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Changing Responses of Land Dynamics and Vulnerability to Flooding Under Policy and Environmental Change near Poyang Lake, China
Project Start Date
01/01/2005
Project End Date
01/01/2008
Project Call Name
Region
Solicitation
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Team Members:

Person Name Person role on project Affiliation
Daniel Brown Principal Investigator University of Washington, Seattle, US
Kathleen Bergen Co-Investigator University of Michigan, Ann Arbor, United States
Abstract

We have examined the performance of the CUBIST regression tree in filling cloud covered and/or areas affected by the Scan-Line Corrector (SLC) failure on Landsat Imagery. Tests were performed on two terrain- and atmospherically-corrected yet cloudy Landsat 5 scenes and a subset of three L7 images each acquired a month apart over the Upper Delaware River Basin. Regression tree results are within 0.5% reflectance in the visible wavelengths, between 1.5 and 2.7% for Landsat Band 4, and between 0.6 and 1.4% reflectance in Bands 5 and 7. Visual examination of the regression-filled images shows that, IF clouds and shadows in the input scenes can be accurately detected before processing of the data, regression trees are an effective tool to mitigate not only the image gaps due to the SLC failure, but also clouds and cloud shadows.