A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE. By CHARLES T.

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1 LAND USE / LAND COVER CLASSIFICATION: METHODS TO OVERCOME PIXEL CONFUSION AND THE EFFECTS OF TREE SHADOWS IN VERY HIGH RESOLUTION MULTISPECTRAL IMAGERY A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE By CHARLES T. NICHOLS NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI JUNE, 2012

2 LAND USE / LAND COVER CLASSIFICATION Methods to Overcome Pixel Confusion and the Effects of Tree Shadows in Very High Resolution Multispectral Imagery Charles T. Nichols Northwest Missouri State University THESIS APPROVED Thesis Advisor, Dr. Ming-Chih Hung Date Dr. Yi-Hwa Wu Date Dr. Patricia Drews Date Dean of Graduate School, Dr. Gregory Haddock Date

3 LAND USE / LAND COVER CLASSIFICATION Methods to Overcome Pixel Confusion and the Effects of Tree Shadows in Very High Resolution Multispectral Imagery Abstract Very-high resolution aerial imagery, while providing a very high level of detail of the land surface, also introduces new challenges for land use / land cover classification. Higher spatial resolution increases the spectral heterogeneity in the land cover features being classified, and markedly increases the shadows in the image resulting in very large numbers of confused pixels which can exceed 55% of an aerial photo image. This paper describes a methodology that resulted in a LULC classification accuracy of 89% with a Kappa of 85% (five classes: coniferous, deciduous, bare ground, water, and roads) by overcoming the intense pixel confusion caused by very heavy tree shadows and the use of very-high resolution (30cm) multispectral imagery (Green, Red, NIR) in a forested, non-urban area. ISODATA classification using a large number of clusters with subsequent iterative majority filtering handled the heterogeneity well. Shadowed areas were identified, removed, and underwent a separate ISODATA re-classification to effectively assign those pixels to the base LULC classes to overcome the confusion caused by shadows. A process of elimination, using a hierarchical class model, was a simple, effective way to overcome the challenges of having to deal with the spectrally complex, highly confused deciduous class. iii

4 Table of Contents Page Abstract.. List of Figures.. List of Tables.. Acknowledgements... List of Abbreviations.. iii v vi vii viii Chapter 1: Introduction.. 1 Research Objective Chapter 2: Literature Review. 6 Classification of Very High Spatial Resolution Multispectral Imagery 6 Initial Land Cover Classification Approaches 7 Refinements to Improve Classification Accuracy. 12 Challenges with the Use of Very High Resolution Multispectral Imagery 15 Chapter 3: Conceptual Framework and Methodology 18 Study Area Description.. 18 Data Sources and Conditions 19 Software Utilized Conceptual Basis for Methodology.. 20 Image Classification.. 26 Methodology Accuracy Assessment 32 Chapter 4: Results and Discussions 34 Classification Results Error Matrix Results Chapter 5: Conclusions. 45 Research Limitations and Areas for Further Development 46 References. 49 iv

5 List of Figures Page Figure 1. False color aerial photo of 1.5 x 1.5 km study area Figure 2. Hierarchical class decision flow Figure 3. Vastly different spectral characteristics for roads due to shadows, snow, slush, and dry pavement surfaces Figure 4. Image classification flow chart Figure 5. ERDAS IMAGINE data model for combining non-road classes Figure 6. Stratified random sample locations Figure 7. Results of the aggregated ISODATA classification Figure 8. ISODATA classification of shadow mask to other classes Figure 9. Results of reclassifying the shadows separately Figure 10. Coniferous mask before and after MMU consolidation Figure 11. Side by side comparison of final LULC map and aerial image v

6 List of Tables Page Table 1. Samples per stratum Table 2. Results of initial ISODATA clustering and coding Table 3. Results of matrix-combining of initial and shadows only classes Table 4. Results of reassigning confused pixels based on proximate dominant class.. 40 Table 5. Initial versus final proportion of pixels belonging to each land cover class.. 42 Table 6. Accuracy report of the land use land cover classification vi

7 Acknowledgements I am very grateful to Dr. Ming-Chih Hung for chairing my thesis committee and for his encouragement, guidance, expertise, and support. I also thank Dr. Yi-Hwa Wu and Dr. Patricia Drews for serving on my thesis committee and contributing their perspectives and insight while I was developing my thesis. Northwest Missouri State University and the faculty of the Humanities and Social Sciences department have earned my respect and thanks for their support for the outstanding online Masters in Geographic Information Science program that they developed. vii

8 List of Abbreviations AOI DALA ISODATA LULC NIR OOC PBC Area of Interest Data Assisted Labeling Approach Iterative Self-Organizing Data Analysis Technique Algorithm Land Use / Land Cover classification Near Infrared Object Oriented Classification Pixel Based Classification viii

9 CHAPTER 1 INTRODUCTION Very-high spatial resolution imagery (less than ½ meter) over forested lands offers land managers special benefits and challenges for land use / land cover (LULC) classification mapping compared to lower spatial resolution multispectral imagery. Important benefits include higher detail and potentially higher accuracy. Yet, despite these benefits, challenges include higher intra-class spectral variability and confused pixels that can adversely affect classification accuracy (Yu et al. 2006; Orny et al. 2010; Im and Jensen 2005; Sawaya et al. 2003). The primary benefit is that the higher resolution offers the ability to see much higher detail of the land surface providing sharper, more accurate delineation of LULC boundaries where there are distinctive differences between classes such as roads, field boundaries, buildings, and parking areas. Plus, very high resolution imagery offers a greater ability to distinguish smaller features such as individual buildings and trees, and small areas of individual vegetation species, whereas pixels in lower resolution imagery average out the spectral reflectance of individual features and vegetation species within each pixel s coverage area. Sawaya et al. (2003) stated that the clarity of high resolution imagery provides for more visual interpretation and assessment than previously possible with lower resolution imagery such as Landsat. However, higher resolution may not improve classification performance and accuracy (Hsieh et al. 2001; Su et al. 2004). This is because the higher intra-class spectral variability associated with very high resolution imagery reduces the separability between classes resulting in high pixel classification confusion (a relatively high number 1

