Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/6533
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dc.contributor.authorMupfiga Upenyuen_US
dc.contributor.authorMutanga Onisimoen_US
dc.contributor.authorDube Timothyen_US
dc.date.accessioned2025-04-28T07:45:32Z-
dc.date.available2025-04-28T07:45:32Z-
dc.date.issued2024-11-19-
dc.identifier.urihttps://cris.library.msu.ac.zw//handle/11408/6533-
dc.description.abstractVegetation fires are known to profoundly impact ecosystem structure and composition, posing threats to ecosystem stability and human safety. In Zimbabwe, uncontrolled fires have been recur rent, yet a rigorous analysis of the key drivers is still lacking. Previous studies in Zimbabwe have predominantly focused on spatio-temporal dynamics of the occurrence of vegetation fire, leaving a gap in understanding the underlying drivers. Accurate prediction of fire occurrence and identifica tion ofthe major driversisimperative for effective fire managementstrategies. The study employsthe Maxent model, a machine-learning approach, to analyze historical MODIS fire data alongside bioclimatic, topographic, anthropogenic, and vegetation variables, to assess the likelihood of fire occurrence in Zimbabwe. The research also aims to elucidate the major factors that influence fire occurrence within the region. The independent contributions of predictor variables to the model’s goodness of fit are evaluated using a jackknife test, while model accuracy is assessed using the AUC (area underthe receiver operating characteristic curve). Resultsindicate that elevation, precipitation seasonality, temperature annual range and human footprint emerge as the major factors influencing fire occurrence in Zimbabwe. The model demonstrates an acceptable accuracy, with an average AUC of 0.77. This study underscores the utility of the Maxent model in elucidating the contributions of various environmental factors to vegetation fire occurrence. Moreover, the ability of the model to predict the probability of fire occurrence offers valuable insights for fire managers, facilitating the assessment of the spatial vulnerability of vegetation to fire occurrence. Overall, this research contributes to an improved understanding of the drivers of vegetation fires in Zimbabwe and provides a practical tool for enhancing fire management efforts in the region and beyond.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofRemote Sensing Applications: Society and Environmenten_US
dc.subjectFire risken_US
dc.subjectMaxenten_US
dc.subjectProbabilityen_US
dc.subjectVulnerabilityen_US
dc.subjectZimbabween_US
dc.titleAssessing drivers of vegetation fire occurrence in Zimbabwe: Insights from Maxent modelling and historical data analysisen_US
dc.typeresearch articleen_US
dc.identifier.doihttps://doi.org/10.1016/j.rsase.2024.101404-
dc.contributor.affiliationDiscipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3201, South Africa; Department of Geography, Environmental Sustainability and Resilience Building, Midlands State University, Gweru, 9055, Zimbabween_US
dc.contributor.affiliationDiscipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3201, South Africaen_US
dc.contributor.affiliationInstitute for Water Studies, Department of Earth Sciences, The University of the Western Cape, Private Bag X17, Bellville, 7535, South Africaen_US
dc.relation.issn2352-9385en_US
dc.description.volume37en_US
dc.description.startpage1en_US
dc.description.endpage13en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetyperesearch article-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
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