FIRES IN BRAZILIAN BIOMES
Abstract
Increasingly, forest fires are occurring in large parts of the world due to warmer weather, more frequent and severe droughts and continuous changes in land use. In Brazil, the weakening of environmental public policies has further aggravated forest fires with widespread impacts throughout the country. This study aimed to evaluate the association between short-term variations in fire focis by precipitation, temperature in
biomes in the state of Mato Grosso do Sul (MS) – Midwest Brazil. Generalized additive negative binomial regression models with distributed nonlinear lag terms were adjusted with daily counts of fire focis as results and total daily precipitation and other meteorological variables as predictors, adjusting for seasonality and trend. In general, higher precipitation was associated with fewer fire focis, with higher relative risks for the
cerrado biome for the dry cutting and for higher temperatures the number of fire focis with higher relative risk for the pantanal biome. Stronger associations were observed in dry cutting (winter/spring). It was found that higher temperatures are associated with more fire focis. Fire focis are strongly associated with precipitation and temperature variation, but in opposite directions. Higher precipitation can become more clearly associated with fewer fire focis and higher temperature to more fire focis. If we maintain the burning culture to clear pastures and planting areas, the burnings will become increasingly uncontrollable.
Keywords: Hot Spots, Precipitation, Temperature, Weather, Risk Assessement, Regression Models, Fire Foci, Burned Area.Keywords: hot spots, precipitation, temperature, weather, risk assessement, regression models, fire foci, burned area.
References
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BUSICO, G. GIUDITTA, E.; KAZAKIS, N.; COLOMBANI, N. A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability.11, 7166, 2019. doi: 10.3390/su11247166
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CHUVIECO, E.; GIGLIO, L.; JUSTICE, C. Global characterization of fire activity: toward defining fire regimes from Earth observation data. Global Change Biology 14, 1488–1502, 2008. doi: 10.1111/j.1365-2486.2008.01585.x
CONEDERA, M.; TINNER, W. The interaction between forest fires and human activity in southern Switzerland. Advances in Global Change Research 3, 247–261, 2000.
DURIGAN, G.; RATTER, J.A. The need for a consistent fire policy for Cerrado conservation. Journal of Applied Ecology 53, 11-15, 2016.
EMBRAPA. Embrapa Biome Cerrado Information Agency. Vegetation types of the Cerrado Biome. Available from: http://www.agencia.cnptia.embrapa.br/Agencia16/AG01/arvore/AG01_23_911200585232.html Access on: Aug. 2022." http://www.agencia.cnptia.embrapa.br/Agencia16/AG01/arvore/AG01_23_911200585232.html
FORTIN, M.; DEBLOIS, J. Modeling tree recruitment with zero-inflated models: the example of hardwood stands in Southern Quebec, Canada. Forest Science 53, 529–539, 2007. doi: 10.1093/forestscience/53.4.529
GABBAN, A.; SAN-MIGUEL-AYANZ, J.; VIEGAS, D.X. A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment. International Journal of Remote Sensing 29, 5677–5687, 2008. doi: 10.1080/01431160801958397
GASPARRINI, A.; ARMSTRONG, B. DLNM: Distributed Lag Non-Linear Models, R Package Version 1.2.4. The Comprehensive R Archive Network, Vienna. 2010.
GREEN, J.; ZÊZERE, J. Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Sciences 10, 485–497, 2010. doi: 10.5194/nhess-10-485-2010
GUO, F.T.; WANG, G.Y;, SU, Z.W.; LIANG, H.L.; WANG, W.H.; LIN, F.F.; LIU, A.Q. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. International Journal of Wildland Fire 25, 505–519, 2016. doi: 10.1071/WF15121
HARDIN, J.; HILBE, J. Generalized Linear Models and Extensions. Stata Corporation, College Station.2011
HU, T.; ZHOU, G. Drivers of lightning-and human-caused fire regimes in the Great Xing'an Mountains. Forest Ecology and Management 329, 49–58, 2014. doi: 10.1016/j.foreco.2014.05.047
IBGE. Brazilian Institute of Geography and Statistics. Map of Biomes and Vegetation. 2004. Available at:http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm Access at: Aug. 2022." http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm
KEELEY, J.E.; FOTHERINGHAM, C.J. Historic fire regime in Southern California shrublands. Conservation Biology 15, 1536–1548, 2001. doi: 10.1046/j.1523-1739.2001.00097.x
LAMBERT, D. Zero-inflated Poisson regression, with an application defects to in manufacturing. Technometrics 34, 1–14, 1992.
