Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya
Rangeland degradation represents a significant environmental and socio-economic challenge in dryland ecosystems, particularly within Sub-Saharan Africa, where pastoral livelihoods depend on fragile landscapes.The proliferation of Prosopis juliflora, an aggressive woody invasive species, has emerged...
| Autores principales: | , , , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR System Organization
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/177711 |
| _version_ | 1855522639804628992 |
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| author | Cherotich, Fredah Galgallo, Diba Dhulipala, Ram Whitbread, Anthony M Paliwal, Ambica |
| author_browse | Cherotich, Fredah Dhulipala, Ram Galgallo, Diba Paliwal, Ambica Whitbread, Anthony M |
| author_facet | Cherotich, Fredah Galgallo, Diba Dhulipala, Ram Whitbread, Anthony M Paliwal, Ambica |
| author_sort | Cherotich, Fredah |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Rangeland degradation represents a significant environmental and socio-economic challenge in dryland ecosystems, particularly within Sub-Saharan Africa, where pastoral livelihoods depend on fragile landscapes.The proliferation of Prosopis juliflora, an aggressive woody invasive species, has emerged as a critical ecological and management concern. Accurate monitoring of Prosopis juliflora invasion is imperative for effective management. However, traditional field-based surveys, while valuable, are hindered by high costs, logistical challenges, and limited spatial coverage, rendering them inadequate for tracking invasions across extensive rangelands. Recent advancements in remote sensing and machine learning present a transformative alternative.The study employs Random Forest classification across multiple satellite datasets to assess sensor performance in detecting Prosopis juliflora along with community validation using PGIS. It further utilizes ground-truth observations to predict the spread of this invasive species across the region. This integrative approach bolsters the evidence base for decision-making, thereby supporting adaptive, inclusive, and sustainable management of rangelands. |
| format | Brief |
| id | CGSpace177711 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | CGIAR System Organization |
| publisherStr | CGIAR System Organization |
| record_format | dspace |
| spelling | CGSpace1777112025-11-25T02:05:49Z Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya Cherotich, Fredah Galgallo, Diba Dhulipala, Ram Whitbread, Anthony M Paliwal, Ambica invasive species machine learning citizen science remote sensing rangelands Rangeland degradation represents a significant environmental and socio-economic challenge in dryland ecosystems, particularly within Sub-Saharan Africa, where pastoral livelihoods depend on fragile landscapes.The proliferation of Prosopis juliflora, an aggressive woody invasive species, has emerged as a critical ecological and management concern. Accurate monitoring of Prosopis juliflora invasion is imperative for effective management. However, traditional field-based surveys, while valuable, are hindered by high costs, logistical challenges, and limited spatial coverage, rendering them inadequate for tracking invasions across extensive rangelands. Recent advancements in remote sensing and machine learning present a transformative alternative.The study employs Random Forest classification across multiple satellite datasets to assess sensor performance in detecting Prosopis juliflora along with community validation using PGIS. It further utilizes ground-truth observations to predict the spread of this invasive species across the region. This integrative approach bolsters the evidence base for decision-making, thereby supporting adaptive, inclusive, and sustainable management of rangelands. 2025-09-30 2025-11-10T14:55:12Z 2025-11-10T14:55:12Z Brief https://hdl.handle.net/10568/177711 en Open Access application/pdf CGIAR System Organization Cherotich, F., Galgallo, D., Dhulipala, R., Whitbread, A. and Paliwal, A. 2025. Integrating Earth Observation, Machine Learning, and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya. DTA Research Brief. Montpellier, France: CGIAR System Organization. |
| spellingShingle | invasive species machine learning citizen science remote sensing rangelands Cherotich, Fredah Galgallo, Diba Dhulipala, Ram Whitbread, Anthony M Paliwal, Ambica Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title | Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title_full | Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title_fullStr | Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title_full_unstemmed | Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title_short | Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya |
| title_sort | integrating earth observation machine learning and citizen science to map prosopis juliflora invasion in semi arid rangelands of kenya |
| topic | invasive species machine learning citizen science remote sensing rangelands |
| url | https://hdl.handle.net/10568/177711 |
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