PROTEGE: A Near Real-Time Geospatial Information System for Wildfire Detection and Response in the Brazilian Amazon
Resumo
Em 2024, a degradação por fogo na Amazônia brasileira atingiu o pior nível em duas décadas, mas órgãos estaduais ainda dependem de portais de dados que não roteiam alertas por jurisdições operacionais. Este artigo apresenta o PROTEGE, um sistema de informação geoespacial que automatiza ingestão de focos ativos, interseção espacial em PostGIS e notificação jurisdicional ao Corpo de Bombeiros. O sistema integra dados de três satélites VIIRS via NASA FIRMS e envia alertas por email e WhatsApp com mapas e rotas personalizadas. Em 2025, processou 180.326 focos com mediana de 2,9 s e sucesso de 99,3%; entre setembro e dezembro, gerou 9.609 interseções e 7.096 alertas ao CBM.
Referências
Alencar, A. A. C., Arruda, V. L. S., da Silva, W. V., et al. (2022). Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using deep learning. Remote Sensing, 14(11):2510.
Arienti, M. C., Cumming, S. G., Krawchuk, M. A., and Boutin, S. (2006). Road network and fire-management effects on forest fire initial attack in Alberta. Canadian Journal of Forest Research, 36(12):3155–3166.
Bourgoin, C., Beuchle, R., Branco, A., et al. (2025). Extensive fire-driven degradation in 2024 marks worst Amazon forest disturbance in over 2 decades. Biogeosciences, 22(19):5247–5256.
Carvalho, R. B., Oliveras Menor, I., Schmidt, I. B., Berlinck, C. N., Genes, L., and Dirzo, R. (2025). Brazil on fire: Igniting awareness of the 2024 wildfire crisis. Journal of Environmental Management, 389:126190.
Cavalcante, R. B. L., Souza, B. M., Ramos, S. J., et al. (2021). Assessment of fire hazard weather indices in the eastern Amazon: a case study for different land uses. Acta Amazonica, 51(4):352–362.
Coskuner, K. A. (2022). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest – Biogeosciences and Forestry, 15(2):85–94.
Davies, D. K., Ilavajhala, S., Wong, M. M., and Justice, C. O. (2009). Fire information for resource management system: Archiving and distributing MODIS active fire data. IEEE Transactions on Geoscience and Remote Sensing, 47(1):72–79.
Drusch, M., Del Bello, U., Carlier, S., et al. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120:25–36.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226–231. AAAI Press.
Giglio, L., Schroeder, W., and Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31–41.
Haklay, M. and Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4):12–18.
Huang, K. and Wichmann, B. (2024). Machine learning estimates on the impacts of detection times on wildfire suppression costs. PLOS ONE, 19(11):e0313200.
INPE (2025a). BDQueimadas — banco de dados de queimadas. Accessed: 2025-12-31.
INPE (2025b). TerraBrasilis — plataforma de monitoramento ambiental. Accessed: 2025-12-31.
Li, W., Dodwell, E., and Cook, D. (2023). A clustering algorithm to organize satellite hotspot data for the purpose of tracking bushfires remotely. The R Journal, 15(1):17–33.
Lima, C. F., Torres, F. T. P., Minette, L. J., et al. (2024). Is there a relationship between forest fires and deforestation in the Brazilian Amazon? PLOS ONE, 19(6):e0306238.
Ljubojevic, S. and Avramovic, Z. Z. (2021). Architecture of GIS solutions for detection and development of wildfire database. JITA – Journal of Information Technology and Applications, 11(2):123–130.
Macul, M. d. S., Campanharo, W., Morelli, F., et al. (2022). Using earth observation data cube to mapping burned areas. In Proceedings of the XXIII Brazilian Symposium on Geoinformatics (GEOINFO 2022), pages 264–269. INPE.
Mangiameli, M., Mussumeci, G., and Cappello, A. (2021). Forest fire spreading using free and open-source GIS technologies. Geomatics, 1(1):50–64.
