetation consists mainly of herbaceous layers of short shrubs and grasses. These areas are located in flat regions below 200 meters above sea level with temperatures ranging between 27 and 30 C. They are usually very dry but may be subject to floods during heavy rain seasons. Burnt areas are also included in this category as a result of the traditional antrophic practice of land management. The dynamic variability of savannahs, burnt areas and grasslands evidence the impact of human activities and the climate characteristics of the region. Figure 3-2 illustrates the distribution of the five classes described earlier according to the land cover map provided by Humboldt Institute introduced further in this chapter. In the extension of these areas a sampling strategy is carried out to identify radar brightness values that will be analysed along the range. The sampling methodology and results are presented in section 5.5. As it can be seen from the figure, the most representative class is the dense wet forest which is imaged in a wide range of incident angles. Class 2, dense wet forest is conveniently present along the range in the three available images. Class 3 is present in the far range of images 1 and 3 but in the close range of image 2. Class 4, short forest is present sufficiently in image 1 and 3 but it is not represented in the extension and size of image 2. Class 5, dry savannah can be observed in the middle and far range of images 1 and 3 and in the close and middle ranges of image 2. Appendix IV illustrates in a radar ALOS image the spatial distribution of land cove types and the variation in brightness and texture of the pixels in the study area. 31

44 3.2. Materials Radar images Figure 3-2 Distribution of considered land cover classes Three radar scenes captured by the satellite ALOS PalSAR (see section 2.6) were used in this research. The images were captured in ScanSAR mode after the calibration phase which took place between the months of July and October of The ScanSAR images cover a swath of 350 kilometres at 100 meters resolution with 8 processing looks. For better clarity and short addressing of the images in this document, they are labelled by ordinal numbers which follow a chronological order (see Table 3-1). In consequence, the image captured in July 17 is addressed as Image 1, the image corresponding to July 22 as Image 2 and the latest image in time, October 22 as Image 3. Table 3-1 Radar image ID used in this document ID ALOS image Date Image 1 July Image 2 July Image 3 October

45 The images were acquired in level 1.5 (section 2.6) which is equivalent to a georreferenced SAR focused multi look processed image in ground range geometry. The acquisition time is around 10 am local time (GMT-5) with a descending right looking orbit for which the range increases from east to west. Image 1 and Image 3 cover the same extent and thus comparisons can be established between them paying careful attention at time lapse between image acquisitions. Image 2 overlaps 50% of its area with those images. The metadata for the images is presented in Appendix V. The spatial extent of the images 1-3 is visualized in Figure 3-3. Figure 3-3 Layout of radar images investigated in the research The radar images were acquired from ASF ALOS node in Alaska which is the distributor for the images in areas of the American continent. The processing steps done in ASF Alaska centre are the same as those carried out by the JAXA Japanese agency, the main distributor of the data. For a review of the processing level 1.5 refer to section and for detailed documentation consult the product description from JAXA in [51]. 33

46 Satellite images LandSat and Aster images have been employed for the verification of land cover types and to support the identification of sampling areas. Although the satellite images in this study do not correspond to the acquisition date of the SAR data, they were acquired after late 2003 and provide clear interpretation clues in areas were radar backscattering does not offer clear differentiation. A total of 7 LandSat and 7 Aster images were used. The images and their acquisition dates are listed in Table 3-2. Table 3-2 Satellite images for the study area LandSat Path-Row Date Aster Lat-Long Date LandSat Aster LandSat Aster LandSat Aster LandSat Aster LandSat Aster Landsat Aster LandSat Aster Figure 3-4 shows the layout of the satellite images used for the research. LandSat images cover most of the extent of the SAR area and Aster cover conveniently good part of the ALOS images overlap. Figure 3-4 Layout of the satellite images in the study area 34

47 Digital elevation model As it was explained in section 2.4, terrain relief plays an important role in the radar backscattering measured at the antenna. Terrain slope should be considered during SAR processing as it affects the radar backscattering values and the effective incident angle. The use of a DTM is also relevant to this research for the derivation of terrain corrected images as described in section 4.1. The digital elevation model used is part of the global coverage Shuttle Topographic Mission Radar (SRTM) model. The SRTM model was constructed by NGA and DLR using radar interferometry processing of data collected by the Space Shuttle Endeavour in February The derived elevation model was provided in WGS 84 projection and with a resolution of one arc second, equivalent to 30 meters. According to the data provider specifications the SRTM elevation model yields a vertical accuracy of 16 meters and circular relative geolocation error of less than 20 meters. The SRTM obtained from X band radar observation produces elevation values from near the top of the canopies [79]. The DEM is displayed in Figure 3-5. Figure 3-5 Digital elevation model for study area 35

48 Ecosystem map An ecosystem map available for the study area is used for the consideration of land cover types in the sampling procedure as it is explained later in chapter 5. The ecosystem map was published by the Von Humboldt Institute of Colombia in The vector map was acquired at scale 1:1 000,000, where ecosystems have been identified using satellite images and field survey verification. It is of special importance for this study as besides ecosystems data, it gives other relevant information such as biome, geomorphology, land cover, climate and humidity. Along with the digital information the metadata and technical document of the map can be consulted in [80], which describes the methodology used for its construction as well as the metadata for the spatial product. The quality of the ecosystem map was assessed by Humboldt Institute by means of confusion matrix analysis. The kappa coefficient derived from confusion matrix was higher than 77% for small polygons and 84% for medium size polygons [80]. The ecosystem map is shown in Figure 3-6. Figure 3-6 Ecosystem map for study area 36

49 3.3. Methods This section describes the approach and workflow used to achieve the objectives of this research and to address the questions defined in the outline of this thesis in Chapter 1. The method consists of observations of particular experiments or cases aiming to translate the obtained results to more general principles that are still valid in a wider range of conditions. Analysis of radar backscattering variation with incident angle was performed on the three radar images. To achieve this, geo spatial information such as satellite images, thematic maps and digital elevations models are used to sample areas of interest within specific classes. The results of the experiments are afterwards used to normalize the radar backscattering values using a correction method which is based on statistical properties or on land cover physical characteristics. Furthermore, an alternative method for radiometry correction and understanding of radar backscattering is proposed by means of a radar backscattering model which considers the electromagnetic interactions between the radar microwaves and the structural elements of the land cover classes. The general workflow followed to achieve the aimed objectives is shown in the next figure. Radar images Data acquisition Data preparation and processing Land cover definition Data processing Sampling stage Spatial information Ecosystem map Optical images Digital elevation map Previous studies Radar backscattering analysis by image and by land cover Objective: determination and quantification of incident angle influence on radar backscattering Analysis Development of correction method Normalization of radar returns along the range by land cover type Backscattering modelling Radiative transfer model for radar backscattering Model assessment Objective: Correction method for influence of incident angle on radar backscattering Figure 3-7 Workflow methodology applied in this research 37

50 The methods applied in this research are in the realm of physics, remote sensing, geographic information systems tools, statistics and mathematics. In Figure 3-8 a list of implemented methods in this research are listed. Image processing techniques Image modelling Object oriented classification GIS analysis Radiative transfer modelling Statistical analysis Spatial data modelling Image segmentation BFGS fitting method Figure 3-8 Methods implemented in research For every main method presented, a group of operations or techniques is implemented and described in this section of the report. The image processing techniques needed for data preparation and the sampling stage includes operations such as geo-referencing, mosaicking, image enhancement, raster editing, masking, raster modelling, surface analysis and raster statistics. ERDAS Imagine 9.0 and RSI ENVI 4 applications are used for the implementation of these techniques. The GIS methods incorporate different GIS geo-processing operations implemented on the spatial information in order to combine different spatial layers. Basic operations such as digitizing, classification, spatial querying and analysis, overlay operations, proximity computation, masking, multi layer modelling and representation are carried out during the experiments of this research. These have been performed in ArcGIS ESRI software version 9.0. Radar processing techniques such as image import, incident angle image calculation, metadata reading, radiometry conversion, terrain correction, image geocoding and multi-looking are used. The radar application ASF Mapping 1.0 developed by ASF Alaska was used for radar image processing [30]. Figure 3-9 Segmentation output implemented in SegSAR 38

51 The segmentation applied during the sampling stage aims to overcome the noise-like effect of radar images and identify significant homogenous regions which represent the mean backscattering of a given area. The segmentation method followed in this research implements the cartoon method described in the Oliver and Quegan work [32]. The algorithm for radar segmentation used in this research is implemented in SegSAR in IDL and was produced by A. Sousa Jr. at INPE Institute in Brazil [81]. Figure 3-9 presents a typical segmentation output for a sector of an ALOS image. Conventional pixel based classification presents limitations for classification of radar images; therefore region based classification of the images is implemented during the sampling stage. It launches more convenient results for radar sampling and assures that the values being sampled correspond to homogeneous patches of radar backscattering, minimizing the effect of radar speckle. The region based classification is implemented in Developer Definiens software version 7.0, which implements a supervised classification based in region attributes, fuzzy logic and nearest neighbour classifier [82]. Statistical techniques are required for the analysis of the radar backscattering. As brightness values follow certain probability density functions, theory on probability distributions is needed. Spatial statistics are needed during the sampling design and basic statistics are involved in the analysis of return values along the range component. Regression techniques are used in the backscattering model fitting stage to find the contributions of independent variables to the total radar return. Regression techniques are used as well to find the coefficients of the model which normalize the radar brightness values along the range. For statistical analysis the R project for statistical computing software version was used [83]. The radiative transfer model used in this research for the representation of the radar backscattering from specific land cover classes is based on radiative transfer theory. The fundamentals of radiative transfer theory can be found in physics in the field of electromagnetic energy. The backscattering model of this research is based on the water cloud model proposed by Attema and Ulaby [68] with the modification and extension presented by Richards et al [39]. Broyden-Fletcher-Goldfarb-Shanno (BFGS) fitting algorithm is implemented in the backscattering model to solve an unconstrained non linear system. The BFGS mathematical method is a derived Newton optimization method to find roots of equations or to find local minima of a function. The BFGS fitting algorithm was introduced in [84] and was implemented here by means of the code available in R software version [83]. The BFGS procedure is known to estimate coefficients of a mathematical system while minimizing the least square difference between the observations and the function. The minimization is given by: 3-1 Where are the values of the non linear system, are known values of the function at a given x and A,B,P,Cr are the parameters of the model to optimize. 39

