Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis

Jonathan Asher Greenberg; Solomon Z. Dobrowski; Susan L. Ustin

doi:10.1016/j.rse.2005.02.015

Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis

Jonathan Asher Greenberg, Solomon Z. Dobrowski, Susan L. Ustin

W. A. Franke College of Forestry and Conservation

Research output: Contribution to journal › Article › peer-review

70 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	15-25
Number of pages	11
Journal	Remote Sensing of Environment
Volume	97
Issue number	1
DOIs	https://doi.org/10.1016/j.rse.2005.02.015
State	Published - Jul 15 2005

Funding

This project was funded by the Tahoe Regional Planning Agency (TRPA) and the Forest Service (USFS). Special thanks to Mike Vollmer (TRPA), Shane Romsos (USFS), Hugh Safford (USFS), George Scheer, Lawrence Ross, Shawn Kefauver, Jaylee Tuil, Missy Voight, Ben Addlestone, Jennifer Riddell, Dylan Burge, Shannon Murphy and Jessica Gorin.

Funders	Funder number
Tahoe Regional Planning Agency
U.S. Forest Service-Retired

Keywords

Allometry
Biomass
Crown area
DBH
Forestry
Hyperspatial imagery
IKONOS
Lake Tahoe Basin
Shadow
Stem density
Trees
Vectorization

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10.1016/j.rse.2005.02.015

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title = "Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis",

abstract = "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.",

keywords = "Allometry, Biomass, Crown area, DBH, Forestry, Hyperspatial imagery, IKONOS, Lake Tahoe Basin, Shadow, Stem density, Trees, Vectorization",

author = "Greenberg, {Jonathan Asher} and Dobrowski, {Solomon Z.} and Ustin, {Susan L.}",

note = "Funding Information: This project was funded by the Tahoe Regional Planning Agency (TRPA) and the Forest Service (USFS). Special thanks to Mike Vollmer (TRPA), Shane Romsos (USFS), Hugh Safford (USFS), George Scheer, Lawrence Ross, Shawn Kefauver, Jaylee Tuil, Missy Voight, Ben Addlestone, Jennifer Riddell, Dylan Burge, Shannon Murphy and Jessica Gorin.",

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AU - Dobrowski, Solomon Z.

AU - Ustin, Susan L.

N1 - Funding Information: This project was funded by the Tahoe Regional Planning Agency (TRPA) and the Forest Service (USFS). Special thanks to Mike Vollmer (TRPA), Shane Romsos (USFS), Hugh Safford (USFS), George Scheer, Lawrence Ross, Shawn Kefauver, Jaylee Tuil, Missy Voight, Ben Addlestone, Jennifer Riddell, Dylan Burge, Shannon Murphy and Jessica Gorin.

PY - 2005/7/15

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N2 - 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.

AB - 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.

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KW - Forestry

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KW - Stem density

KW - Trees

KW - Vectorization

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Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis

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