Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis

We present a novel approach to generating regional scale aboveground biomass estimates for tree species of the Lake Tahoe Basin using hyperspatial (< 1 m2 ground resolution) remote sensing imagery. Tree crown shadows were identified and delineated as individual polygons. The area of shadowed vegetation for each tree was related to two tree structural parameters, diameter-at-breast height (DBH) and crown area. We found we could detect DBH and crown area with reasonable accuracy (field measured to image derived cross correlation results were 0.67 and 0.77 for DBH and crown area, respectively). Furthermore, the counts of the delineated polygons in a region generated overstory stem densities validated to manually photointerpreted stem densities (photointerpreted vs. image-derived stem densities correlation was 0.87). We demonstrate with accurate classification maps and allometric equations relating DBH or crown area to biomass, that these crown-level parameters can be used to generate regional scale biomass estimates without the signal saturation common to coarse-scale optical and RADAR sensors.