52 40 ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR

53 4. Data processing The data used for the research was collected and processed in the first step of the investigation. This stage is to an extent time consuming as data was requested to different organizations and validation, processing and assessment of it required an additional effort. This section presents the procedure used to gather, process and make the data ready to be analysed and used for the final scope of the research. After the data is processed and integrated using image processing software and GIS applications the next step is to go deep into the analysis of radar backscattering as presented in chapter Radar data Three ALOS PalSAR images are acquired from ASF Alaska node, authorized distributor for American territories images. The datasets in processing level 1.5 are focused processed amplitude images and multi look averaged. The images are first converted from the original format to a geotiff product compatible with most of the remote sensing packages. The original information comes in CEOS format specification, and each dataset consist of 4 individual files which contain the raster image structure, a descriptor file and the associated metadata. Not all the image processing applications are able to handle the ALOS PalSAR 1.5 level structure. For this research the ASF Map Ready 1.0 software and ENVI 4.2 were used to process the original information. Figure 4-1 presents the steps involved in processing the radar images. Radar images Acquisition from ASF Alaska node via FTP Format conversion Terrain correction Geocoding Brightness backscattering conversion Sigma image Beta image Gamma image Radar brightness assessment Figure 4-1 Workflow for radar data processing ALOS PalSAR images in level 1.5 still need to be corrected for variations in terrain relief. For this a DEM of the area of the image in the same geometric projection and preferably of a better resolution is 41

54 needed. The effects of terrain variation such as layover, foreshortening and shadows are addressed in this processing step. The terrain correction applied does not really improve the information content of areas subject to those effects. Compressed backscattering information such as those of layover or foreshortening is not recoverable. The same principle applies to the shadows areas where information is not present. The real effect of terrain correction is to adjust the location of those areas to the real position with the help of a DEM. This is certainly of relevant importance at the moment of overlaying the images with other spatial data sets. The areas with terrain distortion are ignored in the analysis or radar brightness in chapter 5. Presents an example of terrain correction applied to image 1. As it can be seen no new information or improved values are generated. The effect is only the correct geolocation of the shadows and layover areas. Figure 4-2 Original image (left) and terrain corrected image (right) The terrain correction applied produces a terrain corrected geocoded image. The information for geocoding is extracted from the metadata as the original information is in georreferenced state with map projection information attached but not applied. The images are resampled to an UTM projection WGS84 reference ellipsoid. Figure 4-3 provides an example of this processing step on ALOS image 1. Figure 4-3 Georreferenced image (left) and geocoded image (right) 42

55 ALOS images in level 1.5 are made available in amplitude values. The digital values of the raster image represent detected backscattering brightness according to the square law detector. From this original amplitude values the objective is to translate to brightness expressions such as power or intensity, sigma and gamma values. These expressions are also converted into decibel values in order to facilitate comparison with other studies which uses the decibel equivalent as a standard for reporting. The radar brightness expressions are found in literature works such as [16] but the specific sensor should also be considered as different calibrations require particular terms in the calculations. Appendix VI presents a table with the formulas used to calculate brightness expressions for ALOS data as well as an example for calculations at a given pixel in radar image 1. These mathematical expressions were obtained from [50] and from electronic communication with ASF Alaska support team. In order to quantitative compare radar brightness values with other reported results it is necessary to find consistent sigma values for land cover in each radar image. The final step with the original radar images is to validate and assess the brightness values obtained for land cover types. Table 4-1 shows typical averaged sigma nought values for different land cover types in ALOS image 1. Table 4-1 Observed sigma values for different land cover types Land cover Mean Amplitude Std. deviation Sigma Forest Flooded forest Grassland Water bodies It has been demonstrated how the statistical properties of logarithm scaled speckle, that is sigma values, are altered and are slightly different from those values of those of the original amplitude or power expressions. In [85] and [86], it is demonstrated how the transformed sigma (db) values follow different statistical properties after a non linear transformation is applied. The practical recommendation that follows from these observations is to do the analysis of the data in amplitude values and process all the statistics in this brightness domain. For effects of reporting and comparison this statistical values obtained, such as the mean, shall be converted to sigma nought values using the formulas of Appendix VI. This is the approach taken in this report Satellite images The role of the optical satellite images in this research is to support radar interpretation and define sampling areas for analysis of radar brightness using the spatial and spectral resolution characteristics. Processing of the satellite images involved basic steps such as image importing, image georreferencing and resampling. For the georreferencing step an image to image approach was taken using as master image the ALOS radar datasets. The final verification of the result consists simply on the overlay of the LandSat and Aster processed images and the ALOS radar images. Figure 4-4 shows the steps needed to process the satellite optical images. 43

56 Satellite image data Acquisition from GLCF via FTP Acquisition from SIMCI via FTP LandSat Aster Format importing Image to image geocoding Geocoded images Image overlay Figure 4-4 Workflow for optical satellite image processing 4.3. Digital elevation model The use of a digital elevation model is to correct the ALOS images for terrain distortions such as layover, foreshortening and shadows and to define sampling areas as presented in chapter 2. Two sources of DEM model were used. SIMCI provided elevation information for the Colombian territory. Although the study area lies exclusively in Colombia, international areas of SRTM of Brazil and Venezuela were obtained from USGS in internet with the aim of producing a continuous rectangular dataset with height information. This was needed as a pre requisite to apply terrain correction in ASF Alaska SAR utility. The DEM available in 30 meters for the Colombian area was resampled and reprojected to the UTM 19WGS 84. Once available the two DEM inputs, a mosaic of the area was done with help of image processing software. After mosaicking, some small areas were subject to interpolation and smoothing to improve the matching of the two datasets. A final verification of the height elevation values is performed as well as the quality of the final dataset which is integrated in a GIS application with the other spatial data. Figure 4-5 presents the general workflow for the processing of the DEM. 44

57 Elevation data Acquisition from SIMCI via FTP Acquisition from USGS via FTP DEM Colombian territory DEM non Colombian territory DEM reprojection DEM reprojection and resampling to 30M DEM Mosaicking DEM editing DEM assessment and verification DEM Figure 4-5 Workflow for DEM processing 4.4. Ecosystem map The ecosystem map provided by the Humboldt institute plays a central role in the sampling process and analysis of the brightness values by land cover types. The ecosystem map was offered in digital shapefile vector format. A carefully reading of the technical document is necessary to understand the considerable amount of information that populates the database. Using GIS operations the dataset is reprojected, clipped and subsetted. Attribute reclassification is needed to generate new thematic datasets from the original product. This operation produces biome, geomorphology, humidity, land cover and ecosystem maps that will be the input of the sampling process in section 5.5. Figure 4-6 presents the general workflow implemented for the processing of the ecosystem map. 45

58 Ecosystem map Acquisition from Humboldt via FTP Ecosystem vector data Reprojection Editing and clipping Analysis of technical document and metadata Reclassification of vector data Humidity Biome Land cover Geomorphology Ecosystem Figure 4-6 Workflow for processing of ecosystem map 46

59 5. Radar backscattering analysis and modelling After applying the necessary data processing techniques, radar backscattering analysis and modelling of land cover types is implemented. In order to achieve this, the generation of basic statistics is required (5.1) along with the calculation of incident angle images that allows to stratify the results according to incident angle variations (5.2). Furthermore, the antenna pattern correction affecting SAR images should be addressed (5.3), in order to obtain estimates of the achievable radiometric accuracy. Having done this, the research moves to the quantification of the incident angle effect (5.4 and 5.5), its correction (5.6) and its explanation by means of a backscattering model (5.7) Radar backscattering statistics Statistical properties of the radar data are fundamental to understand the reflection of targets when they are observed under microwave radiation. Statistics of the image and the study of their data distribution is the first step to the posterior analysis carried out in the data to find the effects of different viewing geometry. The statistical properties of radar observations are well known for single and multi-look data [85]. A complete study of the theoretical data distributions can be found in [32]. As reported by Oliver phase provides no information in the study of distributed targets since its distribution is target independent. Here as the complex and phase observations were not available the study is focused on the intensity, amplitude and log amplitude values of the multi-look data which will be relevant for the posterior analysis of radar measurements with different viewing geometry. A practical estimation of a RCS at a given position is to average the intensity values over some neighbouring pixels surrounding the pixel of interest. The estimation of RCS over homogeneous regions by averaging in intensity will also reduce the error originated from speckle in individual pixels. For this analysis a homogeneous forest area was selected over the radar image 1 as shown by the grey polygon in Figure 5-1. The intensity statistics for the selected region are: Figure 5-1 Forest window for PDF test 47

60 Table 5-1 Statistics for test region Matrix 192x176 (33792 ) Mean 6.61 e+7 Standard deviation 1.74e+7 min 2.09e+7 max 1.75e+8 According to the radar model the L looks averaged intensity values have mean σ and follow a gamma distribution with order parameter L. 5-1 The average intensity (I) has moments (m): 5-2 From 5-2 the equivalent or effective number of looks (ENL) can be calculated as: 5-3 Using equation 5-3 the effective number of looks is In this case there is a difference between the nominal number of looks and the effective number of looks (ENL). It is common to register a difference between the number of looks physically affected by the processor and the effective number of looks calculated from data statistics[16]. After the data is multi-look processed there is an unavoidable spatial correlation that will always make the effective looks smaller than the nominal number of look filters or independent observations. In the case of the PalSAR image an effective number of looks of the order less than 8 should be expected and on the contrary a higher number is found. This may has its roots in the processing operations applied to the image and it proves the importance of carefully determining the effective number of independent looks from the data. The study of forest backscattering in the three ALOS images and the calculation of ENL produces the following results: Table 5-2 ENL for ALOS images Image 1 Image2 Image 3 ENL The ENL obtained for the radar images is used for the estimation of the minimum sampling size of the considered land cover classes and as input parameter of the segmentation processes in section 5.5. The histograms with the Intensity values for a homogeneous forest scene in the three available radar images along with the L-looks theoretical gamma distribution of equation 5-1 are plotted in Figure

61 Figure 5-2 Comparison of intensity theoretical distribution with observation for test region The figure shows that the distribution of intensity values of image 1 for the selected region follow very closely a gamma distribution except for a low deviation in the middle range intensity values. If amplitude values are considered, with, then theory predicts a square root gamma distribution, which is derived from equation 5-1 and yields: 5-4 Likewise, the amplitude histograms for forest area in radar image 1 selected along with the L-looks theoretical gamma distribution (equation 5-4) is plotted in Figure 5-3. It can be observed the neat adjustment between the PalSAR observation and the theory prediction. It can be observed from the histograms and the PDF that both present a small variation in the peak of the histogram, which means that the observations have a central value with frequencies or probabilities a little higher than predicted. Figure 5-3 Comparison of amplitude theoretical distribution with observation for test region 49

