Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil

Detalhes bibliográficos
Autor(a) principal: Carvalho, Rita de Cássia Freire
Data de Publicação: 2024
Tipo de documento: Tese
Idioma: por
Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
Texto Completo: http://repositorio.ufes.br/handle/10/17381
Resumo: Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome.
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spelling 117Santos, Alexandre Rosa dos https://orcid.org/0000-0003-2617-9451http://lattes.cnpq.br/7125826645310758Carvalho, Rita de Cássia Freirehttps://orcid.org/0000-0003-1912-2430http://lattes.cnpq.br/6449713515797354Peluzio, Telma Machado de Oliveirahttps://orcid.org/0000-0003-0462-9239http://lattes.cnpq.br/2216111713065095Ferrari, Jéferson Luizhttps://orcid.org/0000-0001-5663-6428http://lattes.cnpq.br/5213847780149836Fiedler, Nilton Cesarhttps://orcid.org/0000-0002-4376-3660http://lattes.cnpq.br/8699171075880935Dias, Henrique Machadohttps://orcid.org/0000-0003-2217-7846http://lattes.cnpq.br/55658525088730922024-06-19T09:53:03Z2024-06-19T09:53:03Z2024-02-22Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome.Os incêndios florestais emergiram como uma problemática ambiental de relevância nos últimos anos, o que demanda atenção dos pesquisadores. Ecossistemas florestais, como o bioma Amazônia, estão, cada vez mais, ameaçados por incêndios florestais. Nesse contexto, o principal objetivo desse estudo foi desenvolver um modelo capaz de prever a localização e a quantificação de incêndios florestais, por meio da lógica Fuzzy para o bioma Amazônia, Brasil. Para a elaboração do modelo de risco de incêndio baseado na lógica Fuzzy, foram utilizadas as variáveis temperatura, deficiência hídrica, precipitação, altitude, declividade, aspectos das vertentes, densidade demográfica, rodovias e uso e cobertura da terra. Foram realizadas três modelagens de risco de incêndio florestal com o propósito de testar diferentes funções de pertinência para uma mesma variável, denominado, neste estudo, como Risco de Incêndio Florestal 1 (RIF 1), em que utilizou-se as funções de pertinência Fuzzy linear crescente, Fuzzy linear decrescente, Fuzzy Generalized Bell e Fuzzy Gaussian; na modelagem Risco de Incêndio Florestal 2 (RIF 2), utilizou-se as funções de pertinência Fuzzy Large e Fuzzy Small e para a modelagem Risco de Incêndio Florestal 3 (RIF 3), foi utilizada a função Fuzzy Large em todas as variáveis. Posteriormente, um mapa de densidade de incêndios foi gerado usando dados de cicatrizes de fogo do banco de dados MapBiomas, seguido por uma análise de correlação de Pearson para verificar a consistência dos mapas e identificar o modelo mais eficaz. Dentre as três modelagens, o RIF 1 apresentou o maior valor de correlação de Pearson, assim, esse foi constatado como o melhor modelo capaz de prever a localização de incêndios florestais. O bioma Amazônia é classificado como médio risco a ocorrência de incêndios, o que corresponde a 30,05 % de toda região de estudo, uma área de aproximadamente 1.263.561,00 km². Portanto, entende-se que a modelagem de risco de incêndios florestais foi eficaz para prever e quantificar o risco de incêndios florestais para o bioma Amazônia brasileiro.FAPESTexthttp://repositorio.ufes.br/handle/10/17381porUniversidade Federal do Espírito SantoDoutorado em Ciências FlorestaisPrograma de Pós-Graduação em Ciências FlorestaisUFESBRCentro de Ciências Agrárias e Engenhariassubject.br-rjbnÁrea(s) do conhecimento do documento (Tabela CNPq)Modelagem de riscobioma Amazôniageotecnologiaslógica FuzzyLógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasiltitle.alternativeinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)instname:Universidade Federal do Espírito Santo (UFES)instacron:UFESemail@ufes.brORIGINALRitadeCassiaFreireCarvalho-2024-tese.pdfRitadeCassiaFreireCarvalho-2024-tese.pdfapplication/pdf6373263http://repositorio.ufes.br/bitstreams/601c876d-308d-46b7-9129-06f3bc61957c/download185cb505688b5eb0603d63f199adc869MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufes.br/bitstreams/d31e0322-607c-4564-96c4-0686a789047e/download8a4605be74aa9ea9d79846c1fba20a33MD5210/173812024-08-29 11:25:09.042oai:repositorio.ufes.br:10/17381http://repositorio.ufes.brRepositório InstitucionalPUBhttp://repositorio.ufes.br/oai/requestopendoar:21082024-10-15T18:00:54.507021Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)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
dc.title.none.fl_str_mv Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
dc.title.alternative.none.fl_str_mv title.alternative
title Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
spellingShingle Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
Carvalho, Rita de Cássia Freire
Área(s) do conhecimento do documento (Tabela CNPq)
Modelagem de risco
bioma Amazônia
