Environmental vulnerability evolution in the Brazilian Amazon
Abstract Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to determine the areas of greatest vulnerabi...
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| Format: | Article |
| Language: | English |
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Academia Brasileira de Ciências
2023-07-01
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| Series: | Anais da Academia Brasileira de Ciências |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000301603&lng=en&tlng=en |
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| author | NILTON C. FIEDLER RICARDO M.M. DE JESUS FELIPE Z. MOREIRA ANTONIO H.C. RAMALHO ALEXANDRE R. DOS SANTOS KAÍSE B. DE SOUZA |
| author_facet | NILTON C. FIEDLER RICARDO M.M. DE JESUS FELIPE Z. MOREIRA ANTONIO H.C. RAMALHO ALEXANDRE R. DOS SANTOS KAÍSE B. DE SOUZA |
| author_sort | NILTON C. FIEDLER |
| collection | DOAJ |
| description | Abstract Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to determine the areas of greatest vulnerability to human activities, in Amazon biome, through MODIS images of Land use and land cover (LULC) from the 2001 and 2013. Remote sensing, Euclidean distance, Fuzzy logic, AHP method and analysis of net variations were applied to specialize the classes of vulnerability in the states belonging to the Amazon Biome. From the results, it can be seen that the class that most evolved in a positive net gain during the evaluated period was “very high” and the one that most reduced was “high”, showing that there was a transition from “high” to “very high” risk areas. The states with the largest areas under “very high” risk class were Mato Grosso (101,100.10 km2) and Pará (81,010.30 km2). It is concluded that the application of remote sensing techniques allows the determination and assessment of the environmental vulnerability evolution. Mitigation measures urgently need to be implemented in the Amazon biome. The methodology can be extended to any other area of the planet. |
| first_indexed | 2024-03-13T01:30:59Z |
| format | Article |
| id | doaj.art-65dcf960d40048cf8f8f296f3b1c35ea |
| institution | Directory Open Access Journal |
| issn | 1678-2690 |
| language | English |
| last_indexed | 2024-03-13T01:30:59Z |
| publishDate | 2023-07-01 |
| publisher | Academia Brasileira de Ciências |
| record_format | Article |
| series | Anais da Academia Brasileira de Ciências |
| spelling | doaj.art-65dcf960d40048cf8f8f296f3b1c35ea2023-07-04T07:46:16ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902023-07-0195210.1590/0001-3765202320210333Environmental vulnerability evolution in the Brazilian AmazonNILTON C. FIEDLERhttps://orcid.org/0000-0002-3895-661XRICARDO M.M. DE JESUShttps://orcid.org/0000-0002-3843-1426FELIPE Z. MOREIRAhttps://orcid.org/0000-0001-9480-0735ANTONIO H.C. RAMALHOhttps://orcid.org/0000-0002-0037-5422ALEXANDRE R. DOS SANTOShttps://orcid.org/0000-0003-2617-9451KAÍSE B. DE SOUZAhttps://orcid.org/0000-0002-0230-7992Abstract Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to determine the areas of greatest vulnerability to human activities, in Amazon biome, through MODIS images of Land use and land cover (LULC) from the 2001 and 2013. Remote sensing, Euclidean distance, Fuzzy logic, AHP method and analysis of net variations were applied to specialize the classes of vulnerability in the states belonging to the Amazon Biome. From the results, it can be seen that the class that most evolved in a positive net gain during the evaluated period was “very high” and the one that most reduced was “high”, showing that there was a transition from “high” to “very high” risk areas. The states with the largest areas under “very high” risk class were Mato Grosso (101,100.10 km2) and Pará (81,010.30 km2). It is concluded that the application of remote sensing techniques allows the determination and assessment of the environmental vulnerability evolution. Mitigation measures urgently need to be implemented in the Amazon biome. The methodology can be extended to any other area of the planet.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000301603&lng=en&tlng=enGeographic Information Systemsartificial intelligence techniquesanthropismanthropism, environmental evolutionMODIS |
| spellingShingle | NILTON C. FIEDLER RICARDO M.M. DE JESUS FELIPE Z. MOREIRA ANTONIO H.C. RAMALHO ALEXANDRE R. DOS SANTOS KAÍSE B. DE SOUZA Environmental vulnerability evolution in the Brazilian Amazon Anais da Academia Brasileira de Ciências Geographic Information Systems artificial intelligence techniques anthropism anthropism, environmental evolution MODIS |
| title | Environmental vulnerability evolution in the Brazilian Amazon |
| title_full | Environmental vulnerability evolution in the Brazilian Amazon |
| title_fullStr | Environmental vulnerability evolution in the Brazilian Amazon |
| title_full_unstemmed | Environmental vulnerability evolution in the Brazilian Amazon |
| title_short | Environmental vulnerability evolution in the Brazilian Amazon |
| title_sort | environmental vulnerability evolution in the brazilian amazon |
| topic | Geographic Information Systems artificial intelligence techniques anthropism anthropism, environmental evolution MODIS |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652023000301603&lng=en&tlng=en |
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