Skip to main content
Log in

Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The objective is to evaluate the fire foci dynamics via environmental satellites and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Brazil. Data considered the period between 2000 and 2017 and was obtained from CPTEC/INPE. Annual and monthly analyzes were performed based on descriptive, exploratory (boxplot) and multivariate statistics analyzes (cluster analysis (CA), principal component analysis (PCA)) and Poisson regression models (based on 2000 and 2010 census data). CA based on the Ward method identified five fire foci homogeneous groups (G1 to G5), while Coruripe did not classify within any group (NA); therefore, the CA technique was consistent (CCC = 0.772). Group G1 is found in all regions of Alagoas, while G2, G5, and NA groups are found in Baixo São Francisco, Litoral, and Zona da Mata regions. Most fire foci were observed in the Litoral region. Seasonally, the largest records were from October to December months for all groups, influenced by the sugarcane harvesting period. The G4 group and Coruripe accounted for 60,767 foci (32.1%). The highest number of fire foci occurred in 2012 and 2015 (between 8000 and 9000 foci), caused by the action of the El Niño–Southern Oscillation. The Poisson regression showed that the dynamics of fire foci are directly associated with the Gini index and Human Development Index (models 1 and 3). Based on the PCA, the three components captured 78.8% of the total variance explained, and they were strongly influenced by the variables: population, GDP, and demographic density. The municipality of Maceió has the largest contribution from the fire foci, with values higher than 40%, and in PC1 and PC2 are related to urban densification and population growth.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.

  • Alencar, A. A., Solórzano, L. A., & Nepstad, D. C. (2004). Modeling forest understory fires in an eastern Amazonian landscape. Ecological Applications, 14, 139–149. https://doi.org/10.1890/01-6029.

    Article  Google Scholar 

  • Alves, J. M. B., Silva, E. M., Sombra, S. S., Barbosa, A. C. B., & Santos, A. C. S. (2017). Eventos Extremos Diários de Chuva no Nordeste do Brasil e Características Atmosféricas. Revista Brasileira de Meteorologia, 32, 227–233. https://doi.org/10.1590/0102-77863220012.

    Article  Google Scholar 

  • Barros Santiago, D., Correia Filho, W. L. F., Oliveira-Júnior, J. F., & Junior, S. (2019). Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian northeast. Modeling Earth Systems and Environment, 5, 1429–1441. https://doi.org/10.1007/s40808-019-00614-x.

    Article  Google Scholar 

  • Barros, A.H.C., Araújo Filho, J.C., Silva, A.B., & Santiago, G.A.C.F. (2012). Climatologia do Estado de Alagoas. Dados eletrônicos. Recife: Embrapa Solos.

  • Belo, C., & Santos, S. S. (2013). A paisagem canavieira em União dos Palmares-Alagoas e seus impactos socioambientais. Revista Ambientale, 4, 1–13.

    Google Scholar 

  • Bem, P. P., Carvalho Júnior, O. A., Trondoli, M. E. A., Guimarães, R. F., & Gomes, R. A. T. (2018). Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District. International Journal of Wildland Fire, 28, 35–45. https://doi.org/10.1071/WF18018.

    Article  Google Scholar 

  • Biagiotti, D., Sarmento, J. L. R., Rego Neto, A. A., Santos, G. V., Silva Santos, N. P., Torres, T. S., & Neri, V. S. (2013). Caracterização fenotípica de ovinos da raça Santa Inês no Estado do Piauí. Revista Brasileira de Saúde e Produção Animal, 14, 29–42. https://doi.org/10.1590/S1519-99402013000100004.

    Article  Google Scholar 

  • Bontemps, S., et al. (2015). Multi-year global land cover mapping at 300 M and characterization for climate modelling: achievements of the land cover component of the ESA climate change initiative. ISPRS Arch. 40-7W3, 323–328. https://doi.org/10.5194/isprsarchives-XL-7-W3-323-2015.

  • Brando, P. M., Balch, J. K., Nepstad, D. C., Morton, D. C., Putz, F. E., Coe, M. T., Silvério, D., Macedo, M. N., Davidson, E. A., Nóbrega, C. C., Alencar, A., & Soares-Filho, B. S. (2014). Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proceedings of the National Academy of Sciences, 111, 6347–6352. https://doi.org/10.1073/pnas.1305499111.

    Article  CAS  Google Scholar 

  • Caúla, R. H., Oliveira-Júnior, J. F., Lyra, G. B., Delgado, R. C., & Heilbron Filho, P. F. L. (2015). Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011. Environmental Earth Sciences, 74, 1497–1508. https://doi.org/10.1007%2Fs00703-016-0481-x.

