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| Registros recuperados : 180 | |
| 121. |  | 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. Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2. ISPRS International Journal of Geo-Information, v. 12, n. 7, 263, July 2023.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente. |
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| 124. | | PORTINHO, J. L.; GOMES, A. C. C.; KOGA-VICENTE, A.; MANZATTO, C. V.; MILANI, F. C. C.; ARAUJO, L. S. de; VICENTE, L. E. An interactive webgis platform for enhanced support of integrated environmental management of aquaculture In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Artigo 96069. p. 1-4.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 125. | | ESTEVES, V. P. P.; ESTEVES, E. M. M.; ARAUJO, O.; MORGADO, C.; VICENTE, L. E.; BUNGENSTAB, D. J.; LOEBMANN, D. G. dos S. W.; VICTORIA, D. de C. Life Cycle Assessment (LCA) of soybean-baed biodiesel in brazilian midwest using satellite monitoring to evaluate land use change impacts. In: CONFERENCE ON SUSTAINABLE DEVELOPMENT OF ENERGY, WATER AND ENVIRONMENT SYSTEMS, 10., 2015. Dubrovnik, Croatia. Book of Abstracts... Zagreb, Croatia: Faculty of Mechanical Engineering and Naval Architecture, 2015. p. 489. Organizers: University of Zagreb, Zagreb, Croatia and Instituto Superior Técnico, Lisbon, Portugal.| Biblioteca(s): Embrapa Gado de Corte. |
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| 126. | | VEDOVATO, L. B.; VICENTE, L. E.; PIQUEIRA, J. R. C.; LOEBMANN, D. G. dos S. W.; MATTOS, S. H. V. L. Landscape complexity analysis based on texture patterns and satellite image for a São Paulo's Cerrado site. In: CONGRESSO BRASILEIRO DE ECOLOGIA DE PAISAGENS, 2.; SIMPÓSIO SCGIS-BR, 2., 2012, Salvador, BA. Anais... Salvador, BA: IALE-BR, 2012. 1 p.| Biblioteca(s): Embrapa Territorial. |
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| 127. | | MORAES, M. C. DE; VICENTE, L. E.; GREGO, C. R.; PAIM, F. A. de P.; PIQUEIRA, J. R. C.; MATTOS, S. H. V. L. DE. Mapas de complexidade de fragmentos de Cerrado a partir da variabilidade espacial de dados do sensor Thematic Mapper5. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 3927-3934.| Biblioteca(s): Embrapa Territorial. |
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| 129. | | SANO, E. E.; BOLFE, E. L.; PARREIRAS, T. C.; BETTIOL, G. M.; VICENTE, L. E.; DEL'ARCO SANCHES, I.; VICTORIA, D. de C. Estimating double cropping plantations in the Brazilian Cerrado through PlanetScope monthly mosaics. Land, v. 12, n. 3, 581, 2023.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente. |
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| 130. | | VICENTE, L. E.; SILVA, A. F. da; BUENO, A. de F.; KOGA-VICENTE, A.; ARAUJO, L. S. de; MACIEL, R. M. A.; ROGGIA, S. Dados hiperespectrais de variedades de soja sob diferentes tempos de ataque do percevejo Euschistus heros. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. Ref. 155756. p. 960-963.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Soja. |
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| 134. | | LOEBMANN, D. G. dos S. W.; VICENTE, L. E.; DE PAULA, S. C.; VICTORIA, D. de C.; ANDRADE, R. G.; SILVA, R. F. B. DA; AGNESE, M. L. Comparative assessment of atmospheric correction of Landsat imagery using Modtran and dark object subtraction. In: SYMPOSIUM SELPER, 15., 2012, Cayenne, French Guiana. Abstracts... Cayenne: SELPER, 2012. 1 p.| Biblioteca(s): Embrapa Territorial. |
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| 135. | | PORTINHO, J. L.; GOMES, A. C. C.; KOGA-VICENTE, A.; MILANI, F. C. C.; PENTEAN, R. P.; MANZATTO, C. V.; ARAUJO, L. S. de; VICENTE, L. E. The pathways influence of agricultural expansion on water quality of fish farming in Ilha Solteira reservoir, São Paulo, Brazil. Aquaculture, v. 536, article 736405, 2021.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 136. | | GAROFALO, D. F. T.; KONDO, V. Y.; VICENTE, L. E.; SILVA, M. G. da; KOGA-VICENTE, A.; RAMOS, N. P.; PACKER, A. P. Pre-harvesting biomass burning for sugarcane crop: comparing emission factors for realist results. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Ref. 96710. p. 1-4.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 137. | | FRANCESCHINI, M. H. D.; DEMATTÊ, J. A. M.; TERRA, F. DA S.; ARAÚJO, S. R.; SOUZA FILHO, C. R. DE; VICENTE, L. E. Qualificação d atributos fisico-químicos do solo através de dados espectrais (Vis-NIR-SWIR) obtidos em laboratório e por imagem aérea hiperespectral. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 16., 2013, Foz do Iguaçú. Anais... São José dos Campos: INPE, 2013. p.0530-0538 1 CD-ROM| Biblioteca(s): Embrapa Territorial. |
