Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys

Andrew T. Hudak, Eva K. Strand, Lee A. Vierling, John C. Byrne, Jan U.H. Eitel, Sebastián Martinuzzi, Michael J. Falkowski

Research output: Contribution to journalArticlepeer-review

222 Scopus citations


Sound forest policy and management decisions to mitigate rising atmospheric CO2 depend upon accurate methodologies to quantify forest carbon pools and fluxes over large tracts of land. LiDAR remote sensing is a rapidly evolving technology for quantifying aboveground biomass and thereby carbon pools; however, little work has evaluated the efficacy of repeat LiDAR measures for spatially monitoring aboveground carbon pools through time. Our study objective was therefore to evaluate the use of discrete return airborne LiDAR for quantifying biomass change and carbon flux from repeat field and LiDAR surveys. We collected LiDAR data in 2003 and 2009 across ~20,000ha of an actively managed, mixed conifer forest landscape in northern Idaho. The Random Forest machine learning algorithm was used to impute aboveground biomass pools of trees, saplings, shrubs, herbaceous plants, coarse and fine woody debris, litter, and duff using field-based forest inventory data and metrics derived from the LiDAR collections. Separate predictive tree aboveground biomass models were developed from the 2003 and 2009 field and LiDAR data, and biomass change was estimated at the plot, pixel, and landscape levels by subtracting 2003 predictions from 2009 predictions. Traditional stand exam data were used to independently validate 2003 and 2009 tree aboveground biomass predictions and tree aboveground biomass change estimates at the stand level. Over this 6-year period, we found a mean increase in tree aboveground biomass due to forest growth across the non-harvested portions of 4.1Mg/ha/yr. We found that 26.3% of the landscape had been harvested during this time period which outweighed growth at the landscape level, resulting in a net tree aboveground biomass change of -5.7Mg/ha/yr, and -2.3Mg/ha/yr in total aboveground carbon, summed across all the aboveground biomass pools. Change in aboveground biomass was related to forest successional status; younger stands gained two- to three-fold less biomass than did more mature stands. This result suggests that even the most mature forest stands are valuable carbon sinks, and implies that forest management decisions that include longer harvest rotation cycles are likely to favor higher levels of aboveground carbon storage in this system. A 30-fold difference in LiDAR sampling density between the 2003 and 2009 collections did not affect plot-scale biomass estimation. These results suggest that repeat LiDAR surveys are useful for accurately quantifying high resolution, spatially explicit biomass and carbon dynamics in conifer forests.

Original languageEnglish (US)
Pages (from-to)25-40
Number of pages16
JournalRemote Sensing of Environment
StatePublished - Aug 2012

Bibliographical note

Funding Information:
Primary funding to the University of Idaho for this study was provided by the Department of Energy (DOE) Big Sky C Sequestration Partnership with Montana State and Washington State Universities , with supplemental funding from the U.S. Forest Service Rocky Mountain Research Station through Joint Venture Agreement 08-JV-11221633-159 . Cliff Todd, Sean Taylor, Brendon Newman, and John Kyle Parker-Mcglynn did the bulk of the 2009 fieldwork. We thank Linda Tedrow and Patrick Adam for LiDAR processing help, and David Brown and Steven Mulkey for helpful discussions. We thank Halli Hemingway from Bennett Lumber Products, Inc., Brant Steigers and Rob Taylor from Potlatch Forest Holdings, Inc., and Brian Austin from the University of Idaho Experimental Forest, for providing stand exam data used for stand-level validation. Finally, we thank Ralph Dubayah and two anonymous reviewers for their helpful suggestions on an earlier draft of the manuscript.


  • Aboveground carbon
  • Biomass change
  • Carbon Measuring Reporting and Verification (MRV)
  • Discrete return LiDAR
  • Imputation
  • Mixed conifer forest
  • Multi-temporal
  • Random forest algorithm


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