62 5.2. Calculation of incident angle From the geocoded radar images incident angle images were generated. Each pixel in the incident angle image at the i,j position stores a digital value that represents the average incident angle value at that respective pixel. As it is presented in Appendix V, the values for incident angles are firstly extracted from metadata information and afterwards derived from an application tool provided by the ASF Alaska team. Figure 5-4 presents a reconstruction of the range of incident angles at the moment the radar images were taken with a descending right looking orbit. The position of ALOS satellite is derived from metadata information. As it can be seen, image 1 and image 3 lie in the same extent with the same range of incident angles, On the other hand, image 2 has the same range of incident angle values but the near range lies half way the range of images 1 and 3. The overlapping area between images allows the observation of equivalent targets from different angle illumination. That is, the study of radar brightness will be performed first considering image by image independently and then focusing on the overlap area between image 1 and image 2. This comparison is acceptable given the short time difference between their acquisition dates (5 days). Figure 5-4 Schematic diagram of incident angle values for the three ALOS radar images Accurate calculations of the incident angle are obtained using the software provided by ASF Alaska team. This application calculates values from the original non geocoded images and then the result can be map oriented using the metadata information. From direct communication with the ASF Alaska team, it is known that incident angle images are calculated iteratively using (lat/long) coordinates of the image pixels together with satellite orbit information (state vectors), to calculate satellite look angles. The algorithm calculating the incident angle for each pixel is implemented in a standalone command line tool which produces a raster output. The result images are checked to verify consistency and agreement with the values provided in the metadata information. 50

63 An example of an incident angle image is presented in Figure 5-5. In this image, the diagonal lines in north-south direction represent constant values of incident angle and their separation is equal to 1 degree of incident angle variation. This arrangement is used in the sampling stage, where backscattering values are analysed and averaged for each of those strips. For instance, a total of 36 strips of one degree are contained in the incident angle image. It is worth mentioning, that the strips have different width as they lie from the close range to the far range. The width of a strip in the close range for the example of Figure 5-5 is 11.5 km while a strip in the far range has a width of 9.9 km. This effect is due to the oblique radar observation and the projection from slant range to ground range. Figure 5-5 Calculation of incident angle image for ALOS image Antenna pattern effect After the consideration on ALOS antenna pattern done in section the first evaluation of the antenna pattern correction may result from the visual inspection and examination of the radar images. For the conducted research, different data visualizations and image enhancements were used with the aim of revealing range patterns effects, product of the antenna pattern correction algorithm applied in JAXA, the institution on charge of the processing of the images. Figure 5-6 presents the three SAR images with image enhancements that emphasize and somehow magnify the presence of radiometric unevenness as a result of antenna pattern corrections applied in the processing steps. In the colour scheme used in the figure reddish tones represent dark pixels, bluish medium values and yellowish the brighter pixels. As it can be seen, the antenna pattern effect, estimated from the artifactual tone variations, is stronger in image 1 where an area of brighter pixels appears towards the right of the image. The effect is less evident in image 2 and 3. 51

64 The effect of antenna pattern in the images used for this research was discussed with ASF Alaska, distributor of PalSAR images for American area. It comes out that using the current ALOS antenna pattern correction it is not feasible to remove completely the effect, in particular for PalSAR images. It is estimated as a result of the ALOS calibration and validation phase [52], that the effect should be less than 1 db and ideally less than 0.5 db. In order to quantify the antenna pattern effect in radar backscattering for the 3 processed images, homogeneous regions of interest were taken in forest areas inside and outside the range bins, represented by black lines in Figure 5-6. The backscattering for the regions was averaged per image and the obtained values are presented in Table 5-3. Figure 5-6 Radiometric azimuth bins product of antenna pattern correction. Image 1 (left), Image 2 (center) and Image 3 (right) Table 5-3 Antenna pattern regions in radar images Image ID Dark pattern (db) Bright Pattern (db) Difference (db) Image 1 ALPSRS Image 2 ALPSRS Image 3 ALPSRS From Table 5-3 it can be seen that the maximum effect of antenna pattern unevenness is found to be 0.67 db for image 1 and the lowest is 0.49 for image 3. From this, one can effectively conclude that the variation due to antenna pattern processing is about 0.5 db for the considered patches. As those patches represent the highest perceptible effect in the image, it makes sense to predict that in most cases the antenna pattern effect introduces backscattering variations below 0.5 db Radar backscattering analysis at image level In this section a general examination of the radar backscattering values at image level is done. A first approach to investigate the variation of radar backscattering along the range in the given images is to plot the brightness values moving from near to far range, as seen on Figure 5-7. Image 1 and 3 are brought here as they cover the same range. As range extension is 360 Km in width, the images are divided conveniently into 36 range bins each one with an extension of 10 Km. The amplitude values falling in each bin are then averaged and reported here in sigma values. The resulted graph is overlaid here on top of their respective radar images to facilitate understanding and comparison of the output. 52

65 sigma far range close range close range ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR From Figure 5-7, both images exhibit a very similar behaviour. It can be seen a still backscattering level in the close range with sigma values around -6 db. In both images, between the ranges eleven and eighteen, a sudden bright hill which reaches a maximum peak at -5 db is observed. This peak might be partially related with the antenna pattern effect studied in 5.3, but it is explained in major extent considering the flooded forest present in the mentioned bins. It is also clear that the hill is more pronounced in Image 1; a fact that may be explained if the moisture content at the moment of image acquisition is considered. Image 1 was acquired in July, in the middle of a heavy rainy season in the area. The effect of heavy rains can be observed not only in the bins but in all the ranges of the left image where flooded forests are clearly identified for their bright values. On the contrary, image 3 exhibits rather constant radar backscattering for forest targets and the influence of flooded forest is less evident. In fact, Image 3 was acquired after the rainy season (October 2007), when the rains decrease in amount and intensity. Rain statistics for the image acquisition dates are presented in Appendix VII far range Figure 5-7 Backscattering values along the range. Left: Image 1. Right: Image 3 From Figure 5-7 it can be also assumed that there is a strong correlation between range direction and radar backscattering. As the bin number increase from the close range to the far range the backscattering values decrease from -5.5 to -8 db. This decrease can be attributed to the incident angle dependence, which is expected to behave in a similar way as the dropping curves from right to left in the figure. Nevertheless this will be a wrong conclusion. For the given case there is a strong correlation between radar backscattering and land cover types of the image. The highly reflecting land covers such as flooded forest are located in the close range, which accounts for the high sigma values in the close range. On the contrary, very low reflecting targets such as grasslands and bare soils appear in the middle of the image and occupy larger areas as moving to the far range. This accounts for the low sigma values observed on the left in the presented plot. In consequence, a general analysis of the radar backscattering along the range is difficult to explain in terms of incident angle variation as it is strongly dependent on land cover types which are not homogenously distributed over the image. Section 5.5 investigates the variation of radar backscattering along range direction separately for different land cover classes. 53

66 5.5. Radar backscattering analysis by reflecting targets Considering previous radar studies (section 2.7) it is expected that if angle dependency is observed in the radar images this occurs in different amounts and behaviours according to the considered land cover type. This section reviews in detail the radar backscattering values across the azimuth component regarding reflecting targets. To this end, radar backscattering values are analyzed by image and by land cover type categories. Land cover categories were previously described in section 3.1 as well as their spatial distribution from the consideration on the land cover map and on the radar images (Appendix IV). On these areas a sampling design is carried out. The selection of samples representative of land covers types is the result of a careful consideration of radar images, land cover maps of the area, optical images and previous studies involving ground truth data. The unit of analysis and reporting for radar backscattering are range bins of incident angles. Each bin in the image stands for one degree extent of incident angle observation. Thus 27 range bins are found in a radar image with incident angle variation between 17 and 43 degrees, the case of the used PalSAR data. Nevertheless, as the land covers do not appear homogeneously along the range in the images, usually less than 27 bins per class are found in the stats report. In addition, it should be said that the pixels that belong to a class are thoroughly selected. For this two approaches are followed. When a class, such as forest can be identified completely using the ancillary data, a series of spatial conditions are implemented to obtain the final suitable area of each class. This is called here the spatial conditions criteria. On the other hand, when the category is rather complex and mixed such as grasslands and bare soils, a region classification based approach is adopted. Once homogeneous backscattering areas of a specific class are determined, a series of spatial conditions are considered afterwards to obtain sampling suitable areas. Depending on the class the methodology can also implement the two criteria, spatial and brightness. The explained sampling approach by land cover type is show in Figure 5-8. Detailed sampling strategy by class is presented in this chapter for each class considered. Radar image Ancillary data Segmentatioin Spatial conditions geoprocessing Classification Suitable area by spatial conditions criteria Suitable area by brightness criteria Intersecction Sampling areas Averaging of radar backscattering by 1 degree range bins Stats report, graphs and analysis Figure 5-8 Land cover sampling strategy for analysis of radar backscattering 54

67 Class 1 - Dense wet forest The samples for the class dense wet forest are identified by spatial modelling criteria. Spatial constrains on land cover type, forest height, humidity, slope and distance to other land covers, are sufficient to identify brightness pixels which belong to the samples of class 1. After a verification and improvement is done, the average sigma values are reported by range bins in degrees. The methodology applied for the sampling of class 1 is shown in Figure 5-9. Identical methodology was followed for the sampling process of the three studied radar images. Class 1. Dense wet forest Workflow Backscattering analysis Spatial Conditions Land cover type: Dense forest Suitable area Class 1 Height: Medium Humidity: Wet and very wet Verification and improvement with optical data Distance to pastures and grasslands: >2 Km Distance to major rivers: >8 Km Slope : <7% Distance to minor rivers and water streams: >2 Km Overlay with 1 incident angle strips Backscattering statistics report class 1 Figure 5-9 Workflow sampling class 1 The following figure presents in reddish tones the sampling areas resulted from applying the methodology of Figure 5-9. In this figure the sampling areas for image 1 and 3 are displayed on top of their respective radar images. Figure 5-10 Sampling areas class 1. Image 1(left). Image 2 (right) 55

68 sigma values sigma values ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Figure 5-11 presents the sigma values for class 1 in images 1 and 3 for incident angles variation between 26 and 42 degrees. It is important to mention that these images cover the same area extent and they were taken with four months difference Image 1 Image Incident angle Image Image Figure 5-11 Sigma values Image 1 and 3 for class 1 along the range Figure 5-12 presents the sigma values angular variation for class 1 in image 2. This is presented in different chart from images 1 and 3 as they cover different area extents Image Incident angle Image Figure 5-12 Sigma values Image 2 for class 1 along the range Table 5-4 presents the correlation coefficients of the incident angles and the average backscatter values for class 1. Table 5-4 Correlation coefficients class 1 Class1 image 1 image 2 image 3 r

