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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
24/02/1994 |
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Data da última atualização: |
24/02/1994 |
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Autoria: |
FARIA, R. T. de; BOISVERT, J.; COSTA, A. C. S. da; MARUR, C. J.; OKUYAMA, L. A.; OLIVEIRA, D. de. |
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Afiliação: |
1 IAPAR. 2 LRRI. 4-6 IAPAR. |
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Título: |
Utilização de técnicos de modelagem na estimativa da umidade do solo sob culturas anuais no estado do Paraná. |
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Ano de publicação: |
1993 |
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Fonte/Imprenta: |
Londrina: IAPAR, 1993. |
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Páginas: |
39 p. |
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Série: |
(IAPAR. Boletim Técnico, 40). |
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ISSN: |
0100-3054 |
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Idioma: |
Português |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00573nam a2200193 a 4500
001 1848934
005 1994-02-24
008 1993 bl uuuu u0uu1 u #d
022 $a0100-3054
100 1 $aFARIA, R. T. de
245 $aUtilização de técnicos de modelagem na estimativa da umidade do solo sob culturas anuais no estado do Paraná.
260 $aLondrina: IAPAR$c1993
300 $a39 p.
490 $a(IAPAR. Boletim Técnico, 40).
700 1 $aBOISVERT, J.
700 1 $aCOSTA, A. C. S. da
700 1 $aMARUR, C. J.
700 1 $aOKUYAMA, L. A.
700 1 $aOLIVEIRA, D. de
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Registro original: |
Embrapa Trigo (CNPT) |
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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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Circulação/Nível: |
A - 1 |
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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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