Monitoring Coastline Change Using Remote Sensing and GIS Technologies

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1 2012 International Conference on Earth Science and Remote Sensing Lecture Notes in Information Technology, Vol.30 Monitoring Coastline Change Using Remote Sensing and GIS Technologies Arzu Erener 1,a,*, Murat Yakar 2,b 1 Department of Geomatics Engineering, Selçuk University, 42250, Konya, Turkey 2 Department of Geomatics Engineering, Selçuk University, 42250, Konya, Turkey a ae76@hotmail.com.tr, b yakar@selcuk.edu.tr *Corresponding author Keywords: Change detection, Coastline, RS, GIS, Change matrix. Abstract. Meke Lake, known as the world's evil eye hosts numerous bird species and has a natural beauty. It is a crater lake which is not like one another in the world and located in the district of Konya Karapinar. Meke Lake's water, drawn from day to day which may be due to unconscious influence of agricultural irrigation and/or lack of adequate rainfall in the region. In order to identify the size of the problem and take necessary precautions, the current situation should be identified and the changes should be determined. In this context, the main objective of this study includes to analyze the temporal and spatial change and to identify the current situation of Meke lake by using multi-temporal satellite image data integrated with GIS. This study employed Landsat images during the period from 1987 to Coastline change of Meke lake identified by using algorithms of change. The change algorithm involves initially classification of remote sensing images for each period. Employing the pixel-based classification, the images has been cross-classified in order to identify the "where to where" change. The type, rate, and pattern of the changes among three years time is analyzed by quantitatively by change matrix and also qualitatively by evaluation of spatial change map. As a result, this study demonstrates the ability of GIS and Remote Sensing in capturing spatial-temporal data for determining and updating LULC changes. 1. Introduction Rapid changes in land use/cover changes (LULC) may adversely affect the environment and may cause degradation in ecosystems. Accurate and timely information about the spatial dynamics of LULC is essential for decision-making, global change and ecosystem monitoring. Use of remote sensing (RS), Photogrammetry and Geographic Information System (GIS) technologies proved to be economic and useful sources in large areas in order produce information in short-time and to generate information for current situation and LULC changes. The shoreline is defined as intersection of water and land surfaces. Multi-year lake zone mapping and generating geospatial data is considered to be a valuable task for coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Traditionally, mapping lake dynamics in small areas is carried out using conventional field surveying methods [1]. Mapping lake dynamics and extraction of coastline in various times using traditional methods is a time consuming work, since in temporal and spatial domain the water level has a dynamic nature [2]. Remote sensing (RS) and Geographic Information System (GIS) technologies proved to be economic and useful sources in order to display the dynamic nature of lakes. Determination and delineation of water bodies with remote sensing data are done most easily in near-ir wavelengths, /10/$ IERI ESRS

because of water absorption property of these bands [3]. Optic systems has been investigated and evaluated to generate shorelines by different researchers.

2 because of water absorption property of these bands [3]. Optic systems has been investigated and evaluated to generate shorelines by different researchers. [4,5,6,7,8 and 9] while using Landsat data, [10,11,12,13 and 14] used high resolution images (Ikonos, Quickbird etc.). The present research aims to monitor the dynamics of Meke lake and investigation of coastline change. In this context, the temporal and spatial change of Meke lake was analyzed by using multi-temporal satellite image data integrated with GIS. 2. Research area The Meke lake lies in the province of Konya in the Middle Southern part of Turkey. It is located between latitude 37 43' N to 37 40' N and longitude 33 36'E to 33 41'E, at the southeastern part of Konya city and it is approximately 95 km from the city centre. Meke Lake has a unique shape containing two intertwined crater lake (Fig. 1). The first cone was formed 5 million years ago with a volcanic eruption. It was filled with water gradually and turned into a crater lake. The second volcano in the middle of the lake was formed by the second explosion. This eruption caused a chimney to rise in the lake. In geology, this rise is called as "Secondary Rise". Later on with various eruptions seven other formations were formed which are called "parasite cones in geology. The dept of the lake never exceeds 12 m. In the main Meke which is 981 m. from sea level there is a volcanic cone. This cone is 50 m. high from the water level (Fig. 1). Fig 1. Image of study region Meke lake, showing the intertwined crater lakes 3. Data sets and methodology In this study, Landsat TM and Landsat ETM+ data sets were used in order to monitor the dynamics of Meke lake and investigation of coastline change. Images were acquired for the study area on three occasions spanning a period of nineteen years (Table 1). A Landsat TM image from 06 September 1987, Landsat ETM+ image from 05 June 2000, and a Landsat ETM+ image from 28 July 2006 were used in the analyses. These images were downloaded freely from Global Land Cover Facility (GLCF) web page [15]. The GeoCover data set was provided in a standard GeoTIFF format with a UTM projection, using the WGS-84 datum. 311

