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

International Journal of Remote Sensing

By:
, ,
DOI: 10.1080/01431161003667455

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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.

Additional Publication Details

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
First page:
2197
Last page:
2203