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81. | | MANJOLIN, R.; GREGO, C. R.; NOGUEIRA, S. F.; SILVA, G. B. S. da; TRABAQUINI, K.; SANCHES, I. D. Variabilidade espacial da fertilidade, carbono e nitrogênio do solo em áreas de pastagem e cana-de-açúcar no estado de São Paulo. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017. p. 7163-7170. Biblioteca(s): Embrapa Territorial. |
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82. | | TEIXEIRA, A. H. de C.; LEIVAS, J. F.; RONQUIM, C. C.; GARCON, E. A. M.; SILVA, G. B. S. da. Water and vegetation indices by using MODIS products for eucalyptus, pasture, and natural ecosystems in the eastern São Paulo state, Southeast Brazil. In: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 19., 2017, Warsaw. Proceedings... Warsaw: SPIE, 2017. (SPIE proceedings, v. 10421). p. 1042112-1-1042112-12. Biblioteca(s): Embrapa Territorial. |
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83. | | LEIVAS, J. F.; TEIXEIRA, A. H. de C.; SILVA, G. B. S. da; GARCON, E. A. M.; RONQUIM, C. C. Water indicators based on SPOT 6 satellite images in irrigated area at the Paracatu River Basin, Brazil. In: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 19., 2017, Warsaw. Proceedings... Warsaw: SPIE, 2017. (SPIE proceedings, v. 10421). p. 104211l-1-104211l-7. Biblioteca(s): Embrapa Territorial. |
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84. | | LORENSINI, C. L.; LOEBMANN, D. G. dos S. W.; SILVA, G. B. S. da; VICENTE, L. E.; VICTORIA, D. de C. Alterações do uso da terra em municípios com expansão de área plantada com cana-de-açúcar. In: CONGRESSO INTERINSTITUCIONAL DE INICIAÇÃO CIENTÍFICA, 7., 2013, Campinas, SP. Anais... Campinas: IAC, 2013. 8 p. Biblioteca(s): Embrapa Territorial. |
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85. | | GALDINO, S.; BOLFE, E. L.; NOGUEIRA, S. F.; ARAUJO, L. S. de; VICTORIA, D. de C.; GREGO, C. R.; SILVA, G. B. S. da. Análise geoespacial entre níveis de degradação de pastagens e parâmetros físicos em sub-bacias de Pindamonhangaba, SP. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 1161-1168. Biblioteca(s): Embrapa Territorial. |
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86. | | LEIVAS, J. F.; TEIXEIRA, A. H. de C.; ANDRADE, R. G.; SILVA, G. B. S. da; NOGUEIRA, S. F.; ARAUJO, L. S. de. Aplicação do modelo agrometeológico espectral SAFER e imagens Rapid Eye na FLONA Tapajós. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 2552-2558. Biblioteca(s): Embrapa Territorial. |
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88. | | SILVA, G. B. S. da; TEIXEIRA, A. H. de C.; VICTORIA, D. de C.; NOGUEIRA, S. F.; LEIVAS, J. F.; COAGUILA, D. N.; HERLIN, V. R. Energy balance model applied to pasture experimental areas in São Paulo State, Brazil. Proceedings of SPIE, v. 9998, p. 99981C-1-99981C-10, 2016. Biblioteca(s): Embrapa Territorial. |
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89. | | SILVA, G. B. S. da; TEIXEIRA, A. H. de C.; VICTORIA, D. de C.; NOGUEIRA, S. F.; LEIVAS, J. F.; COAGUILA, D. N.; HERLIN, V. R. Energy balance model applied to pasture experimental areas in São Paulo State, Brazil. In: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 18., 2016, Edinburg. Proceedings... Bellingham: SPIE, 2016. p. 99981C-1-99981C-10. (SPIE proceedings, v. 9998). Biblioteca(s): Embrapa Agricultura Digital. |
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92. | | TEIXEIRA, A. H. de C.; REIS, J. B. R. DA S.; LEIVAS, J. F.; SILVA, G. B. S. da; STRUIVING, T. B. Componentes da produtividade da água modelados por sensoriamento remoto em limoeiros irrigados de Minas Gerais. Agrometeoros, Passo Fundo, v. 25, n. 1, p. 237-247, ago. 2017. Biblioteca(s): Embrapa Territorial. |
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93. | | GREGO, C. R.; MANJOLIN, R. C.; NOGUEIRA, S. F.; RODRIGUES, C. A. G.; SILVA, G. B. S. da; CONCEIÇÃO, M. P. C.; HERLING, V. R. Geostatistical Analysis of NDVI in rotational and continuous grazing pastures In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande, MS. Proceedings... Brasília, DF: Embrapa, 2016. p. 121-125. (Embrapa Gado de Corte. Documentos, 216). Biblioteca(s): Embrapa Territorial. |
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94. | | VICTORIA, D. de C.; GARCON, E. A. M.; OLIVEIRA, B. P. de; SILVA, G. B. S. da; LOEBMANN, D. G. dos S. W. Geoprocessamento. In: TÔSTO, S. G.; RODRIGUES, C. A. G.; BOLFE, E. L.; BATISTELLA, M. (Ed.). Geotecnologias e geoinformação. Brasília, DF: Embrapa, 2014. p. 93-105. (Coleção 500 Perguntas, 500 Respostas) Biblioteca(s): Embrapa Territorial. |
