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Estimating forest canopy bulk density using six indirect methods

Author(s): Robert E. Keane, Elizabeth D. Reinhardt, Joe H. Scott, Kathy L. Gray, James J. Reardon
Year Published: 2005

Canopy bulk density (CBD) is an important crown characteristic needed to predict crown fire spread, yet it is difficult to measure in the field. Presented here is a comprehensive research effort to evaluate six indirect sampling techniques for estimating CBD. As reference data, detailed crown fuel biomass measurements were taken on each tree within fixed-area plots located in five important conifers types in the western United States, using destructive sampling following a series of four sampling stages to measure the vertical and horizontal distribution of canopy biomass. The six ground-based indirect measurement techniques used these instruments: LI-COR LAI-2000, AccuPAR ceptometer,CID digital plant canopy imager, hemispherical photography, spherical densiometer, and point sampling. These techniques were compared with four aggregations of crown biomass to compute CBD: foliage only, foliage and small branchwood, foliage and all branchwood (no stems), and all canopy biomass components. Most techniques had the best performance when all canopy biomass components except stems were used. Performance dropped only slightly when the foliage and small branchwood canopy biomass aggregation (best approximates fuels available for crown fires) was employed. The LAI-2000, hemispherical photography, and CID plant canopy imager performed best. Regression equat ons that predict CBD from gap fraction are presented for all six techniques.

Citation: Keane, Robert E.; Reinhardt, Elizabeth D.; Scott, Joe H.; Gray, Kathy L.; Reardon, James. 2005. Estimating forest canopy bulk density using six indirect methods. Canadian Journal of Forest Research. 35(3): 724-739.
Topic(s): Fuels Inventory & Monitoring, Fuels
Ecosystem(s): Montane wet mixed-conifer forest, Montane dry mixed-conifer forest, Ponderosa pine woodland/savanna
Document Type: Book or Chapter or Journal Article
NRFSN number: 7952
FRAMES RCS number: 4443
Record updated: May 24, 2018