Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1134/S0097807821010140 http://hdl.handle.net/11449/207239 |
Resumo: | Abstract: The objective of this study is to develop an anthropic exposure indicator for river basins using quantitative and qualitative aspects of the landscape and morphometric analysis based on fuzzy logic and geoprocessing. The indicator was developed from a Mamdani type fuzzy inference system by integrating information regarding the calculation of the anthropic transformation index and the circularity index of the river basin and its watersheds. The anthropic transformation was obtained from the mapping of land and forest use plotted by visual interpretation of the orthorectified multispectral satellite image of RapidEye. The circularity index was calculated using the area of the territorial limits of the study area. The basin presented thirteen classes of uses, with a greater predominance of the anthropic agricultural area, occupied by temporary crops in approximately 3472 ha (36.33%). The vegetation cover has a greater predominance of forest fragments of dense Ombrophylous forest that measure approximately 3589 ha (37.05%). The indicator showed a medium to high anthropogenic exposure for the basin. Watershed 8 showed a high to very high exposure. The exposure indicator is a tool that details the anthropic exposure of watersheds based on the reality of the activities that occur within it and the morphometric capacity. It can be used for similar areas. |
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Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inferencegeoprocessingland usememberships functionswater resourcesAbstract: The objective of this study is to develop an anthropic exposure indicator for river basins using quantitative and qualitative aspects of the landscape and morphometric analysis based on fuzzy logic and geoprocessing. The indicator was developed from a Mamdani type fuzzy inference system by integrating information regarding the calculation of the anthropic transformation index and the circularity index of the river basin and its watersheds. The anthropic transformation was obtained from the mapping of land and forest use plotted by visual interpretation of the orthorectified multispectral satellite image of RapidEye. The circularity index was calculated using the area of the territorial limits of the study area. The basin presented thirteen classes of uses, with a greater predominance of the anthropic agricultural area, occupied by temporary crops in approximately 3472 ha (36.33%). The vegetation cover has a greater predominance of forest fragments of dense Ombrophylous forest that measure approximately 3589 ha (37.05%). The indicator showed a medium to high anthropogenic exposure for the basin. Watershed 8 showed a high to very high exposure. The exposure indicator is a tool that details the anthropic exposure of watersheds based on the reality of the activities that occur within it and the morphometric capacity. It can be used for similar areas.Federal University of the South of Bahia Universitary Campus Sosígenes CostaState Goiás University, Doctor Deusdete Ferreira de Moura AvenueSão Paulo State University (UNESP) Science and Technology Institute Sorocaba Geoprocessing and Environmental Mathematical Modeling LaboratoryTechnological Research Institute of São Paulo (IPT), University CitySão Paulo State University (UNESP) Science and Technology Institute Sorocaba Geoprocessing and Environmental Mathematical Modeling LaboratoryUniversitary Campus Sosígenes CostaState Goiás UniversityUniversidade Estadual Paulista (Unesp)Technological Research Institute of São Paulo (IPT)Elfany Reis do Nascimento Lopes,Carlos de Souza, JoséPaixão de Sousa, Jocy Ana [UNESP]Filho, José Luiz AlbuquerqueLourenço, Roberto Wagner [UNESP]2021-06-25T10:51:46Z2021-06-25T10:51:46Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article29-40http://dx.doi.org/10.1134/S0097807821010140Water Resources, v. 48, n. 1, p. 29-40, 2021.1608-344X0097-8078http://hdl.handle.net/11449/20723910.1134/S00978078210101402-s2.0-85100443143Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengWater Resourcesinfo:eu-repo/semantics/openAccess2021-10-23T16:37:17Zoai:repositorio.unesp.br:11449/207239Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:09:01.844939Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
title |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
spellingShingle |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference Elfany Reis do Nascimento Lopes, geoprocessing land use memberships functions water resources |
title_short |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
title_full |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
title_fullStr |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
title_full_unstemmed |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
title_sort |
Anthropic Exposure Indicator for River Basins Based on Landscape Characterization and Fuzzy Inference |
author |
Elfany Reis do Nascimento Lopes, |
author_facet |
Elfany Reis do Nascimento Lopes, Carlos de Souza, José Paixão de Sousa, Jocy Ana [UNESP] Filho, José Luiz Albuquerque Lourenço, Roberto Wagner [UNESP] |
author_role |
author |
author2 |
Carlos de Souza, José Paixão de Sousa, Jocy Ana [UNESP] Filho, José Luiz Albuquerque Lourenço, Roberto Wagner [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universitary Campus Sosígenes Costa State Goiás University Universidade Estadual Paulista (Unesp) Technological Research Institute of São Paulo (IPT) |
dc.contributor.author.fl_str_mv |
Elfany Reis do Nascimento Lopes, Carlos de Souza, José Paixão de Sousa, Jocy Ana [UNESP] Filho, José Luiz Albuquerque Lourenço, Roberto Wagner [UNESP] |
dc.subject.por.fl_str_mv |
geoprocessing land use memberships functions water resources |
topic |
geoprocessing land use memberships functions water resources |
description |
Abstract: The objective of this study is to develop an anthropic exposure indicator for river basins using quantitative and qualitative aspects of the landscape and morphometric analysis based on fuzzy logic and geoprocessing. The indicator was developed from a Mamdani type fuzzy inference system by integrating information regarding the calculation of the anthropic transformation index and the circularity index of the river basin and its watersheds. The anthropic transformation was obtained from the mapping of land and forest use plotted by visual interpretation of the orthorectified multispectral satellite image of RapidEye. The circularity index was calculated using the area of the territorial limits of the study area. The basin presented thirteen classes of uses, with a greater predominance of the anthropic agricultural area, occupied by temporary crops in approximately 3472 ha (36.33%). The vegetation cover has a greater predominance of forest fragments of dense Ombrophylous forest that measure approximately 3589 ha (37.05%). The indicator showed a medium to high anthropogenic exposure for the basin. Watershed 8 showed a high to very high exposure. The exposure indicator is a tool that details the anthropic exposure of watersheds based on the reality of the activities that occur within it and the morphometric capacity. It can be used for similar areas. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:51:46Z 2021-06-25T10:51:46Z 2021-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1134/S0097807821010140 Water Resources, v. 48, n. 1, p. 29-40, 2021. 1608-344X 0097-8078 http://hdl.handle.net/11449/207239 10.1134/S0097807821010140 2-s2.0-85100443143 |
url |
http://dx.doi.org/10.1134/S0097807821010140 http://hdl.handle.net/11449/207239 |
identifier_str_mv |
Water Resources, v. 48, n. 1, p. 29-40, 2021. 1608-344X 0097-8078 10.1134/S0097807821010140 2-s2.0-85100443143 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Water Resources |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
29-40 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129495032397824 |