A self-trained classification technique for producing 30 m percent-water maps from Landsat data
International Journal of Remote Sensing
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Abstract
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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A self-trained classification technique for producing 30 m percent-water maps from Landsat data |
| Series title | International Journal of Remote Sensing |
| DOI | 10.1080/01431161003667455 |
| Volume | 31 |
| Issue | 8 |
| Year Published | 2010 |
| Language | English |
| Publisher | Taylor and Francis |
| Publisher location | Philadelphia, PA |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 7 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | International Journal of Remote Sensing |
| First page | 2197 |
| Last page | 2203 |
| Google Analytic Metrics | Metrics page |