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Improved forest change detection with terrain illumination corrected Landsat images

1 Introduction</h5> Landsat imagery is widely used to monitor changes in land surface conditions, including changes in forest cover, which impact Earth's energy balance, carbon cycle, water cycle and biogeochemical processes ( Band, 1993; Huang et al., 2008; Pandey, 2002 ). To quantify such changes, two Landsat images acquired before and after the forest change are typically examined by visual interpretation or automated analysis. With the Landsat archive becoming freely available, the main challenge for generating continental or global forest change maps at Landsat resolution (30 m) is an effective and accurate change detection algorithm ( Huang et al., 2008 ). Various computer based change-detection algorithms have been developed ( Foody & Mathur, 2004; Huang et al., 2010a, 2010b; Masek et al., 2008; Townshend et al., 2012 ). The key process in these algorithms is the spectral analysis of a set of training pixels and discriminating appropriate spectral thresholds that can be applied to the whole image scene or multiple scenes to define the area of forest change.</P>One important assumption underlying these algorithms is that the spectral characteristics of the training pixels represent those of the forest pixels within the study region. However, topographic illumination effects (shadow, slope, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier
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