10 of pixels that are not readily assignable to a known class). This is manifested with a salt-and-pepper appearance from the confused pixels in the classification results because individual pixels are classified differently from their neighbors (Yu et al. 2006). For instance, any single 30 cm (1 foot) pixel within a tree crown may have a distinctly different spectral reflectance versus the tree pixels surrounding it. Orny et al. (2010) also found that class boundaries derived from very high resolution imagery are often poorly defined in forested areas because of the greater heterogeneity of spectral reflectance within the same class, such as the folds, gaps, shadows, and convolutions in a tree canopy, and in the gradual transition areas on the ground from one class to another, such as coniferous to deciduous. Moreover, Im and Jensen (2005) stated that high spatial resolution imagery, unlike moderate or coarse resolution imagery, exhibits high-frequency components with high contrasts (such as shadow pixels) where there are horizontal overlays of objects that protrude above the terrain (e.g. tall trees) caused by off nadir look angles. In this environment, an individual tree has considerable spectral variability (e.g., pixels representing sunlit crown, shaded crown, and the influence of branches) that puts limitations on a single unique spectral signature for tree classification. Likewise, Sawaya et al. (2003) stated that the spatial detail of high resolution imagery is impressive but the spectral-radiometric similarity between certain classes is compounded with the presence of confused pixels and the greater variability within classes. Hsieh et al. (2001) studied the effect that spatial resolution has on classification errors of pure and mixed pixels in remote sensing. Pure pixels are those containing only one class of land cover, while mixed pixels are those composed of two or more classes. 2

11 As spatial resolution gets finer, the proportion of pure pixels increases, while the proportion of mixed pixels may go down. There is also an increase in spectral covariance of pure pixels on LULC classification error because of reduced class separability. The increase in classification error becomes sharper where there is higher within-class variability. The overall classification accuracy of mixed pixels may or may not decline with increasing spatial resolution depending on how extensive the class boundaries are in the study area. The boundaries between two or more land cover classes often create mixed pixels consisting of varying proportions of each class. This is known as the boundary effect. Where the study area has small, convoluted, or linear surface features, higher spatial resolution often gives a lower classification error. This error reduction is partly due to the decreased proportion of mixed pixels to total pixels in the image. So, Hsieh et al. (2001) pointed out that there is a counterbalancing effect of within-class variability and the boundary effect on land cover classification error with higher resolution imagery. Therefore, as suggested by Orny et al. (2010), very high resolution imagery requires new image analysis methods to address the challenges of additional noise (confused pixels) as a result of more details in texture and shadows, plus the fact that structurally homogeneous groups of trees are represented by heterogeneous pixels. Further class confusion occurs in heavily shadowed areas of forests. This is caused by a combination of low sun angle and tall trees that result in long shadows where pixels have very low brightness values. Very low pixel brightness values can make it very difficult to distinguish the LULC classification type hidden in the shadows because the spectral signatures are so similar between them. Sawaya et al. (2003) found 3

12 that pixel classification confusion of dark features include open water and shadows. Pixel classification confusion of moderately dark features included wetlands, shadows and forest damage. And, pixel classification confusion of bright features included concrete and bare fields. Sugumaran et al. (2003) discovered that in images with 1 m resolution, tree crowns could be identified with a minimum shadow effect. However in 25 cm very-high resolution images, the trees are seen more clearly but the resolution is so high that the number of shadow pixels also increases. In 4 m images tree crowns cannot be separated from shadows as they span only one or two pixels. The LULC mapping project of the Lake Maumelle watershed for the Arkansas Central Water District demonstrates an excellent case study of the issues described above. The District desired an LULC map to aid in its resource management, land use planning, surface water runoff management, and timber management. The LULC classification was to be derived from 30cm (1-foot) February 2009 leaf-off multispectral aerial imagery (Green, Red, and NIR). They desired extraction of five LULC classes composed of coniferous forest, deciduous forest, bare ground, water (ponds, rivers, open streams), and roads (all types) with a minimum mapping unit (MMU) of 0.25 acres (1011m 2 ). The.25 acre MMU was chosen for practical purposes because it was the smallest area involved in any land management decisions. The LULC classification accuracy had to be at least 85% as measured from an error matrix table. The deliverable was a digital LULC map of a 1.5 x 1.5 km pilot area with a 30cm (1-foot) pixel resolution. The two issues of particular interest in this project were how to deal with the high pixel confusion associated with very high spatial resolution multispectral imagery 4

13 and the effects that heavy tree shadows had on land use / land cover (LULC) classification accuracy in non-urban, forested areas. In the Arkansas study area, there were significant heavy tree shadows in the aerial imagery that hid many types of land cover classes over the land north of the trees. These shadows were so dark that it was not possible to distinguish the correct land cover class in the shadowed area using a simple image classification approach that was heavily reliant on image-wide spectral reflectance alone. So, an alternative methodology was warranted. There were also large, mixed, transitional land cover areas between many of the coniferous and deciduous areas. The LULC classes specified by the Arkansas Central Water District did not include a mixed coniferous-deciduous class. Therefore, it was necessary to test a methodology that logically assigns any particular pixel to its most likely classification within the 1101m 2 (0.25 acre) MMU size requirements. Research Objective With these concerns in mind, the objective of this study was to develop and test a methodology that resulted in a LULC classification accuracy exceeding 85% (five classes: coniferous, deciduous, bare ground, water, and roads) by overcoming pixel confusion caused by very heavy tree shadows and mixed transitional land cover in nonurban areas with the use of very-high resolution (30cm) multispectral imagery (Green, Red, NIR). 5

14 CHAPTER 2 LITERATURE REVIEW An understanding of land use/land cover patterns and land surface characteristics in forested areas is very important for effective resource management. Accurate land use / land cover (LULC) maps depicting the location, size, and shape of various land cover classes and their proximity and access to roads, lakes, and streams provide critical information for land use planning, timber and natural resources management, environmental impact assessment, and surface water runoff management. Classification of Very High Spatial Resolution Multispectral Imagery Decades of manual photointerpretation for detailed vegetation and land use mapping have been replaced with new digital technologies (Sandmann and Lertzman, 2003). Over the past 30 years, the spatial resolution of digital remote sensing has dramatically improved since the launch of the first Landsat low spatial resolution (80m) multispectral satellite beginning in the early 1970s. These improvements led researchers to create new computer-aided analysis tools for creating land use and land cover (LULC) maps (Bauer et al. 1994). Since then, the launch of IKONOS and Quickbird, plus the advent of ultra-high resolution (15 cm) digital airborne cameras, has resulted in imagery being captured at the highest spatial, spectral, and temporal resolution in history (Ehlers et al. 2003). Ehlers et al. (2003) further stated that these very high and ultra high resolution digital cameras offered new possibilities for mapping of the environment very accurately but that standard classification techniques must be supplemented by developing new analysis procedures. This is because the homogeneity of LULC classes 6