LAMPIN-MAILLET, C.; JAPPIOT, M.; LONG, M.; BOUILLON, C.; MORGE, D.; FERRIER, J.P. Mapping wildland-urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the South of France. Journal of Environmental Management 91, 732–741, 2010. doi: 10.1016/j.jenvman.2009.10.001
LENTILE, L.B.; SMITH, F.W.; SHEPPERD, W.D. Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA. International Journal of Wildland Fire 15, 557–566, 2006.
LI, P.; XIAO, C.; FENG, Z.; LI, W.; ZHANG, X. Occurrence frequencies and regional variations in Visible Infrared Imaging Radiometer Suite (VIIRS) global active fires. Global Change Biology 26, 2970–2987, 2020. doi: 10.1111/gcb.15034
LIBONATI, R.; DACAMARA, C.C.; PERES, L.F.; Sander de Carvalho, L.A.; Garcia, L.C. Rescue Brazil's burning Pantanal wetlands. Nature 588, 217–219, 2020. doi: 10.1038/d41586-020-03464-1
LIU, Z. YANG, J. CHANG, Y. WEISBERG, P.J.; HE, H.S. Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biology 18, 2041–2056., 2012 doi: 10.1111/j.1365-2486.2012.02649.x
LV, A.; YANG, P. The relationships of forest fire with temperature and precipitation in China and its spatial-temporal variability. Agricultural Science and Technology 39, 15332–15336, 2011.
MACNEIL, M.A.; CARLSON, J.K. BEERKIRCHER, L.R. Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic. ICES Journal of Marine Science 66, 708–719. doi: 10.1093/icesjms/fsp022
MARENGO, J.A.; CUNHA, A.P.; CUARTAS, L.A.; LEAL, K.R.D.; BROEDEL E, SELUCHI ME, MICHELIN CM, BAIÃO CFP, ANGULO EC, ALMEIDA EK, KAZMIERCZAK ML, MATEUS NPA, SILVA RC, BENDER F (2021) Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts. Frontiers in Water 3, 1-20. doi: 10.3389/frwa.2021.639204
McCulloch, C.E.; Searle, S.R. Generalized, Linear and Mixed Models; John Wiley & Sons: New York, NY, USA, 2001; pp. 1–184.
MMA. Ministry of the Environment. Biomes. Available from: http://www.mma.gov.br/biomas Access on: 1 Aug. 2022a.' http://www.mma.gov.br/biomas
MMA. Ministry of the Environment. National System of Nature Conservation Units. Law No. 9,985 of July 18, 2000. Decree No. 4,340 of August 22, 2002. Decree No. 5,746 of April 5, 2006. Available from:http://www.mma.gov.br/images/arquivos/areas_protegidas/snuc/Livro%20SNUC%20PNAP.pdf Access on:Aug. 2022.' http://www.mma.gov.br/images/arquivos/areas_protegidas/snuc/Livro%20SNUC%20PNAP.pdf
MUNNS E.N. Evaporation and forest fires. Monthly Weather Review 49, 149–152, 1921. doi: https://doi.org/10.1175/1520-0493(1921)49<149:EAFF>2.0.CO;2
MYERS, R.H.; MONTGOMERY, D.C.; VINING, G.G. Generalized Linear Models; John Wiley & Sons: New York, NY, USA, 2002; pp. 100–194.
NUNES, M.T.O.; SOUSA, G.M.; TOMZHINSKI, G.W.; OLIVEIRA-JÚNIOR, J.F.; FERNANDES, M.C. Factors Influênciang on Susceptibility Forestry Fire in Itatiaia National Park. Anuário do Instituto do IGEO 38, 54-62, 2015. doi: https://doi.org/10.11137/2015_1_54_62
OLIVEIRA-JÚNIOR, J.F.; CORREIA FILHO, W.L.F, ALVES, L.E.R. Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil. Environmental Monitoring And Assessment 192, 654, 2020 doi: 10.1007/s10661-020-08588-5
OLIVEIRA-JÚNIOR JF, MENDES D, CORREIA FILHO WLF, SILVA JUNIOR CA, GOIS G, JARDIM AMRF, SILVA MV, LYRA GB, et al.Fire Foci in South America: Impact and Causes, Fire Hazard and Future Scenarios. Journal Of South American Earth Sciences 112, 103623. doi: 10.1016/j.jsames.2021.103623
POURTAGHI, Z.S.; POURGHASEMI, H,R.; ARETHANE, R.; SEMERARO, T. Investigation of general indicators influence ng on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators 64, 72–84,, 2016.
