Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning
Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine l...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402309970X |
| _version_ | 1797383907766697984 |
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| author | Blessing Kavhu Zama Eric Mashimbye Linda Luvuno |
| author_facet | Blessing Kavhu Zama Eric Mashimbye Linda Luvuno |
| author_sort | Blessing Kavhu |
| collection | DOAJ |
| description | Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km2) covered a larger area compared to the natural ones (Categories B∼33 407 km2) and (Categories C∼15 040 km2). The findings of this study emphasize the value of ensemble ML modelling in identifying PMAs and guiding transboundary land use planning. Overall, this study highlights the role of anthropogenic activities in driving land use changes in Transboundary Drainage Basins (TDBs) and suggests a need to promote sustainable practices in predicted PMAs through comprehensive planning to ensure water availability in the Okavango basin. |
| first_indexed | 2024-03-08T21:27:49Z |
| format | Article |
| id | doaj.art-0e43649b6f7247e5ae2b8579e9c51f20 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T21:27:49Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0e43649b6f7247e5ae2b8579e9c51f202023-12-21T07:34:33ZengElsevierHeliyon2405-84402023-12-01912e22762Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learningBlessing Kavhu0Zama Eric Mashimbye1Linda Luvuno2Department of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa; Centre for Sustainability Transitions, Stellenbosch University, Stellenbosch, 7600, South Africa; Scientific Services Unit, Zimbabwe Parks and Wildlife Management Authority, Headquarters, P. O. Box 140, Causeway, Harare, Zimbabwe; Corresponding author. Department of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa.Department of Geography and Environmental Studies, Stellenbosch University, Private Bag X1, Matieland, 7602, South AfricaCentre for Sustainability Transitions, Stellenbosch University, Stellenbosch, 7600, South AfricaRemote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km2) covered a larger area compared to the natural ones (Categories B∼33 407 km2) and (Categories C∼15 040 km2). The findings of this study emphasize the value of ensemble ML modelling in identifying PMAs and guiding transboundary land use planning. Overall, this study highlights the role of anthropogenic activities in driving land use changes in Transboundary Drainage Basins (TDBs) and suggests a need to promote sustainable practices in predicted PMAs through comprehensive planning to ensure water availability in the Okavango basin.http://www.sciencedirect.com/science/article/pii/S240584402309970XLandcover trajectoriesWater resourcesTransboundary basinPost-Angolan warMachine learningEnsemble modelling |
| spellingShingle | Blessing Kavhu Zama Eric Mashimbye Linda Luvuno Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning Heliyon Landcover trajectories Water resources Transboundary basin Post-Angolan war Machine learning Ensemble modelling |
| title | Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning |
| title_full | Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning |
| title_fullStr | Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning |
| title_full_unstemmed | Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning |
| title_short | Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning |
| title_sort | predicting priority management areas for land use cover change in the transboundary okavango basin based on machine learning |
| topic | Landcover trajectories Water resources Transboundary basin Post-Angolan war Machine learning Ensemble modelling |
| url | http://www.sciencedirect.com/science/article/pii/S240584402309970X |
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