Mataveli, G., de Oliveira, G., Silva-Junior, C. H. L., et al. (2022). Record-breaking fires in the Brazilian Amazon associated with uncontrolled deforestation. Nature Ecology & Evolution, 6(12):1792–1793.
Nunes, F. S. M., Soares-Filho, B. S., Oliveira, A. R., et al. (2024). Lessons from the historical dynamics of environmental law enforcement in the Brazilian Amazon. Scientific Reports, 14(1):1828.
Obe, R. O. and Hsu, L. S. (2021). PostGIS in Action. Manning Publications, 3rd edition.
Ribeiro, T. M., de Mendonça, B. A. F., de Oliveira-Junior, J. F., and Fernandes-Filho, E. I. (2021). Fire foci assessment in the Western Amazon (2000–2015). Environment, Development and Sustainability, 23(2):1485–1498.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A. (2014). The new VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143:85–96.
Setzer, A. W., Souza, R., and Morelli, F. (2019). O Banco de Dados de Queimadas do INPE. Biodiversidade Brasileira, 9(1):68–75.
Silva, I. D. B., Valle, M. E., Barros, L. C., and Meyer, J. F. C. A. (2020). A wildfire warning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing, 89:106075.
Silveira, M. V. F., Silva-Junior, C. H. L., Anderson, L. O., and Aragão, L. E. O. C. (2022). Amazon fires in the 21st century: The year of 2020 in evidence. Global Ecology and Biogeography, 31(10):2026–2040.
Soares, B. L., Nascimento, M. L. F., da Rocha, L. S., et al. (2025). Desenvolvimento de um protótipo de detecção e prevenção de incêndios para a agricultura familiar no sudeste do pará. In Anais do LII Seminário Integrado de Software e Hardware (SEMISH), pages 417–428. SBC.
Tavella, R. A., de Moura, F. R., and da Silva Junior, F. M. R. (2025). Ashes of governance: Brazil’s 2024 wildfire crisis and its implications. Regional Environmental Change, 25(4):123.
Urbanelli, A., Frisiello, A., Bruno, L., and Rossi, C. (2024). The ERMES chatbot: A conversational communication tool for improved emergency management and disaster risk reduction. International Journal of Disaster Risk Reduction, 112:104792.
Zacharakis, I. and Tsihrintzis, V. A. (2023). Environmental forest fire danger rating systems and indices around the globe: A review. Land, 12(1):194.
Arienti, M. C., Cumming, S. G., Krawchuk, M. A., and Boutin, S. (2006). Road network and fire-management effects on forest fire initial attack in Alberta. Canadian Journal of Forest Research, 36(12):3155–3166.
Bourgoin, C., Beuchle, R., Branco, A., et al. (2025). Extensive fire-driven degradation in 2024 marks worst Amazon forest disturbance in over 2 decades. Biogeosciences, 22(19):5247–5256.
Carvalho, R. B., Oliveras Menor, I., Schmidt, I. B., Berlinck, C. N., Genes, L., and Dirzo, R. (2025). Brazil on fire: Igniting awareness of the 2024 wildfire crisis. Journal of Environmental Management, 389:126190.
Cavalcante, R. B. L., Souza, B. M., Ramos, S. J., et al. (2021). Assessment of fire hazard weather indices in the eastern Amazon: a case study for different land uses. Acta Amazonica, 51(4):352–362.
Coskuner, K. A. (2022). Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey. iForest – Biogeosciences and Forestry, 15(2):85–94.
Davies, D. K., Ilavajhala, S., Wong, M. M., and Justice, C. O. (2009). Fire information for resource management system: Archiving and distributing MODIS active fire data. IEEE Transactions on Geoscience and Remote Sensing, 47(1):72–79.
Drusch, M., Del Bello, U., Carlier, S., et al. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120:25–36.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226–231. AAAI Press.
Giglio, L., Schroeder, W., and Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31–41.
Haklay, M. and Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4):12–18.
Huang, K. and Wichmann, B. (2024). Machine learning estimates on the impacts of detection times on wildfire suppression costs. PLOS ONE, 19(11):e0313200.