PRADHAN, B.; DINI, H.; BIN, S.M.; ARSHAD, B.A.M. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management 16, 344–352, 2007. doi: 10.1108/09653560710758297
PRICE, O.; BRADSTOCK, R. Countervailing effects of urbanization and vegetation extent on fire frequency on the wildland urban interface: Disentangling fuel and ignition effects. Landscape and Urban Planning 130, 81–88, 2014. doi:10.1016/j.landurbplan.2014.06.013
PYNE, S.J.; ANDREWS, P.L.; LAVEN, R.D. Introduction to wildland fire. John Wiley and Sons: New York; 1996.
SAURA-MAS, S,.; PAULA, S.; PAUSES, J.G.; LLORET, F. Fuel loading and flammability in the Mediterranean Basin woody species with different post-fire regenerative strategies. International Journal of Wildland Fire 19, 783–794, 2010. doi: 10.1071/WF09066
Schmidt DA, Taylor AH, Skinner CN (2008) The influence of fuels treatment and landscape arrangement on simulated fire behavior, Southern Cascade Range, California. Forest Ecology and Management 255, 3170–3184. doi: 10.1016/j.foreco.2008.01.023
Schmidt IB, Eloy L (2020) Fire regime in the Brazilian Savanna: recent changes, policy and management. Flora 268, 151613. doi: 10.1016/j.flora.2020.151613
Sirca C, Cocoon F, Bouillon C, García BF, Ramiro MMF, Molina BV, Spano D (2017) A wildfire risk-oriented GIS tool for mapping Rural-Urban Interfaces. Environmental Modelling & Software 94, 36–47. doi: 10.1016/j.envsoft.2017.03.024
SOUZA, A.; GUO, Y.; PAVÃO, H.G.; FERNANDES, W.A. Effects of Air Pollution on Respiratory Disease: Structures Lag. Health 6, 1333-1339, 2014. doi: 10.4236/health.2014.612163
TIAN, X.; ZHAO, F.; SHU, L.; WANG, M. Distribution characteristics and the influence factors of forest fires in China. Forest Ecology and Management 310, 460–467, 2013. doi: 10.1016/j.foreco.2013.08.025
TORRES, F.T.P.; RIBEIRO, G.A.; MARTINS, S.V.; LIMA, G.S. Determination of the period most conducive to the occurrence of vegetation fires in the urban area of Juiz de Fora, MG. Revista Árvore 34, 297-303, 2010. doi: 10.1590/S0100-67622010000200012
VIGANÓ, H.H.G.; SOUZA, C.C.; CRISTALDO, M.F.; REIS NETO, J.F.; JESUS, N.L. Fires in the Pantanal: modeling and forecasting using multivariate analysis techniques. Revista Ambiente & Água 13, 1-13, 2018. doi: 10.4136/ambi-agua.2024
WALLENIUS TH, KUULUVAINEN T, VANHA-MAJAMAA I. Fire history in relation to site type and vegetation in Viennansalo wilderness in eastern Fennoscandia, Russia. Canadian Journal of Forest Research 34, 1400–1409, 2004. doi:10.1139/x04-023
WU, Z.; HE, H.S.; YANG, J.; LIANG, Y. Defining fire environment zones in the boreal forests of northeastern China. Science of the Total Environment 518, 106–116, 2014. doi: 10.1016/j.scitotenv.2015.02.063
XYSTRAKIS, F.; KOUTSIAS, N. Differences of fire activity and their underlying factors among vegetation formations in Greece. Iforest-Biogeosciences and Forestry 6, 132-140, 2013. doi: 10.3832/ifor0837-006
ZHU, B. LIU, J.; XIAO J. Correlation analysis between forest fire and meteorological elements in Jinggang mountain. Meteorological Disasters Reduction Res. 30, 65–68, 2007.