15 can no longer be achieved by the integrating effect engendered by large 20-80m pixel sizes. They also advocated making use of GIS integration, context-based interpretation schemes, and multi-sensor approaches to achieve higher accuracy. Furthermore, Blaschke and Strobl (2001) pointed out that simple pixel-based analyses are no longer applicable because of the difficulty of classifying very high resolution data where each pixel is related not to the character of an object or an area as a whole, but its components. Thus, the salt and pepper sprinkling of confused pixels using a pixelbased approach increases as spatial resolution increases. So, to address this problem, other approaches such as object-oriented classification and further post-classification of pixel-based techniques emerged. Initial Land Cover Classification Approaches LULC classifications from digital imagery generally use either pixel-based classification (PBC) approaches or object-oriented classification (OOC) approaches. PBC consists of supervised or unsupervised approaches. Lillesand et al. (2004) stated that a supervised approach uses samples of known information classes (training sets) as a guide to automatically classify pixels of unknown identity. There are two stages training and classification. In the training stage, small sets of pixels are selected by an analyst within each land cover class that best represent that class so that the spectral attributes of that class are captured. In the classification stage, the spectral signatures in the training sets are compared with those of other pixels in the image and the other pixels are then automatically categorized into the land cover class that they correspond to. Jenson (1996) emphasized that the accuracy of a supervised classification depends largely on the quality of the training data. Generally, it is best that these training data 7

16 are collected at homogenous sites. This can be difficult when using very high spatial resolution imagery because of the highly heterogeneous nature of features within each land cover class. Sugumaran et al. (2003) discouraged using a supervised classification approach because this high spectral heterogeneity would increase the number of errors. High heterogeneity would also require a large number of training sets for a complete classification of a LULC within the image, which would be very labor intensive. Lillesand et al. (2004) described an unsupervised classification approach as founded on the natural, inherent grouping of spectral values within an image. There are two stages automated classification and manual class labeling. In the classification stage, pixels are statistically segmented over multiple iterations into groups of spectral clusters each cluster having its own spectral characteristics. The analyst then labels each cluster to an information class based on expert knowledge and/or ground truth data. Lang (2007), Sawaya et al. (2003), and Germaine and Hung (2011) used an ISODATA unsupervised classification approach on the very high resolution imagery used in their research. With this approach, it is common for the analyst to set parameters for the initial number of spectral cluster classes, convergence threshold, and maximum number of iterations. Lang (2007) created and compared 24 LULC maps composed of six land cover types representing a varying number of spectral cluster classes (4, 8, 16, 32, 64, 128, 256, and 512) and convergence thresholds (95%, 97%, and 99%). Classification accuracy dramatically improved with higher numbers of initial cluster classes, and to a lesser extent with an increase in the convergence threshold setting. Classification accuracy stopped improving when the number of cluster classes reached 64 cluster classes for convergence thresholds of 95% and 97%, but continued to rise when the 8

17 number of cluster classes was increased from 64 to 128 using a convergence threshold of 99%. In all cases, continued increases in the number of spectral cluster classes beyond 128 did not significantly improve classification accuracy. Germaine and Hung (2011) used 120 spectral cluster classes at 95% convergence for six land cover categories. Sawaya et al. (2003) initially used 10 spectral cluster classes, but then conducted second and third iterations using 100 classes each. So, all three of them used a large number of initial cluster classes in their processes to achieve high-accuracy classification results exceeding 90%. After the initial ISODATA unsupervised classification in the studies addressed above, each of the spectral clusters was assigned (labeled) to a specific land use/cover class. This aggregated the high number of spectral clusters to the few target land cover classes. This approach effectively identified and labeled the heterogeneous pixels belonging within each specific land use/cover class; however, there were some spectral clusters that appeared to belong to multiple land use/cover classes (confused pixels) during the labeling process. These clusters had to be segregated into a temporary confused class for subsequent processing. To summarize, accuracy results were maximized when a large number of initial clusters was used to capture the considerable spectral heterogeneity within each class and later aggregated for further processing. However, further processing was needed at this point to deal with the confused pixels. Lang (2007) pointed out that a key weakness with unsupervised classification is that the labeling of spectral clusters can be very subjective because the analyst is forced to choose which spectral clusters belongs to a corresponding information class when there is often ambiguity - particularly when there are a high number of spectral clusters. 9

18 So, they built an automated data-assisted labeling approach (DALA) consisting of three steps. The first step involved creating a large number of ISODATA classified maps with a varying number of spectral clusters. The second step involved automated labeling assisted by cross-referencing with a reference map that was built on information classes instead of spectral clusters. The third step used a second reference map to estimate the accuracy of each of the multiple classification maps using a traditional confusion matrix. The classification map having the highest accuracy was the one chosen. This approach has merit because it reduces subjectivity, increases repeatability, and reduces time/costs. However, the subjectivity that Lang (2007) referred to can be offset to a large extent if the analyst labels only pure clusters to a particular core LULC class, while ambiguous clusters would be labeled as confused pixels for additional processing. Object oriented classification (OOC) is a land cover classification technique that differs from pixel-based classification techniques (PBC). In an OOC approach, the processing units are image objects (groups of contiguous pixels) instead of individual pixels. Xiaoxia et al. (2005) and Ouyang et al. (2011) described the OOC process. The first step involves grouping spatially adjacent pixels into regions of spectrally homogeneous objects starting with a one pixel object, and then iteratively merging adjacent pairs of image objects having the smallest increase in homogeneity - a userdefined combination of color, smoothness, and compactness - until the smallest increase becomes greater than a user-defined threshold. In the next step, the image objects are classified to specific land use/cover classes based on knowledge-based classification 10