  • Caúla, R. H., Oliveira-Júnior, J. F., Gois, G., Delgado, R. C., Pimentel, L. C. G., & Teodoro, P. E. (2016). Nonparametric statistics applied to fire foci obtained by meteorological satellites and their relationship to the MCD12Q1 product in the state of Rio de Janeiro, Southeast - Brazil. Land Degradation & Development, 28, 1056–1067. https://doi.org/10.1002/ldr.2574.

    Article  Google Scholar 

  • Chuvieco E., Aguado I., Jurdao S., Pettinari M. L., Yebra M., Salas J., et al. (2012). Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire, 23, 606–619. https://doi.org/10.1071/WF12052.

  • Clemente, C. (2011). Aspectos da vida comunitária e da cultura política de um assentamento rural em Murici: reflexões em torno de uma das comunidades visitadas pela equipe da UFU no Projeto Rondon em Alagoas. Revista Em Extensão, 9, 2.

    Google Scholar 

  • Clemente, S. S., Oliveira Júnior, J. F., & Louzada, M. A. P. (2017). Focos de Calor na Mata Atlântica do Estado do Rio de Janeiro. Revista Brasileira de Meteorologia, 32, 669–677. https://doi.org/10.1590/0102-7786324014.

    Article  Google Scholar 

  • Corrar, L.J., Paulo, E., & Dias Filho, J.M. (2007). Análise Multivariada - Para os Cursos de Administração, Ciências Contábeis e Economia, Ed. Atlas, São Paulo, 1ª Edição, 344 p.

  • Correia Filho, W. L. F., & Silva Aragão, M. R. (2014). Padrões Temporais do Vento à Superfície em Mesorregiões do Estado da Bahia. Ciência e Natura, 36, 402–414.

    Article  Google Scholar 

  • Correia Filho, W. L. F., Lucio, P. S., & Spyrides, M. H. C. (2016). Caracterização dos Extremos de Precipitação diária no Nordeste do Brasil. Boletim Goiano de Geografia, 36(3), 539–554. https://doi.org/10.5216/bgg.v36i3.44557.

    Article  Google Scholar 

  • Correia Filho, W. L. F., Santos, T. V., Diogo, A. M., & Amorim, R. F. C. (2018). Diagnóstico da Precipitação e EVI em Dois Eventos de Seca no Nordeste do Brasil. Revista do Departamento de Geografia, 35, 102–112. https://doi.org/10.11606/rdg.v35i0.140068.

    Article  Google Scholar 

  • Correia Filho, W. L. F., Oliveira-Júnior, J. F., Barros Santiago, D., Terassi, P. M. B., Teodoro, P. E., Gois, G., Blanco, C. J. C., Souza, P. H. A., Costa, M., & Santos, P. J. (2019a). Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product. Big Earth Data, 3, 315–337. https://doi.org/10.1080/20964471.2019.1692298.

    Article  Google Scholar 

  • Correia Filho, W. L. F., Barros Santiago, D., Oliveira-Júnior, J. F., & Silva Junior, C. A. (2019b). Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió, Brazil. Land Use Policy, 87, 104026. https://doi.org/10.1016/j.landusepol.2019.104026.

    Article  Google Scholar 

  • CPTEC—Centro de Previsão do Tempo e Estudos Climáticos. (2018). BDQUEIMADAS. http://pirandira.cptec.inpe.br/queimadas/. Accessed 14 Feb 2018.

  • ESA – European Space Agency. (2018). Climate Change Initiative, Land Cover Maps - v2.0.7, Land Covers Maps 2000 and 2015. https://storage.googleapis.com/cci-lc-v207/ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992_2015-v2.0.7.zip. Accessed 14 Apr 2018.

  • Eugenio, F. C., Santos, A. R., Pedra, B. D., Pezzopane, J. E. M., Mafia, R. G., Loureiro, E. B., Martins, L. D., & Saito, N. S. (2019). Causal, temporal and spatial statistics of wildfires in areas of planted forests in Brazil. Agricultural and Forest Meteorology, 266, 157–172. https://doi.org/10.1016/j.agrformet.2018.12.014.

    Article  Google Scholar 

  • Everitt, B. S., & Dunn, G. (1991). Applied multivariate analysis (400p). London: Edward Arnold.

