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

(859 KB)

Title: Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

Author: Hudak, Andrew T.; Crookston, Nicholas L.; Evans, Jeffrey S.; Falkowski, Michael K.; Smith, Alistair M. S.; Gessler, Paul E.; Morgan, Penelope

Date: 2006

Source: Canadian Journal of Remote Sensing. 32(2): 126-­138.

Publication Series: Scientific Journal (JRNL)

Description: We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived from discrete-return lidar data (2 m post spacing), advanced land imager (ALI) multispectral (30 m) and panchromatic (10 m) data, or geographic X, Y, and Z location. In general, the lidar-derived variables had greater utility than the ALI variables for predicting the response variables, especially basal area. The variables most useful for predicting basal area were lidar height variables, followed by lidar intensity; those most useful for predicting tree density were lidar canopy cover variables, again followed by lidar intensity. The best integrated models selected via a best-subsets procedure explained ~90% of variance in both response variables. Natural-logarithm-transformed response variables were modeled. Predictions were then transformed from the natural logarithm scale back to the natural scale, corrected for transformation bias, and mapped across the two study areas. This study demonstrates that fundamental forest structure attributes can be modeled to acceptable accuracy and mapped with currently available remote sensing technologies.

Dataset for this publication

Keywords: regression modeling and mapping, lidar and multispectral data, discrete-return light detection and ranging, multispectral satellite imagery, basal area, tree density, advanced land imager, ALI

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.; Crookston, Nicholas L.; Evans, Jeffrey S.; Falkowski, Michael K.; Smith, Alistair M. S.; Gessler, Paul E.; Morgan, Penelope 2006. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data. Canadian Journal of Remote Sensing. 32(2): 126-­138.

 


 [ 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.