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Author(s):
Jennifer L. Rooker Jensen, Karen S. Humes, Tamara Conner, Christopher Jason Williams, John DeGroot
Year Published:

Cataloging Information

Topic(s):
Fuels Inventory & Monitoring
Fuels
Ecosystem(s):
Montane wet mixed-conifer forest, Montane dry mixed-conifer forest, Ponderosa pine woodland/savanna

NRFSN number: 8254
FRAMES RCS number: 9760
Record updated:

Although lidar data are widely available from commercial contractors, operational use in North America is still limited by both cost and the uncertainty of large-scale application and associated model accuracy issues. We analyzed whether small-footprint lidar data obtained from five noncontiguous geographic areas with varying species and structural composition, silvicultural practices, and topography could be used in a single regression model to produce accurate estimates of commonly obtained forest inventory attributes on the Nez Perce Reservation in northern Idaho, USA. Lidar-derived height metrics were used as predictor variables in a best-subset multiple linear regression procedure to determine whether a suite of stand inventory variables could be accurately estimated. Empirical relationships between lidar-derived height metrics and field-measured dependent variables were developed with training data and acceptable models validated with an independent subset. Models were then fit with all data, resulting in coefficients of determination and root mean square errors (respectively) for seven biophysical characteristics, including maximum canopy height (0.91, 3.03 m), mean canopy height (0.79, 2.64 m), quadratic mean DBH (0.61, 6.31 cm), total basal area (0.91, 2.99 m^2/ha), ellipsoidal crown closure (0.80, 0.08%), total wood volume (0.93, 24.65 m^3/ha), and large saw-wood volume (0.75, 28.76 m^3/ha). Although these regression models cannot be generalized to other sites without additional testing, the results obtained in this study suggest that for these types of mixed-conifer forests, some biophysical characteristics can be adequately estimated using a single regression model over stands with highly variable structural characteristics and topography.

Citation

Rooker Jensen, Jennifer L.; Humes, Karen S.; Conner, Tamara; Williams, Christopher J.; DeGroot, John. 2006. Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar. Canadian Journal of Forest Research. 36(5): 1129-1138.

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