69 From Figure 5-11 it can be seen that both images present a rather constant backscattering along the range for class 1. Image 1 and 3 presents a very similar behaviour in their average sigma and their variability. The average sigma value for image 1 is -5.4 db while for image 3 is 5.2 db a small difference of only 0.2 db. A maximum variation of sigma values along the range of 0.6 and 0.7 db is found for image 1 and 3 respectively. The results of Image 2 for class 1 are presented in Figure The sampled areas of this class exhibit slightly brighter values of radar returns. The average backscattering of class 2 for image 2 is -5 db presenting a maximum variation of 0.7 db. Furthermore this variation is no constant along the range and do not reveal any trend. Correlation coefficient values from Table 5-4 reveal a very weak relationship between radar backscattering and incident angle. Although in all cases the coefficient is negative, meaning that an increase in range leads to a decrease in backscattering, the calculated values are too small to be significant Class 2 - Dense flooded forest Class Dense flooded forest is sampled using a combination of the spatial criteria conditions and radar brightness classification of homogeneous regions. Figure 5-10 presents the adopted methodology for sampling of class 2. Spatial conditions such as land cover type, forest height, humidity and slope are established for suitable areas. For the brightness criteria region segmentation and classification is done to identify those areas which exhibit homogenous and characteristic radar backscattering of a flooded forest. Class 2. Dense flooded forest Workflow Backscattering analysis Spatial Conditions Land cover type: Dense forest Suitable regions by spatial conditions criteria for Class 2 Height: Medium or High Humidity: Flooded Spatial Intersection of brightness and spatial criteria Verification and improvement with optical data Slope : <7% Suitable regions class 2 Overlay with 1 incident angle strips Segmentation SegSAR 4 levels NN Classification Mean and Std Dev Suitable regions by radar brightness criteria Backscattering statistics report class 2 Figure 5-13 Workflow sampling class 2 57

70 sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR The following figure presents in reddish tones the suitable areas identified for sampling of class 2 in image 1 and 2. Figure 5-14 Sampling areas class 1. Image 1(left). Image 2 (right) Average values of radar returns for class 2 in image 1 and 3 are shown in the following figure. 0-1 Image 1 Image incident angle Image Image Figure 5-15 Sigma values Image 1 and 3 for class 2 along the range The figure below presents the angular variation of sigma values for class 2 in image 2. 58

71 sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR 0-1 Image Incident angle Image Figure 5-16 Sigma values Image 2 for class 2 along the range The following table presents the correlation coefficients for the incident angle and the averaged sigma values for class 2. Table 5-5 Correlation coefficients class 2 Class 2 image 1 image 2 image 3 r From Figure 5-15 analysis on backscattering values can be done on images 1 and 3 for class 2. Class 2 represents dense forest areas subject to continuous flooding during the year. The average sigma values of image 1 shows clearly a decreasing pattern as the incident angle increases. This pattern is constant along the range. It starts with a maximum sigma value of -2.9 db at 27 degrees and decreases continuously reaching the minimum in the far range, at 41 degrees with -3.9 db. The variation in sigma from near range to far range is 1 db, a significant and considerate amount compared to the antenna pattern effect. The strong relationship between radar backscattering and incident angle in image 1 is supported by the correlation coefficient of Image 3 observed in the same figure presents a contrasting effect, the average backscattering present very constant values along the range. The average backscattering for image 3 is -5.0 db and it presents a variation of 0.4 db with a correlation coefficient of -0.48, a very small value indicating weak relationship of incident angle and radar backscattering in image 3. Figure 5-16 shows the sampling results of class 2 in image 2. It can be seen a maximum radar return in the near range (17 ) of -2.5 db. The average backscattering decreases steadily along the range and moving towards the far range, when it reaches a minimum value of -3.9 db. The total variation is 1.54 db, an effect so strong that cannot be attributed to antenna pattern or any other system effect. Besides from Table 5-5, a very strong and negative correlation between incident angle and radar values is confirmed. For this analysis, it is critical to mention that image 3 was taken 5 days after image 1, under previous rains and a considerable amount of accumulated rain (Appendix VII). 59

72 Class 3 - Tall and dense wet forest Sampling of class 3 follows the implementation of spatial constrains on areas obtained from the ecosystem map. Constrains on land cover type, height, humidity, slope and distance to major and minor rivers are necessary to obtain the sampling areas for radar backscattering analysis. Figure 5-17 presents the adopted methodology and conditions for the sampling of class 3. Class 3. Tall very wet and dense forest Workflow Backscattering analysis Spatial Conditions Land cover type: Tall dense forest Suitable regions by spatial conditions criteria for Class 3 Height: High Slope : <7% Humidity: Very wet Distance to minor rivers and water streams: >0.8 Km Verification and improvement with optical data Suitable regions class 3 Distance to major rivers: >1 Km Overlay with 1 incident angle strips Figure 5-17 Workflow sampling class 3 Backscattering statistics report class 3 The image below presents in reddish tones the suitable areas for sampling of class 3 as identified in image 1 and 3. Figure 5-18 Sampling areas class 3. Left: Image 1.Right: Image 2 60

73 sigma sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR The following graph presents the variation of class 3 sigma values for images 1 and 3 along the range for incident angles between 31 and 42 degrees Image 1 Image incident angle Image Image Figure 5-19 Sigma values Image 1 and 3 for class 3 along the range The plot below displays the class 3 sigma values for Image 2 as they vary in incident angles between 17 and 34 degrees Image incident angle Image Figure 5-20 Sigma values Image 2 class 3 along the range The following table presents the correlation values between radar backscattering of class 3 and incident angle for the three studied images. Table 5-6 Correlation coefficients class 3 Class 3 image 1 image 2 image 3 r

74 Figure 5-19 presents the backscattering curves of image 1 and 3 for class 3. As it is seen, they cover the same extent presenting similar variation of sigma average values along the range. Both images present the highest return in the close range and then a decreasing average of backscattering which reaches a steady minimum at 42 incident angle. Image 1 has variations of 0.7 db with a maximum of -5 db in the close range and a minimum of -5.7 db in the far range. Meanwhile, image 3 presents variations of 0.9 db along the range, with a maximum return of 4.8 db in the close range and a minimum of -5.7 db in the far range. The correlation coefficients of both images are very consistent, with negative values of 0.82 and The curve of Figure 5-20 presents the variation of backscattering values of image 2 along the range. This is rather random along the range with a mean sigma average of -5.6 db and a maximum variation of 0.6 db. The pattern of image 2 backscattering values departs from the trend of images 1 and 3, where a consistent decrease of radar backscattering along the range was observed. A positive correlation coefficient of 0.49 is observed for image 2. Nevertheless, analysing the overlapping area of the three images the trend can still be observed. The near and the middle range, from 17 till 26 degrees presents a slight decrease which accounts 0.3 db. Afterwards, from the 27 until 33 degrees this pattern not longer exists Class 4 - Short forest The sampling design of class 4 follows and approach of spatial conditions and radar brightness constrains. The spatial conditions are set on the land cover type, geomorphology unit and the slope. On the other hand, the segmentation and classification stage seeks to identify those homogeneous areas of radar backscattering which are representative of short forest returns. Figure 5-21 explains the methodology adopted for class 4 sampling design. This design is applied in image 1 and image 3. Image 2 is not considered for class 4 since this land cover is not sufficiently present in the extent of that image. Class 4. Short forest wet and very wet Workflow Backscattering analysis Spatial Conditions Land cover type: Short forest Suitable regions by spatial conditions criteria for Class 5 Verification and improvement with optical data Geomorphology: Craton Slope : <7% Spatial Intersection of brightness and spatial criteria Suitable regions class 4 Segmentation SegSAR 4 levels NN Classification Mean and Std Dev Suitable regions by radar brightness criteria Overlay with 1 incident angle strips Backscattering statistics report class 4 Figure 5-21 Workflow sampling class 4 Figure 5-22 presents in reddish tones the suitable areas for class 4 which are considered for backscattering analysis of image 1 and image 3. 62

75 Figure 5-22 Sampling areas class 4. Left: Image 1. Right: Image 3 The table below presents the correlation coefficient between radar values and incident angle for class 4. Table 5-7 Correlation coefficients class 4 Class 4 image 1 image 2 image 3 r The plot below presents the average sigma values for class 5 in image 1 and image 3 as they vary between 20 and 41 degrees in incident angle. Figure 5-23 Sigma values Image 1 and 3 for class 4 along the range 63

76 In Figure 5-23 two rather constant and smooth curves are observed for average radar returns of image 1 and 3 respect to class 4. Image 3 is brighter than image 1. The mean backscattering value of image 1 is -5.6 db while for image 3 it is -5.1 db. The variations are very small along the range. For image 1 the maximum variation is 0.4 db and for image 3 it is 0.5 db. From the correlation coefficients, a very weak relationship between backscattering values and incident angle variation is found. From image 3 this relationship is stronger and negative but is due more to a constant decreasing behaviour of backscattering than to a clear decrease of the sigma values Class 5 Savannah The sampling design of class 5 considers spatial constrains and brightness values constrains. On the spatial constrains, land cover type, humidity, slope and distance to major and minor rivers are necessary to identify suitable areas for sampling of class 5. The brightness constrain is implemented by a segmentation and classification stage where characteristic sigma values for savannah backscattering are selected. Finally the intersection of these conditions result in the suitable areas which are subject to study of incident angle influence. Figure 5-24 presents an overview of class 5 sampling approach. Class 5. Grassland Workflow Backscattering analysis Spatial Conditions Land cover type: Grassland Suitable regions by spatial conditions criteria for Class 5 Verification and improvement with optical data Humidity: Dry Slope : <7% Spatial Intersection of brightness and spatial criteria Suitable regions class 5 Distance to major rivers: >0.7 Km Distance to minor rivers and water streams: >1.5 Km Overlay with 1 incident angle strips Segmentation SegSAR 4 levels NN Classification Mean and Std Dev Suitable regions by radar brightness criteria Backscattering statistics report class 3 Figure 5-24 Workflow sampling class 5 Figure 5-25 presents in reddish tones the sampling savannah areas as identified in image 2 and 3. Figure 5-25 Sampling areas class 5. Left: Image 2. Right: Image 3 64

77 sigma sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR The plot below presents the class 5 angular backscattering variation for image 1 and Image 1 Image incident angle Image Image Figure 5-26 Sigma values Image 1 and 3 for class 5 along the range The plot below presents the class 5 angular backscattering variation for image Image incident angle Image Figure 5-27 Sigma values Image 2 for class 5 along the range Table 5-8 presents the correlation coefficients between radar returns and incident angle for class 5. Table 5-8 Correlation coefficients class 5 Class 5 image 1 image 2 image 3 r In Figure 5-26 the variation of sigma values with incident angle for image 1 and 3 can be observed. From this figure it can be seen that image 1 appears much brighter than image 3. In the image there is a consistent pattern of sigma values decrease as moving from 32 on incident angle towards 42. The maximum return for image 1 is db in the close range while the minimum is db in the far 65

