Identifying agricultural flood damage using Landsat imagery

Identifying agricultural flood damage using Landsat imagery During the first two weeks of July 2003, heavy precipitation occurred across the northern and central portions of Indiana, resulting in flooding and ponded water that damaged crops. Landsat 5 Thematic Mapper images were used to identify the level of damage in fields. A supervised classification and temporal change detection were performed with the help of ERDAS Imagine. To examine the recovery rate of crops over time, two methods were used: a change detection matrix and Delta Normalized Difference Vegetation Index. Both methods indicated an improvement in the conditions of the crops two weeks after the end of the heavy precipitation. Correlations between precipitation, crop damage, yield and unharvested area were weak. At the end of the season, the damage caused by flooding and excess precipitation did not greatly affect the yield of crops, especially corn. Soybeans suffered slightly from these rainfall events, and their yield was smaller than in previous years. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Identifying agricultural flood damage using Landsat imagery

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2007 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-006-9026-5
Publisher site
See Article on Publisher Site

Abstract

During the first two weeks of July 2003, heavy precipitation occurred across the northern and central portions of Indiana, resulting in flooding and ponded water that damaged crops. Landsat 5 Thematic Mapper images were used to identify the level of damage in fields. A supervised classification and temporal change detection were performed with the help of ERDAS Imagine. To examine the recovery rate of crops over time, two methods were used: a change detection matrix and Delta Normalized Difference Vegetation Index. Both methods indicated an improvement in the conditions of the crops two weeks after the end of the heavy precipitation. Correlations between precipitation, crop damage, yield and unharvested area were weak. At the end of the season, the damage caused by flooding and excess precipitation did not greatly affect the yield of crops, especially corn. Soybeans suffered slightly from these rainfall events, and their yield was smaller than in previous years.

Journal

Precision AgricultureSpringer Journals

Published: Feb 7, 2007

References

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