Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests

The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially...

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Bibliographic Details
Published in:	Forest ecology and management Vol. 208; no. 1; pp. 189 - 209
Main Authors:	Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R., Stoker, J.M.
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 05-04-2005 Elsevier
Subjects:	Animal and plant ecology Animal, plant and microbial ecology Biological and medical sciences Carbon stocks Fire-behavior-model input layers Fundamental and applied biological sciences. Psychology Information–theoretic techniques Lidar Low density forests Pinus ponderosa Stand structure Synecology Terrestrial ecosystems United States North America Colorado America USA, Colorado Fire-behavior-model input layers Carbon stocks Lidar Information–theoretic techniques Low density forests Stand structure Forests Carbon stocks: Fire-behavior-model input layers Information-theoretic techniques Biomass Linear model Remote sensing Pinus ponderosa Gymnospermae Fire Foliage Coniferales Spermatophyta Behavior Stock Canopy(vegetation)
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Summary:	The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially explicit estimates of stand structural characteristics. Some of these characteristics can be used as inputs to fire behavior models, increasing our understanding of the effect of fuels on fire behavior. Others provide estimates of carbon stocks, allowing us to quantify the carbon consequences of fire. Our objective was to use discrete-return lidar to estimate such variables, including stand height, total aboveground biomass, foliage biomass, basal area, tree density, canopy base height and canopy bulk density. We developed 39 metrics from the lidar data, and used them in limited combinations in regression models, which we fit to field estimates of the stand structural variables. We used an information–theoretic approach to select the best model for each variable, and to select the subset of lidar metrics with most predictive potential. Observed versus predicted values of stand structure variables were highly correlated, with r 2 ranging from 57% to 87%. The most parsimonious linear models for the biomass structure variables, based on a restricted dataset, explained between 35% and 58% of the observed variability. Our results provide us with useful estimates of stand height, total aboveground biomass, foliage biomass and basal area. There is promise for using this sensor to estimate tree density, canopy base height and canopy bulk density, though more research is needed to generate robust relationships. We selected 14 lidar metrics that showed the most potential as predictors of stand structure. We suggest that the focus of future lidar studies should broaden to include low density forests, particularly systems where the vertical structure of the canopy is important, such as fire prone forests.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0378-1127 1872-7042
DOI:	10.1016/j.foreco.2004.12.001