78 range. This represents a variation of 0.6 db. Likewise, image 3 presents a maximum return in the near range of db and a minimum in the far range of db, resulting in a total variation of 0.6 db. The curve of radar backscattering for image 2 is shown in Figure This figure presents a constant and strong decrease pattern of radar returns as moving from the close range towards the far range. The maximum radar returns occurs at 20 incident angle with db and the minimum at 36 degrees in the far range with db. This represents a total variation of 3.30 db. From Table 5-8 correlation coefficient of the three studied images, confirm the evidence of a strong relationship between radar returns and variations of incident angle. This relationship is negative and in all cases close to Correction model for incident angle dependency Once the effect on incident angle was detected and quantified as it was done in section 5.5, a model is necessary to counteract for its effects or to understand the behaviour and predict its influence on images. The former is done in section by means of a normalization algorithm which basically removes the decrease of backscattering along the range. The later is achieve in section 5.7 by means of a semi-empirical model which seeks to explain the variation of radar backscattering using radiative transfer theory Correction model In this section a model to normalize or remove the incident angle effect is proposed and tested. The model is based on the Lambert s cosine law reflection properties as it was already explained in section It was presented in [14] and its practical application can be found on [19]. The following equation relates the observed values on the image with the backscattering corrected values as presented in [14] : Where is the normalized backscattering value, are the sigma values observed and is the wave incident angle. In this equation n is the power to which cosine should be calculated in order to normalize this values. Different values of n will address different surface roughness characteristics. If n=1 the reflecting target is considered as a perfect lambertian surface and thus will correspond to a volume scatterer (reference). If n= 2 the model is considering a surface which is rougher than a lambertian surface, something not possible in theory but which has effectively work before in practical applications. In order to remove the incident angle effect the author initially tried the conventional method of applying cosine terms to the first or second power finding acceptable but not perfect results. A better estimation of n was sought as it will produce the best normalization curve in term of its flatness along the range. For this purpose a regression model was performed using the model of equation 5-5. The coefficients of a linear regression model were calculated in R software statistics package with the following regression equation:

79 sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Where y = log (σ) x = log (cos(θ)), with θ in radians, and n= power of cos(θ) After the model is run in R the n coefficient of the cosine power is found for the correction of sigma values. The intercept of the model is found by simply averaging the observed sigma values along the range. This procedure returns a normalized curve with a zero slope trend line, as shown in the results of this section Normalization class 2 (flooded forest) After running the regression model of equation 5-6 the coefficients present in Table 5-9 are found for the correction of class 2 in images 1 and 2. Table 5-9 Coefficient values for correction backscattering model class 2 Class 2 n b Correction model trend line slope Image Image Radar backscattering variation for class 2 is presented in figures 5-28 and For every plot the observed and normalized values can be seen for the two radar images considered Trend line equation y = x Measured sigma Corrected sigma Linear (Corrected sigma) Measured sigma Corrected sigma Figure 5-28 Sigma values corrected for class 1 image 1 67

80 sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Trend line equation y = x Measured sigma Corrected sigma Trend Measured sigma Corrected sigma Normalization class 5 (Savannah) Figure 5-29 Sigma values corrected for class 1 image 2 The application of the regression model (equation 5-6) estimates the following coefficients for the normalization of class 5, Savannah, in the three radar images Table 5-10 Coefficient values for correction backscattering model class 5 Class 2 n b Correction model trend line slope Image Image Image Radar backscattering variation for class 5 is presented in figures 5-30, 5-31 and For every plot the observed and normalized values can be seen for each of the three radar images considered in this research. 68

81 sigma sigma sigma ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Measured sigma Corrected sigma Trend Trend line equation y = x Measured sigma Corrected sigma Figure 5-30 Sigma values corrected for class 5 image 1-9 Measured sigma Corrected sigma Trend Trend line equation y = x Measured sigma Corrected sigma Figure 5-31 Sigma values corrected for class 5 image Trend line equation y = 0.001x Measured sigma Corrected sigma Trend Measured sigma Corrected sigma Figure 5-32 Sigma values corrected for class 5 image 3 69

82 For the five models considered in this section n values of the correction model vary between 0.56 and 2.29 found for image 1 and image 2 in class 2 and 5 respectively. The other three models return n coefficients close to 1. All the corrected models present a trend slope which is very close to zero Backscattering modelling The water cloud extended backscatter model implemented in this research was introduced and explained in section Four backscattering contributions are considered in the model: volume, surface, volume to surface and trunk to ground. Recalling their mathematical definition previously presented: Volume and surface contribution 5-7 Volume to surface contribution 5-8 Trunk to ground contribution 5-9 The definition of every term participating in the equations and their units were equally previously presented in section Initial parameter values for model fitting The initial estimation of A is taken from the backscattering statistics of L band HH polarization reported for the observation of trees in [15]. The values found in that work are consistent with observations done in forest ecosystems [39, 69, 71]. For the initial value of the attenuation coefficient (B) different studies from literature where consulted [39, 73, 87, 88]. As diverse studies focus on radar attenuation giving somewhat different values, only works on forested areas and L band observation were considered. This exercise allows the definition of the initial values as well as the bounding values this coefficient can take. The P value expresses the leaf area index. Experimentally this value varies from 0 for dry soils, to 9 for very dense forests. The work World s forest biomass reports LAI values measurements from different studies [89]. Observations of LAI for tropical wet forest ecosystems can be found in [90], where ground measurements are carried out for various species in Costa Rica. A report for LAI in different tropical areas of the Amazon can be found in [91]. Values of LAI considered for setting the initial P parameter can be found in Appendix VIII. 70

83 The initial estimation of C value and its domain is taken from the radar backscattering statistics compiled by Ulaby [15]. The backscattering of soil found there is consistent with many studies on radar observation published later for L band. The initial value parameter and their domains for the model fitting were mindfully taken from the sources mentioned just mentioned. The following table presents the estimated parameter values as well as their possible range of variation. Table 5-11 Initial and range of variation parameters values for model fitting Parameter Term Units Initial value Lower bound Upper bound A Volume backscattering coefficient db B Canopy attenuation coefficient db.m P Leaf area index C Surface backscattering coefficient db Bt Trunk attenuation coefficient db ht Trunk height to the canopy m a Trunk radius m For model computations all the parameters are entered in natural units, instead of decibels. Likewise the angle values are expressed in radians rather than in decimal degrees. Decibels are used in this document only for reporting backscattering values, which is a common practice in radar studies Backscattering modelling of dense wet forest (class 1) Prior exploration of the model with all the interactions reveals very low contribution from the underlying surface. In this case, backscattering contributions of very low magnitude were detected. Contributions of the order of 10-6 in natural units were found. Thus it was decided to neglect the soil term in the model. This observation agrees with those values found in [39]. The model fitted then does not consider anymore the surface reflection in the forthcoming results. Considering the observations of class 1, dense wet forest, in the rainy and dry season (images 1 and 3 respectively), the following parameter values are provided by BFGS fitting: Table 5-12 Fitting value parameters class 1 Image 1 Rainy season Image 3 Dry season A db db B 0.17 db.m db.m -1 P Bt db.m db.m -1 RMS

84 Using the values obtained by the fitting process the individual contributions of the considered backscattering interactions were calculated. Figure 5-33 presents the plots for the estimated backscattering contributions as calculated in the model for class 1. From the plots it can be observed that the larger backscattering contributions come from the canopy elements, which is around -7 db at nadir. The trunk to soil reflections are as significant as the canopy to ground returns in the very close rage, under 11 in the rainy season and under 20 in the dry season. For larger angles, the canopy to ground reflections play a more important role in the total backscattering. The trunk component is in both cases small, around -10 db in the close range for the rainy season and -9 db in the dry season. Figure 5-33 Backscattering modelling class 1. Dense wet forest. Individual contributions. Up: Image 1, rainy season. Bottom: Image 3, dry season. 72

85 Backscattering modelling of flooded forest (class 2) For flooded forest class the soil contribution is neglected as the layer of water prevents any reflections from the surface. The calculation of flooded forest model is done for the three radar images. The following parameter values are found for every image after BFGS fitting: Table 5-13 Fitting value parameters class 2 A B P Bt RMS Image 1 Rainy season Image 2 Rainy season Image 3 Dry season db db db 0.17 db.m db.m db.m db.m db.m db.m Using the values obtained by the fitting process the individual contributions of the considered backscattering interactions were calculated for class 2. Figure 5-34 presents the plots for the estimated backscattering contributions. The most remarkable fact which can be observed is the strong contribution of trunk to surface interactions specially in the close range. For the rainy season the trunk reflection is in the order of -4/-5 db and is more important than the canopy contribution itself. In image 1 the trunk contribution is larger and more influential than canopy scattering up to 41 degrees when it drops to a minimum at 56. The second image, acquired in the rainy season, presents strong trunk signal contribution up to 35, where it decreases below the canopy levels. In the three studied images the canopy to ground interaction is below -10 db. As it is evident and mentioned before, the total radar returns are much stronger in the rainy season than in the dry season for the flooded areas. This is confirmed by the model, where backscattering at nadir of -2 db is observed. 73

86 Sigma (db) ASSESSMENT OF THE INCIDENCE ANGLE ON RADAR BACKSCATTERING AND MODEL FOR CORRECTION IN SCANSAR PALSAR Incident angle Observed values Canopy Trunk to soil Total contributions Canopy to ground Trunk to soil Observed values Total contributions Canopy to ground Canopy Observed values Canopy Total contributions Canopy to ground Trunk to soil Figure 5-34 Backscattering modelling class 2. Flooded forest. Individual contributions. Up: Image 1, rainy season. Middle: Image 2, rainy season. Bottom: Image 3, dry season 74

87 Model assessment To determine how well the model and the estimated parameters explain the observed values in each of the images, regression analysis was implemented and estimation of the regression coefficient R² is done. The result of this analysis is presented in Figure 5-35 for class 1, and Figure 5-36for class 2. From the images it can be observed that the model and the estimated parameters fit very well the data for flooded forest in the rainy season. The model has the best fitting with Image 2 backscattering values, where a strong relationship is found between predicted and observed sigma values (R²=0.93). The confidence boundaries for the mean enclosed the 1:1 line, which confirms the good fitting of the model. Image 1, also acquired in the rainy seasons presents a rather good fit between the observed and predicted values (R²=0.78), although the 1:1 line is not enclosed in the confident interval limits at 95% of significance. The model for the dry seasons does not present a good fit. A weak relationship between the modelled backscattering and the observed values is found (R²=0.34). The 1:1 line also deviates from the mean regression line and presents a wider area for confidence limits at 95% significance. On the other hand, the model provides poor quality fit between observations and modelled backscatter in the two analysed image for class 1 (dense wet forest). Small values for the correlation coefficient (R²=0.22, R²=0.07) and wide confidence intervals confirm this result. There is also a strong deviation of the mean line respect to the 1:1 line. 75

