Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat
image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained
with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are
not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.
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Document Type: Research Article
USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, USA
ASRC Research and Technology Solutions, Contractor to the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, USA
Publication date: 2010-03-01
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