Review on applications of remote sensing in urban flood modeling

Xu, Zongxue; Cheng, Tao; Hong, Siyang; Wang, Linxia

doi:10.1360/n972018-00239

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The loss per unit area caused by floods and waterlogging is approximately 2.38 times higher than that of Luoyang, which has the second highest GDP. The higher the degree of urbanization is, the more developed the regional economy, which results in a large population density and a high asset value per unit area in the urban center [7,18]. Therefore, under this condition, the sensitivity to urban flood disaster losses increases, resulting in a great need for flood control and waterlogging removal standards and a great risk of suffering from flood disasters [22].…”

Section: Discussionmentioning

confidence: 99%

“…Based on satellite and precipitation data, Vecere et al [16] divided index insurance thresholds to achieve flood damage monitoring. In the field of urban flood disasters, Kim et al [14] used high-resolution digital terrain to improve urban flood disaster estimation [3,17,18]. Wang et al [19] proposed a multiband water index algorithm based on Landsat 8 images, which automatically extracted urban surface floods using K-means clustering methods and analyzed the characteristics of flood disasters [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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

Duan

Zheng

Jin

et al. 2022

Water

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Duan

Zheng

Jin

et al. 2022

Water

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“…According to the assessment results based on the flood losses model of remote sensing pixels (Table 2), Zhengzhou City has a direct loss of RMB 0.52 billion/km 2 per unit area, which is much higher than other cities. Due to the leading urbanization development in the region, the population density and net asset value per unit are high [75]. In this context, the vulnerability and exposure of urban flood disasters have increased [53], resulting in higher demands for urban flood resistance.…”

Section: Discussionmentioning

confidence: 99%

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

Zheng

Chen

et al. 2023

Sustainability

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To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R2 = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.

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“…As the most frequent and extensive natural disaster within urban areas, urban rainstorms have been the subject of deep scrutiny by scholars worldwide. Comprehensive studies span various aspects, such as formation mechanisms [3,4], simulation and prediction [5][6][7], and risk assessment [8,9].…”

Section: Introductionmentioning

confidence: 99%

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Zou,

Huang,

Wang

et al. 2024

Water

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Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

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Review on applications of remote sensing in urban flood modeling

Abstract: Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area SCIENCE CHINA Earth Sciences 60, 659 (2017); Advances in urban information extraction from high-resolution remote sensing imagery

Cited by 8 publications

References 13 publications

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

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

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

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

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