19 rules. These hierarchical classification rules can take into account spectral, shape, texture, context, and size characteristics. Ouyang et al. (2011) and Yu et al. (2006) compared object oriented classification approaches as an alternative to standard pixel based approaches to try to overcome the salt-and-pepper noise associated with classification of very high resolution imagery. Ouyang et al. (2011) used Quickbird satellite imagery (61cm) to compare eleven OOC and PBC models defined by classification types, feature spaces, classifiers, and hierarchical approaches. Their results showed that the accuracy of OOC (87%) exceeded PBC (82%) for classifying herbaceous plant species in salt marshes. However, they used only 30 initial ISODATA classes in their pixel-based classification approach which does not adequately capture the full range of within-class spectral variability. As noted earlier, Lang (2007) found in their research that there was a dramatic improvement in accuracy when 64 or more classes were used. Therefore, a much larger number of initial ISODATA classes may put OOC and PBC on a more even footing. Also, these comparisons did not account for the improvement of classification results when postprocessing techniques are used in conjunction with PBC or OOC. Yu et al. (2006) evaluated the capability of high (1m) resolution imagery for detailed vegetation classification to create 43 vegetation alliances. They stated that one of the weaknesses of PBC inherent to global behavior-based algorithms is that PBC only considers spectral space differences and does not include spatial adjacency to delineate the boundaries of homogenous patches. This high local variation often results in over segmenting the regions within a small spatial extent. However, they found that using objects as minimum classification units helped to overcome the problem of salt-and-pepper effects 11

20 resulting from traditional pixel-based classification methods. However, they also noted that PBC can be optimized by merging, deleting, and splitting clusters to reduce noise. Furthermore, while Yu et al. (2006) demonstrated that OOC has advantages over PBC for highly detailed vegetation mapping, they also pointed out that detailed vegetation classification is quite different from generic land use/cover classification because of differences in the associated representations of shadow, density, size, and transition zones. Refinements to Improve Classification Accuracy Image processing using an ISODATA classification approach gets better results with additional processing and supplemental supporting datasets after the initial classification and labeling. For instance, Germaine and Hung (2011) used additional processing steps and data sources from the initial ISODATA classification to improve overall LULC classification accuracy results from 91% to 94% for extracting impervious surfaces. They processed 15.4 cm (0.5 ft) aerial imagery over an urban area using ISODATA unsupervised classification of a multispectral image to identify 120 spectral cluster classes. Then they manually examined each of these spectral clusters to the aerial imagery to aggregate them to six land cover categories (vegetation, bare soil, asphalt, concrete, rubberized, and gravel) which were further aggregated into pervious or impervious surface parent classes. They subsequently refined this classification with additional processing using a hierarchical, rule-based KBES (knowledge based expert systems) approach incorporating supplemental LiDAR data to focus on the confused pixels along the borders of land classes. The eight rules used in the KBES classification addressed cover height, slope model, shadow identification, removal of trees, removal of 12

21 ground shadows, and vegetation refinement at bare earth. The final step involved filling data voids with surrounding land cover types using a spatial autocorrelation algorithm to replace pixels that were removed during processing based on the rule hierarchy. These voids represented areas that were obscured by vegetative cover. Germaine and Hung (2011) also noted that though not measured in their study, a Void Fill algorithm and associated procedures to identify shadowed areas, remove them, and then fill them with surrounding land use/cover types was a promising way to classify land use/cover within areas of shadow. Likewise, Sawaya et al. (2003) refined their initial unsupervised classification through additional processing because they concluded that while the spatial detail of high resolution imagery is impressive, the problem of spectral-radiometric similarity between certain classes is compounded especially with dark features. To help mitigate these issues, they used a combination of cluster busting and masking to cut through the spectral confusion in shadows where spectral signatures are so similar that it is difficult to distinguish between land cover classes. They did this through a stepwise series of unsupervised classification processes. After initially separating emergent from submerged vegetation classes through an unsupervised classification, cluster busting involved stratifying the emergent vegetation further using 100 cluster classes and then recoding those classes to 5 core classes. They used a similar technique to produce a separate water only classification. Because water features have very different spectral characteristics than terrestrial features, they put water into a distinct, easily identifiable class as a mask, so that they could perform a second unsupervised classification on the water only portion of the image. This methodology distinguished clear water, turbid 13

22 water, shallow water, and shadowed water more clearly because within a mask, the spectral clusters would be created only from the water pixels, and not be overwhelmed by the wide ranges of spectral variability from using all the pixels in image. A combination of classification hierarchy, masking, and filtering are effective tools for determining how confused and shadow pixels can be assigned to their most likely LULC classification. For example, Ehlers et al. (2003) used a hierarchical classification procedure composed of three levels. Level 1 used vegetation indices and elevation to create separate masks of non-vegetation/sparse vegetation, shadow, low vegetation (< 12m tall), and high vegetation (> 12m tall). In Level 2, the non-vegetation and sparse vegetation layers underwent ISODATA clustering, while the herbaceous vegetation layers underwent a maximum likelihood supervised classification to identify over 20 different biotype classes. Level 3 involved post-processing whereby each of the individually classified mask layers were filtered by minimum area to reduce noise. These masked layers were then hierarchically combined using a predefined, logical priority order to create the final LULC map. Shadowed areas were then eliminated using a combination of majority filtering based on biotope type and altitude to assign shadowed areas to appropriate classes. The result was a map of 21 biotope types. They found tremendous advantages with this hierarchical approach integrating ultra high (15cm or 6in) resolution multispectral data, elevation data, and GIS techniques because it resulted in much higher richness of detail and higher geometric accuracy than that garnered through manual photo-interpretation of land cover, or ground-based analysis. Classification accuracy exceeded 95% for several classes. Pasher and King (2009) also filtered out salt and pepper noise by subjecting binary masks of dead wood in forested 14

