Research Papers

Techniques for developing land-use classification using moderate resolution imaging spectroradiometer imagery

[+] Author Affiliations
Alan J. Stern

Hydrology and Remote Sensing Lab, USDA Agricultural Research Service, Bldg 007, Rm 104, Beltsville, MD 20705

Paul C. Doraiswamy

Hydrology and Remote Sensing Lab., USDA Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, MD 20705

Bakhyt Akhmedov

Science Systems and Applications, Inc., Suite 600, Lanham, MD 20706

J. Appl. Remote Sens. 3(1), 033517 (March 6, 2009). doi:10.1117/1.3106716
History: Received October 1, 2008; Revised February 25, 2009; Accepted March 3, 2009; March 6, 2009; Online March 06, 2009
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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.

© 2009 Society of Photo-Optical Instrumentation Engineers

Topics

MODIS

Citation

Alan J. Stern ; Paul C. Doraiswamy and Bakhyt Akhmedov
"Techniques for developing land-use classification using moderate resolution imaging spectroradiometer imagery", J. Appl. Remote Sens. 3(1), 033517 (March 6, 2009). ; http://dx.doi.org/10.1117/1.3106716


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