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Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. Many studies have focused on flood impact assessments for administrative districts, but fewer have focused 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 precipitation data, combined with crop growth period data to construct a crop flood damage assessment index (CFAI). First, the analytic hierarchy process (AHP) was used to assign weights to the impact parameters; then, the Weighted Composite Score Method was used to calculate the CFAI; and finally, the impact was classified as sub-slight, slight, moderate, sub-severe, or severe based on the magnitude of the CFAI. This method was used for the Missouri River floods of 2019 in the United States and the Henan flood of 2021 in China. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to compare the results. Compared with the DVDI, the CFAI underestimated the evaluation results. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments. Full article

The southeast coastal area of China (SCAC), a typhoon-prone area with a long coastline, suffers severe damage from typhoons almost every year. Exploring the spatial characteristics of historical typhoon-induced vegetation damage (VD) is crucial to predicting VD after severe typhoon landfalls and improving strategies for vegetation protection and restoration. Remote sensing is an efficient and feasible approach for measuring large-scale VD caused by natural disasters. This paper, by exploring the spatial distribution of VD of every severe landfalling typhoon with Google Earth Engine (GEE), aims to reveal the spatial characteristics of typhoon-induced VD in SCAC. Firstly, the values of disaster vegetation damage index (DVDI), difference in enhanced vegetation index (DEVI), and normalized difference vegetation index (DNDVI) for the 28 selected landing typhoons in SCAC were calculated and compared by using moderate resolution imaging spectroradiometer (MODIS) data in GEE. Secondly, every DVDI image was overlaid with land cover, elevation, relative aspect and typhoon path layers in ArcGIS. Thirdly, spatial characteristics of VD were revealed with the aid of spatial statistical analysis. The study found that: (1) DVDI is a more effective index for evaluating VD caused by typhoons. (2) The Pearl River Delta is the most severe VD region. The severe VD regions for four typhoon groups have significantly spatial correlation with typhoon-landing locations. (3) Forests are ranked the first in terms of damaged areas by typhoon in every year, followed by sparse forests. (4) Topography has no influence on VD by a single typhoon event, and relative aspect has no correlation with VD caused by typhoons in SCAC. Full article