88 Figure 5-35 Class 1 Backscattering observations Vs modelled backscattering. Interval confidence 95%. Left: Image 1 rainy season. Right: Image 3 dry season Figure 5-36 Class 2 Backscattering observations Vs modelled backscattering. Interval confidence 95%. Left: Image 1 rainy season. Right: Image 2 Rainy season. Bottom: Image 3 dry season 76

89 6. Discussion This chapter presents a discussion of the findings of this research, the methods implemented to achieve the outlined objectives and the implications of this study on the context of the radar remote sensing field On backscattering analysis by reflecting targets As part of the study on radar backscattering dependence on ScanSAR L band images, 3 ALOS datasets recorded in 2007 were examined. The test site is in the Colombian Orinoco region, characterized by diversity in land cover classes, presence of dense forest areas and seasonal variation of moisture content according to precipitation conditions. The first examination on range dependence was done on the three radar datasets revealing a strong variation along the range component. This is attributed to the uneven spatial distribution of the land covers in the images rather than to an observable effect of viewing geometry of the radar sensor. Comparing the brightness values between images, the results of this study showed a strong difference in the average level of backscattering for every acquisition date. The first two datasets, recorded during the rainy season, presented, overall, larger backscattering values than the image acquired during the dry season. The backscatter differences are remarkable, up to 2 db, especially in the forest areas near the main rivers and in the Mataven forest. The association of brighter returns with moisture content of the land covers at the time of image acquisition is the clearest factor for the observed difference. As a matter of fact, after reviewing the values of precipitation on the area (Appendix VII) for the days of image acquisition, this hypothesis was strongly supported. The data shows such strong accumulated precipitation in the area, that at the time when image 1 and 2 were acquired, flooding levels had reached large extents in the study area. The difference in brightness is clearly attributed to the variation of land covers moisture content between seasons. This is not surprising, considering that several authors have observed similar patterns in other studies [38, 47]. In order to determine the influence of incident angle on radar backscattering, analysis of radar returns was done separately for each land cover type. Five land cover classes were defined, according to a review of land covers distribution on the area based on reports and existing classification maps (section 3.1.1). A specifically designed sampling procedure, involving spatial datasets and GIS as well as image processing techniques was implemented in terms of testing the relationship between radar backscatter and range component. Once the sampling areas were selected, statistical analysis of brightness values of those areas was performed stratifying these areas in strips of 1 degree of incident angle variation. Based on the results of the backscattering study (section 5.5), analysing on the different classes can be done starting with the classes for which incident angle effect was found and continuing with those where effect was not revealed or concluding effect was not proved. Savannah - class 5 For the savannah class a clear and strong effect of incident angle was found. Variations of 0.6, 1.5 and 3.3 db were observed along the range for the three radar images. The correlation coefficients support the evidence of incident angle effect, as they are in all cases larger than Considering the physical 77

90 structure composition of this class, the backscattering behaviour associated with it can be easily explained. The definition of savannah class encompasses areas with short or no vegetation with the presence of pastures, small shrubs, bare soils, burnt areas or crops in different phenological state due to agricultural practices. When these surfaces are observed by microwaves in the low frequencies, the presence of small elements appear transparent to the incoming wave, the surface behaves as a smooth layer and therefore most of the energy is reflected away from the radar antenna. As a result, the probability of recording energy at the antenna decreases, and thus very low backscattering values are registered at the image. The further the target is from the sensor, the lower the probability of receiving returns at the radar antenna is. The brightness behaviour of savannah class, besides being understandable from a wave propagation point of view, results in consistency with previous radar works which presents similar findings. In [18] a backscattering decrease of nearly 10 db is found for ENVISAT images at incident angle ranges between 15 and 45º for dry and sandy soils. In [15] a total of 141 observations were reported on grasslands for L band frequency HH polarizations, an average decrease of 15 db was found for incident angle variation of 20 degrees. Flooded forest - class 3 Radar backscattering dependency on incident angle was found for the flooded forest category. Although this dependency is subject to the presence of flooding conditions as for the dry season no significant variation of brightness levels was found. Along with the evident incidence angle effect for the rainy season images, stronger returns were found for those images, where values as high as -2.5 db were found in the close range, for a later decrease of at least 1 db in the backscattering level towards the far range (Figure 5-15). Meanwhile the flooded forest class in the dry season presents lower brightness levels and insignificant variation along the range, with stable average return of -5 db. The backscattering values of the rainy season images suggest clear range effect, with correlation coefficients of and In contrast, image 3 presents a correlation of (Table 5-5). The backscattering trend of image 1 and 2 differs from that of image 3; nevertheless this should be revised in the context of precipitation conditions. In section 2.7 it was shown that studies have confirmed a combined effect of incident angle and moisture content on radar backscattering. Class 3 presents a semi permanent layer of water which is thicker during the rainy season. The presence of an underlying layer of water in open or semi open canopy produces double bounce scattering which is more evident at closer ranges. It can be stated that images 1 and 2, taken during the rainy season with a considerable amount of accumulated rain (see Appendix VII), exhibit relationship with incident angle observation because of double bounce returns in the close range. For the forested area under investigation, where a layer of water of a few meters is present during strong rainy seasons, the flooding conditions plays a very important role in the bright radar return values registered at the antenna. These strong values are explained by the physical interaction between the microwaves and the scatterers. The waves interacting with the flooded forest reflect on the trunks and water surface, two specular surfaces that being perpendicular to each other reflect most of the incident energy in a similar way as a dihedral corner reflector will do (Appendix II). As a result, strong reflections are detected at the antenna. Given the L band penetration, the reflections that occur more frequently at close ranges, are still observable at middle range and are unobservable in the far range. This fact fully explains the radar values registered for the ALOS images of the rainy season. In 78

91 contrast, during dry seasons, there is not river overflow, and thus the water does not stay in the understory. In that case, although the microwaves can penetrate and hit the underlying surface that behaves as a lambertian layer attenuating or reflecting the incident energy in different directions. The effect of corner reflector by the configuration trunk-underlying surface has been registered before in radar literature. The importance of double bounce scattering in forest areas when radar observation is done in L band frequency is mentioned in [39]. In [92] it is shown that L band flooded forest backscattering is dominated by a component which undergoes reflection from the forest floor. In [93], L band JERS images over the Brazilian Amazonia where analyzed during different rainy seasons. Radar backscattering dependence on water levels was manifest by angular radar signatures, detecting backscattering variations up to 3 db during strong flooding seasons. In [38], a review on the radar enhancement effect of flooded forest is done. It documents applications in different forest ecosystems where stronger returns of flooded forest are detected under low incident angles. Dense forest class 1 Regarding the land cover classes for which incident angle effect was not observed. Very important in terms of its presence and distributions is class 1, dense forest. This particular class was the most dominant in the investigated images and therefore its findings are the most reliable. Besides due to the good spatial distribution of this class along the range, it was possible to collect sampling areas along a wide variation of incident angles. For example, for image 2, radar brightness between 17 and 38 degrees of incident angle were selected (Figure 5-12). The presence of incident angle effect was absolutely ruled out for this class and in contrast, the most stable sigma values were found here, independently of the range position and of the moisture conditions. A maximum variation of 0.7 db was found for this class but no clear pattern was detected. Low values of the correlation coefficient, below 0.5 in all cases, support the independence of range backscattering from incident angle. In fact, apart from the statistical analysis and from the empirical manipulation of the images, there is a clear evident homogeneity of brightness values for this class as well as very uniform tones when examining the images along the range and when comparing the three images taken under different moisture conditions. The observed homogeneity of the dense wet forest along the range and between seasonal images brings the point of ALOS calibration stage. In [52] (Figure 2-14) it is seen that the radar calibration of the ALOS system was done using targets in the tropical Brazilian Amazon forest, which might be to a large extent, very similar to the areas studied here. The stable effect of class 1 may be the result of a dedicated radar calibration on tropical forests. A physical explanation of the backscattering homogeneity observed in class 1 is considered here. As the structure of this forest presents very small spatial variation within the image, it can be expected that the scatterers are homogeneous along the range. Furthermore, if these forested areas are not influenced by the rainy season as it was observed, the homogeneous returns come as no surprise. The high density of trees of this class, and the totally closed canopies might explain why even during the rainy seasons no corner reflections from the underlying surface are observed. The forest canopy of this class is so dense and closed that even a low frequency microwave, such as L band, cannot trespass or penetrate the upper and medium levels of the canopies, which will scatter most of the incoming energy. In [38], it is noted how for closed canopy mangrove no enhanced returns were observed in 79

92 SeaSat imagery. The authors argue that the attenuation by the canopy or understory vegetation was too great to allow significant double bounce returns. Another possible factor which can explain the backscattering homogeneity observed for class 1 and which should be further investigated is the issue of radar saturation. Several studies (some of them are reviewed in [41]) have proved that after a certain limit of biomass volume, the radar signal is saturated, and no variations are registered after that limit. According to published studies [40, 41], the saturation point depends on the radar wavelength. Estimates for L band radar have shown that saturation point is reached at biomass volumes varying between 100 and 250 ton/ha. For tropical forest in Guaviare, Colombia biomass volume between up to 300 ton/ha were measured in a radar study [4]. If in this research images of forest with biomass levels above the saturation point for L band are being observed, it might be possible that even for a variation of 20 degrees of incident angle no range effect will be observed, as it is being offset by the biomass saturation effect. In such case, the observations of class 2 - flooded forest, would remain consistent, as lower biomass levels might be expected for that class. Anyhow, a conclusion cannot be drawn on the issue of biomass saturation for forest ecosystems, as the author lacks the necessary data to validate this hypothesis. Nevertheless, it is a point that can be a matter of a further study using field verification. Short forest class 4 The backscattering values of short forest class did not vary with incident angle. Under the examination of two images, very stable backscattering values along the range were found. There is an interesting pattern observed in Figure The image captured in the dry season presents brighter returns as compared with those of the rainy season, approximately by 0.6 db. This goes against radar theory expectations [15], as a surface with higher moisture content, resulting in a higher dielectric permittivity, should exhibit stronger backscattering. Considering the obtained results for this class, and the observations during the rainy and the dry seasons, it is likely that the moisture content does not play a key role in radar backscattering for the short forest class, and the radar returns will be more related to the structural characteristics of the area. Tall dense forest class 3 Regarding the results of class 3, tall dense forest, no concluding results could be obtained on the effect of the incident angle. Images 1 and 3, taken during the rainy and the dry season, suggest a clear range effect, as the backscattering values decrease along the range 0.7 and 0.9 db respectively (Figure 5-19). The correlation coefficient is also higher than 0.8 in both cases (Table 5-6). Nevertheless for image 2, taken during the rainy season, its backscattering values show a lower correlation coefficient (0.49) with incident angle. From an observation of the backscattering plots of image 1 and image 3 the variation and pattern is constant and of enough magnitude to state that there is a relationship between incident angle and radar returns. Nevertheless the image 2 backscattering values behave in such a way that is very difficult to maintain this statement. Although it could be argued that an antenna pattern effect can influence the observed backscattering values. It is clear that, more observations are needed to draw a strong conclusion on the angular dependence of the backscattering values of this class. 80

