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Registro Completo |
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
21/11/1996 |
Data da última atualização: |
25/08/2022 |
Tipo da produção científica: |
Nota Técnica/Nota Científica |
Autoria: |
PANIZZI, A. R.; SLANSKY JUNIOR, F. |
Afiliação: |
Embrapa-Soja. Londrina, PR. |
Título: |
Piezodorus guildinii (Hemiptera: Pentatomidae) - an unusual host of the tachinid Trichopoda pennipes. |
Ano de publicação: |
1985 |
Fonte/Imprenta: |
Florida Entomologist, v. 68, n. 3, p. 485-486, Sep. 1985. |
Idioma: |
Inglês |
Palavras-Chave: |
Agroecosistema; EUA; Insect parasitoids; Inseto prarasita; Soybean agroecosystems; Trichopoda pennipes; USA. |
Thesagro: |
Piezodorus Guildinii; Soja. |
Thesaurus Nal: |
Florida. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 00720naa a2200241 a 4500 001 1458063 005 2022-08-25 008 1985 bl uuuu u00u1 u #d 100 1 $aPANIZZI, A. R. 245 $aPiezodorus guildinii (Hemiptera$bPentatomidae) - an unusual host of the tachinid Trichopoda pennipes.$h[electronic resource] 260 $c1985 650 $aFlorida 650 $aPiezodorus Guildinii 650 $aSoja 653 $aAgroecosistema 653 $aEUA 653 $aInsect parasitoids 653 $aInseto prarasita 653 $aSoybean agroecosystems 653 $aTrichopoda pennipes 653 $aUSA 700 1 $aSLANSKY JUNIOR, F. 773 $tFlorida Entomologist$gv. 68, n. 3, p. 485-486, Sep. 1985.
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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.
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