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A self-trained classification technique for producing 30 m percent-water maps from Landsat data

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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

Affiliations: 1: USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, USA 2: 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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