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| Registros recuperados : 10 | |
| 3. |  | PANTOJA, N.; BEUCHLE, R.; OLIVEIRA, M. V. N. d'; CASSOL, H.; LUI, G. The potential for operational monitoring of selectively logged forest using vegetation index in the brazilian Amazon. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos, SP. Anais... São José dos Campos: Inpe, 2019. 4 p. Editores: Douglas Francisco Marcolino Gherardi; Ieda Del'Arco Sanches; Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Acre. |
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| 6. | | CASSOL, H. L. G.; ARAI, E.; SANO, E. E.; DUTRA, A. C.; HOFFMANN, T. B.; SHIMABUKURO, Y. E. Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil. Land, v. 9, n. 5, 2020.| Biblioteca(s): Embrapa Cerrados. |
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| 9. | | 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.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 10. | | PERIPATO, V.; LEVIS, C.; MOREIRA, G. A.; GAMERMAN, D.; STEEGE, H. ter; PITMAN, N. C. A.; SOUZA, J. G. de; IRIARTE, J.; ROBINSON, M.; JUNQUEIRA, A. B.; TRINDADE, T. B.; ALMEIDA, F. O.; MORAES, C. de P.; LOMBARDO, U.; TAMANAHA, E. K.; MAEZUMI. S. Y.; OMETTO, J. P. H. B.; BRAGA, J. R. G.; CAMPANHARO, W. A.; CASSOL, H. L. G.; LEAL, P. R.; ASSIS, M. L. R. de; SILVA, A. M. da; PHILLIPS, O. L.; COSTA, F. R. C.; FLORES, B. M.; HOFFMAN, B.; HENKEL, T. W.; UMAÑA, M. N.; MAGNUSSON, W. E.; SANDOVAL, E. H. V.; BARLOW, J.; MILLIKEN, W.; LOPES, M. A.; SIMON, M. F.; ANDEL, T. R. van; LAURANCE, S. G. W.; LAURANCE, W. F.; TORRES-LEZAMA, A.; ASSIS, R. L.; MOLINO, J.-F.; MESTRE, M.; HAMBLIN, M.; COELHO, L. de S.; LIMA FILHO, D. de A.; WITTMANN, F.; SALOMÃO, R. P.; AMARAL, I. L.; GUEVARA, J. E.; MATOS, F. D. de A.; CASTILHO, C. V. de; CARIM, M. de J. V.; LÓPEZ, D. C.; SABATIER, D.; IRUME, M. V.; MARTINS, M. P.; GUIMARÃES, J. R. da S.; BÁNKI, O. S.; PIEDADE, M. T. F.; RAMOS, J. F.; LUIZE, B. G.; NOVO, E. M. M. de L.; VARGAS, P. N.; SILVA, T. S. F.; VENTICINQUE, E. M.; MANZATTO, A. G.; REIS, N. F. C.; TERBORGH, J.; CASULA, K. R.; DEMARCHI, L. O.; CORONADO, E. N. H.; MENDOZA, A. M.; MONTERO, J. C.; SCHÖNGART, J.; FELDPAUSCH, T. R.; QUARESMA, A. C.; AYMARD C., G. A.; BARALOTO, C.; ARBOLEDA, N. C.; ENGEL, J.; PETRONELLI, P.; ZARTMAN, C. E.; KILLEEN, T. J.; MARIMON, B. S.; MARIMON-JUNIOR, B. H.; SCHIETTI, J.; SOUSA, T. R.; VASQUEZ, R.; RINCÓN, L. M.; BERENGUER, E.; FERREIRA, J. N.; MOSTACEDO, B.; AMARAL, D. D. do; CASTELLANOS, H.; MEDEIROS, M. B. de; ANDRADE, A.; CAMARGO, J. L.; FARIAS, E. de S.; MAGALHÃES, J. L. L.; NASCIMENTO, H. E. M.; QUEIROZ, H. L. de; BRIENEN, R.; REVILLA, J. D. C.; STEVENSON, P. R.; ARAUJO-MURAKAM, A.; CINTRA, B. B. L.; FEITOSA, Y. O.; BARBOSA, F. R.; CARPANEDO, R. de S.; DUIVENVOORDEN, J. F.; NORONHA, J. da C. de; RODRIGUES, D. de J.; MOGOLLÓN, H. F.; FERREIRA, L. V.; HOUSEHOLDER, J. E.; LOZADA, J. R.; COMISKEY, J. A.; DRAPER, F. C.; TOLEDO, J. J. de; DAMASCO, G.; DÁVILA, N.; GARCÍA-VILLACORTA, R.; LOPES, A.; VALVERDE, F. C.; ALONSO, A.; DALLMEIER, F.; GOMES, V. H. F.; JIMENEZ, E. M.; NEILL, D.; MORA, M. C. P.; AGUIAR, D. P. P. de; ARROYO, L.; CARVALHO, F. A.; SOUZA, F. C. de; FEELEY, K. J.; GRIBEL, R.; PANSONATO, M. P.; PAREDES, M. R.; SILVA, I. B. da; FERREIRA, M. J.; FINE, P. V. A.; FONTY, E.; GUEDES, M. C.; LICONA, J. C.; PENNINGTON, T.; PERES, C. A.; ZEGARRA, B. E. V.; PARADA, G. A.; MOLINA, G. P.; VOS, V. A.; CERÓN, C.; MAAS, P.; SILVEIRA, M.; STROPP, J.; THOMAS, R.; BAKER, T. R.; DALY, D.; HUAMANTUPA-CHUQUIMACO, I.; VIEIRA, I. C. G.; ALBUQUERQUE, B. W.; FUENTES, A.; KLITGAARD, B.; MARCELO-PEÑA, J. L.; SILMAN, M. R.; TELLO, J. S.; VRIESENDORP, C.; CHAVE, J.; DI