Table 1. Properties of satellite images. Images Time # of Bands Spectral Range (μm) Spatial Resolution (m) Landsat TM 06 September 1987 1,2,3,4,5,7 0.45-2.

3 Table 1. Properties of satellite images. Images Time # of Bands Spectral Range (μm) Spatial Resolution (m) Landsat TM 06 September ,2,3,4,5, Landsat ETM+ 05 June July ,2,3,4,5, , The methodology adopted for the study is provided in Fig 2. Coastline change of Meke lake was identified by using algorithms of change. Prior to change detection analysis pre-analysis were applied. The remote sensing data cannot be used for mapping purposes in the case of it is not geometrically corrected [3]. Archived Landsat data downloaded at GLCF consist of georeferenced image products at various levels of geometric correction, such as: orthorectification, terrain Correction or SLC-Off. The Landsat GeoCover data sets archived at the GLCF are orthorectified image products with a positioning accuracy [root mean square error (rmse)] of about two pixels [15,16]. The two-pixel georeferencing error of the orthorectified Landsat images is unacceptable for detection of coastline change. Instead, subpixel image registration between TM, and more recent ETM+ imagery is required. Therefore, geometric rectification process was applied to the images acquired at different times in which one image (i.e., the master) was used as the reference and the other images as the slave [16]. Fig 2. The flowchart of the study In order to ensure the greatest possible benefit from RS data, the influence of objects outside the focus of interest should be removed. In this study ATCOR model developed by [17], German Aerospace Center - DLR was used for determination of image areas affected by atmospheric disturbances such as cloud and haze. Following the pre-analysis process the images were prepared for change analysis. The change algorithm involves initially classification of remote sensing images for each period. In order to assess the classification accuracy of the results, they were compared with ground truth observations. Employing the pixel-based classification, the images were cross-classified in order to identify the "where to where" change. The type, rate, and pattern of the changes among three years time were analyzed quantitatively by change matrix and also qualitatively by evaluation of spatial change map. 312

4 4. Image classification and change map The first step was to identify Meke lake in the images acquired at different times. A supervised classification method was applied to the rectified and pre-analyzed data set in order to analyze change. The Maximum Likelihood (MLC) decision rule, which calculates a Bayesian probability function from the inputs for classes established from training sites was used for lake detection. Each pixel was then assigned to a class to which it most probably belongs [3]. Based on the training sets, the maximum-likelihood classifier produces a lake map from all the four bands of the data set. In this study, LULC classes were identified as water and the rest terrain features other than water class. The lake maps for 1987, 2000 and 2006 years are presented in Fig. 3. Classified maps for different years were compared qualitatively by evaluation of spatial change map. It is simply the subtraction of initial state image from the final state image. The image obtained initially was considered as the initial image. The results of change maps for 1987 to 2000, 1987 to 2006 and 2000 to 2006 is presented in Fig. 4. Fig 3. The MLC lake maps for 1987, 2000 and 2006 Fig 4. The results of change maps for each pair Positive change means the water level is increased in the time of initial image. And negative change means the water level decreased in the time of initial image. When visually analyzed these maps presents that from 1987 to 2000 and 1987 to 2006 the highest negative change provided. From 2000 to 2006 both positive and negative changes could be identified. In order to quantitatively identify the changes a change matrix was computed. The change matrix report includes a class-for-class image difference. The analysis focuses primarily on the Initial State classification changes. That is, for each Initial State class, the analysis identifies the classes into which those pixels changed in the Final State image. The matrix in Table 2 lists the initial state classes (1987) in the columns and the final state classes (2000 and 2006) in the rows. For each class in 1987, the matrix indicates how these pixels are classified in the 2000 and 2006 image. The class changes row indicates the percent of initial state pixels that have changed classes. Between 1987 to 2000 the water class change 15.05%. The class change in 2006 increased to 15.36%. An image difference that is 313