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| 138. | | GOMES, A. C. C.; PENTEAN, R.; PORTINHO, J. L.; VICENTE, L. E.; ARAUJO, L. S. de; MANZATTO, C. V.; ALCÂNTARA, E. Short-term retrieval of water transparency from a caged-fish farm using Landsat-8/OLI images. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. 4 p.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 139. | | LOEBMANN, D. G. dos S. W.; VICENTE, L. E.; GARCON, E. A. M.; SILVA, G. B. S. da; ANDRADE, R. G.; TORRESAN, F. E. Spectral angle mapping multitemporal approach in SPOT VEGETATION Time-Series to detect sugarcane areas in São Paulo State, Southeastern Brazil. In: AGU FALL MEETING, 2011, San Francisco. Anais... San Francisco: AGU, 2011.| Biblioteca(s): Embrapa Territorial. |
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| 140. | | SALAZAR, D. U.; DEMATTÊ, J. A. M.; VICENTE, L. E.; GUIMARÃES, C. C.; SOUZA, A. B. de; INFORSATO, L.; CARVALHO, H. W. L. de. Spectral range (FRX-VIS-NIR-SWIR-MIR) interaction on the organic matter prediction. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. Trabalho 392.| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 180 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Cerrados; Embrapa Meio Ambiente. |
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Data corrente: |
24/07/2023 |
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Data da última atualização: |
25/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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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. |
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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. |
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Título: |
Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
ISPRS International Journal of Geo-Information, v. 12, n. 7, 263, July 2023. |
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DOI: |
https://doi.org/10.3390/ijgi12070263 |
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Idioma: |
Inglês |
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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. MenosAgricultural 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 co... Mostrar Tudo |
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Palavras-Chave: |
Agricultural Intensification; Aprendizado de máquina; Harmonized Landsat Sentinel-2; HLS; Inteligência artificial; Intensificação agrícola; Machine learning; Mapeamento agrícola; Multisensor. |
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Thesagro: |
Agricultura; Cerrado; Sensoriamento Remoto. |
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Thesaurus NAL: |
Agriculture; Artificial intelligence; Remote sensing. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155214/1/AP-Mapping-agricultural-intensification-2023.pdf
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Marc: |
LEADER 02974naa a2200397 a 4500
001 2155214
005 2023-07-25
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/ijgi12070263$2DOI
100 1 $aBOLFE, E. L.
245 $aMapping agricultural intensification in the Brazilian savanna$ba machine learning approach using harmonized data from Landsat Sentinel-2.$h[electronic resource]
260 $c2023
520 $aAgricultural 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.
650 $aAgriculture
650 $aArtificial intelligence
650 $aRemote sensing
650 $aAgricultura
650 $aCerrado
650 $aSensoriamento Remoto
653 $aAgricultural Intensification
653 $aAprendizado de máquina
653 $aHarmonized Landsat Sentinel-2
653 $aHLS
653 $aInteligência artificial
653 $aIntensificação agrícola
653 $aMachine learning
653 $aMapeamento agrícola
653 $aMultisensor
700 1 $aPARREIRAS, T. C.
700 1 $aSILVA, L. A. P. da
700 1 $aSANO, E. E.
700 1 $aBETTIOL, G. M.
700 1 $aVICTORIA, D. de C.
700 1 $aDEL'ARCO SANCHES, I.
700 1 $aVICENTE, L. E.
773 $tISPRS International Journal of Geo-Information$gv. 12, n. 7, 263, July 2023.
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Embrapa Agricultura Digital (CNPTIA) |
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