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Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2
Electrical & Computer Engineering, College of Engineering, University of Massachusetts, Amherst, MA 01002, USA
3
School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(6), 3007-3036; https://doi.org/10.3390/rs5063007
Received: 20 April 2013 / Revised: 30 May 2013 / Accepted: 4 June 2013 / Published: 14 June 2013
Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements. View Full-Text
Keywords: biomass; allometry; uncertainty; Harvard forest; Howland forest biomass; allometry; uncertainty; Harvard forest; Howland forest
MDPI and ACS Style

Ahmed, R.; Siqueira, P.; Hensley, S.; Bergen, K. Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing. Remote Sens. 2013, 5, 3007-3036.

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