Skip to page content
USDA Forest Service

Research & Development Treesearch

Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help - We Participate  Government Made Easy

Global Forest Information Service

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

(202) 205-8333

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

Publication Information

View PDF (69 KB)

Title: A systematic framework for Monte Carlo simulation of remote sensing errors map in carbon assessments

Author: Healey, S.; Patterson, P.; Urbanski, S.;

Date: 2014

Source: The International Forestry Review. 16(5): 196.

Publication Series: Abstract

Description: Remotely sensed observations can provide unique perspective on how management and natural disturbance affect carbon stocks in forests. However, integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential remote sensing errors as maps are used as inputs in ecosystem carbon assessment. We present a generic approach for coordinating the MC alteration of map values so that specific levels of both pixel-level and map-wide systematic error may be simulated. This approach is based on constructing systems of linear equations and inequalities which incorporate results of map validation exercises. Solution of these systems provides probability functions capable of simulating different levels of error. We illustrate this approach, using error assessments calibrated by the United States (U.S.) national forest inventory data, in an assessment of the effects of wildfire and harvest on carbon storage over 20 years on a forested landscape in the western U.S. This assessment utilized the Forest Carbon Management Framework approach, which is being implemented across the U.S. National Forest System. Results showed that systematic map errors can contribute significant uncertainty in MC analysis, but that impacts of fire and harvest on landscape-level carbon storage can nevertheless be clearly identified and differentiated using remotely sensed maps.

Keywords: Monte Carlo (MC) simulations, remote sensing, carbon stocks

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.
  • You may send email to to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)



Healey, S.; Patterson, P.; Urbanski, S. 2014. A systematic framework for Monte Carlo simulation of remote sensing errors map in carbon assessments. The International Forestry Review. 16(5): 196.


 [ 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.