23 areas to generalization and smoothing. Objects that were separated by a single 20 cm pixel were combined which grouped multiple dead wood objects that were part of a single dead tree crown. Thus, autocorrelation provided a means to reduce noise through aggregation. While LULC classification was not his objective, Dare (2005) described the problem of detection and removal of shadow features when using high resolution 1m satellite imagery. The umbra of the shadow is where the primary light source is completely obscured, and the penumbra represents the edge of the shadow where the light source is partially obscured. Spectrally, the penumbra thus represents a blend of the shadow with its underlying land cover, so the penumbra is spectrally distinct from the umbra and within itself. Dare (2005) found that he could use thresholding to detect shadow from non-shadowed areas, but the challenge was in selecting the right threshold level from the histogram. It was particularly difficult to distinguish shadow from water because the radiometric response between them was so similar (both are very dark). Fortunately, secondary illumination within shadowed regions means that spectral variances are higher in shadows than in water regions. Thus, he could filter water from shadows based on variance. Dare (2005) also tried radiometric enhancement of the shadows based on histogram matching in order to make the shadow pixels brighter to match the surrounding non-shadowed areas. This was an attempt to normalize the differences between shadow and non-shadow areas. However, the spectral complexity of the penumbra pixels provided difficulties using this approach. Challenges with the Use of Very High Resolution Multispectral Imagery The research highlights the fact that very high spatial resolution multispectral imagery introduces new challenges for LULC classification that need further study. 15

24 While the higher detail offers the opportunity for more accurate and precise land class boundaries, it also brings much higher within-class spectral heterogeneity and a significantly higher proportion of confused pixels. In addition, shadows caused by a low sun angle on tall trees make it particularly difficult to determine the land cover type hidden in those shadows which adds to the confusion. Previous studies described above provide a foundation to synthesize a methodology to address these issues. First of all, it is essential to use a large number of spectral clusters when using an unsupervised classification approach as concluded by Lang (2007) in his research and verified in the work done by Germaine and Hung (2011). This approach very effectively captures the considerable within-class spectral heterogeneity inherent with very high resolution imagery for classification by isolating the pure LULC pixels from the confused pixels. Secondly, dark areas such as water and shadows, where the spectral character of the different land types hidden within is particularly nuanced, can be processed separately as described by Sawaya et al. (2003). This bolsters the ability to differentiate LULC classes within those areas. Thirdly, autocorrelation using a majority filter, as used by Ehlers et al. (2003), Germaine and Hung (2011), and others can aggregate confused pixels to their most likely class since the spectral reflectance of the different individual pixels that makes up an object and the mutual relationships between them can be considered as a whole. Fourthly, hierarchal classification techniques as used by Ouyang et al. (2011) and Ehlers et al. (2003) can offer effective means to prioritize LULC classifications. Finally, Ehlers et al. (2003) demonstrated that the use of GIS integration, multiple data sources, and context-based interpretation schemes would further improve classification accuracy. While these and 16

25 other studies described above used ancillary data sources to improve results, it is also worthwhile to confine further research to just the use of imagery for LULC land use classification so as to determine the extent that imagery alone as a data source contributes towards LULC classification since supplemental datasets such as LiDAR, DEMS, and additional imagery, may not be available for other similar projects. 17

26 CHAPTER 3 CONCEPTUAL FRAMEWORK AND METHODOLOGY Study Area Description The 2.32 square kilometer (574 acre) study area is located in Perry County, Arkansas. The study site is 48 kilometers west-northwest of Little Rock, Arkansas just west of the tiny hamlet of Williams Junction where State Highways 9 and 10 intersect (see Figure 1). The study area is part of the 479 square kilometer Lake Maumelle Figure 1. False color aerial photo of the 1.5 x 1.5 km study area. 18

27 watershed. The Maumelle River curves along the western and southern sides of the study area. The study site consists of flat to rolling topography with extensive coniferous and deciduous forest. There are a few small lakes, a few cleared areas for agricultural fields, and areas where timber was harvested. The coniferous trees are primarily mature pine, and the deciduous forest is primarily mature oak and hickory hardwoods. There is very little urban development, and the road network consists of a small number of two-lane asphalt roads, with dirt and gravel access roads into the wooded and harvested timber areas. Data Sources and Conditions The main data source for the LULC classification was leaf-off, orthorectified, multispectral (Green, Red, NIR) aerial imagery of the study area (1.5 km x 1.5km). This imagery was acquired from an Applanix DSS439 digital camera. This imagery has 30cm (1-foot) ground resolution with +/- 3 meter horizontal accuracy at a 90% confidence level. The data was acquired Feb 12, 2009 between 10:30am and 1:30pm resulting in a low (41.5 degree) sun angle which accentuated shadows. The airplane flew an east-west flight line. The aerial photos were taken in the winter during leaf-off conditions to clearly differentiate coniferous versus deciduous trees for land cover assessment purposes. Snow covered most of the area and was most apparent on the bare earth areas in the image. There were some isolated open areas where the snow had melted exposing slush or mud. Road surfaces were mixed varying from snow covered, to slush, to dry pavement. An ancillary data source included georeferenced Google Street View 19

28 pictures taken on the ground in April 2008 from the main highways going through the area which was used for ground truth verification as needed. Software Utilized ERDAS IMAGINE 2011 Version 11.0 Build 304 This is a robust image processing software that performs advanced remote sensing, thematic raster analysis, and spatial modeling to create new information. This software performed the bulk of the image and thematic data processing. ESRI ArcGIS Version 10.0 with Service Pack 3 and Spatial Analyst extension ArcGIS software manipulates vector and raster geospatial data. It was used in this study for digitizing roads, creating a road buffer, and converting the buffer to a raster. It was also used to facilitate data preparation Conceptual Basis for Methodology The NIR, red, and green light reflected from the surface of the land and recorded by the aerial camera have different proportions and intensity (spectral signatures) depending on the nature of the land surface material. Thus, each land surface material has its own pattern of spectral reflectance. Coniferous pixels in the image, for example, express a pattern of spectral reflectance that is associated with coniferous trees (composed of heterogeneous pixels as previously mentioned). Water pixels in the image express a pattern of spectral reflectance that is associated with water. Confusion exists where spectral reflectance of different land cover material co-exists. That is, pixels having similar spectral reflectance could belong to two or more information classes (Hung and Wu 2005). Thus, there are needs for a logical method to allocate these 20

