Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10348/10860 |
Resumo: | The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the “polluter pays principle”, even more in Brazil where the areas occupied by degraded pastures are enormous. |
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Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pasturesWater erosionBrightness indexThe inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the “polluter pays principle”, even more in Brazil where the areas occupied by degraded pastures are enormous.2021-12-09T16:39:29Z2021-02-23T00:00:00Z2021-02-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10348/10860enghttps://doi.org/10.1016/j.scitotenv.2021.146019Vieira, Alessandra Soaresdo Valle Junior, Renato FariasRodrigues, Vinicius SilvaQuinaia, Thiago Luiz da SilvaMendes, Rafaella GouveiaValera, Carlos AlbertoFernandes, Luis Filipe SanchesPacheco, Fernando António Lealinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-02T12:36:11Zoai:repositorio.utad.pt:10348/10860Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:01:23.988268Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
title |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
spellingShingle |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures Vieira, Alessandra Soares Water erosion Brightness index |
title_short |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
title_full |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
title_fullStr |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
title_full_unstemmed |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
title_sort |
Estimating water erosion from the brightness index of orbital images: A framework for the prognosis of degraded pastures |
author |
Vieira, Alessandra Soares |
author_facet |
Vieira, Alessandra Soares do Valle Junior, Renato Farias Rodrigues, Vinicius Silva Quinaia, Thiago Luiz da Silva Mendes, Rafaella Gouveia Valera, Carlos Alberto Fernandes, Luis Filipe Sanches Pacheco, Fernando António Leal |
author_role |
author |
author2 |
do Valle Junior, Renato Farias Rodrigues, Vinicius Silva Quinaia, Thiago Luiz da Silva Mendes, Rafaella Gouveia Valera, Carlos Alberto Fernandes, Luis Filipe Sanches Pacheco, Fernando António Leal |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Vieira, Alessandra Soares do Valle Junior, Renato Farias Rodrigues, Vinicius Silva Quinaia, Thiago Luiz da Silva Mendes, Rafaella Gouveia Valera, Carlos Alberto Fernandes, Luis Filipe Sanches Pacheco, Fernando António Leal |
dc.subject.por.fl_str_mv |
Water erosion Brightness index |
topic |
Water erosion Brightness index |
description |
The inadequate management of soils and the absence of conservation practices favor the degradation of pastures and can trigger adverse environmental alterations and damage under the terms of Brazilian Federal Law no. 6.938/1981. Based on this premise, this study aimed to estimate soil losses caused by water erosion in pasture areas using the brightness index (BI) from the annual series of Landsat 8 images in different geological formations. A specifically prepared Google Earth Engine (GEE) script automatically extracted the BI from the images. The study occurred in the Environmental Protection Area (EPA) of Uberaba River basin (Minas Gerais, Brazil). To accomplish the goal, 180 digital 500-wide random buffers were selected from 3 geologic types (60 points per type), and then analyzed for zonal statistics of USLE (Universal Soil Loss Equation) soil loss and BI in a Geographic Information System. The regression models BI versus USLE soil loss allowed estimating BI soil losses over the pastures of EPA. The model fittings were remarkable. The validation of soil loss maps in the EPA occurred in pasture phytophysiognomies through the probing of penetration resistance in 37 randomly selected locations. The results were satisfactory, mostly those based on the BI. The BI losses increased for greater resistances. Amplified losses also occurred in regions exposed to environmental land use conflicts (actual uses that deviate from land capability or natural use). Overall, the BI approach proved efficient to accurately track soil losses and pasture degradation over large areas, with the advantage of standing on a single parameter easily accessed through remote sensed data. From an environmental standpoint, this is an important result, because the accurate diagnosis and prognosis of degraded pastures is paramount to implement mitigation measures following the “polluter pays principle”, even more in Brazil where the areas occupied by degraded pastures are enormous. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-09T16:39:29Z 2021-02-23T00:00:00Z 2021-02-23 |
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://hdl.handle.net/10348/10860 |
url |
http://hdl.handle.net/10348/10860 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://doi.org/10.1016/j.scitotenv.2021.146019 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137097665216512 |