93 6.2. On simplified correction model Once the effect of the incident angle was determined and quantified for the considered classes, a method for the correction or normalization of brightness values along the range within the affected classes was sought (section 5.6). As it was observed, the backscattering values are affected by the incident angle differently according to the land cover class in consideration. Thus a method which accounts for corrections at the level class, rather than at the whole image is necessary. The simplified correction method applied here is presented in [14] and takes into account the reflecting targets as lambertian reflectors which present diffuse scattering. In its mathematical form, the model associates the reflections with the cosine of the incident angle to the power index n. The task consisted in estimating the n values which normalizes or corrects for the backscattering drop of the sigma values along the range. Using linear fit regression of log-transformed sigma values, the n coefficient was found for the affected classes, namely flooded forest and savannah (Table 5-9 and Table 5-10). Once the values are corrected by the cosine model, flat and more stable backscattering curves along the range are obtained. The normalization on flooded forest produced very flat curves with a trend line slope of zero. For the Savannah areas the correction works satisfactorily for image 1 and 3, but the incident angle effect is so strong in image 2 that even after correction a trend can still be seen towards the close range. Some aspects should be commented on the application of the simplified model. Firstly, from the radar backscattering analysis it was clear that the range variation does not affect in equal proportion each land cover classes present in the image. Therefore a method which considers the correction at the land cover level is needed. Although most of the SAR distributors consistently apply a first order cosine correction to counteract for the range backscattering variation, improving the overall values of the whole image, they implement this correction ignoring the spatial variation of the land covers. It is worth to say that this might be acceptable from the distributor point, as time as well as dedicated work for every image will be needed to correct for the range dependency. Nevertheless, if required by the application, the radar operator should consider applying a more thorough correction where the distribution of the land cover classes is taken into account. Considering the application of the simplified correction model, if a correction for incident angle is used at all, the cosine normalization is normally applied to the whole image. For more accurate results it is needed to implement this method at the land cover level. In a forest monitoring system application, where there is a need for an analysis of the radar images in a limited time, the use of existing land cover maps or the implementation of simple supervised land cover classifications will be sufficient to produce a layer with the land covers distribution of the area. Using these spatial datasets and actual estimated or historical estimation of n power indexes, it will be feasible to apply the cosine correction method at individual land cover class level On backscattering modelling After the application of the simplified correction model, a different and more sophisticated solution which permits to understand the radar backscattering under different incident angles was sought with the implementation of a semi empirical backscattering model based on radiative transfer propagation of the radar energy (section 5.7). The implemented backscattering model was based on the Ulaby 81

94 water cloud model [68] and the later extended by Richards for application in forested areas [39]. The considerations taken into account for selecting the water cloud model for this research are: o o o The water cloud model is a semi empirical model which uses a simple approximation for the modelling of radiative transfer energy and has been widely used with satisfactory results in vegetation studies. This is the case even though most of the studies have been done in very homogeneous targets such as potato [73] and sugar beet crops [11]. Although many more advanced and sophisticated models have been developed in recent years (section 2.7.3), the water cloud model is still attractive in practice given the few input parameters, its simplicity and its theoretical foundations. The use of a simple backscattering model results in an easier inversion and thus it is feasible to obtain physical variables of the reflecting targets. Depending on its application, variables such as moisture content, leaf area index, energy attenuation or biomass volume can be estimated. Nevertheless, as the original water cloud model considers only the interactions of vegetation and the underlying soil, it cannot fully explain the radar backscattering of land covers such as dense forest in wet and flooded conditions (class1, class2), which presents a more complicated structure. Moreover, the physical structure and multiple scatterings of forest areas have a more noticeable influence when the incoming microwave radiations are in the low frequencies, such as the L band (1.3 GHz) observed in this study. In consequence, the model adopted for backscattering modelling in this research was based on the originally proposed Attema and Ulaby water cloud model [68] and the energy interaction types derived in Richard s model [39]. The extended version of the water cloud model considers the canopy to ground and trunk to ground interactions (section ). These last two considerations are relevant when modelling the radar backscattering of forested areas. Furthermore, the extended version of the water cloud model was considered appropriate due to the very bright values observed during the radar brightness analysis in section 5.5.2, in particular for flooded forest areas. According to previous studies [47, 72] and based on the analysis of the digital values of the images during the research, it can be expected that a major part of the high reflection of flooded forest during the raining season in L band is explained by trunk to ground and canopy to ground interactions. The model required several parameters to describe the expected L band backscatter in the considered areas. The values of the parameters were found by means of a least squares minimization algorithm, where initial parameter estimation and domain bounds were required as an input. As the success of the minimization method depends to a large extent on the initial values of the parameters, a thorough selection of the parameters of the model was done. This step involved the consideration of the physical conditions of the test area and research on previous studies to achieve the best possible estimation. A successful implementation of the model in R software produced modelled backscattering curves of the individual contributions of considered energy interactions along the range component. It is important to mention that the implementation of the model for the dense forest (class 1) and flooded forest (class 2) permits measuring the variations of the total backscattering along the range as well as the individual contributions (Figure 5-33 and Figure 5-34). For the dense forest, the model predicts a maximum return of -4 db in the nadir point (0º incident angle) which decreases 82

95 progressively until reaching a minimum of -10 db in the far range. The backscattering modelling of the dense forest class supports the previous considerations about the lower contribution of the trunk to soil reflections due to a dominant backscattering of the canopy elements in the upper and middle strata. Backscattering modelling of the flooded forest class produced curves which besides explaining the overall backscattering along the range provide additional support for the hypothesis of strong reflections in the close range caused by corner backscattering returns originating in the layer of water present during the rainy season. According to the modelling results, under flooding conditions, the trunk to soil contribution is even more dominant than the canopy term. This effect is confirmed for near and middle range values. In contrast, for incident angle larger than 40º the most dominant contribution comes from the canopy. The finding on corner reflections as mentioned before was confirmed by some studies on tropical forest areas, and the results found here are supported by that reported evidence. This also suggest the validity of the extended applied model, as the original model, considering only vegetation and soil contributions, will be insufficient to explain the radar backscattering of a land cover category complex in structure such as the flooded forest areas considered here. The statistical evaluation of the implemented backscattering model revealed a remarkably good fit for the flooded areas during the rainy season (Figure 5-36). Meanwhile the model failed to provide a good fit for the dense forest class. This difference between modelled and observed backscattering can be traced back to the backscattering analysis stage, where no range dependency was found. The behaviour observed for this class does not follow the assumption of the backscattering model. Furthermore, the cosine reflection law and the exponential attenuation law were not manifested here. Nevertheless, the model cannot be totally neglected for the consideration of dense forest class 2 as it consistently explains the backscattering contributions resulting from canopy, trunk and trunk to ground interactions. Regarding the application of the model implemented here it is important to mention that the water cloud model, originally designed for the radar backscattering modelling of vegetation with simple structure, has had a wide application for modelling of crops and species where homogeneous structure is almost always the case. The use of the water cloud model has had limited application on tropical forested areas. For these areas, heterogeneous and complex structure is the rule rather than the exception. Several considerations should be done on the specific implementation of a model. The double bounce interactions addressed in this research is one of those variables which a model should include. As it was mentioned before, a fact which makes the water cloud model and its extended version attractive for this research is its simplicity, facility of inversion and the considered inputs, which are feasible and relatively easy to measure. Besides, if the model is successfully inverted, as it is the trend in environmental science, valuable variables sought by forest studies and applications would be easily measured. The water cloud model, if successfully applied, provides variables such as leaf area index, soil moisture content, biomass levels, and phonological state information on crops. Although new scattering models emerge with the progress of the new research during the nineties and the beginning of this century, many of them remain inapplicable due to their complexity or the many inputs required from the observed areas. There is also a risk of overfitting, coming from models which produce high accuracies or fit perfectly at the expense of vary detailed and impractical ground 83

96 measurements. For a model to be of practical in terms of its applications, it should provide acceptable predictions while keeping its simplicity to the lowest possible limit so that its formulation and input requirements do not prevent its application in areas of difficult accessibility. In a forest monitoring scenario, such model will be feasible to apply and will motivate applications such as the correction of incident angle variation on backscattering values On used materials As this is one of the first studies which have the privileged opportunity to make use of the ALOS PalSAR images after the calibration phase, some remarks should be made. The use of the ScanSAR images allowed conducting the experiments and observation of the L band radar returns with no difficulty. The images were of good quality and of overall good radiometry for the examination of the land cover categories considered here. Of relevant issue is the consideration of the radiometric accuracy provided by the PalSAR sensor. The JAXA agency after the calibration process, warranties a radiometric accuracy varying between db [52]. Although a larger accuracy is often required, the estimated value can be considered sufficient for the conducted experiments, especially if the accuracy remains in the lower level of the estimated values. In fact, for the classes where incident angle effect was detected, their variations along the range were larger than 0.5 db. An issue that deserves special attention is the antenna pattern correction effect on the PalSAR images. This disturbance of the image results somehow more complicated for the ScanSAR PalSAR product due to the acquisition mode. As it was presented in section 2.6.3, the ScanSAR image is formed by five consecutive scans of the radar antenna across the track. This results in a 350 km wide swath image where five antenna patterns effects will be observed. A successfully correction becomes more difficult to achieve and therefore the effect after antenna pattern processing is still visible, producing variations at least of 0.5 db as it was presented in 5.3. This effect, which is not exclusive of the PalSAR images, affecting all spaceborne and airborne SAR data products, is considered to be among the acceptable limits so that the results of the experiments are not substantially affected by it On applied methods As no field survey was involved in this research, is has been a challenge to select the most suitable sampling areas representative for each considered class in terms of testing the range dependency. Spatial information datasets consisting of optical satellite images, digital elevation model, land cover maps and several thematic maps were used as data input for the sampling process. GIS and image processing tools were used to process and relate the spatial data sets used during the sampling stage. Methods such as geo-referencing, mosaicking, image enhancement, raster editing, masking, raster modelling, surface analysis and raster statistics were used in order to determine radar pixels addressing the spatial criteria. As radar speckle is an issue that should be considered in radar processing and which inhibits the efficient use of pixel based classification procedures, image segmentation was implemented during the sampling stage. This method allows the generation of segments which follow certain brightness homogeneity as defined in the parameters of the segmentation process. The generated segments address a certain level of homogeneity and have a significant size, so that they can be used overcoming the limitations of radar speckle, yet small enough to ensure constant incident angle. 84

