Developments in land use and land cover classification techniques in remote sensing: a review.
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140199 https://doi.org/10.4236/jgis.2022.141001 |
Resumo: | Abstract. Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem´s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing. |
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Developments in land use and land cover classification techniques in remote sensing: a review.Cobertura da terraDados espaciaisComputação em nuvemAprendizado de máquinaBig dataBig Spatial DataCloud ComputingMachine LearningSensoriamento RemotoUso da TerraRemote sensingLand useLand coverAbstract. Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem´s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.LUCRÊNCIO SILVESTRE MACARRINGUE, UNICAMP, Instituto Politécnico de Ciências da Terra e Ambiente, Matola, Mozambique; EDSON LUIS BOLFE, CNPTIA, Unicamp; PAULO ROBERTO MENDES PEREIRA, UNICAMP.MACARRINGUE, L. S.BOLFE, E. L.PEREIRA, P. R. M.2022-02-18T02:00:18Z2022-02-18T02:00:18Z2022-02-172022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleJournal of Geographic Information System, v. 14, n. 1, p. 1-28, Feb. 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140199https://doi.org/10.4236/jgis.2022.141001enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-02-18T02:00:27Zoai:www.alice.cnptia.embrapa.br:doc/1140199Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-02-18T02:00:27falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-02-18T02:00:27Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Developments in land use and land cover classification techniques in remote sensing: a review. |
title |
Developments in land use and land cover classification techniques in remote sensing: a review. |
spellingShingle |
Developments in land use and land cover classification techniques in remote sensing: a review. MACARRINGUE, L. S. Cobertura da terra Dados espaciais Computação em nuvem Aprendizado de máquina Big data Big Spatial Data Cloud Computing Machine Learning Sensoriamento Remoto Uso da Terra Remote sensing Land use Land cover |
title_short |
Developments in land use and land cover classification techniques in remote sensing: a review. |
title_full |
Developments in land use and land cover classification techniques in remote sensing: a review. |
title_fullStr |
Developments in land use and land cover classification techniques in remote sensing: a review. |
title_full_unstemmed |
Developments in land use and land cover classification techniques in remote sensing: a review. |
title_sort |
Developments in land use and land cover classification techniques in remote sensing: a review. |
author |
MACARRINGUE, L. S. |
author_facet |
MACARRINGUE, L. S. BOLFE, E. L. PEREIRA, P. R. M. |
author_role |
author |
author2 |
BOLFE, E. L. PEREIRA, P. R. M. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
LUCRÊNCIO SILVESTRE MACARRINGUE, UNICAMP, Instituto Politécnico de Ciências da Terra e Ambiente, Matola, Mozambique; EDSON LUIS BOLFE, CNPTIA, Unicamp; PAULO ROBERTO MENDES PEREIRA, UNICAMP. |
dc.contributor.author.fl_str_mv |
MACARRINGUE, L. S. BOLFE, E. L. PEREIRA, P. R. M. |
dc.subject.por.fl_str_mv |
Cobertura da terra Dados espaciais Computação em nuvem Aprendizado de máquina Big data Big Spatial Data Cloud Computing Machine Learning Sensoriamento Remoto Uso da Terra Remote sensing Land use Land cover |
topic |
Cobertura da terra Dados espaciais Computação em nuvem Aprendizado de máquina Big data Big Spatial Data Cloud Computing Machine Learning Sensoriamento Remoto Uso da Terra Remote sensing Land use Land cover |
description |
Abstract. Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem´s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-02-18T02:00:18Z 2022-02-18T02:00:18Z 2022-02-17 2022 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Journal of Geographic Information System, v. 14, n. 1, p. 1-28, Feb. 2022. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140199 https://doi.org/10.4236/jgis.2022.141001 |
identifier_str_mv |
Journal of Geographic Information System, v. 14, n. 1, p. 1-28, Feb. 2022. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140199 https://doi.org/10.4236/jgis.2022.141001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
institution |
EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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