Evaluation of typhoon-induced inundation losses associated with LULC using multi-temporal SAR and optical images

We utilized a two-branch end-to-end network (MultiSenCNN) for land use and land cover (LULC) classification and flood event mapping using multispectral (MS), panchromatic (Pan) and synthetic aperture radar (SAR) images, where flooding was induced by typhoon Lekima in August 2019. Flood damages were assessed by considering both the LULC and flood maps. We defined three strategies to compare the MS + SAR and MS + Pan images to demonstrate the ability of the MultiSenCNN algorithm for LULC classification. The three strategies yielded an average overall accuracy of similar to 98% and an average Kappa of similar to 0.98 for LULC classification. The overall accuracy of the fused MS + SAR images is slightly higher than the MS + Pan images when using the same model training samples. The flood mapping shows an overall accuracy of 97.22% and a Kappa of 0.94, with a flood inundation area of 101 km(2) that mainly inundated cropland and urban areas. Compared to other LULC types, the flooded cropland has caused more loss of ecosystem service values during typhoon Lekima, accounting for 81.19% of the total. Using SAR mages can well monitor the start/end states of flood events and the inundated areas, providing the flood status information to rescuers and governments for making timely decisions.

Automated summary

In this study, we used multispectral, panchromatic, and synthetic aperture radar (SAR) images for land use and land cover (LULC) classification and flood event mapping. The results showed that the MultiSenCNN algorithm, which fused the multispectral and SAR images, achieved high accuracy in LULC classification. The flood mapping also demonstrated high accuracy and highlighted significant damage to cropland. SAR images proved to be effective in monitoring flood events and providing crucial information for rescuers and governments to make timely decisions.