    Google Scholar 

  • Fernandes, R. C., & Correia Filho, W. L. F. (2013). Espacialização temporal dos focos de queimadas e de poluentes atmosféricos (CO, CH4, NO2, N2O) em Alagoas. Ciência e Natura, 35, 287–294. https://doi.org/10.5902/2179460X12580.

    Article  Google Scholar 

  • Flanningan, M. D., Stocks, B. J., & Wotton, B. M. (2000). Climate change and forest fires. Science of the Total Environment, 262, 221–229. https://doi.org/10.1016/S0048-9697(00)00524-6.

    Article  Google Scholar 

  • Forino, G., von Meding, J., & Brewer, G. J. (2015). A conceptual governance framework for climate change adaptation and disaster risk reduction integration. International Journal of Disaster Risk Science, 6, 372–384. https://doi.org/10.1007/s13753-015-0076-z.

    Article  Google Scholar 

  • Funk, C., Pete, P., Martin, L., Diego, P., James, V., Shraddhanand, S., Gregory, H., James, R., Laura, H., Andrew, H., & Joel, M. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, California-USA. Scientific Data, 2, 10–66. https://doi.org/10.1038/sdata.2015.66.

    Article  Google Scholar 

  • Gois, G., Souza, J. L., Silva, P. R. T., & Oliveira-Júnior, J. F. (2005). Caracterizacão da desertificação no estado de Alagoas utilizando variáveis climáticas. Revista Brasileira de Meteorologia, 20, 301–314.

    Google Scholar 

  • Hammer, R. B., Stewart, S. I., & Radeloff, V. C. (2009). Demographic trends, the wildland–urban interface, and wildfire management. Society & Natural Resources An International Journal, 22, 777–782. https://doi.org/10.1080/08941920802714042.

    Article  Google Scholar 

  • Harzallah, A., Rocha de Aragão, J. O., & Sadourny, R. (1996). Interannual rainfall variability in North–East Brazil: observation and model simulation. International Journal of Climatolology, 16, 861–878. https://doi.org/10.1002/(SICI)1097-0088(199608)16:8<861::AID-JOC59>3.0.CO;2-D.

    Article  Google Scholar 

  • Heinl, M., Silva, J., Tacheba, B., & Bredenkamp, G. J. (2004). Vegetation changes after single fire-events in the Okavango Delta wetland, Botswana. South African Journal of Botany, 70(5), 695–704.

    Article  Google Scholar 

  • Hongyu, K., Sandanielo, V. L. M., & Oliveira Junior, G. J. (2016). Análise de componentes principais: resumo teórico, aplicação e interpretação. E&S Engineering and Science, 5, 83–90. https://doi.org/10.18607/ES201653398.

    Article  Google Scholar 

  • IBGE - Instituto Brasileiro de Geografia e Estatística. (2018a). Censos 2000 e 2010, https://ww2.ibge.gov.br/home/estatistica/populacao/censo2010/indicadores_sociais_municipais/. Accessed 13 Apr 2018.

  • IBGE - Instituto Brasileiro de Geografia e Estatística. (2018b). Censo Agropecuário, https://www.ibge.gov.br/estatisticas-novoportal/economicas/agricultura-e-pecuaria/9117-producao-agricola-municipal-culturas-temporarias-e-permanentes.html?=&t=downloads. Accessed 13 Apr 2018.

  • Jiang, Z., Lian, Y., & Qin, X. (2014). Rocky desertification in Southwest China: impacts, causes, and restoration. Earth-Science Reviews, 132, 1–12. https://doi.org/10.1016/j.earscirev.2014.01.005.

    Article  Google Scholar 

  • Justino, F., Peltier, W. R., & Barbosa, H. A. (2010). Atmospheric susceptibility to wildfire occurrence during the Last Glacial Maximum and mid-Holocene. Palaeogeography, Palaeoclimatology, Palaeoecology, 295, 76–88. https://doi.org/10.1016/j.palaeo.2010.05.017.

    Article  Google Scholar 

  • Kayano, M. T., & Andreoli, R. V. (2006). Relationships between rainfall anomalies over northeastern Brazil and the El Nin o Southern Oscillation. Journal of Geophysical Research, 111, D13101. https://doi.org/10.1029/2005JD006142.

    Article  Google Scholar 

  • Kayano, M. T., & Capistrano, V. B. (2014) . How the Atlantic multidecadal oscillation (AMO) modifies the ENSO influence on the South American rainfall. International Journal of Climatology, 34, 162–178. https://doi.org/10.1002/joc.3674.

