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PRESENTING AND COMPARING THE OBJECT BASED IMAGE ANALYSIS AND STANDARD IMAGE ANALYSIS FOR CHANGE DETECTION OF FOREST AREAS, USING LOW-RESOLUTION SATELLITE IMAGERY
Abstract
The objectives of this study were to present and compare methods for change detection of forest areas in low-resolution satellite imagery, using object based image analysis and standard image analysis techniques (pixel ba sed). Considering that huge area in Serbia is covered with forest, this study is of great importance for detecting and tracking changes over time. The data used in this st udy were optical remote sensing satellite images Landsat TM (22-08-1986) and Landsat ETM+ (23-06-2002). These images were processed in order to classify and identify f our main land cover classes, namely: forest, agricultural, urban and water areas. The images are analyzed and the process of change detection was applied on forest areas. The re sults show the type and the place of land- use changes occurred in these main four cl asses during those years. Change analyses were performed based on object image analysis and standard image analysis techniques on the other hand. The research reported here shows that the object-oriented classification method is most accurate when it comes to the classification of land cover. The research also shows that the method of post-classification on object classification results is the most accurate method of change detection in forest areas.
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References22
Keith R. McCloy "Resource Management Information Systems: Remote Sensing, GIS and Modelling", Taylor & Francis, 2006.
Meyer, W.B. "Past and Present Land-use and Land-cover in the U.S.A. Consequences", 1995.
Change detection in land use and land cover using remote sensing data and gis. http://www.gisdevelopment.net/thesis/ visited in December 2007.
Singh, A. "Digital Change Detection Techniques Using Remotely Sensed Data", International Journal of Remote Sensing. Vol. 10, No. 6, p. 989-1003, 1989.
Shimabukuro Y.E., Batista G.T., Mello E.M.K., Moreira J.C., Duarte V. "Using shade fraction image segmentation to evaluate deforestation in Landsat TM images of the Amazon Region" International Journal of Remote Sensing 19:3, 535-541, 1998.
Pal, N.R., Pal, Sankar K. "A review on image segmentation techniques. Pattern Recognition" 26:9, 1277-1294, 1993.
Blaschke, T., Strobl, J. "What’s wrong w ith pixels?: some recent developments interfacing remote sensing and GIS" GIS- Zeitschrift für Geoinformationssysteme 6, 12- 17, 2001.
Schiewe, J., Tufte, L., Ehlers, M. 2001. Potential and problems of multi-scale segmentation methods in remote sensing. GIS-Zeitschrift für Geoinformations systeme 6, 34-39.
Hill, R.A. "Image segmentation for hum id tropical forest classification in Landsat TM data" International Journal of Remote Sensing 20:5, 1039-1044, 1999.
Macleod & Congalton. "A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelgrass from Re motely Sensed Data ", Photogrammetric Engineering & Remote Sensing. Vol. 64. No. 3. p. 207-216, 1998.
Coppin, P. & Bauer, M. "Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery", Remote Sensing Reviews. Vol. 13. p. 207-234, 1996.
Maillard, P., Clausi, D.A., "Comparing classification metrics for labeling segmented remote sensing images", Comput er and Robot Vision, 2005. Proceedings, p. 42–428.
Balasubramanian, R. Rajan, S. Dorais wami, R. Stevenson, H., "A reliable composite classification strategy", Electrica l and Computer Engineering, p. 914-917 vol.2, 1998.
John C. Russ, The Image Processing Handbook, Springer-Verlag; October 1998.
Spitzer, H.; Franck, R.; Ko llewe, M.; Rega, N.; Rothkirch, A.; Wiemker, R., "Change detection with 1 m resolution satellite and aerial images", Geosciences and Remote Sensing Symposium, 2001, p. 2256 - 2258 vol.5
L. Bruzzone and S. B. Serpico, "An iterative technique for the detection of land- cover transitions in multi-temporal remo te-sensing images", IEEE Trans. Geosci. Remote Sensing, vol. 35, p. 858-867, July 1997.
Radke R.J., Andra S., Al-Kofani O. and Roysan B, "Image change detection algorithms: A systematic survey", IEEE Trans. Image Processing, 14 (3):291-307, 2005.
Shashi Shekar, Hui Xiong (Eds .), Encyclopedia of GIS, © 2008 SpringerScience, ISBN: 978-0-387-35973-1
Change detection and assessment using multi temporal satellite image for North- East Mediterranean Coast, Tun cay Kuleli, University of Cukurova Faculty of Fisheries Adana, Turkey.
Adia S.O, "Change detection of vegeta tion cover using multi-temporal remote sensing data and GIS techniques", Depart ment of Meteorology, Rabiu A. B Space Physics Lab, Physics Department Federal University of Technology, Akure Ondo State, India.
Dušan Jovanovi ć, "Monitoring Land-Use change area of Vojvodina, Serbia using LANDSAT ETM+ AND TM DATA", 4th International Conference, Bulgaria, InterGeo East 2007.
Dušan Jovanović, "A Comparison of object-oriented classification and standard techniques of image analyses in change detection of forest areas", msc thesis, Faculty of Technical Sciences, Serbia, 2010.
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