Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data
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) classifi...
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| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3512 |
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| author | Natalia C. Wiederkehr Fabio F. Gama Paulo B. N. Castro Polyanna da Conceição Bispo Heiko Balzter Edson E. Sano Veraldo Liesenberg João R. Santos José C. Mura |
| author_facet | Natalia C. Wiederkehr Fabio F. Gama Paulo B. N. Castro Polyanna da Conceição Bispo Heiko Balzter Edson E. Sano Veraldo Liesenberg João R. Santos José C. Mura |
| author_sort | Natalia C. Wiederkehr |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:20:13Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:20:13Z |
| publishDate | 2020-10-01 |
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| spelling | doaj.art-e4bdcdd079c34fe59bdc968f8cd794842023-11-20T18:36:03ZengMDPI AGRemote Sensing2072-42922020-10-011221351210.3390/rs12213512Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric DataNatalia C. Wiederkehr0Fabio F. Gama1Paulo B. N. Castro2Polyanna da Conceição Bispo3Heiko Balzter4Edson E. Sano5Veraldo Liesenberg6João R. Santos7José C. Mura8National Institute for Space Research, Av. dos Astronautas, 1.758, São José dos Campos, São Paulo 12227-010, BrazilNational Institute for Space Research, Av. dos Astronautas, 1.758, São José dos Campos, São Paulo 12227-010, BrazilCampus Universitário, Federal University of Ouro Preto, Morro do Cruzeiro, Ouro Preto, Minas Gerais 35400-000, BrazilDepartment of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UKCentre for Landscape and Climate Research (CLCR), University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UKEmbrapa Cerrados, BR-020, Planaltina, Federal District 73310-970, BrazilForest Engineering Department, Santa Catarina State University, Avenida Luiz de Camões 2090, Lages, Santa Catarina 88520-000, BrazilNational Institute for Space Research, Av. dos Astronautas, 1.758, São José dos Campos, São Paulo 12227-010, BrazilNational Institute for Space Research, Av. dos Astronautas, 1.758, São José dos Campos, São Paulo 12227-010, BrazilWe 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.https://www.mdpi.com/2072-4292/12/21/3512BrazilAmazonforestland useland coverforest degradation |
| spellingShingle | Natalia C. Wiederkehr Fabio F. Gama Paulo B. N. Castro Polyanna da Conceição Bispo Heiko Balzter Edson E. Sano Veraldo Liesenberg João R. Santos José C. Mura Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data Remote Sensing Brazil Amazon forest land use land cover forest degradation |
| title | Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data |
| title_full | Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data |
| title_fullStr | Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data |
| title_full_unstemmed | Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data |
| title_short | Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data |
| title_sort | discriminating forest successional stages forest degradation and land use in central amazon using alos palsar 2 full polarimetric data |
| topic | Brazil Amazon forest land use land cover forest degradation |
| url | https://www.mdpi.com/2072-4292/12/21/3512 |
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