  • Kayano, M. T., Andreoli, R. V., & Souza, R. A. F. (2013). Relations between ENSO and the South Atlantic SST modes and their effects on the South American rainfall. International Journal of Climatology, 33, 2008–2023. https://doi.org/10.1002/joc.3569.

  • Kouadio, Y. K., Servain, J., Machado, L. A. T., & Lentini, A. D. (2012). Heavy rainfall episodes in the Eastern Northeast Brazil linked to large-scale ocean-atmosphere conditions in the tropical Atlantic. Advances in Meteorology, 1-16.

  • Lall, S., & Mathibela, B. (2016). The application of artificial neural networks for wildfire risk prediction. In: 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA) (pp. 1-6). IEEE. https://doi.org/10.1109/RAHA.2016.7931880.

  • Lima, M., Vale, J. C. E., Medeiros Costa, G., Santos, R. C., Correia Filho, W. L. F., Gois, G., Oliveira Junior, J. F., Teodoro, P. E., Rossi, F. S., & Silva Junior, C. A. (2020). The forests in the indigenous lands in Brazil in peril. Land Use Policy, 90, 104258. https://doi.org/10.1016/j.landusepol.2019.104258.

    Article  Google Scholar 

  • Lyra, G. B., Santos, M. J., Souza, J. L., Lyra, G. B., & Santos, M. A. (2011). Espacialização da temperatura do ar anual no estado de Alagoas com diferentes modelos digitais de elevação e resoluções espaciais. Ciência Florestal, 21, 275–287. https://doi.org/10.5902/198050983231.

    Article  Google Scholar 

  • Lyra, G. B., Oliveira-Júnior, J. F., & Zeri, M. (2014). Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas State, northeast of Brazil. International Journal of Climatology, 34, 3546–3558. https://doi.org/10.1002/joc.3926.

    Article  Google Scholar 

  • Lyra, G. B., Oliveira-Júnior, J. F., Gois, G., Cunha-Zeri, G., & Zeri, M. (2017). Rainfall variability over Alagoas under the influences of SST anomalies. Meteorology and Atmospheric Physics, 129, 157–171. https://doi.org/10.1007%2Fs00703-016-0461-1.

  • Malik, K. (2013). Human development report 2013—the rise of the south: human progress in a diverse world. Online at http://hdr.undp.org/en/2013-report, p 216.

  • Marengo, J. A., Torres, R. R., & Alves, L. M. (2017a). Drought in Northeast Brazil—past, present and future. Theoretical and Applied Climatology, 129, 1189–1200. https://doi.org/10.1007/s00704-016-1840-8.

    Article  Google Scholar 

  • Marengo, J. A., Alves, L. M., Alvala, R. C. S., Cunha, A. P., Brito, S., & Moraes, O. L. L. (2017b). Climatic characteristics of the 2010-2016 drought in the semiarid Northeast Brazil region. Anais da Academia Brasileira de Ciências, 90, 1973–1985. https://doi.org/10.1590/0001-3765201720170206.

    Article  Google Scholar 

  • Melo, M.G.D.S. (2011). Gestão ambiental no setor sucroalcooleiro de Pernambuco: entre a inesgotabilidade dos recursos naturais e os mecanismos de regulação (Master’s thesis, Universidade Federal de Pernambuco).

  • Molion, L. C. B., & Bernardo, S. O. (2002). Uma revisão da dinâmica das chuvas no Nordeste Brasileiro. Revista Brasileira de Meteorologia, 17, 1–10.

    Google Scholar 

  • Mollmann Júnior, R. A., Silva Junior, R. S., Coelho, S. M. S. C., & Medina, B. L. (2015). Estudo da dispersão de monóxido de carbono emitido por queimadas na Amazônia Legal em 19 agosto de 2010 baseado em: Simulações do modelo WRF-Chem e sensoriamento remoto. Ciência e Natura, 37, 144–152. https://doi.org/10.5902/2179460X16230.

    Article  Google Scholar 

  • Moreno, M. V., & Chuvieco, E. (2013). Characterising fire regimes in Spain from fire statistics. International Journal of Wildland Fire, 22(3), 296–305.

    Article  Google Scholar 

  • Moura, A. D., & Shukla, J. (1981). On the dynamics of droughts in Northeast Brazil—observations, theory and numerical experiments with a general-circulation model. Journal Atmospheric Science, 38, 2653–2675. https://doi.org/10.1175/1520-0469(1981)038\2653:Otdodi[2.0.Co;2.

