Development of a Flood Dynamic Monitoring and Forecasting System Based on Remote Sensing Big Data

To minimize the economic losses and casualties caused by flooding, early warning of flood disasters is crucial. This paper develops a flood dynamic monitoring and forecasting system based on remote sensing big data. By using remote sensing image processing techniques, the system extracts temporal changes in water body and surface information, identifies the extent and variations of water bodies, and determines the flooded areas. Based on flood disaster level assessments, the system forecasts flood dynamics and visualizes the results through GIS technology. The results show that the maximum flood inundation area in Fengzhan City increased from 196 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to 3320 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and then decreased to 2219 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. The flood-affected areas in Fengzhan City, Wahe City, Yunxiang City, and Guannan City account for 30%–70% of the total sowing area, which classifies them as moderately affected. Guikou City and Changyu City, with flood-affected areas accounting for less than 30% of the total sowing area, are classified as lightly affected. The relative error between the fitted inundation area in the system and the area extracted from remote sensing images does not exceed 4.00%, indicating that the system has high accuracy and reliability in monitoring water body inundation areas and assessing disaster severity.