29 confused pixels to the most appropriate land use/cover class. The following describes the methodology used to allocate road, water, coniferous, bare land, and deciduous pixels to their respective land cover classes. It also describes the methodology to logically allocate confused and shadow pixels to the most appropriate land cover class. The general approach began with ISODATA clustering as used by Lang (2007) and Germaine and Hung (2011) to create a large number of spectral classes which were then assigned to the appropriate LULC class through photointerpretation. The use of a large number of spectral classes offered a way to consolidate the spectral classes to the appropriate information classes even though any one LULC type is composed of complex heterogeneous pixels. Distinguishing LULC classes within shadowed areas required more refinement because the image brightness values were so low that the spectral signatures between LULC classes were very similar. So, in a process similar to that used by Ehlers et al. (2003), Sawaya et al. (2003), and Germaine and Hung (2011), a separate ISODATA classification/lulc class assignment of just the shadowed areas was performed using the same process described in the paragraph above. The result of the re-classified shadow pixels was then added to the initial ISODATA LULC classification. This boosted the number and proportion of classified pixels, and reduced the number and proportion of essentially unclassified pixels (confused). Each LULC class was then exported as a mask image consisting of two classes - either that class or non-such pixels - which enabled further refinement and elimination of small islands of pixels less than the.25 acre MMU within each class. The LULC classes were then combined using hierarchical image stacking. The classes having the 21

30 highest classification accuracy had priority over those with lower classification accuracy (Figure 2). The LULC classification hierarchy was established empirically by each class s relative classification accuracy as determined by photointerpretation during the ISODATA class assignment process. The following describes the factors that influenced the structure of that hierarchy: roads, water, coniferous, bare ground, and deciduous. Figure 2. Hierarchical class decision flow 22

31 Roads were the most accurate and highest priority class because a directly measured buffer was created from road centerlines directly digitized from the image. Trees, tree shadows, snow, and slush were hiding road pixels so badly that an image processing solution alone would have resulted in considerable classification errors. So, to achieve exceptionally high LULC classification accuracy, road centerlines were easily and quickly digitized as a simple line shapefile off the original very high resolution aerial image based on photointerpretation. Then, a polygon buffer was created around the road centerlines based on the road width as directly measured from the image. Figure 3. Vastly different spectral characteristics for roads due to shadows, snow, slush, and dry pavement surfaces. 23

32 The polygon buffer was then converted to a raster road mask (composed only of road and non-road pixels) and imported into ERDAS IMAGINE. The result was a very high LULC classification accuracy for the roads because obstructions hiding road segments from view, such as snow cover, slush, and overhanging trees or tree shadows, were addressed by simply connecting road centerlines across these obstructions during digitizing. And, direct measurements of road width for buffering were within two feet because of the very high resolution of the aerial image. The high-accuracy roads were superimposed over the top of all other classes as the last step. Water pixels were very distinct from other pixels in the image due to the relatively high homogeneity of spectral signatures for water and because its NIR reflectance values were distinctly lower than the other classes. The NIR band is very good for distinguishing open water bodies from other land cover types because water absorbs almost all NIR light which has a wavelength of about 750 to 900 nm (Govender et al. 2007). Thus, based on photo interpretation of the aerial image, water pixels extracted using all four bands in the ISODATA unsupervised classification left an unambiguous distinction of water versus non-water areas - except where open water was hidden behind tree shadows, there were overhanging branches along shorelines, or where there were shoals. Coniferous pixels also had very distinct spectral signatures, but they were not as homogeneous as water, since there were internal shadows and folds in the tree canopy to contend with. Living, leafy vegetation reflects NIR light considerably more than other types of land cover. In winter, the only large masses of living, leafy vegetation in Arkansas are coniferous. That is why the coniferous vegetation in the aerial photo 24

33 (displayed in bright red) is so clearly distinguished from other land cover types and why the spectral reflectance is also quite distinct. The effect of coniferous tree shadows adversely affecting the LULC classification was considerable on land cover not of the same height (bare earth and deciduous). However, the effects of shadows on coniferous trees of similar height (other coniferous trees) were considerably less than the bare earth or deciduous land cover classes based on empirical observations from photointerpretation. Bare soil was distinct because of the snow cover, but there were a few areas of melted snow and slush that created confusion, and many mixed class transitional areas (smaller bare soil areas interspersed within other classes) also increased the complexity and reduced the spatial accuracy of the bare class compared to water or coniferous classes. The deciduous class would be the most complex and difficult class to accurately extract from the image because the spectral signatures of tree trunks, branches and their shadows, and snow cover often matched those of the other classes. So, a simpler approach was used. First, all the other classes were determined (road, water, coniferous, and bare earth). Thus, all remaining pixels would be deciduous class based on a process of elimination. This approach thus avoids having to do any separate classification of the deciduous class at all. 25

34 Image Classification The flowchart below (Figure 4) depicts the multi-step image classification process based on the conceptual methodology described above. The following section describes each step in detail. Figure 4. Image classification flowchart. 26

35 Methodology Step 1. Initial ISODATA classification The first step was to use the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method of unsupervised classification. The ISODATA method assigned pixels having similar spectral reflectance to arbitrary clusters (spectral clusters) based on the mean and standard deviation of the spectral values in each band (Im and Jenson 2005). The 25 million 1-foot pixels in the image were clustered into temporary spectral clusters on the first iteration using a minimum-distance-to-means algorithm. On the subsequent iterations, the cluster spectral means were recalculated to include the new pixels assigned to each cluster. Eventually, through successive iterations, all of the pixels in the image were reassigned to spectral clusters based on the minimum distance to the new cluster means. This process repeated itself until either a maximum number of iterations or a maximum percentage of unchanged pixels were reached. In this case, a 95% convergence threshold was achieved with 13 iterations. One hundred clusters was a sufficiently large number of classes to ensure adequate differentiation of each of the different types of land cover in the image. Each cluster had its own finely tuned range of spectral reflectance which at first was not affiliated with any particular land cover class. The geographic location of the pixels associated with each of the 100 clusters was visually identified on the original aerial photo. Each cluster was then recoded to one of six land cover classes water, coniferous, bare land, deciduous, shadows, and confused based on visual photointerpretation. For example, if the pixels of a certain cluster happened to be clearly 27

