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Registro Completo |
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
16/11/2021 |
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Data da última atualização: |
16/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
KUCK, T. N.; SANO, E. E.; BISPO, P. da C.; SHIGUEMORI, E. H.; SILVA FILHO, P. B. F.; MATRICARDI, E. A. T. |
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Afiliação: |
TAHISA NEITZEL KUCK; EDSON EYJI SANO, CPAC; POLYANNA DA CONCEIÇÃO BISPO; ELCIO HIDEITI SHIGUEMORI; PAULO FERNANDO FERREIRA SILVA FILHO; ERALDO APARECIDO TRONDOLI MATRICARDI. |
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Título: |
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Remote Sensing, v. 13, n. 3341, 2021. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon. MenosAbstract: The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selecti... Mostrar Tudo |
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Thesagro: |
Desmatamento; Sensoriamento Remoto. |
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Thesaurus Nal: |
Synthetic aperture radar. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/227792/1/Sano-Livre-A-comparative-assessment-of-machine-learning.pdf
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Marc: |
LEADER 02269naa a2200217 a 4500
001 2136170
005 2021-11-16
008 2021 bl uuuu u00u1 u #d
100 1 $aKUCK, T. N.
245 $aA Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images.$h[electronic resource]
260 $c2021
520 $aAbstract: The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.
650 $aSynthetic aperture radar
650 $aDesmatamento
650 $aSensoriamento Remoto
700 1 $aSANO, E. E.
700 1 $aBISPO, P. da C.
700 1 $aSHIGUEMORI, E. H.
700 1 $aSILVA FILHO, P. B. F.
700 1 $aMATRICARDI, E. A. T.
773 $tRemote Sensing$gv. 13, n. 3341, 2021.
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Registro original: |
Embrapa Cerrados (CPAC) |
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Biblioteca |
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| Registros recuperados : 12 | |
| 2. |  | SANO, E. E.; MATRICARDI, E. A. T.; CAMARGO, F. F. Estado da Arte do Sensoriamento Remoto de Radar: Fundamentos, Sensores, Processamento de Imagens e Aplicações. State-of-the-art of Radar Remote Sensing: Fundamentals, Sensors, Image Processing, and Applications. Revista Brasileira de Cartografia, v. 72, n. 50th Anniversary Special Issue, 2020. p. 1484-1508| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Cerrados. |
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| 5. | | MIGUEL, E. P.; REZENDE, A. V.; LEAL, F. A.; MATRICARDI, E. A. T.; VALE, A. T. do; PEREIRA, R. S. Redes neurais artificiais para a modelagem do volume de madeira e biomassa do cerradão com dados de satélites. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 9, p. 829-839, set. 2015. Título em inglês: Artificial neural networks for modeling wood volume and aboveground biomass of tall Cerrado using satellite data.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 6. | | CAMELO, A. P.; SANCHES, K.; MATRICARDI, E. A. T.; SANO, E. E.; SOUZA, A. N. de; MIGUEL, E. P. Effects of landscape fragmentation on soil loss in the Cerrado Biome, Brazil. Environmental Management and Sustainable Development, v. 13, n. 1, 2024.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 4 |
| Biblioteca(s): Embrapa Cerrados. |
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| 9. | | BRICEÑO CASTILLO, G. V.; FREITAS, L. J. M. de; CORDEIRO, V. A.; ORELLANA, J. B. P.; REATEGUI-BETANCOURT, J. L.; NAGY, L.; MATRICARDI, E. A. T. Assessment of selective logging impacts using UAV, Landsat, and Sentinel data in the Brazilian Amazon rainforest. Journal of Applied Remote Sensing, v. 16, n. 1, 014526, Mar. 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 12. | | SILVA JUNIOR, C. H. L.; CARVALHO, N. S.; PESSÔA, A. C. M.; REIS, J. B. C.; PONTES-LOPES, A.; DOBLAS, J.; HEINRICH, V.; CAMPANHARO, W.; ALENCAR, A.; SILVA, C.; LAPOLA, D. M.; ARMENTERAS, D.; MATRICARDI, E. A. T.; BERENGUER, E.; CASSOL, H.; NUMATA, I.; HOUSE, J.; FERREIRA, J. N.; BARLOW, J.; GATTI, L.; BRANDO, P.; FEARNSIDE, P. M.; SAATCHI, S.; SILVA, S.; SITCH, S.; AGUIAR, A. P.; SILVA, C. A.; VANCUTSEM, C.; ACHARD, F.; BEUCHLE, R.; SHIMABUKURO, Y. E.; ANDERSON, L. O.; ARAGÃO, L. E. O. C. Amazonian forest degradation must be incorporated into the COP26 agenda. Nature Geoscience, v. 14, p. 634-635, Sep. 2021.| Tipo: Nota Técnica/Nota Científica |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 12 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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