Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155214
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dc.contributor.authorBOLFE, E. L.
dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorSILVA, L. A. P. da
dc.contributor.authorSANO, E. E.
dc.contributor.authorBETTIOL, G. M.
dc.contributor.authorVICTORIA, D. de C.
dc.contributor.authorDEL'ARCO SANCHES, I.
dc.contributor.authorVICENTE, L. E.
dc.date.accessioned2023-07-24T15:23:23Z-
dc.date.available2023-07-24T15:23:23Z-
dc.date.created2023-07-24
dc.date.issued2023
dc.identifier.citationISPRS International Journal of Geo-Information, v. 12, n. 7, 263, July 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1155214-
dc.descriptionAgricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021-2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectMultisensor
dc.subjectInteligência artificial
dc.subjectAprendizado de máquina
dc.subjectIntensificação agrícola
dc.subjectMapeamento agrícola
dc.subjectHarmonized Landsat Sentinel-2
dc.subjectHLS
dc.subjectMachine learning
dc.subjectAgricultural Intensification
dc.titleMapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroCerrado
dc.subject.nalthesaurusAgriculture
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusArtificial intelligence
riaa.ainfo.id1155214
riaa.ainfo.lastupdate2023-07-24
dc.identifier.doihttps://doi.org/10.3390/ijgi12070263
dc.contributor.institutionEDSON LUIS BOLFE, CNPTIA; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; LUCAS AUGUSTO PEREIRA DA SILVA, Universidade Federal de Uberlândia; EDSON EYJI SANO, CPAC; GIOVANA MARANHAO BETTIOL, CPAC; DANIEL DE CASTRO VICTORIA, CNPTIA; IARA DEL´ARCO SANCHES, INSTITUTO DE PESQUISAS ESPACIAIS; LUIZ EDUARDO VICENTE, CNPMA.
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