Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/11609 |
Resumo: | Protected areas were created mainly for the conservation of biodiversity in the Amazon. However, there are high rates of deforestation within them, caused by the concession of roads, settlements and occupations. The use of geoprocessing techniques is of paramount importance to detect changes in land use and occupation. This study aims to model future scenarios in the Gurupi-MA Biological Reserve using the DYNAMIC EGO software, using the transition method to simulate deforestation trajectories until 2030, based on the variables: altitude, slope, roads, settlement and hydrographic area. As a result of the transition matrix, four transitions were computed: forest for deforestation, forest for illegal logging, illegal logging for deforestation and illegal logging. The forest-class areas showed the highest number of cells with changes, with a percentage of 0.25% deforestation and 6.08% of cells for illegal exploitation. It was found that several factors contribute to the increase in deforestation close to roads and settlements: illegal logging, cattle raising, hunting and human occupation, compromising the region's fauna and flora. From the simulation of the future scenario (2030), it was observed that the class of deforestation tends to grow north of REBIO. By 2030, there may be a total reduction of 9.17% in forest cover in this UC. Through environmental modeling, together with the command, control and monitoring plans, it is possible to guide socioeconomic and environmental development in protected areas in the Amazon region of Maranhão, for the maintenance and protection of their natural wealth. |
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Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MAModelización ambiental y uso de inteligencia artificial para el pronóstico de la deforestación: el caso de Rebio do Gurupi-MAModelagem ambiental e uso da inteligência artificial para prognóstico de desmatamento: o caso da Rebio do Gurupi-MASpatial analysisDeforestationAmazon.Análisis espacialDeforestaciónAmazonas.Análise espacialDesmatamentoAmazônia.Protected areas were created mainly for the conservation of biodiversity in the Amazon. However, there are high rates of deforestation within them, caused by the concession of roads, settlements and occupations. The use of geoprocessing techniques is of paramount importance to detect changes in land use and occupation. This study aims to model future scenarios in the Gurupi-MA Biological Reserve using the DYNAMIC EGO software, using the transition method to simulate deforestation trajectories until 2030, based on the variables: altitude, slope, roads, settlement and hydrographic area. As a result of the transition matrix, four transitions were computed: forest for deforestation, forest for illegal logging, illegal logging for deforestation and illegal logging. The forest-class areas showed the highest number of cells with changes, with a percentage of 0.25% deforestation and 6.08% of cells for illegal exploitation. It was found that several factors contribute to the increase in deforestation close to roads and settlements: illegal logging, cattle raising, hunting and human occupation, compromising the region's fauna and flora. From the simulation of the future scenario (2030), it was observed that the class of deforestation tends to grow north of REBIO. By 2030, there may be a total reduction of 9.17% in forest cover in this UC. Through environmental modeling, together with the command, control and monitoring plans, it is possible to guide socioeconomic and environmental development in protected areas in the Amazon region of Maranhão, for the maintenance and protection of their natural wealth.Las áreas protegidas fueron creadas principalmente para la conservación de la biodiversidad en la Amazonía. Sin embargo, existen altos índices de deforestación dentro de ellos, provocados por la concesión de caminos, asentamientos y ocupaciones. El uso de técnicas de geoprocesamiento es de suma importancia para detectar cambios en el uso y ocupación del suelo. Este estudio tiene como objetivo modelar escenarios futuros en la Reserva Biológica Gurupi-MA utilizando el software DYNAMICS EGO, utilizando el método de transición para simular trayectorias de deforestación hasta 2030, en base a las variables: altitud, pendiente, caminos, asentamiento y área hidrográfica. Como resultado de la matriz de transición, se calcularon cuatro transiciones: bosque para deforestación, bosque para tala ilegal, tala ilegal para deforestación y tala ilegal. Las áreas de clase forestal presentaron el mayor número de celdas con cambios, con un porcentaje de 0.25% de deforestación y 6.08% de celdas para explotación ilegal. Se encontró que varios factores contribuyen al aumento de la deforestación cerca de caminos y asentamientos: tala ilegal, ganadería, caza y ocupación humana, comprometiendo la fauna y la flora de la región. A partir de la simulación del escenario futuro (2030), se observó que la clase de deforestación tiende a crecer al norte de REBIO. Para el 2030, puede haber una reducción total de 9.17% en la cobertura forestal en esta UC. A través de la modelización ambiental, junto con los planes de comando, control y seguimiento, es posible orientar el desarrollo socioeconómico y ambiental en las áreas protegidas de la región amazónica de Maranhão, para el mantenimiento y protección de sus riquezas naturales.As áreas protegidas foram criadas principalmente para a conservação da biodiversidade na Amazônia. No entanto, existem altas taxas de desmatamento dentro das mesmas, ocasionado pela concessão de estradas, assentamentos e ocupações. O uso de técnicas de geoprocessamento é de suma importância para detectar mudanças no uso e ocupação do solo. Tal estudo objetiva modelar cenários futuros na Reserva Biológica Gurupi-MA no software DINAMICA EGO, usando o método de transição para simular trajetórias de desmatamento até 2030, com base nas variáveis: altitude, declividade, estradas, assentamento e hidrográfica. Como resultado da matriz de transição, quatro transições foram computadas: floresta para desmatamento, floresta para extração ilegal de madeira, extração ilegal de madeira para desmatamento e exploração ilegal de madeira. As áreas da classe florestal apresentaram maior número de células com alteração, com um percentual de 0,25% de desmatamento e 6,08% de células para exploração ilegal. Constatou-se que vários fatores contribuem para o aumento do desmatamento próximo a estradas e assentamentos: extração ilegal de madeira, criação de gado, caça e ocupação humana, comprometendo a fauna e a flora da região. A partir da simulação do cenário futuro (2030), observou-se que a classe de desmatamento tende a crescer ao norte de REBIO. Até 2030, pode haver uma redução total de 9,17% da cobertura florestal nesta UC. Por meio da modelagem ambiental, juntamente com os planos de comando, controle e monitoramento, é possível orientar o desenvolvimento socioeconômico e ambiental em áreas protegidas da Amazônia maranhense, para a manutenção e proteção de sua riqueza natural.Research, Society and Development2021-02-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1160910.33448/rsd-v10i2.11609Research, Society and Development; Vol. 10 No. 2; e13810211609Research, Society and Development; Vol. 10 Núm. 2; e13810211609Research, Society and Development; v. 10 n. 2; e138102116092525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/11609/11066Copyright (c) 2021 Luana Helena Oliveira Monteiro Gama; Paula Fernanda Pinheiro Ribeiro Paiva; Orleno Marques da Silva Junior; Maria de Lourdes Pinheiro Ruivohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessGama, Luana Helena Oliveira Monteiro Paiva, Paula Fernanda Pinheiro RibeiroSilva Junior, Orleno Marques daRuivo, Maria de Lourdes Pinheiro2021-03-02T09:32:39Zoai:ojs.pkp.sfu.ca:article/11609Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:33:23.321527Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA Modelización ambiental y uso de inteligencia artificial para el pronóstico de la deforestación: el caso de Rebio do Gurupi-MA Modelagem ambiental e uso da inteligência artificial para prognóstico de desmatamento: o caso da Rebio do Gurupi-MA |
title |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
spellingShingle |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA Gama, Luana Helena Oliveira Monteiro Spatial analysis Deforestation Amazon. Análisis espacial Deforestación Amazonas. Análise espacial Desmatamento Amazônia. |
title_short |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
title_full |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
title_fullStr |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
title_full_unstemmed |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
title_sort |
Environmental modeling and use of artificial intelligence for prognosis of deforestation: the case of Rebio do Gurupi-MA |
author |
Gama, Luana Helena Oliveira Monteiro |
author_facet |
Gama, Luana Helena Oliveira Monteiro Paiva, Paula Fernanda Pinheiro Ribeiro Silva Junior, Orleno Marques da Ruivo, Maria de Lourdes Pinheiro |
author_role |
author |
author2 |
Paiva, Paula Fernanda Pinheiro Ribeiro Silva Junior, Orleno Marques da Ruivo, Maria de Lourdes Pinheiro |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Gama, Luana Helena Oliveira Monteiro Paiva, Paula Fernanda Pinheiro Ribeiro Silva Junior, Orleno Marques da Ruivo, Maria de Lourdes Pinheiro |
dc.subject.por.fl_str_mv |
Spatial analysis Deforestation Amazon. Análisis espacial Deforestación Amazonas. Análise espacial Desmatamento Amazônia. |
topic |
Spatial analysis Deforestation Amazon. Análisis espacial Deforestación Amazonas. Análise espacial Desmatamento Amazônia. |
description |
Protected areas were created mainly for the conservation of biodiversity in the Amazon. However, there are high rates of deforestation within them, caused by the concession of roads, settlements and occupations. The use of geoprocessing techniques is of paramount importance to detect changes in land use and occupation. This study aims to model future scenarios in the Gurupi-MA Biological Reserve using the DYNAMIC EGO software, using the transition method to simulate deforestation trajectories until 2030, based on the variables: altitude, slope, roads, settlement and hydrographic area. As a result of the transition matrix, four transitions were computed: forest for deforestation, forest for illegal logging, illegal logging for deforestation and illegal logging. The forest-class areas showed the highest number of cells with changes, with a percentage of 0.25% deforestation and 6.08% of cells for illegal exploitation. It was found that several factors contribute to the increase in deforestation close to roads and settlements: illegal logging, cattle raising, hunting and human occupation, compromising the region's fauna and flora. From the simulation of the future scenario (2030), it was observed that the class of deforestation tends to grow north of REBIO. By 2030, there may be a total reduction of 9.17% in forest cover in this UC. Through environmental modeling, together with the command, control and monitoring plans, it is possible to guide socioeconomic and environmental development in protected areas in the Amazon region of Maranhão, for the maintenance and protection of their natural wealth. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-02-07 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11609 10.33448/rsd-v10i2.11609 |
url |
https://rsdjournal.org/index.php/rsd/article/view/11609 |
identifier_str_mv |
10.33448/rsd-v10i2.11609 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11609/11066 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 2; e13810211609 Research, Society and Development; Vol. 10 Núm. 2; e13810211609 Research, Society and Development; v. 10 n. 2; e13810211609 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052744699215872 |