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    Author(s): Andrew T. HudakNicholas L. CrookstonJeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan
    Date: 2006
    Source: Canadian Journal of Remote Sensing. 32(2): 126-­138.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (859.5 KB)

    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.

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    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.

    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

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