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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
11/04/2012 |
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Data da última atualização: |
18/01/2013 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LU, D.; CHEN, Q.; WANG, G.; MORAN, E.; BATISTELLA, M.; ZHANG, M.; LAURIN, G. V.; SAAH, D. |
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Afiliação: |
DENGSHENG LU, INDIANA UNIVERSITY; QI CHEN, ZHEJIANG A&F UNIVERSITY; GUANGXING WANG, SOUTHERN ILLINOIS UNIVERSITY AT CARBONDALE; EMILIO MORAN, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; MAOZHEN ZHANG, ZHEJIANG A&F UNIVERSITY; GAIA VAGLIO LAURIN, UNIVERSITY OF TOR VERGATA; DAVID SAAH, SPATIAL INFORMATICS GROUP. |
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Título: |
Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
International Journal of Forestry Research, v. 2012. p. 16, 2012 |
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Páginas: |
16 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data. |
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Thesagro: |
Biomassa. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/57333/1/MateusFR.pdf
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Marc: |
LEADER 01955naa a2200229 a 4500
001 1922003
005 2013-01-18
008 2012 bl uuuu u00u1 u #d
100 1 $aLU, D.
245 $aAboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.
260 $c2012
300 $a16 p.
520 $aLandsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.
650 $aBiomassa
700 1 $aCHEN, Q.
700 1 $aWANG, G.
700 1 $aMORAN, E.
700 1 $aBATISTELLA, M.
700 1 $aZHANG, M.
700 1 $aLAURIN, G. V.
700 1 $aSAAH, D.
773 $tInternational Journal of Forestry Research$gv. 2012. p. 16, 2012
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Embrapa Territorial (CNPM) |
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| 1. |  | 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.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
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