| |
|
|
| Registros recuperados : 13 | |
| 9. |  | LI, G.; LU, D.; MORAN, E.; CALVI, M. F.; DUTRA, L. V.; BATISTELLA, M. Examining deforestation and agropasture dynamics along the Brazilian TransAmazon Highway using multitemporal Landsat imagery. GIScience & Remote Sensing, v. 56, n. 2, p. 161-183, 2019.| Biblioteca(s): Embrapa Agricultura Digital. |
|    |
| 11. | | CAK, A. D.; MORAN, E. F.; FIGUEIREDO, R. de O.; LU, D.; LI, G.; HETRICK, S. Urbanization and small household agricultural land use choices in the Brazilian Amazon and the role for the water chemistry of small streams. Journal of Land Use Science, Abingdon, v. 11, n. 2, p. 203-221, 2016.| Biblioteca(s): Embrapa Meio Ambiente. |
| |
| 12. | | AN, F.; LI, G.; LI, Q. X.; LI, K.; CARVALHO, L. J. C. B.; OU, W.; CHEN, S. The comparatively proteomic analysis in response to cold stress in cassava plantlets. Plant Molecular Biology Reporter, v. 34, n. 6, p. 1095-1110, 2016.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
|  |
| 13. | | LU, D.; BATISTELLA, M.; LI, G.; MORAN, E.; HETRICK, S.; FREITAS, C. DA C.; SANT'ANNA, S. J. Land use/cover classification in the Brazilian Amazon using satellite images. Pesquisa Agropecuária Brasileira, Brasilia, DF, v. 47, n. 9, p. 1185-1208, set. 2012. p. 1185-1208.| Biblioteca(s): Embrapa Territorial; Embrapa Unidades Centrais. |
| |
| Registros recuperados : 13 | |
|
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Territorial (CNPM) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
