Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern
Help
 

GeoTreesearch


Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

(2.8 MB)

Title: Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys

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

Date: 2012

Source: Remote Sensing of Environment. 123: 25-40.

Publication Series: Journal/Magazine Article (JRNL)

Description: 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 ~ 20,000 ha 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 4.1 Mg/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 -5.7 Mg/ha/yr, and -2.3 Mg/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.

Keywords: discrete return LiDAR, multi-temporal, aboveground carbon, mixed conifer forest, random forest algorithm, imputation, biomass change, Carbon Measuring Reporting and Verification (MRV)

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

XML: View XML

Citation:


Hudak, Andrew T.; Strand, Eva K.; Vierling, Lee A.; Byrne, John C.; Eitel, Jan U. H.; Martinuzzi, Sebastian; Falkowski, Michael J. 2012. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sensing of Environment. 123: 25-40.

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.