Developments in land use and land cover classification techniques in remote sensing: a review.

Detalhes bibliográficos
Autor(a) principal: MACARRINGUE, L. S.
Data de Publicação: 2022
Outros Autores: BOLFE, E. L., PEREIRA, P. R. M.
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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spelling 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
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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
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reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
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