INPE (2025a). BDQueimadas — banco de dados de queimadas. Accessed: 2025-12-31.
INPE (2025b). TerraBrasilis — plataforma de monitoramento ambiental. Accessed: 2025-12-31.
Li, W., Dodwell, E., and Cook, D. (2023). A clustering algorithm to organize satellite hotspot data for the purpose of tracking bushfires remotely. The R Journal, 15(1):17–33.
Lima, C. F., Torres, F. T. P., Minette, L. J., et al. (2024). Is there a relationship between forest fires and deforestation in the Brazilian Amazon? PLOS ONE, 19(6):e0306238.
Ljubojevic, S. and Avramovic, Z. Z. (2021). Architecture of GIS solutions for detection and development of wildfire database. JITA – Journal of Information Technology and Applications, 11(2):123–130.
Macul, M. d. S., Campanharo, W., Morelli, F., et al. (2022). Using earth observation data cube to mapping burned areas. In Proceedings of the XXIII Brazilian Symposium on Geoinformatics (GEOINFO 2022), pages 264–269. INPE.
Mangiameli, M., Mussumeci, G., and Cappello, A. (2021). Forest fire spreading using free and open-source GIS technologies. Geomatics, 1(1):50–64.
Mataveli, G., de Oliveira, G., Silva-Junior, C. H. L., et al. (2022). Record-breaking fires in the Brazilian Amazon associated with uncontrolled deforestation. Nature Ecology & Evolution, 6(12):1792–1793.
Nunes, F. S. M., Soares-Filho, B. S., Oliveira, A. R., et al. (2024). Lessons from the historical dynamics of environmental law enforcement in the Brazilian Amazon. Scientific Reports, 14(1):1828.
Obe, R. O. and Hsu, L. S. (2021). PostGIS in Action. Manning Publications, 3rd edition.
Ribeiro, T. M., de Mendonça, B. A. F., de Oliveira-Junior, J. F., and Fernandes-Filho, E. I. (2021). Fire foci assessment in the Western Amazon (2000–2015). Environment, Development and Sustainability, 23(2):1485–1498.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A. (2014). The new VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143:85–96.
Setzer, A. W., Souza, R., and Morelli, F. (2019). O Banco de Dados de Queimadas do INPE. Biodiversidade Brasileira, 9(1):68–75.
Silva, I. D. B., Valle, M. E., Barros, L. C., and Meyer, J. F. C. A. (2020). A wildfire warning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing, 89:106075.
Silveira, M. V. F., Silva-Junior, C. H. L., Anderson, L. O., and Aragão, L. E. O. C. (2022). Amazon fires in the 21st century: The year of 2020 in evidence. Global Ecology and Biogeography, 31(10):2026–2040.
Soares, B. L., Nascimento, M. L. F., da Rocha, L. S., et al. (2025). Desenvolvimento de um protótipo de detecção e prevenção de incêndios para a agricultura familiar no sudeste do pará. In Anais do LII Seminário Integrado de Software e Hardware (SEMISH), pages 417–428. SBC.
Tavella, R. A., de Moura, F. R., and da Silva Junior, F. M. R. (2025). Ashes of governance: Brazil’s 2024 wildfire crisis and its implications. Regional Environmental Change, 25(4):123.
Urbanelli, A., Frisiello, A., Bruno, L., and Rossi, C. (2024). The ERMES chatbot: A conversational communication tool for improved emergency management and disaster risk reduction. International Journal of Disaster Risk Reduction, 112:104792.
Zacharakis, I. and Tsihrintzis, V. A. (2023). Environmental forest fire danger rating systems and indices around the globe: A review. Land, 12(1):194.
Publicado
19/07/2026
Como Citar
GONÇALVES, Jáder; BARROSO, Guilherme Mota; BARATA, Charlles Silva.
PROTEGE: A Near Real-Time Geospatial Information System for Wildfire Detection and Response in the Brazilian Amazon. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 542-553.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.22629.
