Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios
Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fi...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/8/1/8 |
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| author | Rodrigo N. Vasconcelos Mariana M. M. de Santana Diego P. Costa Soltan G. Duverger Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro Washington J. S. Franca Rocha |
| author_facet | Rodrigo N. Vasconcelos Mariana M. M. de Santana Diego P. Costa Soltan G. Duverger Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro Washington J. S. Franca Rocha |
| author_sort | Rodrigo N. Vasconcelos |
| collection | DOAJ |
| description | Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions. |
| format | Article |
| id | doaj-art-b0f5568faf1c4c1b8bf5d8a4fb357246 |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-b0f5568faf1c4c1b8bf5d8a4fb3572462025-01-24T13:32:15ZengMDPI AGFire2571-62552024-12-0181810.3390/fire8010008Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future ScenariosRodrigo N. Vasconcelos0Mariana M. M. de Santana1Diego P. Costa2Soltan G. Duverger3Jefferson Ferreira-Ferreira4Mariana Oliveira5Leonardo da Silva Barbosa6Carlos Leandro Cordeiro7Washington J. S. Franca Rocha8Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana-UEFS, Feira de Santana 44036-900, BA, BrazilForest Engineering Institute (FEI/UEAP), State University of Amapá—UEAP, Av. Pres. Getúlio Vargas, 650 Centro, Macapa 68900-070, AP, BrazilGEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, BrazilGEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj, Pinheiros 05422-030, SP, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj, Pinheiros 05422-030, SP, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj, Pinheiros 05422-030, SP, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj, Pinheiros 05422-030, SP, BrazilPostgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana-UEFS, Feira de Santana 44036-900, BA, BrazilWildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions.https://www.mdpi.com/2571-6255/8/1/8semiariddrylandsfire susceptibilityfire dynamicsMaxentclimate change |
| spellingShingle | Rodrigo N. Vasconcelos Mariana M. M. de Santana Diego P. Costa Soltan G. Duverger Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro Washington J. S. Franca Rocha Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios Fire semiarid drylands fire susceptibility fire dynamics Maxent climate change |
| title | Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios |
| title_full | Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios |
| title_fullStr | Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios |
| title_full_unstemmed | Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios |
| title_short | Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios |
| title_sort | machine learning model reveals land use and climate s role in caatinga wildfires present and future scenarios |
| topic | semiarid drylands fire susceptibility fire dynamics Maxent climate change |
| url | https://www.mdpi.com/2571-6255/8/1/8 |
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