geotecnologias
lógica Fuzzy
subject.br-rjbn
title_short Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
title_full Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
title_fullStr Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
title_full_unstemmed Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
title_sort Lógica Fuzzy Aplicada Para Análise de Riscos de Incêndios Florestais Para o Bioma Amazônia, Brasil
author Carvalho, Rita de Cássia Freire
author_facet Carvalho, Rita de Cássia Freire
author_role author
dc.contributor.authorID.none.fl_str_mv https://orcid.org/0000-0003-1912-2430
dc.contributor.authorLattes.none.fl_str_mv http://lattes.cnpq.br/6449713515797354
dc.contributor.advisor1.fl_str_mv Santos, Alexandre Rosa dos
dc.contributor.advisor1ID.fl_str_mv https://orcid.org/0000-0003-2617-9451
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/7125826645310758
dc.contributor.author.fl_str_mv Carvalho, Rita de Cássia Freire
dc.contributor.referee1.fl_str_mv Peluzio, Telma Machado de Oliveira
dc.contributor.referee1ID.fl_str_mv https://orcid.org/0000-0003-0462-9239
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/2216111713065095
dc.contributor.referee2.fl_str_mv Ferrari, Jéferson Luiz
dc.contributor.referee2ID.fl_str_mv https://orcid.org/0000-0001-5663-6428
dc.contributor.referee2Lattes.fl_str_mv http://lattes.cnpq.br/5213847780149836
dc.contributor.referee3.fl_str_mv Fiedler, Nilton Cesar
dc.contributor.referee3ID.fl_str_mv https://orcid.org/0000-0002-4376-3660
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/8699171075880935
dc.contributor.referee4.fl_str_mv Dias, Henrique Machado
dc.contributor.referee4ID.fl_str_mv https://orcid.org/0000-0003-2217-7846
dc.contributor.referee4Lattes.fl_str_mv http://lattes.cnpq.br/5565852508873092
contributor_str_mv Santos, Alexandre Rosa dos
Peluzio, Telma Machado de Oliveira
Ferrari, Jéferson Luiz
Fiedler, Nilton Cesar
Dias, Henrique Machado
dc.subject.cnpq.fl_str_mv Área(s) do conhecimento do documento (Tabela CNPq)
topic Área(s) do conhecimento do documento (Tabela CNPq)
Modelagem de risco
bioma Amazônia
geotecnologias
lógica Fuzzy
subject.br-rjbn
dc.subject.por.fl_str_mv Modelagem de risco
bioma Amazônia
geotecnologias
lógica Fuzzy
dc.subject.br-rjbn.none.fl_str_mv subject.br-rjbn
description Forest fires have emerged as an environmental issue of relevance in recent years, demanding the attention of researchers. Forest ecosystems, such as the Amazon biome, are increasingly threatened by forest fires. In this context, the main objective of this study was to develop a model capable of predicting the location and quantification of forest fires in the Amazon biome, Brazil, using Fuzzy logic. For the development of the fuzzy logic-based fire risk model, the following variables were used: temperature, water deficiency, precipitation, altitude, slope, aspect, population density, highways, and land use/cover. Three forest fire risk modeling approaches were conducted to test different membership functions for the same variable, referred to in this study as Forest Fire Risk 1 (FFR 1), employing the linear increasing, linear decreasing, generalized bell-shaped, and Gaussian fuzzy membership functions; in Forest Fire Risk 2 (FFR 2) modeling, large and small fuzzy membership functions were used; and in Forest Fire Risk 3 (FFR 3) modeling, the large fuzzy membership function was applied to all variables. Subsequently, a fire density map was generated using fire scar data from the MapBiomas database, followed by a Pearson correlation analysis to check the consistency of the maps and identify the most effective model. Among the three modeling approaches, FFR 1 showed the highest Pearson correlation value; thus, it was determined to be the best model capable of predicting the location of forest fires. The Amazon biome is classified as having a medium risk of fire occurrence, which corresponds to 30.05% of the entire study region, an area of approximately 1,263,561.00 km². Therefore, it is understood that the forest fire risk modeling was effective in predicting and quantifying the risk of forest fires for the Brazilian Amazon biome.
publishDate 2024
dc.date.accessioned.fl_str_mv 2024-06-19T09:53:03Z
dc.date.available.fl_str_mv 2024-06-19T09:53:03Z
dc.date.issued.fl_str_mv 2024-02-22
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dc.publisher.none.fl_str_mv Universidade Federal do Espírito Santo
Doutorado em Ciências Florestais
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Ciências Florestais
dc.publisher.initials.fl_str_mv UFES
dc.publisher.country.fl_str_mv BR
dc.publisher.department.fl_str_mv Centro de Ciências Agrárias e Engenharias
publisher.none.fl_str_mv Universidade Federal do Espírito Santo
Doutorado em Ciências Florestais
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