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 m 2 ground resolution) remote sensing imagery. Tree crown shadows were identified and delineated as individual polygons. The area of shadowed veg...

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Bibliographic Details
Published in:Remote sensing of environment 2005-07, Vol.97 (1), p.15-25
Main Authors: Greenberg, Jonathan Asher, Dobrowski, Solomon Z., Ustin, Susan L.
Format: Article
Language:English
Subjects:
Allometry
Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Biomass
Crown area
DBH
Earth sciences
Earth, ocean, space
Exact sciences and technology
Forestry
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Hyperspatial imagery
IKONOS
Internal geophysics
Lake Tahoe Basin
Shadow
Stem density
Teledetection and vegetation maps
Trees
Vectorization
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Summary:We present a novel approach to generating regional scale aboveground biomass estimates for tree species of the Lake Tahoe Basin using hyperspatial (< 1 m 2 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.
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2005.02.015