Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements

Curtis Edson,Michael G Wing

doi:10.3390/rs3112494

Abstract

Light Detection and Ranging (LiDAR) remote sensing has demonstrated potential in measuring forest biomass. We assessed the ability of LiDAR to accurately estimate forest total above ground biomass (TAGB) on an individual stem basis in a conifer forest in the US Pacific Northwest region using three different computer software programs and compared results to field measurements. Software programs included FUSION, TreeVaW, and watershed segmentation. To assess the accuracy of LiDAR TAGB estimation, stem counts and heights were analyzed. Differences between actual tree locations and LiDAR-derived tree locations using FUSION, TreeVaW, and watershed segmentation were 2.05 m (SD 1.67), 2.19 m (SD 1.83), and 2.31 m (SD 1.94), respectively, in forested plots. Tree height differences from field measured heights for FUSION, TreeVaW, and watershed segmentation were −0.09 m (SD 2.43), 0.28 m (SD 1.86), and 0.22 m (2.45) in forested plots; and 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively, in a plot containing young conifers. The TAGB comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall, LiDAR TAGB estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR TAGB underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons.

Highlights

Forest attribute inventory information and measurements are critical to forest management [1].Historically, forest inventories have focused on timber production [2] but recent inventories have concentrated on fuel biomass and carbon stores due to interest in bioenergy and carbon sequestration and concerns over global climate change [3,4,5]
When using a canopy height model (CHM), any vegetation below the dominant/co-dominant canopy is likely to fall below the CHM surface, we experimented with higher resolutions to determine if smaller trees could be discernible within the Light Detection and Ranging (LiDAR) data
We presented a comparison of FUSION, TreeVaW, and watershed segmentation tree extraction methods in clearcut, even-age, uneven-age, and old growth forest plots using an extensive tree inventory

Summary

Introduction

Forest attribute inventory information and measurements are critical to forest management [1].Historically, forest inventories have focused on timber production [2] but recent inventories have concentrated on fuel biomass and carbon stores due to interest in bioenergy and carbon sequestration and concerns over global climate change [3,4,5]. Biomass is the measurement of plant material mass per unit area. Biomass measurement is sometimes limited to living plant material, but based on the slow deterioration of woody vegetation; the measurement sometimes includes dead material. Ground biomass is the “mass of live or dead organic matter” [6]. The unit of measure is commonly g/m2 or kg/ha. Biomass is measured via four primary means: (a) in situ destructive measurement; (b) in situ non-destructive using equations or conversion; (c) derived from remote sensing; and (d) modeling [4,6]. Allometric equations are used to statistically infer biomass based on in situ field data or remotely sensed data for extrapolation to larger land areas. Allometry assumes that a relationship exists by species based on structural measurements, usually height and stem or base diameter [6]

Methods

Results

Conclusion