Data/hora: 30/05/2024 13:59:31

Biblioteca(s): 	Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente.	
Data corrente: 	24/07/2023	
Data da última atualização: 	25/07/2023	
Autoria: 	BOLFE, E. L.; PARREIRAS, T. C.; SILVA, L. A. P. da; SANO, E. E.; BETTIOL, G. M.; VICTORIA, D. de C.; DEL'ARCO SANCHES, I.; VICENTE, L. E.	
Afiliação: 	EDSON 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.	
Título: 	Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	ISPRS International Journal of Geo-Information, v. 12, n. 7, 263, July 2023.	
DOI: 	https://doi.org/10.3390/ijgi12070263	
Idioma: 	Inglês	
Conteúdo: 	Agricultural 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.	
Palavras-Chave: 	Agricultural Intensification; Aprendizado de máquina; Harmonized Landsat Sentinel-2; HLS; Inteligência artificial; Intensificação agrícola; Machine learning; Mapeamento agrícola; Multisensor.	
Thesagro: 	Agricultura; Cerrado; Sensoriamento Remoto.	
Thesaurus Nal: 	Agriculture; Artificial intelligence; Remote sensing.	
Categoria do assunto: 	--
X Pesquisa, Tecnologia e Engenharia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155214/1/AP-Mapping-agricultural-intensification-2023.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (21745-1) - UPC - AP - DD -  -  -  - DIS
CPAC - (37610-1) - UPC - AP - DD - DIGITAL -  - DIGITAL - DIS
CNPMA - (17471-1) - UPC - AP - DD -  -  -  - DIS