Composite Risk Assessment and Spatial Optimization Configuration Based on Remote Sensing Mapping Process

Abstract

Remote sensing (RS) technology offers a revolutionary tool to research urban climate risks, with its ability to fuse data from multiple sources greatly enhancing the accuracy of compound disaster evaluations. This article examines the risks of combined high temperatures and droughts in Beijing by merging Landsat satellite RS data, meteorological records, and socioeconomic information. We developed a three-dimensional framework consisting of hazard, exposure, and vulnerability to perform a detailed assessment of the compound risk index (CRI), while also modifying the local climate zone (LCZ) framework through a multiobjective optimization approach. Our analysis revealed that the medium and high compound risk level areas were mainly located in the eastern and southern parts of Beijing, with the average CRI of Fengtai District reaching 0.52. Additionally, exposure varied from intermediate to high and followed an outwardly dispersed spatial pattern. Vulnerability exhibited a low to high gradient distribution throughout the study area. Notably, CRI values were consistently higher in architectural LCZs compared to natural ones. Specifically, compact and low-rise building types demonstrate elevated CRI values (LCZ3: 0.501), thus, warranting prioritized optimization efforts. By integrating constraints related to development and population density, and through measures such as increasing LCZ4 and LCZ5 while reducing LCZ3 and LCZ8, the CRI decreased from 6320.56 to 6145.45 (1.48% reduction). The findings of this article present innovative strategies for mitigating compound risks and provide scientific guidance critical for fostering the development and regulatory planning of climate-resilient cities.