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| Registros recuperados : 11 | |
| 3. |  | GAROFALO, D. F. T.; MESSIAS, C. G.; LIESENBERG, V.; BOLFE, E. L.; FERREIRA, C. Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 7, p. 593-604, jul. 2015. Título em inglês: Comparative analysis of digital classifiers of Landsat?8 images for thematic mapping procedures.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 4. | | GAROFALO, D. F. T.; MESSIAS, C. G.; LIESENBERG, V.; BOLFE, E. L.; FERREIRA, M. C. Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático. Pesquisa Agropecuária Brasileira, Brasília, v. 50, n.7, p. 593-604, jul. 2015.| Biblioteca(s): Embrapa Territorial. |
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| 5. | | HESS, A. F.; MINATTI, M.; LIESENBERG, V.; MATTOS, P. P. de; BRAZ, E. M.; COSTA, E. A. Brazilian pine diameter at breast height and growth in mixed Ombrophilous forest in Southern Brazil. Australian Journal of Crop Science, v. 12, n. 5, p. 770-777, May 2018.| Biblioteca(s): Embrapa Florestas. |
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| 6. | | PERTILLE, C. T.; SCHIMALSKI, M. B.; PICINATTO FILHO, V.; LIESENBERG, V.; OLIVEIRA, E. B. de; MIRANDA, F. das D. A. Estimation of sanity of a stand of Pinus taeda L. after the attack of Sapajus nigritus Kerr (1972) using vegetation index. Scientia Forestalis, v. 48, n. 126, e3323, 2020. 14 p.| Biblioteca(s): Embrapa Florestas. |
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| 7. | | PERTILLE, C. T.; OLIVEIRA, E. B. de; NICOLETTI, M. F.; PICCINATTO FILHO, V.; LIESENBERG, V.; SCHIMALSKI, M. B. Wood production from a Pinus taeda L. stand attacked by Sapajus nigritus. Advances in Forestry Science, v. 9, n. 2, p. 1729-1734, 2022.| Biblioteca(s): Embrapa Florestas. |
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| 8. | | WIEDERKEHR, N. C.; GAMA, F. F.; CASTRO, P. B. N.; BISPO, P. da C.; BALZTER, H.; SANO, E. E.; SANTOS, J. R.; LIESENBERG, V.; MURA, J. C. Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data. Remote Sensing, v. 12, n. 21, 2020.| Biblioteca(s): Embrapa Cerrados. |
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| 9. | | 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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| 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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| Registros recuperados : 11 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
15/12/2020 |
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Data da última atualização: |
15/12/2020 |
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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: |
WIEDERKEHR, N. C.; GAMA, F. F.; CASTRO, P. B. N.; BISPO, P. da C.; BALZTER, H.; SANO, E. E.; SANTOS, J. R.; LIESENBERG, V.; MURA, J. C. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 21, 2020. |
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Idioma: |
Português |
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Conteúdo: |
We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude?Pottier, van Zyl, Freeman?Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover. MenosWe discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude?Pottier, van Zyl, Freeman?Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individ... Mostrar Tudo |
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Thesagro: |
Degradação Ambiental; Floresta; Uso da Terra. |
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Thesaurus NAL: |
Amazonia. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219224/1/SANO-DISCRIMINATING-FOREST-SUCCESSIONAL-STAGES.pdf
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Marc: |
LEADER 02958naa a2200265 a 4500
001 2128151
005 2020-12-15
008 2020 bl uuuu u00u1 u #d
100 1 $aWIEDERKEHR, N. C.
245 $aDiscriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data.$h[electronic resource]
260 $c2020
520 $aWe discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude?Pottier, van Zyl, Freeman?Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.
650 $aAmazonia
650 $aDegradação Ambiental
650 $aFloresta
650 $aUso da Terra
700 1 $aGAMA, F. F.
700 1 $aCASTRO, P. B. N.
700 1 $aBISPO, P. da C.
700 1 $aBALZTER, H.
700 1 $aSANO, E. E.
700 1 $aSANTOS, J. R.
700 1 $aLIESENBERG, V.
700 1 $aMURA, J. C.
773 $tRemote Sensing$gv. 12, n. 21, 2020.
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