ZUMBRUNNEN, T. MENÉNDEZ, P.; BUGMANN, H.; CONEDERA, M.; GIMMI, U.; BÜRGI, M. Human impacts on fire occurrence: A case study of hundred years of forest fires in a dry alpine valley in Switzerland. Regional Environmental Change 12, 935–949, 2012. doi: 10.1007/s10113-012-0307-4
ALVES, J.M.B.; DA SILVA, M.S.; ARAÚJO, F.C.; SILVA, L.L. A Study of Heat Outputs in the Caatinga Biome and its Relationships with Meteorological Variables. Revista Brasileira Meteorologia 36, 513-527, 2021. doi:10.1590/0102-77863630015
BUI D.T.; BUI, Q.T.; NGUYEN, Q.P.; PRADHAN, B.; NAMPAK, H.; TRINH, P.T. A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology 233, 32–44, 2017. doi: 10.1016/j.agrformet.2016.11.002
BUSICO, G. GIUDITTA, E.; KAZAKIS, N.; COLOMBANI, N. A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability.11, 7166, 2019. doi: 10.3390/su11247166
CARMO, M.; MOREIRA, F.; CASIMIRO, P.; VAZ, P. Land use and topography influences on wildfire occurrence in northern Portugal. Landscape and Urban Planning 100, 169–176, 2011. Doi: 10.1016/j.landurbplan.2010.11.017
CHUVIECO, E.; GIGLIO, L.; JUSTICE, C. Global characterization of fire activity: toward defining fire regimes from Earth observation data. Global Change Biology 14, 1488–1502, 2008. doi: 10.1111/j.1365-2486.2008.01585.x
CONEDERA, M.; TINNER, W. The interaction between forest fires and human activity in southern Switzerland. Advances in Global Change Research 3, 247–261, 2000.
DURIGAN, G.; RATTER, J.A. The need for a consistent fire policy for Cerrado conservation. Journal of Applied Ecology 53, 11-15, 2016.
EMBRAPA. Embrapa Biome Cerrado Information Agency. Vegetation types of the Cerrado Biome. Available from: http://www.agencia.cnptia.embrapa.br/Agencia16/AG01/arvore/AG01_23_911200585232.html Access on: Aug. 2022." http://www.agencia.cnptia.embrapa.br/Agencia16/AG01/arvore/AG01_23_911200585232.html
FORTIN, M.; DEBLOIS, J. Modeling tree recruitment with zero-inflated models: the example of hardwood stands in Southern Quebec, Canada. Forest Science 53, 529–539, 2007. doi: 10.1093/forestscience/53.4.529
GABBAN, A.; SAN-MIGUEL-AYANZ, J.; VIEGAS, D.X. A comparative analysis of the use of NOAA-AVHRR NDVI and FWI data for forest fire risk assessment. International Journal of Remote Sensing 29, 5677–5687, 2008. doi: 10.1080/01431160801958397
GASPARRINI, A.; ARMSTRONG, B. DLNM: Distributed Lag Non-Linear Models, R Package Version 1.2.4. The Comprehensive R Archive Network, Vienna. 2010.
GREEN, J.; ZÊZERE, J. Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Sciences 10, 485–497, 2010. doi: 10.5194/nhess-10-485-2010
GUO, F.T.; WANG, G.Y;, SU, Z.W.; LIANG, H.L.; WANG, W.H.; LIN, F.F.; LIU, A.Q. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. International Journal of Wildland Fire 25, 505–519, 2016. doi: 10.1071/WF15121
HARDIN, J.; HILBE, J. Generalized Linear Models and Extensions. Stata Corporation, College Station.2011
HU, T.; ZHOU, G. Drivers of lightning-and human-caused fire regimes in the Great Xing'an Mountains. Forest Ecology and Management 329, 49–58, 2014. doi: 10.1016/j.foreco.2014.05.047
IBGE. Brazilian Institute of Geography and Statistics. Map of Biomes and Vegetation. 2004. Available at:http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm Access at: Aug. 2022." http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm
KEELEY, J.E.; FOTHERINGHAM, C.J. Historic fire regime in Southern California shrublands. Conservation Biology 15, 1536–1548, 2001. doi: 10.1046/j.1523-1739.2001.00097.x
LAMBERT, D. Zero-inflated Poisson regression, with an application defects to in manufacturing. Technometrics 34, 1–14, 1992.
LAMPIN-MAILLET, C.; JAPPIOT, M.; LONG, M.; BOUILLON, C.; MORGE, D.; FERRIER, J.P. Mapping wildland-urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the South of France. Journal of Environmental Management 91, 732–741, 2010. doi: 10.1016/j.jenvman.2009.10.001
LENTILE, L.B.; SMITH, F.W.; SHEPPERD, W.D. Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA. International Journal of Wildland Fire 15, 557–566, 2006.
