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

Forest Ecology and Management
By: , and 

Links

Abstract

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 r2 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.

Study Area

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests
Series title Forest Ecology and Management
DOI 10.1016/j.foreco.2004.12.001
Volume 208
Issue 1-3
Year Published 2005
Language English
Publisher Elsevier
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 21 p.
First page 189
Last page 209
Country United States
State Colorado
Other Geospatial Front Range
Online Only (Y/N) N
Additional Online Files (Y/N) N