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 Research Station
Help
 

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

View PDF (363 KB)

Title: Inference for lidar-assisted estimation of forest growing stock volume

Author: McRoberts, Ronald E.; Næsset, Erik; Gobakken, Terje.;

Date: 2013

Source: Remote Sensing of Environment. 128: 268-275.

Publication Series: Scientific Journal (JRNL)

Description: Estimates of growing stock volume are reported by the national forest inventories (NFI) of most countries and may serve as the basis for aboveground biomass and carbon estimates as required by an increasing number of international agreements. The probability-based (design-based) statistical estimators traditionally used by NFIs to calculate estimates are generally unbiased and entail only limited computational complexity. However, these estimators often do not produce sufficiently precise estimates for areas with small sample sizes. Model-based estimators may overcome this disadvantage, but they also may be biased and estimation of variances may be computationally intensive. For a minor region within Hedmark County, Norway, the study objective was to compare estimates of mean forest growing stock volume per unit area obtained using probability- and model-based estimators. Three of the estimators rely to varying degrees on maps that were constructed using a nonlinear logistic regression model, forest inventory data, and lidar data. For model-based estimators, methods for evaluating quality of fit of the models and reducing the computational intensity were also investigated. Three conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the data; estimators enhanced using maps produced greater precision than estimates based on only the plot observations; and third, model-based synthetic estimators benefit from sample sizes for larger areas when applied to smaller subsets of the larger areas.

Keywords: Nonlinear logistic regression model, stratified estimator, model-assisted estimator, model-based estimator

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.
  • This publication may be available in hard copy. Check the Northern Research Station web site to request a printed copy of this publication.
  • Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.

XML: View XML

Citation:


McRoberts, Ronald E.; Næsset, Erik; Gobakken, Terje. 2013. Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment. 128: 268-275.

 


 [ 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.