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Journal of Applied Remote Sensing

Techniques for developing land-use classification using moderate resolution imaging spectroradiometer imagery
Author(s): Alan Stern; Paul C. Doraiswamy; Bakhyt Akhmedov
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Paper Abstract

Abstract. Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type. This study compares four techniques to create land-use classifications using MODIS data. These classifications were compared to the ground data that had been set aside, to a mask where each MODIS sized pixel were at least 80% of a single land-used based on a Landsat TM classification for the same year, and to the Landsat TM classification. Using a decision tree method and comparing the classification to an 80% mask resulted in an accuracy of 73% which was the highest accuracy obtained in this study. The study showed that accuracies could range from 37% to 73% depending on the classification process and if segment data, an 80% mask, or a Landsat TM classification were used for accuracy assessment.

Paper Details

Date Published: 1 March 2009
PDF: 12 pages
J. Appl. Remote Sens. 3(1) 033517 doi: 10.1117/1.3106716
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Alan Stern, U.S. Dept. of Agriculture (United States)
Paul C. Doraiswamy, USDA Agricultural Research Service (United States)
Bakhyt Akhmedov, Science Systems and Applications, Inc. (United States)


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