A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas
As more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming deca...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/17/4288 |
| _version_ | 1797581889483046912 |
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| author | Carolina Salvo Alessandro Vitale |
| author_facet | Carolina Salvo Alessandro Vitale |
| author_sort | Carolina Salvo |
| collection | DOAJ |
| description | As more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming decades. The authors propose an innovative methodology to assess the future multi-hazard exposure of urban areas based on remote sensing technologies and statistical and spatial analysis. The authors, specifically, applied remote sensing technologies combined with artificial intelligence to map the built-up area automatically. They assessed and calibrated a transferable Binary Logistic Regression Model (BLRM) to model and predict future urban growth dynamics under different scenarios, such as the business as usual, the slow growth, and the fast growth scenarios. Finally, considering specific socioeconomic exposure indicators, the authors assessed each scenario’s future multi-hazard exposure in urban areas. The proposed methodology is applied to the Municipality of Rende. The results revealed that the multi-hazard exposure significantly changed across the analyzed scenarios and that urban socioeconomic growth is the main driver of risk in urban environments. |
| first_indexed | 2024-03-10T23:13:02Z |
| format | Article |
| id | doaj.art-111807d4c71b460d9fd3764f42cfa725 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:13:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-111807d4c71b460d9fd3764f42cfa7252023-11-19T08:47:03ZengMDPI AGRemote Sensing2072-42922023-08-011517428810.3390/rs15174288A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban AreasCarolina Salvo0Alessandro Vitale1Department of Civil Engineering, University of Calabria, 87036 Rende, CS, ItalyDepartment of Civil Engineering, University of Calabria, 87036 Rende, CS, ItalyAs more than 75% of the global population is expected to live in urban areas by 2050, there is an urgent need to assess the risk of natural hazards through a future-focused lens so that adequately informed spatial planning decisions can be made to define preventive risk policies in the upcoming decades. The authors propose an innovative methodology to assess the future multi-hazard exposure of urban areas based on remote sensing technologies and statistical and spatial analysis. The authors, specifically, applied remote sensing technologies combined with artificial intelligence to map the built-up area automatically. They assessed and calibrated a transferable Binary Logistic Regression Model (BLRM) to model and predict future urban growth dynamics under different scenarios, such as the business as usual, the slow growth, and the fast growth scenarios. Finally, considering specific socioeconomic exposure indicators, the authors assessed each scenario’s future multi-hazard exposure in urban areas. The proposed methodology is applied to the Municipality of Rende. The results revealed that the multi-hazard exposure significantly changed across the analyzed scenarios and that urban socioeconomic growth is the main driver of risk in urban environments.https://www.mdpi.com/2072-4292/15/17/4288exposureriskurban growthpredictionlogistic regressionremote sensing |
| spellingShingle | Carolina Salvo Alessandro Vitale A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas Remote Sensing exposure risk urban growth prediction logistic regression remote sensing |
| title | A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas |
| title_full | A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas |
| title_fullStr | A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas |
| title_full_unstemmed | A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas |
| title_short | A Remote Sensing Method to Assess the Future Multi-Hazard Exposure of Urban Areas |
| title_sort | remote sensing method to assess the future multi hazard exposure of urban areas |
| topic | exposure risk urban growth prediction logistic regression remote sensing |
| url | https://www.mdpi.com/2072-4292/15/17/4288 |
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