Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas

Abstract

To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R2 ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

Urban Flood Loss Assessment and Index Insurance Compensation Estimation by Integrating Remote Sensing and Rainfall Multi-Source Data: A Case Study of the 2021 Henan Rainstorm

“…Daily rainfall data in Henan Province from 1970 to 2021 was counted. According to the rainfall levels of the China Meteorological Administration [10], the 50-year rainfall data were, respectively, classified into six types: light rain, moderate rain, heavy rain, rainstorm, heavy rainstorm and extraordinary rainstorm. By analyzing the long time series data, the frequency of rainfall in Henan Province was calculated as shown in Table 5.…”

“…(2) The role of remote sensing in the UFIITC model The spatial information and extent of urban water bodies are prone to change due to rainfall, evaporation and human activities, especially when heavy rainfall and flooding occur [10]. With the rapid development of satellite remote sensing technology, remote sensing-based water extraction provides an efficient and reliable technical means for monitoring urban water environments [55].…”

“…β history is the unit area losses of the historical flood level. α is the conversion factor, estimated from the fitting of remote sensing brightness values, and is generally assumed to be 0.04 [10]. γ is the power index, determined from historical years.…”

“…Using the high-precision SHP (Shape is a data format used in ArcGIS 10.2 software) vector boundary in Henan, the area of flooded remote sensing images of each city was extracted and calculated by pixels. Then, we conducted on-site investigations to investigate the flood affected areas in different regions [10]. And through NDWI interpretation of the pre-disaster and post-disaster water bodies, information on the scope of flood disasters was excavated and verified.…”

“…Floods are one of the most severe natural disasters worldwide [1], often causing huge economic losses and serious human casualties [2,3]. In the context of climate warming, extreme rainstorms are becoming more frequent [4] and more prone to causing widespread flooding [5], severely constraining sustainable socio-economic development, for example, floods in Thailand in 2011 [6], flooding along the Elbe River in Germany in 2013 [7], heavy rainfall across central Europe in 2021 [2], an extraordinary rainstorm in Henan in 2021 [8][9][10] and floods in Pakistan in 2022 [11]. Currently, in response to natural disasters, government departments use post-disaster relief to provide assistance [12], with a huge financial burden [13].…”