FIORE, A.; HILÁRIO, R. R.; PHILLIPS, J. F.; RIVAS-TORRES, G.; HILDEBRAND, P. von; PEREIRA, L. de O.; BARBOSA, E. M.; BONATES, L. C. de M.; DOZA, H. P. D.; GÓMEZ, R. Z.; GONZALES, G. P. G.; GONZALES, T.; MALHI, Y.; MIRANDA, I. P. de A.; PINTO, L. F. M.; PRIETO, A.; RUDAS, A.; RUSCHEL, A. R.; SILVA, N.; VELA, C. I. A.; ZENT, E. L.; ZENT, S.; CANO, A.; MÁRQUEZ, Y. A. C.; CORREA, D. F.; COSTA, J. B. P.; GALBRAITH, D.; HOLMGREN, M.; KALAMANDEEN, M.; LOBO, G.; NASCIMENTO, M. T.; OLIVEIRA, A. A.; RAMIREZ-ANGULO, H.; ROCHA, M.; SCUDELLE, V. V.; SIERRA, R.; TIRADO, M.; HEIJDEN, G. van der; TORRE, E. V.; REATEGUI, M. A. A.; BAIDER, C.; BALSLEV, H.; CÁRDENAS, S.; CASAS, L. F.; FARFAN-RIOS, W.; FERREIRA, C.; LINARES-PALOMINO, R.; MENDOZA, C.; MESONES, I.; GIRALDO, L. S. U.; VILLARROEL, D.; ZAGT, R.; ALEXIADES, M. N.; OLIVEIRA, E. A. de; GARCIA-CABRERA, K.; HERNANDEZ, L.; CUENCA, W. P.; PANSINI, S.; PAULETTO, D.; AREVALO, F. R.; SAMPAIO, A. F.; GAMARRA, L. V.; ARAGÃO, L. E. O. C. More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia. Science, v. 382, p. 103-109, 2023. Na publicação: Carolina V. Castilho, Joice Ferreira.| Biblioteca(s): Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Recursos Genéticos e Biotecnologia; Embrapa Roraima. |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
14/12/2020 |
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Data da última atualização: |
14/12/2020 |
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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: |
ARAI, E.; SANO, E. E.; DUTRA. A. C.; CASSOL, H. L. G.; HOFFMANN, T. B.; SHIMABUKURO, Y. E. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 7, 2020. |
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ISSN: |
2072-4292 |
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Idioma: |
Português |
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Conteúdo: |
Abstract: This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring?Global Land Cover (FROM-GLC) and Global Food Security?Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis. MenosAbstract: This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring?Global Land Cover (FROM-GLC) and Global Food Security?Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A co... Mostrar Tudo |
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Palavras-Chave: |
Fração máxima; Mapeamento de terras agrícolas; Mato Grosso. |
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Thesagro: |
Cerrado; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219147/1/SANO-VEGETATION-FRACTION-IMAGES-DERIVED.pdf
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Marc: |
LEADER 02519naa a2200253 a 4500
001 2128068
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
022 $a2072-4292
100 1 $aARAI, E.
245 $aVegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil.$h[electronic resource]
260 $c2020
520 $aAbstract: This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring?Global Land Cover (FROM-GLC) and Global Food Security?Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.
650 $aCerrado
650 $aSensoriamento Remoto
653 $aFração máxima
653 $aMapeamento de terras agrícolas
653 $aMato Grosso
700 1 $aSANO, E. E.
700 1 $aDUTRA. A. C.
700 1 $aCASSOL, H. L. G.
700 1 $aHOFFMANN, T. B.
700 1 $aSHIMABUKURO, Y. E.
773 $tRemote Sensing$gv. 12, n. 7, 2020.
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