36 associated with water, those pixels were classified as water pixels. The choice of land cover classes for this step was different from the required classes because although many clusters were clearly associated with a particular required LULC class (water, coniferous, bare ground, deciduous), not all of them were. For instance, the clusters associated with tree shadows needed to be assigned to a temporary tree shadow class since the ground beneath a tree shadow could represent any of several classes (such as roads, water, or bare earth). So, the clusters that were not clearly distinguished as a specific land cover class or tree shadow class were separated out temporarily to a confused pixels class for further processing. Roads were not included in this step since that land cover classification would be superimposed later. Step 2. Allocate Shadows to the Principal Land Cover Classes To extract land cover classes from shadows, the first step was to create a mask from the shadow class created in step 1. An ISODATA classification was then performed only on the pixels from the original image corresponding to the shadow pixels in the shadow mask. The ISODATA classification yielded 50 spectral clusters, which were subsequently re-classified to water, coniferous, deciduous, bare ground, and confused using the same photo interpretive techniques used in step 1. This result was then added to the initial ISODATA classification/recode from step 1 using the ERDAS IMAGINE Matrix function to add to the total number and proportion of pixels classified to the required LULC classes. 28

37 Step 3. Allocate Confused Land Cover Pixels to Land Cover Classes The ERDAS IMAGINE Neighborhood Functions tool was used to reassign the confused pixels that were liberally sprinkled throughout the image to one of the four land cover classes (water, coniferous, bare, or deciduous) that they were most likely to belong to based on spatial auto-correlation. Thus, a confused pixel was recoded according to the predominant land cover class of its immediate neighboring pixels using a 3 x 3 neighborhood majority filter where the confused pixel had no influence on the calculation. It required only three iterations to recode over 99% of the confused pixels to one of these four LULC classes. Roads were not included since that land cover classification would be superimposed later. Step 4. Create Masks of Each Class It was necessary at this point to remove noise by eliminating the small islands of pixels within and outside of each class (water, coniferous, and bare). This worked best by first creating a mask of each class. The ArcGIS 10 Raster Calculator tool within the Spatial Analyst extension was used to convert the raster values from the files created in step 2 to integers. The ArcGIS 10 Spatial Analyst Reclassify tool was then used to create a separate mask for each land use/cover class layer consisting only of two classes - the target class having a value of 1, and the non-target class having a value of 2. Note that a class value of zero does not work in the upcoming clump/eliminate process. A deciduous mask was not necessary because the deciduous class would be determined later by process of elimination. But, it was necessary to create a temporary dummy deciduous mask where all pixels in the raster had a class value of 1. 29

38 Next, the clump and eliminate functions were consecutively performed within ERDAS IMAGINE to eliminate the small islands of each class s pixels that were less than the.25 acre MMU (10,890 connected pixels). Eight connected neighbors had slightly better results than using four based on empirical photo interpretive comparisons with the original aerial image. The next step eliminated the overlaps and gaps between the individual mask layers when they were overlaid in the map window. Step 5. Combine Mask Classes Hierarchically The land cover class layers had different hierarchical levels of inherent classification accuracy. Roads were at the top of the hierarchy because the road centerlines were directly digitized from the very high resolution aerial photos, and road widths were directly measured to the foot, so no tree shadows or other classes impacted the classification. Water was second because of its homogeneity in this image, its well defined edges along the shoreline, and its clear distinction from the other classes. Next, coniferous tree pixels were well differentiated from other land cover types because of their distinctively strong NIR response. The tree shadows within the coniferous crowns were easily filtered out. Next, unsupervised classification did a good job of identifying zones of bare ground due to the snow cover, except in relatively small areas where the snow was melted. Since all the other pixels were classified, the remaining pixels would be deciduous by process of elimination. The ERDAS IMAGINE Modeler executed the decision tree model (Figure 4 above) for the non-road classes to hierarchically combine the water, coniferous, bare ground, and deciduous mask layers using the CONDITIONAL function. 30

39 CONDITIONAL {($n3_water_final == 1)4, ($n1_conif_final == 1)3, ($n4_bare_final == 1)2, ($n7_decid_temp == 1)1} Under this function, the first expression that was true determined the output pixel value (LULC class) at any one pixel location. Since the deciduous class was defined as what was left over, a temporary deciduous mask was created with all values having a value of 1. Through the Combine Classes Model No Roads model (Figure 5) the other classes essentially replaced the non-deciduous areas of the deciduous mask. A final clump/eliminate on the results of the Combine Classes No Roads model eliminated noise consisting of small islands of pixels less than the.25 acre MMU (10,890 pixels). Figure 5. ERDAS IMAGINE data model for combining non-road classes 31

40 Step 6. Superimpose Roads The final step was to superimpose the roads class over the existing LULC classes. The Con function within the ArcGIS Spatial Analyst was used to perform a conditional if/else evaluation on each of the input cells of the LULC input raster from step 5. In this case, if the roads mask had a value of 1, it would replace the existing LULC classification with a road class. If it had a value of 0, it would keep the existing LULC class. Accuracy Assessment A stratified random sampling approach was used to test the accuracy of the final LULC classification. Within ERDAS Imagine, 216 sample locations (Figure 6) were selected throughout the original image with a minimum of 23 samples per stratum (Table 1). The minimum sample size was not applied to the roads class because it was Figure 6. Stratified random sample locations. 32

41 created by highly accurate direct measurements as opposed to the other land cover classes that used image processing. Photointerpretation was used over each of the sample locations from the aerial image to determine the appropriate LULC class type for the pixel covering each location. The very high resolution aerial photo displayed the land surface very clearly, so photo-interpretation was justifiable for high quality ground truth verification of the LULC classification. The results were posted for each sample location. Note that the LULC image was not displayed during this process so as not to influence the outcome. Table 1. Samples per stratum Stratum Number of Samples Water 23 Coniferous 82 Bare 30 Deciduous 77 Road 4 Total

