Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison

Wafa Mohamed Tahir Nori Detection of land cover changes in El Rawashda forest, Sudan: A systematic comparison Band 11 der Reihe „Fernerkundung und angewandte Geoinformatik“ Herausgegeben von Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden 134 Seiten, Format DIN B5, Zahlreiche Abbildungen, davon 34 farbig. Sprache: Englisch. Preis: 39,80 Euro. ISBN 978-3-944101-20-0. Rhombos-Verlag, Berlin 2014 About this book This research evaluates the potential of remote sensing for monitoring forest cover change in El Rawashda forest, Sudan, using Landsat ETM and Terra ASTER imagery. This was accomplished by performing eight change detection algorithms. Firstly a simplified post-classification with only 4 forest classes, namely close forest, open forest, bare land and grass land, was used. A RGB-NDVI change detection strategy to detect major decrease or increase in forest vegetation was developed as well. This method was found to be more effective than NDVI image differencing as it distinguishes different change classes by different colour tones. The Tasseled Cap green layer (GTC) composite was proposed to detect the change in vegetation of the study area. This method performs better than RGB-NDVI. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The calculated date to date change vectors contain useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the Principal Components Analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. A recently proposed approach, the Multivariate Alteration Detection (MAD), in combination with a posterior Maximum Autocorrelation Factor Transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. As a final step a quantitative accuracy assessment at the level of change/no change pixels was performed. Among the various investigated methods of forest cover change analysis the highest accuracy was obtained using post-classification comparison based on supervised classification.