Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies

Abstract

Climate change has caused an increase in the frequency of flood events. Rapid and accurate flood mapping is essential for disaster monitoring and risk assessment. The normalized difference flood index (NDFI) is a change detection method with the characteristics of efficient processing and less manual intervention, which can quickly obtain flood information. However, the NDFI method would misclassify some permanent water bodies in lakes and rivers into floods. We presented a framework by combining NDFI calculated from synthetic aperture radar images and a summer permanent water bodies (SPWB) exclusion layer derived from optical remote sensing surface reflectance data, abbreviated as NDFI-SPWB. This framework was further verified by the flood event in the Yangtze river basin in July 2020. Results show that the NDFI-SPWB framework can increase the user accuracy by approximately 10% and the Kappa coefficient by approximately 0.08 compared with the original NDFI method, which verifies the feasibility and effectiveness of the proposed framework.