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Registros recuperados : 13 | |
5. | | OSCO, L. P.; ARRUDA, M. S.; GONÇALVES, D. N.; DIAS, A.; BATISTOTI, J.; SOUZA, M.; GOMES, F. D. G.; RAMOS, A. P. M.; JORGE, L. A. de C.; LIESENBERG, V.; LI, J.; MA, L.; MARCATO JUNIOR, J.; GONÇALVES, W. N. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing, v. 174, 2021. 1 - 17 Biblioteca(s): Embrapa Instrumentação. |
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6. | | OSCO, L. P.; NOGUEIRA, K.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVES, W. N.; JORGE, L. A. de C.; MARCATO JUNIOR, J.; SANTOS, J. A. Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. Precision Agriculture, v. 22, n. 4,2021. 1171-1188 Biblioteca(s): Embrapa Instrumentação. |
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7. | | OSCO, L. P.; MARCATO JUNIOR, J.; RAMOS, A. P. M.; JORGE, L. A. de C.; FATHOLAHI, S. N.; SILVA, J. A.; MATSUBARA, E. T.; PISTORI, H.; GONÇALVES, W. N.; LI, J. A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observations and Geoinformation, v. 102, 102456, 2021. 1 - 22 Biblioteca(s): Embrapa Instrumentação. |
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8. | | OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; BLASSIOLI-MORAES, M. C.; MICHEREFF, M. F. F.; AQUUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C. An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. Infrared Physics & Technology, v. 123, 104203, 2022. 13 p. Biblioteca(s): Embrapa Instrumentação. |
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9. | | OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; MORAES, M. C. B.; MICHEREFF, M. F. F.; AQUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C. An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. Infrared Physics & Technology, v. 123, 2022. 104203. Na publicação: Maria Carolina Blassioli-Moraes. Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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10. | | OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. Remote Sensing, n. 12, v. 6, a. 906, 2020. 1 - 21 Biblioteca(s): Embrapa Instrumentação. |
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11. | | RAMOS, A. P. M.; GOMES, F. D. G.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; MICHEREFF, M. F. F.; MORAES, M. C. B.; BORGES, M.; LAUMANN, R. A.; LIESENBERG, V.; JORGE, L. A. de C.; OSCO, L. P. Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. Precision Agriculture, 2021. Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann. Biblioteca(s): Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia. |
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12. | | FURUYA, D. E. G.; MA, L.; PINHEIRO, M. M. F.; GOMES, F. D. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; RODRIGUES, D. de C.; BLASSIOLI- MORAES, M. C.; MICHEREFF, M. F. F.; BORGES, M.; ALAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. 1 - 10 Biblioteca(s): Embrapa Instrumentação. |
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13. | | FURUYA, D. E. G.; MA, L.; PINHEIRO, M. M. F.; GOMES, F. D. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; RODRIGUES, D. de C.; BLASSIOLI- MORAES, M. C.; MICHEREFF, M. F. F.; BORGES, M.; LAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. 1 - 10 Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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Registros recuperados : 13 | |
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Registro Completo
Biblioteca(s): |
Embrapa Cerrados. |
Data corrente: |
05/06/2023 |
Data da última atualização: |
05/06/2023 |
Autoria: |
SCHWAIDA, S. F.; CICERELLI, R. E.; ALMEIDA, T.; SANO, E. E.; PIRES, C. H.; RAMOS, A. P. M. |
Afiliação: |
SAMUEL FERNANDO SCHWAIDA; REJANE ENNES CICERELLI; TATI DE ALMEIDA; EDSON EYJI SANO, CPAC; CARLOS HENRIQUE PIRES; ANA PAULA MARQUES RAMOS. |
Título: |
Defining priorities areas for biodiversity conservation and trading forest certificates in the Cerrado biome in Brazil. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Biodiversity and Conservation, v. 32, 2023. |
Páginas: |
p. 18071820 |
Idioma: |
Inglês |
Conteúdo: |