    Article  Google Scholar 

  • Nelder, A. J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society Series A (General), 135(3), 370–384.

    Article  Google Scholar 

  • Neves, J.A., Santos, C.C., Amaral, A.F.C., Sant’anna, S.A.C., Silva, P.A., & Ivo Mello, W.P. (2018). Emissões de gases de efeito estufa em áreas de cana-de-açúcar colhida crua e queimada. In: Embrapa Tabuleiros Costeiros-Artigo em anais de congresso (ALICE). In: Seminário De Iniciação Científica E Pós-Graduação Da Embrapa Tabuleiros Costeiros, 8., 2018, Aracaju. Anais... Aracaju: Embrapa Tabuleiros Costeiros, 2018. Editor técnico: Ronaldo Souza Resende., 2018.

  • Oliveira Júnior, J. F., Lyra, G. B., Gois, G., Brito, T. T., & Moura, N. S. H. (2012). Análise de homogeneidade de séries pluviométricas para determinação do índice de seca IPP no estado de Alagoas. Floresta e Ambiente, 19, 101–112. https://doi.org/10.4322/floram.2012.011.

    Article  Google Scholar 

  • Oliveira Souza, T. C., Delgado, R. C., Magistrali, I. C., Santos, G. L., Carvalho, D. C., Teodoro, P. E., Silva Junior, C. A., & Caúla, R. H. (2018). Spectral trend of vegetation with rainfall in events of El Niño-Southern Oscillation for Atlantic Forest biome, Brazil. Environmental Monitoring and Assessment, 190, 688–698. https://doi.org/10.1007/s10661-018-7060-1.

    Article  Google Scholar 

  • Oliveira-Júnior, J. F., Sousa, G. M., Nunes, M. T. O., Fernandes, M. C., & Tomzhinski, G. W. (2017). Relationship between SPI and ROI in Itatiaia National Park. Floresta e Ambiente, 24, e20160031. https://doi.org/10.1590/2179-8087.003116.

    Article  Google Scholar 

  • Oliveira-Júnior, J. F., Teodoro, P. E., Silva Junior, C. A., Rojo Baio, F. H., Gava, R., Capristo-Silva, G. F., Gois, G., Correia Filho, W. L. F., Lima, M., Santiago, B., Freitas, W. K., Santos, P. J., & Costa, M. S. (2020). Fire foci related to rainfall and biomes of the state of Mato Grosso do Sul, Brazil. Agricultural and Forest Meteorology, 282–283, 107861. https://doi.org/10.1016/j.agrformet.2019.107861.

    Article  Google Scholar 

  • Paredes-Trejo, F. J., Barbosa, H. A., & Lakshmi Kumar, T. V. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. Journal of Arid Environments, 139, 26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009.

    Article  Google Scholar 

  • Paschalidou, A. K., & Kassomenos, P. A. (2016). What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology. Science of the Total Environment, 539, 536–545. https://doi.org/10.1016/j.scitotenv.2015.09.039.

    Article  CAS  Google Scholar 

  • Pereira, J. A. V., & Silva, J. B. (2016). Detecção de focos de calor no Estado da Paraíba: um estudo sobre as Queimadas. Revista Geográfica Acadêmica, 10, 5–16. https://doi.org/10.18227/1678-7226rga.v10i1.3173.

    Article  Google Scholar 

  • R Development Core Team. (2017). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.r-project.org, ISBN 3-900051-07-0.

  • Rasilla, D. F., García-Codron, J. C., Carracedo, V., & Diego, C. (2010). Circulation patterns, wildfire risk and wildfire occurrence at continental Spain. Physics and Chemistry of the Earth, Parts A/B/C, 35(9–12), 553–560. https://doi.org/10.1016/j.pce.2009.09.003.

    Article  Google Scholar 

  • Ribeiro, H. (2008). Queimadas de cana-de-açúcar no Brasil: efeitos à saúde respiratória. Revista de Saúde Pública, 42, 370–376.

    Article  Google Scholar 

  • Rohlf, F. J. (1970). Adaptative hierarchical clustering schemes. Systematic Zoology, 19, 58–82. https://doi.org/10.1093/sysbio/19.1.58.

    Article  Google Scholar 

  • Santiago, D. B., & Gomes, H. B. (2016). Heat islands in the city of Maceió/AL using orbital data from Landsat 5. Revista Brasileira de Geografia Física, 9, 793–803. https://doi.org/10.5935/1984-2295.20160053.

