Flash flood mapping for mountain streams using high-resolution ALOS-2 data

Abstract

This paper introduces a practical way to improve the risk management capacity and resilience of communities by utilizing a prompt flash flood map produced from very high spatial resolution ALOS-2 data. An improved flood detection algorithm is proposed to achieve a better discrimination capacity to identify flooded areas in the valley floodplain based on cluster analysis by verifying training sites and understanding pixel-based backscattering behaviour focusing on surface roughness changes caused by floodwater and floating debris, i.e., mud flow with gravels, stones and uprooted trees. The results show the possibility of a rapid, straightforward change detection approach to flood mapping, in particular to identify and classify floodwaters, damaged buildings, damaged rice fields, and stacks of driftwood through evidenced-based investigation.