Publicação
Multi-Temporal Geospatial Analysis of Floods Effects on Crop Production in Hadejia Local Government Area, Jigawa State, Nigeria
| Resumo: | Flooding is a global recurring disaster that is hindering crop production and food security. Smallholder farmers in Nigeria suffer severe damages and vulnerable to flooding because most practice rain-fed agriculture. Also, Optical satellite images for flood mapping face cloud interference and free Synthetic Aperture Radar (SAR) lacks temporal frequency to capture flooding dynamics. This research used the integration of time-series PlanetScope and Sentinel-1 images to develop flood progression and extent layers for the 2020 and 2022 flooding in Hadejia, Nigeria. Cropland damage and recovery status was assessed using post-flooding Sentinel-2 images and integration of survey data from the farmers. The result revealed that 58% and 66% of the total cropland was flooded in 2020 and 2022, respectively. The recovered cropland in 2020 was estimated to be 13% of the flooded cropland while 16% recovered in 2022. This study proved the effectiveness of the integration of optical and radar time-series images for detailed and refined flooding mapping. This result is crucial for a comprehensive damage assessment on crop production, which can facilitate transparent and data-driven post-disaster action plans, and valuable for policy making in spatial planning to achieve the Sustainable Development Goals (SDGs) of zero hunger (SDG2) and climate action (SDG13). |
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| Autores principais: | Lateef, Lukumon Olaitan |
| Assunto: | Flood Remote Sensing Satellite Image Cropland Damage Assessment Crop Recovery Random Forest SDG 2 - Zero hunger SDG 13 - Climate action |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Flooding is a global recurring disaster that is hindering crop production and food security. Smallholder farmers in Nigeria suffer severe damages and vulnerable to flooding because most practice rain-fed agriculture. Also, Optical satellite images for flood mapping face cloud interference and free Synthetic Aperture Radar (SAR) lacks temporal frequency to capture flooding dynamics. This research used the integration of time-series PlanetScope and Sentinel-1 images to develop flood progression and extent layers for the 2020 and 2022 flooding in Hadejia, Nigeria. Cropland damage and recovery status was assessed using post-flooding Sentinel-2 images and integration of survey data from the farmers. The result revealed that 58% and 66% of the total cropland was flooded in 2020 and 2022, respectively. The recovered cropland in 2020 was estimated to be 13% of the flooded cropland while 16% recovered in 2022. This study proved the effectiveness of the integration of optical and radar time-series images for detailed and refined flooding mapping. This result is crucial for a comprehensive damage assessment on crop production, which can facilitate transparent and data-driven post-disaster action plans, and valuable for policy making in spatial planning to achieve the Sustainable Development Goals (SDGs) of zero hunger (SDG2) and climate action (SDG13). |
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