Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana
Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood mod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2021.1923384 |
| _version_ | 1797715410809782272 |
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| author | Benjamin Ghansah Clement Nyamekye Seth Owusu Emmanuel Agyapong |
| author_facet | Benjamin Ghansah Clement Nyamekye Seth Owusu Emmanuel Agyapong |
| author_sort | Benjamin Ghansah |
| collection | DOAJ |
| description | Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km2 area of the watershed is flooded every 2–3 years (normal flooding) and about 689.61 km2 is flooded every 7–10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local administrators, national flood disaster management and researchers for flood mitigation and land use planning within the watershed. |
| first_indexed | 2024-03-12T08:07:22Z |
| format | Article |
| id | doaj.art-f0e5a2a4b4f04d54afb111a9f77ca884 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:07:22Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-f0e5a2a4b4f04d54afb111a9f77ca8842023-09-02T19:28:58ZengTaylor & Francis GroupCogent Engineering2331-19162021-01-018110.1080/23311916.2021.19233841923384Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in GhanaBenjamin Ghansah0Clement Nyamekye1Seth Owusu2Emmanuel Agyapong3Kwame Nkrumah University of Science and TechnologyKoforidua Technical UniversityKoforidua Technical UniversityKoforidua Technical UniversityFloods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km2 area of the watershed is flooded every 2–3 years (normal flooding) and about 689.61 km2 is flooded every 7–10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local administrators, national flood disaster management and researchers for flood mitigation and land use planning within the watershed.http://dx.doi.org/10.1080/23311916.2021.1923384random forestlandsat imagesflood prone and flood hazardnasia riverrural landscape |
| spellingShingle | Benjamin Ghansah Clement Nyamekye Seth Owusu Emmanuel Agyapong Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana Cogent Engineering random forest landsat images flood prone and flood hazard nasia river rural landscape |
| title | Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana |
| title_full | Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana |
| title_fullStr | Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana |
| title_full_unstemmed | Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana |
| title_short | Mapping flood prone and Hazards Areas in rural landscape using landsat images and random forest classification: Case study of Nasia watershed in Ghana |
| title_sort | mapping flood prone and hazards areas in rural landscape using landsat images and random forest classification case study of nasia watershed in ghana |
| topic | random forest landsat images flood prone and flood hazard nasia river rural landscape |
| url | http://dx.doi.org/10.1080/23311916.2021.1923384 |
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