LI, P.; XIAO, C.; FENG, Z.; LI, W.; ZHANG, X. Occurrence frequencies and regional variations in Visible Infrared Imaging Radiometer Suite (VIIRS) global active fires. Global Change Biology 26, 2970–2987, 2020. doi: 10.1111/gcb.15034
LIBONATI, R.; DACAMARA, C.C.; PERES, L.F.; Sander de Carvalho, L.A.; Garcia, L.C. Rescue Brazil's burning Pantanal wetlands. Nature 588, 217–219, 2020. doi: 10.1038/d41586-020-03464-1
LIU, Z. YANG, J. CHANG, Y. WEISBERG, P.J.; HE, H.S. Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biology 18, 2041–2056., 2012 doi: 10.1111/j.1365-2486.2012.02649.x
LV, A.; YANG, P. The relationships of forest fire with temperature and precipitation in China and its spatial-temporal variability. Agricultural Science and Technology 39, 15332–15336, 2011.
MACNEIL, M.A.; CARLSON, J.K. BEERKIRCHER, L.R. Shark depredation rates in pelagic longline fisheries: a case study from the Northwest Atlantic. ICES Journal of Marine Science 66, 708–719. doi: 10.1093/icesjms/fsp022
MARENGO, J.A.; CUNHA, A.P.; CUARTAS, L.A.; LEAL, K.R.D.; BROEDEL E, SELUCHI ME, MICHELIN CM, BAIÃO CFP, ANGULO EC, ALMEIDA EK, KAZMIERCZAK ML, MATEUS NPA, SILVA RC, BENDER F (2021) Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts. Frontiers in Water 3, 1-20. doi: 10.3389/frwa.2021.639204
McCulloch, C.E.; Searle, S.R. Generalized, Linear and Mixed Models; John Wiley & Sons: New York, NY, USA, 2001; pp. 1–184.
MMA. Ministry of the Environment. Biomes. Available from: http://www.mma.gov.br/biomas Access on: 1 Aug. 2022a.' http://www.mma.gov.br/biomas
MMA. Ministry of the Environment. National System of Nature Conservation Units. Law No. 9,985 of July 18, 2000. Decree No. 4,340 of August 22, 2002. Decree No. 5,746 of April 5, 2006. Available from:http://www.mma.gov.br/images/arquivos/areas_protegidas/snuc/Livro%20SNUC%20PNAP.pdf Access on:Aug. 2022.' http://www.mma.gov.br/images/arquivos/areas_protegidas/snuc/Livro%20SNUC%20PNAP.pdf
MUNNS E.N. Evaporation and forest fires. Monthly Weather Review 49, 149–152, 1921. doi: https://doi.org/10.1175/1520-0493(1921)49<149:EAFF>2.0.CO;2
MYERS, R.H.; MONTGOMERY, D.C.; VINING, G.G. Generalized Linear Models; John Wiley & Sons: New York, NY, USA, 2002; pp. 100–194.
NUNES, M.T.O.; SOUSA, G.M.; TOMZHINSKI, G.W.; OLIVEIRA-JÚNIOR, J.F.; FERNANDES, M.C. Factors Influênciang on Susceptibility Forestry Fire in Itatiaia National Park. Anuário do Instituto do IGEO 38, 54-62, 2015. doi: https://doi.org/10.11137/2015_1_54_62
OLIVEIRA-JÚNIOR, J.F.; CORREIA FILHO, W.L.F, ALVES, L.E.R. Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil. Environmental Monitoring And Assessment 192, 654, 2020 doi: 10.1007/s10661-020-08588-5
OLIVEIRA-JÚNIOR JF, MENDES D, CORREIA FILHO WLF, SILVA JUNIOR CA, GOIS G, JARDIM AMRF, SILVA MV, LYRA GB, et al.Fire Foci in South America: Impact and Causes, Fire Hazard and Future Scenarios. Journal Of South American Earth Sciences 112, 103623. doi: 10.1016/j.jsames.2021.103623
POURTAGHI, Z.S.; POURGHASEMI, H,R.; ARETHANE, R.; SEMERARO, T. Investigation of general indicators influence ng on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators 64, 72–84,, 2016.
