Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques
The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiven...
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MDPI AG
2024-11-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/7/12/437 |
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| author | Washington J. S. Franca Rocha Rodrigo N. Vasconcelos Soltan Galano Duverger Diego P. Costa Nerivaldo A. Santos Rafael O. Franca Rocha Mariana M. M. de Santana Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro |
| author_facet | Washington J. S. Franca Rocha Rodrigo N. Vasconcelos Soltan Galano Duverger Diego P. Costa Nerivaldo A. Santos Rafael O. Franca Rocha Mariana M. M. de Santana Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro |
| author_sort | Washington J. S. Franca Rocha |
| collection | DOAJ |
| description | The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. |
| format | Article |
| id | doaj-art-ee14d6b39bc04cdebfa0bf5f3b0c7d96 |
| institution | DOAJ |
| issn | 2571-6255 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-ee14d6b39bc04cdebfa0bf5f3b0c7d962025-08-20T02:57:07ZengMDPI AGFire2571-62552024-11-0171243710.3390/fire7120437Mapping Burned Area in the Caatinga Biome: Employing Deep Learning TechniquesWashington J. S. Franca Rocha0Rodrigo N. Vasconcelos1Soltan Galano Duverger2Diego P. Costa3Nerivaldo A. Santos4Rafael O. Franca Rocha5Mariana M. M. de Santana6Ane A. C. Alencar7Vera L. S. Arruda8Wallace Vieira da Silva9Jefferson Ferreira-Ferreira10Mariana Oliveira11Leonardo da Silva Barbosa12Carlos Leandro Cordeiro13Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BrazilPostgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BrazilGEODATIN–Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121–Trobogy, Salvador 41301-110, BrazilPostgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BrazilPostgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BrazilPostgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BrazilForest Engineering Institute (FEI/UEAP), State University of Amapá—UEAP, Av. Pres. Getúlio Vargas, 650 Centro, Macapá 68900-070, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, BrazilWorld Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, BrazilThe semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies.https://www.mdpi.com/2571-6255/7/12/437forest firewildfiresemiariddrylandsburned areafire regime |
| spellingShingle | Washington J. S. Franca Rocha Rodrigo N. Vasconcelos Soltan Galano Duverger Diego P. Costa Nerivaldo A. Santos Rafael O. Franca Rocha Mariana M. M. de Santana Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Jefferson Ferreira-Ferreira Mariana Oliveira Leonardo da Silva Barbosa Carlos Leandro Cordeiro Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques Fire forest fire wildfire semiarid drylands burned area fire regime |
| title | Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques |
| title_full | Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques |
| title_fullStr | Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques |
| title_full_unstemmed | Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques |
| title_short | Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques |
| title_sort | mapping burned area in the caatinga biome employing deep learning techniques |
| topic | forest fire wildfire semiarid drylands burned area fire regime |
| url | https://www.mdpi.com/2571-6255/7/12/437 |
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