97 Summing up, the sampling design is composed of context knowledge spatial criteria and radar brightness criteria. The areas identified by the consideration of both set of criteria are suitable to be studied in the radar land cover backscattering analysis stage. It can be considered that this sampling approach is innovative and interesting not only for the radar processing field but for more general satellite image processing applications. As it is perceived now, after reviewing diverse works of other authors studying radar backscattering and radar land cover detection, the use of criteria such as these applied here, although not new for the scholars, have been scarcely considered. The reason behind this may be that those studies had as the most immediate solution visiting the test site and doing survey campaigns. Nevertheless for scenarios where ground survey is limited or accessibility of the areas is hampered by local conditions, the case of flooded forests considered in this study, the selected approach of using existing spatial data sets and spatial data processing techniques results in an efficient an optimal strategy which is worth considering to apply. On the other hand, the accuracy of the sampling technique applied here is still subject to validation. The accuracy assessment of the sampling stage was not performed due to economic and time allotting factors. An acceptable quality is assumed here so that it does not affect the measured values. Anyway, the assessment of the sampling approach such as the one used here, remains as a proposition which should be addressed by a focused and dedicated research on spatial sampling methods On applications of this study After the examination and modelling of radar backscattering in forested areas of the Colombian Orinoco region and the examination of the incident angle effect it is necessary to elaborate on some possible applications of this study. A first application which is of great importance for the environmental science community is the use of incident angle studies to improve the understanding of radar backscattering values in forest ecosystems. This can provide fundamental information on forested areas of limited accessibility and ecological importance. If factors affecting SAR information are better handled and exploited the application of tropical monitoring systems as sought in [25] and [23] will be in some degree prompted. In this thesis two approaches were followed in respect to the treatment of the incident angle backscattering dependency. The first one is to try to radiometrically correct and normalize the digital values of the images. This solution can be selected when the user requires images without angular system effects and where absolute backscattering values are needed independently of range variation. It was shown, how a correction model relating the sigma values to an n power index can be successfully applied to counteract for angle variations. This procedure, as previously pointed out, should be applied separately for each land cover class, as the range dependency was shown to depend strongly on the observed target. On the other hand, the second approach to handling incident angle variation is implemented by means of a radar backscattering model. In this case we try to understand the backscatter angular variation and exploit this effect to extract information from the observed targets. The implementation of the extended water cloud model here or a more refined version of it, from its simplistic approach and satisfactory results, will be a convenient way to obtain physical and structural information on the observed forest types. 85

98 One can think of an application where the use of backscattering measurements along the range and radar backscattering models can be used to implemented expert knowledge classifiers that will consider the range position of the image pixels to determine their membership to a given land cover class. The consideration of range information besides the radar brightness values of the image will eventually improve the land cover classification results. Considering the effective use of L band data and the satisfactory results of the water cloud model for flooded forest class modelling, the identification of flooded forest areas can be addressed. As it is stated in [25], a flooding map and a forest type map are of great importance in the monitoring of tropical forest. In this research it was shown how the extended water cloud model explains better the variation of radar backscattering along the range for flooded forests. Given the relative simplicity of the model, in terms of the input parameters, it is feasible to further explore it in flooded forest areas and to validate the model results. An additional practical implication comes from this study. So far most of radar applications and studies have consistently neglected the incident angle effect on spaceborne SAR images. With this study it has been demonstrated that for a ScanSAR product even after calibration and first order cosine correction there is still a significant remnant angular variation related to the land cover type. Therefore, the angular variation should be considered in spaceborne large swath images once a definition of the application purpose and needed accuracy for backscattering measurement has been settle. 86

99 7. Conclusions and recomendations 7.1. Conclusions The main objective of this research was to study the influence of the incident angle on radar backscatter in ScanSAR PalSAR images and to develop a model for its correction in forested regions. To achieve this, it was proposed to establish the influence of angular variation on radar backscattering in ScanSAR products considering different land cover types. Afterwards a method was sought for the radiometric correction of backscattering aimed to counteract for the incident angular variation. Influence of incident angle on radar backscattering values The angular variation of backscattering values was measured in three ScanSAR images of the Colombian Orinoco region. Five land cover classes were investigated. After analysis of backscattering values by means of a sampling stage the following conclusions are drawn on incident angle effect: Significantly different angular backscattering effects were observed for each land cover class studied. The report for each considered land cover class is as follows: Class 1. Dense and wet forest: No effect was observed. Very stable backscattering along the range was observed. It may be attributed to biomass saturation limits, and the absence of corner reflections due to high density of trees and closed canopies. Class 2. Dense flooded forest: Strong incident angle effect subject to flooding conditions was detected. Variations up to 1 db sigma-log values were measured. Bright corner reflection returns can be explained with trunk-ground interaction mechanism occurring mainly in the close range. Class 3. Tall dense forest: No conclusive results were found on the incident angle effect. There seems to be an angular backscattering dependency based on the analysis of two images but more observations are needed to confirm this. Class 4. Short forest: No angular dependency was observed. Class 5. Savannah: Strong incident angle effect was observed. Backscattering variations up to 3.3 db sigma-log values were observed along the range. Correction model From the backscattering analysis it is concluded that the normalization of backscattering values must be performed separately for each land cover class. This arises from the different backscattering variation that each land cover has due to its physical structure and intrinsic characteristics. For class 2 and class 5 where backscattering variations were detected, a radiometric correction method was successfully applied. The method works considering the reflecting targets as lambertian surfaces with backscattering related to the n index power of incident angle cosine. After application of correction on images normalized backscattering levels along the range were obtained. 87

100 Backscattering modelling As a more refined approach to understand and exploit the angular dependence of backscattering values, a semi empirical model based on radiative transfer theory for backscattering modelling was applied. The water cloud extended model explained satisfactorily the angular variation of backscattering values for flooded forest (R 2 of the model larger than 0.78). When applied to dense forest, the model did not perform as successfully although it explains consistently the individual contributions of the land cover backscatters. Although the water cloud model was proposed more than twenty years ago and more refined and advanced models have been proposed in recent years, it is an attractive solution for forest backscattering modelling given its simplicity, input parameters and ease of inversion Recommendations An extended water cloud model was applied for backscattering modelling of forested ecosystems. The model performs fairly well explaining the radar returns of flooded forest ecosystems but did not do so for the very dense vegetation. It is argued that the reason may be on the biomass saturation limits observed for L band. A specific study should be directed towards determining the reason behind the observations obtained for very dense forest. The water cloud model is nowadays successfully considered for land cover types of simple physical structure, such as soils and crops. The results found for flooded forest using the extended water cloud model suggest that even for very complex land covers such as tropical forest it is still possible to obtain acceptable results within a low level of model complexity or parameterization. The applicability of the extended water cloud model for tropical forest scenarios with field verification is a potentially attractive study to be implemented using products such as ALOS L band SAR images. Regarding the use of ALOS the antenna pattern correction applied in the provided processing centre still presents disturbing effects in particular for ScanSAR product. Although the CEOS committee and JAXA are making efforts to improve the calibration and quality of SAR products, there is still a gap to be filled in this respect. At the beginning of execution of this research it was intended to use ALOS PalSAR images in a lower processing level than 1.5. Nevertheless the lower level corresponds to 1.0 raw unprocessed unfocused data. Due to the current distribution of PalSAR ScanSAR products it is not possible to obtain a processing level 1.1, which is equivalent to slant range projected and focused data. It would have been better to have access to the products 1.0 in order to evaluate the radiometric implications of processing operations done by the distributor. 88

101 Appendix I. Acquisition modes of ALOS system ALOS observation modes Fine beam single polarization (FBS) Fine beam double polarization (FBD) ScanSAR (WB1) Polarimetry (PLR) Chirp bandwidth (MHz) Polarisation HH HH/HV HH HH/HV+VV/VH Off-nadir angle (deg) Incident angle (deg) Swath width (Km) Bit quantization (bits) Data rate (Mbps) Range resolution (m) Azimuth resolution (m)

102 Appendix II. Radar backscattering of a tree trunk modeled as a dihedral corner reflector The reflections of the cylindrical trunk are modelled as a dihedral corner reflector with area proportional to the projected plane reflected by the trunk. The image below illustrates the geometrical modelling of the trunk to ground reflections (from [39]). The radar cross section of a plate is given by: Where λ is the radar system wavelength and Ae is the area of the projected dihedral structure calculated as: Where is the assumed height of the trunk and W is the width of the projected cylinder given by: Where a is the cylinder radius. A homogeneous trunk height of 13 m and a trunk radius of 0.4 m were assumed for the model. 90

103 Appendix III. Land cover photographs in study area Photographs: Up: Photograph taken at the beginning of the rainy season of The water level rises and causes floods that cover forest ecosystems. At the moment the photograph was taken part of the trunks and canopy of trees is already covered by water. Bottom: Mataven forest flood at the beginning of the rainy season of Souce: Marcela Velasco Gomez. 91

104 Photographs: Area of savannahs located in the NW part of the radar images. Savannahs when observed by the L band radar are flat smooth surfaces that reflect low part of the incoming energy depending on range position and moisture content. It can be seen also the influence of forest along the meandering river. The images were taken during flight survey of the SIMCI project in January Source: SIMCI Project UNODC Colombia 92

105 Photographs of very dense forest in the study area. This land cover class dominates large extensions of the image. These forest present large volumes of biomass and closed canopies that reach an average height between 20 and 30 meters. In the image it is observed as bright homogeneous areas where most part of the backscattering is due to contributions from the upper and middle canopy strata and from branches and trunks, regarding that L band radar offers larger signal penetration. The images were taken during flight survey of the SIMCI project in January Source: SIMCI Project UNODC Colombia 93

106 Appendix IV. Distribution of land cover types in ALOS image 1 Venezuela Savannahs Guaviare river Flooded forest Mataven Short forest Vey dense forest Tall vey dense forest Radar shadows Highest peak 692 m ALOS PalSAR image 1 taken on July 17, Study area 94