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Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates
- Source :
- International Journal of Forestry Research, Vol 2012 (2012), Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), instacron:EMBRAPA
- Publication Year :
- 2012
- Publisher :
- Hindawi Limited, 2012.
-
Abstract
- 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.
- Subjects :
- Data stream
Estimation
Secondary succession
Article Subject
lcsh:QH1-199.5
Reference data (financial markets)
Biomass
Forestry
Plant Science
lcsh:General. Including nature conservation, geographical distribution
Lidar
Thematic Mapper
Biomassa
Environmental science
lcsh:SD1-669.5
lcsh:Forestry
Ecology, Evolution, Behavior and Systematics
Uncertainty analysis
Nature and Landscape Conservation
Remote sensing
Subjects
Details
- Language :
- English
- ISSN :
- 16879376 and 16879368
- Volume :
- 2012
- Database :
- OpenAIRE
- Journal :
- International Journal of Forestry Research
- Accession number :
- edsair.doi.dedup.....c0ad33b46434d56518c74385d771e1a3