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Registros recuperados : 42 | |
25. | | LU, D.; BATISTELLA, M.; ALVES, D. HETRICK, S.; MORAN, E. Mapping of Fractional Forest Cover in Rondonia, Brazil with a Combination of Terra MODIS and Landsat TM Images. In: LBA_ECO Science Team Meeting, 11., 2007, Salvador. Resumos... Salvador: LBA, 2007. p. 31-32. Biblioteca(s): Embrapa Territorial. |
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31. | | LU, D.; BATISTELLA, M.; MORAN, E. F.; MIRANDA, E. E. D. A comparative study of Terra ASTER, Landsat TM, and SPT HRG data for land cover classification in the Brazilian Amazon. In: WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS (WMSCI2005), 9th, 2005, Orlando - Florida. Proceedings... Orlando: International Institute of Informatics and Systemics (IIS), 2005. v. 8, p. 411-416. folhas avulsas Biblioteca(s): Embrapa Territorial. |
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32. | | 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. |
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33. | | BATISTELLA, M.; ALVES, D.; LU, D.; MORAN, E. F.; BRONDIZIO, E. S.; D'ANTONA, A. From the Landscape to the region: scaling up approaches in human and physical dimensions of land-use and land-cover change in the Amazon. In: LBA-ECO SCIENCE TEAM MEETING, 10., 2006. Brasília, DF. Abstracts... Brasília: LBA-ECO, 2006. 1 p. Biblioteca(s): Embrapa Territorial. |
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34. | | LU, D.; BATISTELLA, M.; MORAN, E.; HETRICK, S.; ALVES, D.; BRONDIZIO, E. Fractional forest cover mapping in the Brazilian Amazon with a combination of MODIS and TM images. International Journal of Remote Sensing, v. 32, n. 22, p. 7131-7149, 2011. Biblioteca(s): Embrapa Territorial. |
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36. | | 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. |
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37. | | LU, D.; CHEN, Q.; WANG, G.; MORAN, E.; BATISTELLA, M.; ZHANG, M.; LAURIN, G. V.; SAAH, D. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forestry Research, v. 2012. p. 16, 2012 16 p. Biblioteca(s): Embrapa Territorial. |
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38. | | FENG, Y.; LU, D.; CHEN, Q.; KELLER, M.; MORAN, E.; SANTOS, M. N. dos S.; BOLFE, E. L.; BATISTELLA, M. Examining effective use of data source and modeling algorithms for improving biomass estimation in a moist tropical forest of the brazilian Amazon. International Journal of Digital Earth, London, 2017. Biblioteca(s): Embrapa Unidades Centrais. |
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39. | | 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. |
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40. | | CHEN, Q.; LU, D.; KELLER, M.; SANTOS, M. N. DOS; BOLFE, E. L.; FENG, Y.; WANG, C. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sensing, v. 8, n. 1, p. 1-17, 2015. Biblioteca(s): Embrapa Territorial. |
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Registros recuperados : 42 | |
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Registro Completo
Biblioteca(s): |
Embrapa Territorial. |
Data corrente: |
15/09/2014 |
Data da última atualização: |
15/09/2014 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
Internacional - A |
Autoria: |
LU, D.; LI, G.; VALLADARES, G. S.; BATISTELLA, M. |
Afiliação: |
DENGSHENG LU, INDIANA UNIVERSITY; G. LI, INDIANA STATE UNIVERSITY; GUSTAVO S. VALLADARES, CNPM; MATEUS BATISTELLA, CNPM. |
Título: |
Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using rusle, remote sensing and GIS. |
Ano de publicação: |
2004 |
Fonte/Imprenta: |
Land Degradation & Development, v. 15, p. 499-512, 2004. |
DOI: |
10.1002/ldr.634 |
Idioma: |
Português |
Conteúdo: |
This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall?runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0.2, LS values of less than 2.5, and C values of less than 0.25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. |
Palavras-Chave: |
Brazilian Amazonia; GIS; RUSLE; Soil erosion risk. |
Thesaurus NAL: |
Remote sensing. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/108416/1/4024.pdf
|
Marc: |
LEADER 02146naa a2200229 a 4500 001 1994981 005 2014-09-15 008 2004 bl uuuu u00u1 u #d 024 7 $a10.1002/ldr.634$2DOI 100 1 $aLU, D. 245 $aMapping soil erosion risk in Rondônia, Brazilian Amazonia$busing rusle, remote sensing and GIS.$h[electronic resource] 260 $c2004 520 $aThis article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall?runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0.2, LS values of less than 2.5, and C values of less than 0.25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. 650 $aRemote sensing 653 $aBrazilian Amazonia 653 $aGIS 653 $aRUSLE 653 $aSoil erosion risk 700 1 $aLI, G. 700 1 $aVALLADARES, G. S. 700 1 $aBATISTELLA, M. 773 $tLand Degradation & Development$gv. 15, p. 499-512, 2004.
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