42 CHAPTER 4 RESULTS AND DISCUSSION Classification Results The step 1 process used ISODATA unsupervised classification to create a large number of spectral clusters followed by aggregation of those classes to six classes via photointerpretation. This process effectively and accurately assigned pixels even to highly heterogeneous land classes (Table 2). It was especially apparent, when doing the aggregation, that the pixels at the top of the coniferous trees had spectral characteristics that were quite different from the pixels within the crown folds or the outside edges of the crown. So, the high spectral heterogeneity within the coniferous class resulted in a large number of cluster classes (28) being coniferous. There was also high spectral heterogeneity within the shadow and confused classes. Only 3% of the pixels were decidedly deciduous in the classification (Figure 7) even though a casual look at the original aerial photo showed that deciduous obviously covered a considerably larger area. This is because during the photointepretive class aggregation process, a high proportion of candidate deciduous cluster classes appeared to qualify as both deciduous and other classes as well rather than pure deciduous. So, they were assigned as confused pixels instead. The very low percentage of decidedly deciduous pixels was a strong justification for having the deciduous class as the residual class in the hierarchy. 34

43 Table 2. Results of initial ISODATA clustering and coding Class Water Coniferous Bare Deciduous Shadows Confused Total Total Pixels 1,334,736 5,427,912 3,489, ,295 6,382,812 7,712,781 25,005,000 Percent 5% 22% 14% 3% 26% 31% 100% # Clusters Figure 7. Results of the aggregated ISODATA classification 35

44 The very large proportion of shadow and confused pixels represented 57% of the image (Table 3 and Figure 8). The shadows alone - exacerbated by the low sun angle - accounted for 26% of the image. This was consistent with Hsieh et al. (2001) observations regarding the high level of confusion inherent in very-high resolution imagery. The proportion of shadow was so high, that a test using a majority filter to reassign the confused and shadow pixels to one of the four land cover classes (water, coniferous, bare, or deciduous) resulted in long strings of contiguous pixels that obviously had little relation to reality. This is because there were not enough good LULC pixels (those of a decidedly known class) to provide a solid foundation for successful spatial autocorrelation. Boosting the proportion of good pixels by performing a separate ISODATA classification of the shadowed areas (Figure 8) and adding that result to the initial ISODATA classification as performed in step 2 remedied the problem. This action boosted the proportion of good LULC pixels from only 43% of the image to 60% of the image (Table 3) which was enough to allow the majority filtering process in step 3 to perform well (as verified by detailed, close visual inspection to the original unprocessed aerial image). It also very effectively removed 53% of the confusion associated with shadows. No bare pixels resulted from this second ISODATA classification because during the photointepretive class aggregation process, the spectral cluster classes over bare land also occurred over other types of land class areas. There were no decidedly pure bare pixels. Therefore, these mixed pixels were included in the confused category. The results of reclassifying the shadows separately are shown in Figure 9. Black represents background non-shadow areas. Colored pixels represent re-classed shadows. 36

45 Table 3. Results of matrix-combining of Initial and Shadows-Only Classes Initial ISODATA + Shadows ISODATA = Combined Class Total Pixels Percent Total Pixels Percent Total Pixels Percent Water 1,334,736 5% 159,893 3% 1,494,629 6% Coniferous 5,427,912 22% 2,560,744 40% 7,988,656 32% Bare 3,489,464 14% 0% 3,489,464 14% Deciduous 657,295 3% 1,277,280 20% 1,934,575 8% Shadows 6,382,812 26% 0% - 0% Confused 7,712,781 31% 2,384,895 37% 10,097,676 40% Total 25,005, % 6,382, % 25,005, % Figure 8. ISODATA classification of shadow mask to other classes. 37

46 Figure 9. Results of reclassifying the shadows separately. Notice how this process eliminated much of the tree shadows over water along southern shorelines, as well as filling in coniferous and deciduous areas. In step 3, the 3x3 majority filter provided the means to logically assign each of the confused pixels to the appropriate class based on the predominant class in their 38

47 proximity. It only took three iterations for reassignment of over 99% of the confused pixels to the other classes (Table 4). Since most of the confused pixels were in deciduous areas to begin with, the largest percentage of confused pixels went to deciduous. While the majority filter fulfilled its function well, there was still considerable noise to clean up. This was remedied in Step 4 by first creating separate masks of each class. In the bottom left of Figure 10, you can see the distribution of the noise throughout the coniferous mask. Then, performing the clump/eliminate functions effectively eliminated the small islands of pixels less than the.25 acre MMU (10,890 pixels) within each class s mask to screen out the noise (see bottom right of Figure 10). However, the nature of very high resolution imagery introduced an interesting artifact in this process. The ERDAS algorithm simply eliminated areas of less than 10,890 contiguous pixels. Areas larger than that were kept - even if there was a skinny path of pixels one pixel wide connecting two larger areas. So, the result was a highly convoluted class boundary shape. Further smoothing could reduce this, but close visual comparisons with the source aerial photo would support the higher LULC accuracy without smoothing, and it better conveys to the map users the mixed nature of the land cover classes in those areas (Figure 10). 39

48 Table 4. Results of reassigning confused pixels based on proximate dominant class. Class Beginning Pixels First Second Third Water 6% 8% 9% 9% Coniferous 32% 42% 44% 44% Bare 14% 18% 18% 18% Deciduous 8% 24% 28% 28% Confused 40% 8% 1% 0% Total 100% 100% 100% 100% Figure 10. Coniferous mask before and after MMU consolidation. 40

49 At this point, the land class masks have some overlaps and gaps with each other that need to be reconciled. The hierarchical model in Step 5 that combined each of the individual binary LULC masked classes, and through a process of elimination left the deciduous class, was very effective in eliminating the overlaps and gaps in coverage between the masked classes in a logical manner, and assured that all of the study area was classified. Note that the deciduous class was processed prior to this point the same as water, coniferous, and bare even though it was not used in this step. This was to prevent confused and shadow pixels from attaching themselves to water, coniferous, and bare during step 3 in areas known to be deciduous in order to avoid distortions. In Step 6, the road centerlines were digitized, and then buffered to the directlymeasured width of the road. The very high resolution aerial photo enabled very accurate road width measurements. The result made roads a particularly accurate land cover class. This simple approach very effectively eliminated the need to extract the road class using image processing techniques. Since the roads class was extremely accurate it was superimposed over the top of the other classes (Figure 11). Figure 11. Side-by-side comparison of final LULC map and aerial image. 41