Abstract Habitat loss and natural vegetation fragmentation are significant causes of global biodiversity decline, impacting plant and animal species negatively. This issue is worrisome in the private areas of Cerrado in Brazil, which is the second-largest biome, considered a hotspot for biodiversity conservation, and a provider of ecosystem services. Herein, we present a novel integrated approach to define priority areas for biodiversity conservation and environmental compensation in Cerrado, using multicriteria analysis. Our approach combines variables like deforestation projection, integral index of connectivity, threatened species occurrence, and environmental information of rural properties, ranking the importance of remaining native vegetation for biodiversity conservation and forest certificate issuance. Landscape metrics were used to observe and predict land use and land cover changes from 1988 to 2038. We found a loss of native vegetation in the Cerrado superior to 20% between 1988 and 2018, associated with increased of its fragmentation and its connectivity loss, especially after 2008. Natural cover was replaced mostly by pasture and more recently by agriculture. Moreover, we determined that is expected a loss of native vegetation of around 55% by 2038 in the study area. The proposed approach can predict the consequences of future changes in the landscape of the private areas in the Cerrado biome. It should be replicated in other ecosystems, supporting the decision-making process for biodiversity protection. MenosAbstract Habitat loss and natural vegetation fragmentation are significant causes of global biodiversity decline, impacting plant and animal species negatively. This issue is worrisome in the private areas of Cerrado in Brazil, which is the second-largest biome, considered a hotspot for biodiversity conservation, and a provider of ecosystem services. Herein, we present a novel integrated approach to define priority areas for biodiversity conservation and environmental compensation in Cerrado, using multicriteria analysis. Our approach combines variables like deforestation projection, integral index of connectivity, threatened species occurrence, and environmental information of rural properties, ranking the importance of remaining native vegetation for biodiversity conservation and forest certificate issuance. Landscape metrics were used to observe and predict land use and land cover changes from 1988 to 2038. We found a loss of native vegetation in the Cerrado superior to 20% between 1988 and 2018, associated with increased of its fragmentation and its connectivity loss, especially after 2008. Natural cover was replaced mostly by pasture and more recently by agriculture. Moreover, we determined that is expected a loss of native vegetation of around 55% by 2038 in the study area. The proposed approach can predict the consequences of future changes in the landscape of the private areas in the Cerrado biome. It should be replicated in other ecosystems, supporting the decision-... Mostrar Tudo |
Palavras-Chave: |
Espécies ameaçadas. |
Thesagro: |
Cerrado; Uso da Terra. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1154250/1/Sano-DEFINING-PRIORITIES-AREAS-FOR-BIODIVERSITY-CONSERVATION.pdf
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Marc: |
LEADER 02178nam a2200217 a 4500 001 2154250 005 2023-06-05 008 2023 bl uuuu u0uu1 u #d 100 1 $aSCHWAIDA, S. F. 245 $aDefining priorities areas for biodiversity conservation and trading forest certificates in the Cerrado biome in Brazil. 260 $aBiodiversity and Conservation, v. 32$c2023 300 $ap. 18071820 520 $aAbstract Habitat loss and natural vegetation fragmentation are significant causes of global biodiversity decline, impacting plant and animal species negatively. This issue is worrisome in the private areas of Cerrado in Brazil, which is the second-largest biome, considered a hotspot for biodiversity conservation, and a provider of ecosystem services. Herein, we present a novel integrated approach to define priority areas for biodiversity conservation and environmental compensation in Cerrado, using multicriteria analysis. Our approach combines variables like deforestation projection, integral index of connectivity, threatened species occurrence, and environmental information of rural properties, ranking the importance of remaining native vegetation for biodiversity conservation and forest certificate issuance. Landscape metrics were used to observe and predict land use and land cover changes from 1988 to 2038. We found a loss of native vegetation in the Cerrado superior to 20% between 1988 and 2018, associated with increased of its fragmentation and its connectivity loss, especially after 2008. Natural cover was replaced mostly by pasture and more recently by agriculture. Moreover, we determined that is expected a loss of native vegetation of around 55% by 2038 in the study area. The proposed approach can predict the consequences of future changes in the landscape of the private areas in the Cerrado biome. It should be replicated in other ecosystems, supporting the decision-making process for biodiversity protection. 650 $aCerrado 650 $aUso da Terra 653 $aEspécies ameaçadas 700 1 $aCICERELLI, R. E. 700 1 $aALMEIDA, T. 700 1 $aSANO, E. E. 700 1 $aPIRES, C. H. 700 1 $aRAMOS, A. P. M.
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