Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1171826
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorPEREIRA, P. R. M.
dc.contributor.authorSOUZA, A. M. de
dc.contributor.authorALVES, V. F.
dc.date.accessioned2025-01-21T15:47:21Z-
dc.date.available2025-01-21T15:47:21Z-
dc.date.created2025-01-21
dc.date.issued2025
dc.identifier.citationRemote Sensing Applications: Society and Environment, v. 37, 101448, Jan. 2025.
dc.identifier.issn2352-9385
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1171826-
dc.descriptionABSTRACT. Land use and cover changes significantly impact landscape configuration, climate change, and society. The processes of expansion, conversion, intensification, diversification, and reduction materialize these changes in the agricultural environment. The Cerrado, or Brazilian Savanna, is a materialize biodiversity hotspot, extremely important for water production, and one of the most important biomes for global food production. In this sense, monitoring agricultural dynamics in this environment plays a crucial role in sustainable planning, assessment of environmental impacts, and food security. In this study, we propose to analyze the evolution of the role of multispectral orbital remote sensing in mapping and monitoring agricultural dynamics processes in the Cerrado. Therefore, a narrative review of the literature based on studies developed in the biome was carried out to identify advances in tools, processes, and resources, as well as evaluate the challenges and perspectives for the future. Among other relevant results, monitoring these processes has become faster, more frequent, and more accurate, mainly through the combined use of high temporal resolution time series of spectral data and machine learning algorithms. Promising results have been obtained with Harmonized Landsat Sentinel-2 (HLS) data. The consolidation of deep neural networks has contributed substantially to detecting and delimitating complex intensification and diversification systems, such as central irrigation pivots and intercropping. However, there are challenges and obstacles to be faced, such as expanding the use of Sentinel-2 data, establishing means for sharing field data, and expanding studies to more fragmented landscapes, especially agricultural production on small properties.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectDinâmica agrícola
dc.subjectCobertura da terra
dc.subjectBrazilian Savanna
dc.subjectIntensification
dc.subjectDiversification
dc.subjectConversion
dc.subjectExpansion
dc.titleApplications, challenges and perspectives for monitoring agricultural dynamics in the Brazilian savanna with multispectral remote sensing.
dc.typeArtigo de periódico
dc.subject.thesagroUso da Terra
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroCerrado
dc.subject.thesagroAgricultura
dc.subject.nalthesaurusLand use
dc.subject.nalthesaurusLand cover
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1171826
riaa.ainfo.lastupdate2025-01-21
dc.identifier.doihttps://doi.org/10.1016/j.rsase.2025.101448
dc.contributor.institutionTAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA; PAULO ROBERTO MENDES PEREIRA, UNIVERSIDADE ESTADUAL DE CAMPINAS; ABNER MATHEUS DE SOUZA, UNIVERSIDADE ESTADUAL DE CAMPINAS; VINÍCIUS FERNANDES ALVES, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
AP-Applications-challenges-2025.pdf5.07 MBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace