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International Journal of Forestry Research
Volume 2012, Article ID 436537, 16 pages
Research Article

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

1Anthropological Center for Training and Research on Global Environmental Change (ACT), Indiana University, Bloomington, IN 47405, USA
2Department of Geography, University of Hawaii at Manoa, 2424 Maile Way, Honolulu, HI 96822, USA
3Department of Geography and Environmental Resources, Southern Illinois University at Carbondale (SIUC), Carbondale, IL 62901, USA
4Embrapa Satellite Monitoring, Fazenda Chapadão, 13070-115 Campinas, SP, Brazil
5School of Environment and Resources, Zhejiang A&F University, Lin’An 311300, China
6Department of Computer, System and Production Engineering, University of Tor Vergata, 00133 Rome, Italy
7Spatial Informatics Group, LLC, 3248 Northampton Ct., Pleasanton, CA 94588, USA

Received 1 October 2011; Revised 6 January 2012; Accepted 20 January 2012

Academic Editor: Jingxin Wang

Copyright © 2012 Dengsheng Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.