PRADHAN, B.; DINI, H.; BIN, S.M.; ARSHAD, B.A.M. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management 16, 344–352, 2007. doi: 10.1108/09653560710758297
PRICE, O.; BRADSTOCK, R. Countervailing effects of urbanization and vegetation extent on fire frequency on the wildland urban interface: Disentangling fuel and ignition effects. Landscape and Urban Planning 130, 81–88, 2014. doi:10.1016/j.landurbplan.2014.06.013
PYNE, S.J.; ANDREWS, P.L.; LAVEN, R.D. Introduction to wildland fire. John Wiley and Sons: New York; 1996.
SAURA-MAS, S,.; PAULA, S.; PAUSES, J.G.; LLORET, F. Fuel loading and flammability in the Mediterranean Basin woody species with different post-fire regenerative strategies. International Journal of Wildland Fire 19, 783–794, 2010. doi: 10.1071/WF09066
Schmidt DA, Taylor AH, Skinner CN (2008) The influence of fuels treatment and landscape arrangement on simulated fire behavior, Southern Cascade Range, California. Forest Ecology and Management 255, 3170–3184. doi: 10.1016/j.foreco.2008.01.023
Schmidt IB, Eloy L (2020) Fire regime in the Brazilian Savanna: recent changes, policy and management. Flora 268, 151613. doi: 10.1016/j.flora.2020.151613
Sirca C, Cocoon F, Bouillon C, García BF, Ramiro MMF, Molina BV, Spano D (2017) A wildfire risk-oriented GIS tool for mapping Rural-Urban Interfaces. Environmental Modelling & Software 94, 36–47. doi: 10.1016/j.envsoft.2017.03.024
SOUZA, A.; GUO, Y.; PAVÃO, H.G.; FERNANDES, W.A. Effects of Air Pollution on Respiratory Disease: Structures Lag. Health 6, 1333-1339, 2014. doi: 10.4236/health.2014.612163
TIAN, X.; ZHAO, F.; SHU, L.; WANG, M. Distribution characteristics and the influence factors of forest fires in China. Forest Ecology and Management 310, 460–467, 2013. doi: 10.1016/j.foreco.2013.08.025
TORRES, F.T.P.; RIBEIRO, G.A.; MARTINS, S.V.; LIMA, G.S. Determination of the period most conducive to the occurrence of vegetation fires in the urban area of Juiz de Fora, MG. Revista Árvore 34, 297-303, 2010. doi: 10.1590/S0100-67622010000200012
VIGANÓ, H.H.G.; SOUZA, C.C.; CRISTALDO, M.F.; REIS NETO, J.F.; JESUS, N.L. Fires in the Pantanal: modeling and forecasting using multivariate analysis techniques. Revista Ambiente & Água 13, 1-13, 2018. doi: 10.4136/ambi-agua.2024
WALLENIUS TH, KUULUVAINEN T, VANHA-MAJAMAA I. Fire history in relation to site type and vegetation in Viennansalo wilderness in eastern Fennoscandia, Russia. Canadian Journal of Forest Research 34, 1400–1409, 2004. doi:10.1139/x04-023
WU, Z.; HE, H.S.; YANG, J.; LIANG, Y. Defining fire environment zones in the boreal forests of northeastern China. Science of the Total Environment 518, 106–116, 2014. doi: 10.1016/j.scitotenv.2015.02.063
XYSTRAKIS, F.; KOUTSIAS, N. Differences of fire activity and their underlying factors among vegetation formations in Greece. Iforest-Biogeosciences and Forestry 6, 132-140, 2013. doi: 10.3832/ifor0837-006
ZHU, B. LIU, J.; XIAO J. Correlation analysis between forest fire and meteorological elements in Jinggang mountain. Meteorological Disasters Reduction Res. 30, 65–68, 2007.
ZUMBRUNNEN, T. MENÉNDEZ, P.; BUGMANN, H.; CONEDERA, M.; GIMMI, U.; BÜRGI, M. Human impacts on fire occurrence: A case study of hundred years of forest fires in a dry alpine valley in Switzerland. Regional Environmental Change 12, 935–949, 2012. doi: 10.1007/s10113-012-0307-4
Published
02/12/2023
How to Cite
SOUZA, Amaury de et al.
FIRES IN BRAZILIAN BIOMES.
Mercator, Fortaleza, v. 22, dec. 2023.
ISSN 1984-2201.
Available at: <http://www.mercator.ufc.br/mercator/article/view/3405>. Date accessed: 27 apr. 2024.
doi: https://doi.org/10.4215/rm2023.e22023.
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