Dang, Y.; Yang, L.; Song, J. The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact. Remote Sens.2024, 16, 1527.
Dang, Y.; Yang, L.; Song, J. The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact. Remote Sens. 2024, 16, 1527.
Dang, Y.; Yang, L.; Song, J. The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact. Remote Sens.2024, 16, 1527.
Dang, Y.; Yang, L.; Song, J. The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact. Remote Sens. 2024, 16, 1527.
Abstract
Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. There are many studies focused on flood impact assessments for administrative districts but fewer on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and rainfall data, combined with crop growing season data to construct a crop flood damage assessment index (CFAI). First, an analytic hierarchy process (AHP) was used to assign weights to the impact factors; then, the weighted composite score method was used to calculate the CFAI; and finally, the impact was classified as slight, moderate, or severe based on the magnitude of the CFAI . This method was used for the Missouri River floods of 2019 in the U.S. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to validate the results. The combined approach achieved a high degree of consistency in spatial distribution. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.
Keywords
flooding; crop damage assessment; analytic hierarchy process
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
