Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning
Wildfires in the Brazilian Amazon have raised significant concerns owing to the environmental, social, and global impacts associated with these events. They have led to habitat loss for various species and release of substantial amounts of carbon dioxide into the atmosphere. Thereby contributing to...
- Autores:
-
Camacho-De Angulo, Yineth Viviana
Rosa, Nicolas Cechinel
Solano-Correa, Yady Tatiana
Roisenberg, Mauro
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12732
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12732
- Palabra clave:
- Deep Learning
Remote Sensing
Semantic Segmentation
Wildfires
Brazilian Amazon
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning Deep Learning Remote Sensing Semantic Segmentation Wildfires Brazilian Amazon LEMB |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
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Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning |
| dc.creator.fl_str_mv |
Camacho-De Angulo, Yineth Viviana Rosa, Nicolas Cechinel Solano-Correa, Yady Tatiana Roisenberg, Mauro |
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Camacho-De Angulo, Yineth Viviana Rosa, Nicolas Cechinel Solano-Correa, Yady Tatiana Roisenberg, Mauro |
| dc.subject.keywords.spa.fl_str_mv |
Deep Learning Remote Sensing Semantic Segmentation Wildfires Brazilian Amazon |
| topic |
Deep Learning Remote Sensing Semantic Segmentation Wildfires Brazilian Amazon LEMB |
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LEMB |
| description |
Wildfires in the Brazilian Amazon have raised significant concerns owing to the environmental, social, and global impacts associated with these events. They have led to habitat loss for various species and release of substantial amounts of carbon dioxide into the atmosphere. Thereby contributing to climate change and deterioration of air quality due to pollutants emission. The integration of advanced technologies, including high-spatial resolution satellite data and image processing algorithms, enables a more precise and comprehensive understanding of the wildfire scenario. This research introduces a model based on deep learning that can be applied over Sentinel-2 images to reliably detect fire scars with an accuracy above 90% (92% on training data and 82% on validation data). A SpectrumNet convolutional neural network was employed, incorporating features extracted from spectral bands at 10m and 20m. |
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2024 |
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2024-09-12T14:01:44Z |
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2024-09-12T14:01:44Z |
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2024-07-12 |
| dc.date.submitted.none.fl_str_mv |
2024-09-11 |
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Y. V. Camacho-De Angulo; N. C. Rosa; Y. T. Solano-Correa; M. Roisenberg, "Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10642369. |
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https://hdl.handle.net/20.500.12585/12732 |
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10.1109/IGARSS53475.2024.10642369 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Y. V. Camacho-De Angulo; N. C. Rosa; Y. T. Solano-Correa; M. Roisenberg, "Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10642369. 10.1109/IGARSS53475.2024.10642369 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12732 |
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eng |
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eng |
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4 páginas |
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Cartagena de Indias |
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Ciencias Básicas |
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
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Camacho-De Angulo, Yineth Vivianadf97ad9f-47e5-45c8-922c-d1e2b12c9708Rosa, Nicolas Cechinelb7e1e707-4335-441a-95b7-e64dfee063ecSolano-Correa, Yady Tatianac3d85b81-c6f5-4ad0-80dc-65e4cf4283b1Roisenberg, Maurob82483b1-1b24-4356-8b2f-305190e1ae822024-09-12T14:01:44Z2024-09-12T14:01:44Z2024-07-122024-09-11Y. V. Camacho-De Angulo; N. C. Rosa; Y. T. Solano-Correa; M. Roisenberg, "Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learning," in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 2024. DOI: 10.1109/IGARSS53475.2024.10642369.https://hdl.handle.net/20.500.12585/1273210.1109/IGARSS53475.2024.10642369Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarWildfires in the Brazilian Amazon have raised significant concerns owing to the environmental, social, and global impacts associated with these events. They have led to habitat loss for various species and release of substantial amounts of carbon dioxide into the atmosphere. Thereby contributing to climate change and deterioration of air quality due to pollutants emission. The integration of advanced technologies, including high-spatial resolution satellite data and image processing algorithms, enables a more precise and comprehensive understanding of the wildfire scenario. This research introduces a model based on deep learning that can be applied over Sentinel-2 images to reliably detect fire scars with an accuracy above 90% (92% on training data and 82% on validation data). A SpectrumNet convolutional neural network was employed, incorporating features extracted from spectral bands at 10m and 20m.4 páginasapplication/pdfengIEEE International Geoscience and Remote Sensing Symposium (IGARSS)Fire Scars Mapping Over Brazilian Amazon Forest by Exploiting Sentinel-2 Data and Deep Learninginfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Deep LearningRemote SensingSemantic SegmentationWildfiresBrazilian AmazonLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresS. Singh, “Forest fire emissions: A contribution to global climate change,” Frontiers in Forests and Global Change, vol. 5, 11 2022.F. Carta, C. Zidda, M. Putzu, D. Loru, M. Anedda, and D. Giusto, “Advancements in forest fire prevention: A comprehensive survey,” Sensors, vol. 23, no. 14, p. 6635, 2023.G. Martins, J. Nogueira, A. Setzer, and F. Morelli, “Comparison between different versions of inpe’s fire risk model for the brazilian biomes,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3/W12-2020, pp. 119–124, 2020.K. Covey, F. Soper, S. Pangala, A. Bernardino, Z. Pagliaro, L. Basso, H. Cassol, P. Fearnside, D. Navarrete, S. Novoa, H. Sawakuchi, T. Lovejoy, J. Marengo, C. A. Peres, J. Baillie, P. Bernasconi, J. Camargo, C. Freitas, B. Hoffman, G. B. Nardoto, I. Nobre, J. Mayorga, R. Mesquita, S. Pavan, F. Pinto, F. Rocha, R. de Assis Mello, A. Thuault, A. A. Bahl, and A. Elmore, “Carbon and beyond: The biogeochemistry of climate in a rapidly changing amazon,” Frontiers in Forests and Global Change, vol. 4, 3 2021.A. Saleh, M. A. Zulkifley, H. H. Harun, F. Gaudreault, I. Davison, and M. Spraggon, “Forest fire surveillance systems: A review of deep learning methods,” Heliyon, vol. 10, no. 1, p. e23127, 2024.B. Leblon, L. Bourgeau-Chavez, and J. San-Miguel- Ayanz, “Use of remote sensing in wildfire management,” in Sustainable Development (S. Curkovic, ed.), ch. 3, Rijeka: IntechOpen, 2012.R. Libonati, C. C. DaCamara, A. W. Setzer, F. Morelli, and A. E. Melchiori, “An algorithm for burned area detection in the brazilian cerrado using 4 μm modis imagery,” Remote sensing, vol. 7, no. 11, pp. 15782– 15803, 2015.I. Mancilla-Wulff, J. Carrasco, C. Pais, A. Miranda, and A. Weintraub, “Two scalable approaches for burnedarea mapping using u-net and landsat imagery,” arXiv preprint arXiv:2311.17368, 2023.D. N. Gonc¸alves, J. M. Junior, A. C. Carrilho, P. R. Acosta, A. P. M. Ramos, F. D. G. Gomes, L. P. Osco, M. da Rosa Oliveira, J. A. C. Martins, G. A. D. J´unior, et al., “Transformers for mapping burned areas in brazilian pantanal and amazon with planetscope imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 116, p. 103151, 2023.G. Tejada, E. B. G¨orgens, A. Ovando, and J. P. Ometto, “Mapping data gaps to estimate biomass across brazilian amazon forests,” Forest Ecosystems, vol. 7, pp. 1–15, 2020.P. B. T. das Neves, C. J. C. Blanco, A. A. A. M. Duarte, F. B. S. das Neves, I. B. S. das Neves, and M. H. d. P. dos Santos, “Amazon rainforest deforestation influenced by clandestine and regular roadway network,” Land Use Policy, vol. 108, p. 105510, 2021.C. S. Cronan, “Tropical ecology and deforestation,” in Ecology and Ecosystems Analysis, pp. 241–249, Springer, 2023.R. D. Garrett, F. Cammelli, J. Ferreira, S. A. Levy, J. Valentim, and I. Vieira, “Forests and sustainable development in the brazilian amazon: history, trends, and future prospects,” Annual Review of Environment and Resources, vol. 46, pp. 625–652, 2021.A. A. Ioris, “Rethinking brazil’s pantanal wetland: Beyond narrow development and conservation debates,” The Journal of Environment & Development, vol. 22, no. 3, pp. 239–260, 2013.J. J. Senecal, J. W. Sheppard, and J. A. 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