| |
|
|
| Registros recuperados : 6 | |
| 2. |  | 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. |
|   |
| 4. | | MUELLER, E.; HOFFMANN, T. G.; SCHMITZ, F. R. W.; HELM, C. V.; ROY, S.; BERTOLI, S. L.; SOUZA, C. K. de. Development of ternary polymeric films based on cassava starch, pea flour and green banana flour for food packaging. International Journal of Biological Macromolecules, v. 256, p. 1-12, 2024.| Biblioteca(s): Embrapa Florestas. |
|  |
| 5. | | ANDREAE, M. O.; ACEVEDO, O. C.; ARAUJO, A.; ARTAXO, P.; BARBOSA, C. G. G.; BARBOSA, H. M. J.; BRITO, J.; CARBONE, S.; CHI, X.; CINTRA, B. B. L.; SILVA, N. F. da; DIAS, N. L.; DIAS-JÚNIOR, C. Q.; DITAS, F.; DITZ, R.; GODOI, A. F. L.; GODOI, R. H. M.; HEIMANN, M.; HOFFMANN, T.; KESSELMEIER, J.; KÖNEMANN, T.; KRÜGER, M. L.; LAVRIC, J. V.; MANZI, A. O.; MORAN-ZULOAGA, D.; NÖLSCHER, A. C.; NOGUEIRA, D. S.; PIEDADE, M. T. F.; PÖHLKER, C.; PÖSCHL, U.; RIZZO, L. V.; RO, C.-U.; RUCKTESCHLER, N.; SÁ, L. D. A.; SÁ, M. D. O.; SALES, C. B.; SANTOS, R. M. N. dos; SATURNO, J.; SCHÖNGART, J.; SÖRGEL, M.; SOUZA, C. M. de; SOUZA, R. A. F. de; SU, H.; TARGHETTA, N.; TÓTA, J.; TREBS, I.; TRUMBORE, S.; EIJCK, A. van; WALTER, D.; WANG, Z.; WEBER, B.; WILLIAMS, J.; WINDERLICH, J.; WITTMANN, F.; WOLFF, S.; YÁÑEZ-SERRANO, A. M. The Amazon Tall Tower Observatory (ATTO) in the remote Amazon basin: overview of first results from ecosystem ecology, meteorology, trace gas, and aerosol measurements. Atmospheric Chemistry and Physics Discuss, v. 15, n. 18, p. 11599-11726, 2015.| Biblioteca(s): Embrapa Amazônia Oriental. |
|  |
| 6. | | ANDREAE, M. O.; ACEVEDO, O. C.; ARAUJO, A.; ARTAXO, P.; BARBOSA, C. G. G.; BARBOSA, H. M. J.; BRITO, J.; CARBONE, S.; CHI, X.; CINTRA, B. B. L.; SILVA, N. F. da; DIAS, N. L.; DIAS-JÚNIOR, C. Q.; DITAS, F.; DITZ, R.; GODOI, A. F. L.; GODOI, R. H. M.; HEIMANN, M.; HOFFMANN, T.; KESSELMEIER, J.; KÖNEMANN, T.; KRÜGER, M. L.; LAVRIC, J. V.; MANZI, A. O.; LOPES, A. P.; MARTINS, D. L.; MIKHAILOV, E. F.; MORAN-ZULOAGA, D.; NELSON, B. W.; NÖLSCHER, A. C.; NOGUEIRA, D. S.; PIEDADE, M. T. F.; PÖHLKER, C.; PÖSCHL, U.; QUESADA, C. A.; RIZZO, L. V.; RO, C.-U.; RUCKTESCHLER, N.; SÁ, L. D. A.; SÁ, M. de O.; SALES, C. B.; SANTOS, R. M. N. dos; SATURNO, J.; SCHÖNGART, J.; SÖRGEL, M.; SOUZA, C. M. de; SOUZA, R. A. F. de; SU, H.; TARGHETTA, N.; TÓTA, J.; TREBS, I.; TRUMBORE, S.; EIJCK, A. van; WALTER, D.; WANG, Z.; WEBER, B.; WILLIAMS, J.; WINDERLICH, J.; WITTMANN, F.; WOLFF, S.; YÁÑEZ-SERRANO, A. M. The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols. Atmospheric Chemistry and Physics, v. 15, n. 18, p. 10723-10776, 2015.| Biblioteca(s): Embrapa Amazônia Oriental. |
| |
| Registros recuperados : 6 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Cerrados. |
|
Data corrente: |
14/12/2020 |
|
Data da última atualização: |
14/12/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 2 |
|
Autoria: |
CASSOL, H. L. G.; ARAI, E.; SANO, E. E.; DUTRA, A. C.; HOFFMANN, T. B.; SHIMABUKURO, Y. E. |
|
Afiliação: |
EDSON EYJI SANO, CPAC. |
|
Título: |
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. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
Land, v. 9, n. 5, 2020. |
|
Idioma: |
Português |
|
Conteúdo: |
Abstract: This paper presents a new approach for rapidly assessing the extent of land use and land
cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of
fraction images derived from the linear spectral mixing model (LSMM) instead of original bands.
The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data
composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil,
and shade fraction images. These fraction images highlight the LULC components inside the pixels.
The other new idea is to reduce these time series to only six single bands representing the maximum
and standard deviation values of these fraction images in an annual composite, reducing the volume
of data to classify the main LULC classes. The whole image classification process was conducted in the
Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of
each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land
Imager (OLI) images and divided into training and validation datasets. The performance of the
method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was
92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same
validation data set showed 88% agreement with the LULC map made available by the Landsat-based
MapBiomas project. This proposed method has the potential to be used operationally to accurately
map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels. MenosAbstract: This paper presents a new approach for rapidly assessing the extent of land use and land
cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of
fraction images derived from the linear spectral mixing model (LSMM) instead of original bands.
The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data
composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil,
and shade fraction images. These fraction images highlight the LULC components inside the pixels.
The other new idea is to reduce these time series to only six single bands representing the maximum
and standard deviation values of these fraction images in an annual composite, reducing the volume
of data to classify the main LULC classes. The whole image classification process was conducted in the
Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of
each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land
Imager (OLI) images and divided into training and validation datasets. The performance of the
method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was
92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same
validation data set showed 88% agreement with the LULC map made available by the Landsat-based
MapBiomas project. This proposed method h... Mostrar Tudo |
|
Palavras-Chave: |
Computação em nuvem; Desmistura espectral; Mato Grosso. |
|
Thesagro: |
Sensoriamento Remoto; Uso da Terra. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02433naa a2200241 a 4500
001 2128073
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
100 1 $aCASSOL, H. L. G.
245 $aMaximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil.$h[electronic resource]
260 $c2020
520 $aAbstract: This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels.
650 $aSensoriamento Remoto
650 $aUso da Terra
653 $aComputação em nuvem
653 $aDesmistura espectral
653 $aMato Grosso
700 1 $aARAI, E.
700 1 $aSANO, E. E.
700 1 $aDUTRA, A. C.
700 1 $aHOFFMANN, T. B.
700 1 $aSHIMABUKURO, Y. E.
773 $tLand$gv. 9, n. 5, 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Cerrados (CPAC) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