5 positive indicates that the class size increased, on the other hand negative means the class pixels decreased. Therefore, it is clear that between 1987 to 2000 the water class decreased 9.87% and 1987 to 2006 the water class decreased 14.55%. Table 2. The change matrix report, the initial state classes (1987) in the columns and the final state classes (2000 and 2006) in the rows. Final State Initial State (1987) Water Other 2000 Water Other Class change Image difference Water Other Class change Image difference Conclusion Coastline is one of the most important linear features which display a dynamic nature. Environmental management requires the information about coastlines and their changes. Major coastline changes are believed to be occur in response to climate warming or agricultural irrigation. In Turkey actual rainfall are generally slightly below normal limits in the Post-2001 period and unfortunately, in later years, many parts of Turkey remained below the long-term averages and experienced meteorological drought. Remote sensing appears to be a cost effective way to track the lake dynamics. Using such data for Meke Lake showed 9.87% decrease in the lake level from 1987 to 2000 and 14.55% decrease from 1987 to As a result to be aware from environment and take precautions for global change the remote sensing and GIS technology is one of the essential tool in capturing spatial-temporal data. References [1] A. E. Ingham, Hydrography for Surveyors and Engineers, Blackwell Scientific Publications, London, p.132, 1992 [2] G. Winarso, S. Budhiman, The potential application of remote sensing data for coastal study, Proc. 22nd. Asian Conference on Remote Sensing, Singapore, Available on: pp. 1-5, [3] T. M. Lillesand, R. W. Kiefer, Remote sensing and image interpretation, 4th. Ed., John Wiley and Son, USA (1999), pp. 122, 19, G70.4.L54, [4] K. Whithe and H. M. El Asmar, Monitoring changing position of coastline using thematic mapper imagery, an example from the Nile Delta, Geomorphology, vol. 29, pp , [5] P. S. Frazier and K. J. Page, Water body detection and delineation with Landsat TM data, Photogrammetric Engineering and Remote Sensing, vol. 66, pp , [6] H.Liu and K. C.Jezek, Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptive thresholding methods, International Journal of Remote Sensing, vol. 25, pp ,

6 [7] R. Zakariya, Y. Rosnan, S. A.Saidin, M. H.Yahaya, I.Kasawani and H. Lokman, Shoreline detection and changes for Terengganu river mouth from satellite imagery (Landsat 5 and Landsat 7), Journal of Sustainability Science and Management, vol. 1, pp , [8] A. A. Alesheikh, A. Ghorbanali, N. Nouri, Coastline change detection using remote sensing, Int. J. Environ. Sci. Tech., vol. 4, no. 1, pp , ISSN: , [9] M. A. Marfai, H.Almohammad, S.Dey, B.Susanto and L.King, Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia, Environmental Monitoring and Assessment, vol.142, no.1-3, pp , [10] K. Di, J. Wang, R. Ma and R. Li, Automatic shoreline extraction from high-resolution Ikonos satellite imagery, In proceeding of the Annual ASPRS Conference, Anchorage, Alaska, [11] K. D. J. Wang, R. Ma and R.Li, Automatic shoreline extraction from high resolution Ikonos satellite imagery, In: Proceedings of the ASPRS Annual Conference, Anchorage, Alaska, [12] R. Li, K. Di and R. Ma, 3-D Shoreline Extraction from Ikonos Satellite Imagery, Marine Geodesy, vol. 26, pp , [13] A. Tarig, Analysis of Shoreline-Changes Based on the Geometric Representation of the Shorelines in the GIS Database, Journal of Geography and Geospatial Information Science, vol. 1, no.1, [14] A. Erener, S. Düzgün, A.C.Yalçıner, Evaluating land use/cover change with temporal satellite data and information systems, Procedia-Computer Science Journal, Procedia Technology., vol. 1, pp , ISSN: , [15] GLCF, GeoCover Technical Guide. Last access in [Online]. Available: [16] Y. Sheng, C. A. Shah, and L. C. Smith, Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic, IEEE Geoscience and Remote Sensing Letters, vol. 5, no.3, [17] R. Richter, A spatially adaptive fast atmospheric correction algorithm, International Journal of Remote Sensing, vol. 17, pp ,

Use of Corona, Landsat TM, Spot 5 images to assess 40 years of land use/cover changes in Cavusbasi

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