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95. | | LIMA, I. B. T. de; BULLER, L. S.; SCHWERT, F.; GOULART, T.; ULSENHEIMER, R.; SILVA, G. B. S. da; NOGUEIRA, S. F.; MESA-PEREZ, J. M.; SORIANO, E. Fossil to renewable transition for sustaining food, water and energy. In: In: CONFERÊNCIA INTERNACIONAL LINKS 2015, Florianópolis. Ligações entre o consumo de energia, alimentos e água no Brasil, no contexto das estratégias de mitigação das mudanças climáticas: Anais... Florianópolis: UNISUL, 2015. Biblioteca(s): Embrapa Territorial. |
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96. | | VICTORIA, D. de C.; SILVA, G. B. S. da; NOGUEIRA, S. F.; VICENTE, L. E.; TAKEMURA, C. M.; GOMES, R. da C. Identificação de níveis de degradação de pastagem por meio de reflectância espectral de imagens de alta resolução espacial. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 1640-1647. Biblioteca(s): Embrapa Territorial. |
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97. | | BULLER, L. S.; SILVA, G. B. S. da; ZANETTI, M. R.; ORTEGA, E.; MORAES, A. DE; GOULART, T.; BERGIER, I.; BULLER, L. S. Historical Land-Use Changes in São Gabriel do Oeste at the Upper Taquari River Basin. In: BERGIER, I.; ASSINE, M. L. (Ed.). Dynamics of the Pantanal Wetland in South America. Switzerland: Springer International Publishing, 2016. p. 191-208. (The Handbook of Environmental Chemistry, 37). Biblioteca(s): Embrapa Pantanal. |
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99. | | SILVA, G. B. S. da; TEIXEIRA, A. H. de C.; NOGUEIRA, S. F.; VICTORIA, D. de C.; LEIVAS, J. F.; HERLIN, V. R. Landsat-based above ground biomass estimation in pasture area in São Paulo, Brazil. In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande, MS. Proceedings... Brasília, DF: Embrapa, 2016. p. 140-144. (Embrapa Gado de Corte. Documentos, 216). Coordenador: Roberto Giolo de Almeida. II SIGEE. Biblioteca(s): Embrapa Agricultura Digital. |
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100. | | SILVA, G. B. S. da; TEIXEIRA, A. H. de C.; NOGUEIRA, S. F.; VICTORIA, D. de C.; LEIVAS, J. F.; HERLIN, V. R. Landsat-based above ground biomass estimation in pasture area in São Paulo, Brazil. In: INTERNATIONAL SYMPOSIUM ON GREENHOUSE GASES IN AGRICULTURE, 2., 2016, Campo Grande, MS. Proceedings... Brasília, DF: Embrapa, 2016. p. 140-144. Biblioteca(s): Embrapa Territorial. |
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Registros recuperados : 186 | |
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Registro Completo
Biblioteca(s): |
Embrapa Cerrados; Embrapa Meio Ambiente. |
Data corrente: |
31/03/2021 |
Data da última atualização: |
22/03/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
SANO, E. E.; RIZZOLI, P.; KOYAMA, C. N.; WATANABE, M.; ADAMI, M.; SHIMABUKURO, Y. E.; SILVA, G. B. S. da; FREITAS, D. M. de. |
Afiliação: |
EDSON EYJI SANO, CPAC; PAOLA RIZOLLI, Microwaves and Radar Institute, German Aerospace Center; CHRISTIAN N KOYAMA, Tokyo Denki University; MANABU WATANABE, Tokyo Denki University; MARCOS ADAMI, INPE; YOSIO EDEMIR SHIMABUKURO, INPE; GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPMA; DANIEL MORAES DE FREITAS, IBAMA. |
Título: |
Comparative analysis of the global forest/non-forest maps derived from SAR and optical sensors: case studies from brazilian Amazon and Cerrado biomes. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Remote Sensing, v. 13, n. 3, article 367, 2021. |
DOI: |
https://doi.org/10.3390/rs13030367 |
Idioma: |
Inglês |
Conteúdo: |
Abstract: Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement. MenosAbstract: Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmen... Mostrar Tudo |
Palavras-Chave: |
ALOS-2; Forest mapping; SAR; TanDEM-X. |
Thesagro: |
Cerrado; Floresta Tropical; Mapa; Sensoriamento Remoto. |
Thesaurus NAL: |
Remote sensing; Savannas; Tropical forests. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/227779/1/Sano-comparative-analysis.pdf
|
Marc: |
LEADER 03169naa a2200349 a 4500 001 2136157 005 2022-03-22 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs13030367$2DOI 100 1 $aSANO, E. E. 245 $aComparative analysis of the global forest/non-forest maps derived from SAR and optical sensors$bcase studies from brazilian Amazon and Cerrado biomes.$h[electronic resource] 260 $c2021 520 $aAbstract: Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement. 650 $aRemote sensing 650 $aSavannas 650 $aTropical forests 650 $aCerrado 650 $aFloresta Tropical 650 $aMapa 650 $aSensoriamento Remoto 653 $aALOS-2 653 $aForest mapping 653 $aSAR 653 $aTanDEM-X 700 1 $aRIZZOLI, P. 700 1 $aKOYAMA, C. N. 700 1 $aWATANABE, M. 700 1 $aADAMI, M. 700 1 $aSHIMABUKURO, Y. E. 700 1 $aSILVA, G. B. S. da 700 1 $aFREITAS, D. M. de 773 $tRemote Sensing$gv. 13, n. 3, article 367, 2021.
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