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

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

Research output: Contribution to journalArticlepeer-review

165 Scopus citations


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 geographicX, Y, andZlocation. 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.

Original languageEnglish (US)
Pages (from-to)126-138
Number of pages13
JournalCanadian Journal of Remote Sensing
Issue number2
StatePublished - Apr 2006

Bibliographical note

Funding Information:
This analysis and paper are a product of the Sustainable Forestry component of Agenda 2020, a joint effort of the US Department of Agriculture Forest Service Research and Development and the American Forest and Paper Association. Data collection was supported in part by funds provided by the Rocky Mountain Research Station, Forest Service, and US Department of Agriculture, to the University of Idaho through two Joint Venture agreements (03-JV-11222048-140 and 03-JV-11222063-231), with additional funds for analysis and reporting coming from two others (04-JV-11222063-299 and 05-JV-11221663-014). Research partners included Potlatch, Inc. and Bennett Lumber Products, Inc. Curtis Kvamme, K.C. Murdock, Jacob Young, Tessa Jones, Jennifer Clawson, Bryn Parker, Kasey Prestwich, Stephanie Jenkins, Kris Poncek, and Jeri Stewart assisted in the field. We thank Andrew Lister, William Wykoff, Renate Bush, and Rudy King for their helpful review comments.


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