Data/hora: 27/07/2024 08:17:46

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	17/02/2022	
Data da última atualização: 	17/02/2022	
Autoria: 	MACARRINGUE, L. S.; BOLFE, E. L.; PEREIRA, P. R. M.	
Afiliação: 	LUCRÊNCIO SILVESTRE MACARRINGUE, UNICAMP, Instituto Politécnico de Ciências da Terra e Ambiente, Matola, Mozambique; EDSON LUIS BOLFE, CNPTIA, Unicamp; PAULO ROBERTO MENDES PEREIRA, UNICAMP.	
Título: 	Developments in land use and land cover classification techniques in remote sensing: a review.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	Journal of Geographic Information System, v. 14, n. 1, p. 1-28, Feb. 2022.	
DOI: 	https://doi.org/10.4236/jgis.2022.141001	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Aprendizado de máquina; Big data; Big Spatial Data; Cloud Computing; Cobertura da terra; Computação em nuvem; Dados espaciais; Machine Learning.	
Thesagro: 	Sensoriamento Remoto; Uso da Terra.	
Thesaurus Nal: 	Land cover; Land use; Remote sensing.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/231516/1/AP-Developments-Land-Use-2022.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (21172-1) - UPC - AP - DD -  -  -  - DIS