Remote-sensing-based flood damage estimation using crop condition profiles

Abstract

Flooding introduces significant changes to crop condition profiles that can be derived from remote sensing. These changes correlate to crop damage caused by flood events. Crop condition profiles can be directly or indirectly constructed using different vegetation indices if specific crop are pre-determined. Crop condition profiles may be resulted from different vegetation indices. This study compares different vegetation index algorithms in constructing crop condition profiles and their effect on flood damage estimation. Examined vegetation index algorithms include normalized difference vegetation index (NDVI), vegetation condition index (VCI), mean vegetation condition index (MVCI), and ratio to median vegetation condition index (RMVCI). MODIS data is used as the major source of remotely sensed observations considering its high temporal resolution that is highly desirable for constructing crop condition profiles. Cropland Data Layer (CDL) of USDA National Agricultural Statistics Service is used to differentiate different crop types. Several flooding events have been identified and compared with different condition profiles. The study shows that crop condition profiles can effectively detect the flood damage and estimate the damage due to flood.