Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data
The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural di...
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MDPI AG
2025-06-01
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| author | Miguel A. Belenguer-Plomer Inês Mendes Michele Lazzarini Omar Barrilero Paula Saameño Sergio Albani |
| author_facet | Miguel A. Belenguer-Plomer Inês Mendes Michele Lazzarini Omar Barrilero Paula Saameño Sergio Albani |
| author_sort | Miguel A. Belenguer-Plomer |
| collection | DOAJ |
| description | The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change. |
| format | Article |
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| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-b4284285e7b2433790271bed8eea0bfb2025-08-20T03:29:39ZengMDPI AGRemote Sensing2072-42922025-06-011712208710.3390/rs17122087Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial DataMiguel A. Belenguer-Plomer0Inês Mendes1Michele Lazzarini2Omar Barrilero3Paula Saameño4Sergio Albani5European Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainEuropean Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainEuropean Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainEuropean Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainEuropean Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainEuropean Union Satellite Centre (SatCen), 28850 Torrejon de Ardoz, SpainThe United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change.https://www.mdpi.com/2072-4292/17/12/2087climate securityearth observation (EO)Sustainable Development Goals (SDGs)flood resilience |
| spellingShingle | Miguel A. Belenguer-Plomer Inês Mendes Michele Lazzarini Omar Barrilero Paula Saameño Sergio Albani Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data Remote Sensing climate security earth observation (EO) Sustainable Development Goals (SDGs) flood resilience |
| title | Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data |
| title_full | Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data |
| title_fullStr | Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data |
| title_full_unstemmed | Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data |
| title_short | Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data |
| title_sort | estimating flood affected houses as an sdg indicator to enhance the flood resilience of sahel communities using geospatial data |
| topic | climate security earth observation (EO) Sustainable Development Goals (SDGs) flood resilience |
| url | https://www.mdpi.com/2072-4292/17/12/2087 |
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