期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 7B
页码：552-556
出版社：Copernicus Publications
摘要：This study investigates how to derive water fraction and flood map from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Earth Observing System (EOS) using a Regression Tree (RT) approach. The RT approach can integrate all the possible candidate predictors, such as the MODIS channel 2 reflectance (CH2), reflectance ratio (CH2/CH1), reflectance difference (CH2-CH1) between MODIS channels 2 and 1, vegetation and water indices. Meanwhile, it provides accuracy estimates of the derivation. The recent floods in New Orleans area in August 2005 were selected for the study. MODIS surface reflectance with the matched surface water fraction data were used for the RT training. From the training set, 60% were used for training, and the remaining 40% for test. Rules and regression models from the RT training were applied for real applications to New Orleans flooding in 2005 to calculate water fraction values. Flood distributions in both space and time domains were generated using the differences in water fraction values after and before the flooding. The derived water fraction maps were evaluated using higher resolution Thematic Mapper (TM) data from the Landsat observations. It shows that correlation between the water fractions derived from the MODIS and TM data is 0.97, with difference or "bias" of 2.16%, standard deviation of 3.89%, and root mean square error (rmse) of 4.45%. The results show that the RT approach in dynamic monitoring of floods is promising