    Article  Google Scholar 

  • Santos Silva, A., Santos Silva, F. H., Santos, G., & Holanda Leite, M. J. (2019). Desmatamento multitemporal no bioma Caatinga no município de Delmiro Gouveia, Alagoas. Revista Verde de Agroecologia e Desenvolvimento Sustentável, 14, 654–657.

    Article  Google Scholar 

  • Silva, D. F. (2017). Aplicação de Análises de Ondaletas para Detecção de Ciclos e Extremos Pluviométricos no Leste do Nordeste do Brasil. Revista Brasileira de Meteorologia, 32, 187–198. https://doi.org/10.1590/0102-77863220002.

    Article  Google Scholar 

  • Silva de Souza, L., Landau, L., Moraes, N. O., & Pimentel, L. C. G. (2012). Air quality photochemical study over Amazonia Area, Brazil. International Journal of Environment and Pollution, 48, 194–202. https://doi.org/10.1504/IJEP.2012.049666.

    Article  CAS  Google Scholar 

  • Silva Lopes, D. V., Silva, D. E., Silva, F. M. S., Paraíso, L. A., Soares, T. L., & Souza, M. C. B. (2018). Áreas de Proteção Ambiental (Apa) de Conservação da Bacia do CELMM. Caderno de Graduação-Ciências Exatas e Tecnológicas-UNIT-ALAGOAS, 4, 73.

    Google Scholar 

  • Silva, B. F. P., Fedorova, N., Levit, V., Peresetsky, A., & Brito, B. M. (2011). Sistemas sinóticos associados às precipitações intensas no Estado de Alagoas. Revista Brasileira de Meteorologia, 26, 323–338.

    Article  Google Scholar 

  • Sokol, R. A., & Rohlf, F. J. (1962). The comparison of dendograms by objective methods. Taxon., 11, 33–40. https://doi.org/10.2307/1217208.

    Article  Google Scholar 

  • Souza, E. B., Kayano, M. T., & Ambrizzi, T. (2005). Intraseasonal and submonthly variability over the Eastern Amazon and Northeast Brazil during the autumn rainy season. Theoretical and Applied Climatology, 81, 177–191. https://doi.org/10.1007/s00704-004-0081-4.

    Article  Google Scholar 

  • Stephenson, D. B., Diaz, H. F., & Murnane, R. J. (2008). Definition, diagnosis, and origin of extreme weather and climate events. Climate Extremes and Society, 340, 11–23.

    Article  Google Scholar 

  • Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2015). Attribution of climate extreme events. Nature Climate Change, 5, 725–730. https://doi.org/10.1038/nclimate2657.

    Article  Google Scholar 

  • Valentin, J. L. (2000). Ecologia Numérica – Uma Introdução à Análise Multivariada de Dados Ecológicos. Rio de Janeiro: Interciência.

    Google Scholar 

  • Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244. https://doi.org/10.1080/01621459.1963.10500845.

    Article  Google Scholar 

  • White, B. L. A., & White, L. A. S. (2017). Queimadas e incêndios florestais no estado de Sergipe, Brasil, entre 1999 e 2015. Floresta, 46, 561–570.

    Article  Google Scholar 

  • Zeri, M., Carvalho, V. S. B., Cunha-Zeri, G., Oliveira-Júnior, J. F., Lyra, G. B., & Freitas, E. D. (2016). Assessment of the variability of pollutants concentration over the metropolitan area of São Paulo, Brazil, using the wavelet transform. Atmospheric Science Letters, 17, 87–95. https://doi.org/10.1002/asl.618.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Center for Weather Forecasting and Climate Studies/National Institute of Space Research (CPTEC/INPE) for making available the fire foci data via BDQueimadas data bank. The authors also thank ESA of the CCI-CL group for making available the world’s coverage data via annual maps version 2.0.7. The first author thanks the research productivity scholarship granted by the National Council of Scientific and Technological Development (CNPq) process number 309681/2019-7. The authors also thank CNPq for the funding project 424605/2018-0—linked to the Universal Notice No. 28/2018. The second author thanks CNPq for the Junior Postdoctoral Scholarship No. 161023/2019-3.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Antonio da Silva Junior.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Oliveira-Júnior, J.F., Filho, W.L.F.C., Alves, L.E.R. et al. Fire foci dynamics and their relationship with socioenvironmental factors and meteorological systems in the state of Alagoas, Northeast Brazil. Environ Monit Assess 192, 654 (2020). https://doi.org/10.1007/s10661-020-08588-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-020-08588-5

Keywords

Navigation