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127. | | PALMIERI, R.; FARIA, V.; CERIGNONI, F.; BATISTELLA, M. The impact of forest concessions on deforestation in protected areas of Brazilian Amazonia. Pesquisa Florestal Brasileira, v. 39, e201902043, p. 92, 2019. Título equivalente: Concessões florestais afetam dinâmica de desmatamento nas áreas protegidas na Amazônia brasileira. Special issue. Abstracts of the XXV IUFRO World Congress, 2019, Curitiba. Biblioteca(s): Embrapa Agricultura Digital. |
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133. | | ZONTA, M.; MIRANDA, J. R.; BATISTELLA, M.; JUNGUEIRA, C. B. Carta de uso atual das terras e cobertura vegetal do Município de Jaguariúna, São Paulo. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 8., 1996, Salvador. Anais... São José dos Campos: INPE, 1996; Equador: SELPER, 1996. 2 p. folhas avulsas Biblioteca(s): Embrapa Territorial. |
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Registro Completo
Biblioteca(s): |
Embrapa Territorial. |
Data corrente: |
05/12/2014 |
Data da última atualização: |
09/12/2014 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M. |
Afiliação: |
DENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
Título: |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
International Journal of Remote Sensing, v. 35, n. 24, p. 8188-8207, 2014. |
ISBN: |
0143-1161 |
DOI: |
10.1080/01431161.2014.980920 |
Idioma: |
Inglês |
Conteúdo: |
Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m. MenosTexture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover ty... Mostrar Tudo |
Palavras-Chave: |
Advanced Land Observing Satellite; Land-cover classification; Landsat Thematic Mapper; Phased Array type L-band Synthetic Aperture Radar. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/113201/1/4208.pdf
|
Marc: |
LEADER 02554naa a2200241 a 4500 001 2001846 005 2014-12-09 008 2014 bl uuuu u00u1 u #d 022 $a0143-1161 024 7 $a10.1080/01431161.2014.980920$2DOI 100 1 $aLU, D. 245 $aThe roles of textural images in improving land-cover classification in the Brazilian Amazon.$h[electronic resource] 260 $c2014 520 $aTexture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m. 653 $aAdvanced Land Observing Satellite 653 $aLand-cover classification 653 $aLandsat Thematic Mapper 653 $aPhased Array type L-band Synthetic Aperture Radar 700 1 $aLI, G. 700 1 $aMORAN, E. 700 1 $aDUTRA, L. 700 1 $aBATISTELLA, M. 773 $tInternational Journal of Remote Sensing$gv. 35, n. 24, p. 8188-8207, 2014.
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