Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries

Dan Krofcheck,Christopher Lippitt,Marcy Litvak,Amy Neuenschwander

doi:10.3390/rs8060453

Dan Krofcheck, Christopher Lippitt + Show 2 more

Open Access

https://doi.org/10.3390/rs8060453

Copy DOI

Abstract

The rapid and accurate assessment of above ground biomass (AGB) of woody vegetation is a critical component of climate mitigation strategies, land management practices and process-based models of ecosystem function. This is especially true of semi-arid ecosystems, where the high variability in precipitation and disturbance regimes can have dramatic impacts on the global carbon budget by rapidly transitioning AGB between live and dead pools. Measuring regional AGB requires scaling ground-based measurements using remote sensing, an inherently challenging task in the sparsely-vegetated, spatially-heterogeneous landscapes characteristic of semi-arid regions. Here, we test the ability of canopy segmentation and statistic generation based on aerial LiDAR (light detection and ranging)-derived 3D point clouds to derive AGB in clumps of vegetation in a juniper savanna in central New Mexico. We show that single crown segmentation, often an error-prone and challenging task, is not required to produce accurate estimates of AGB. We leveraged the relationship between the volume of the segmented vegetation clumps and the equivalent stem diameter of the corresponding trees (R2 = 0.83, p < 0.001) to drive the allometry for J. monosperma on a per segment basis. Further, we showed that making use of the full 3D point cloud from LiDAR for the generation of canopy object statistics improved that relationship by including canopy segment point density as a covariate (R2 = 0.91). This work suggests the potential for LiDAR-derived estimates of AGB in spatially-heterogeneous and highly-clumped ecosystems.

Highlights

Semi-arid regions are characterized by lower above ground biomass (AGB) and low fractional cover of vegetation compared to temperate and tropical regions [1,2,3]
The representativeness of the trees used to parametrize the LiDAR-based regressions of biomass was assessed by comparing field-measured canopy height with the mean local maximum canopy height within the LiDAR analysis extent, and the distribution of heights measured by our field plots was representative of the analysis extent (Figure 6)
By using structural metrics derived from the LiDAR 3D point cloud, such as canopy volume, canopy density and canopy closure, we showed that the cumulative equivalent stem diameters (ESD) and, subsequently, AGB agreed well with field-measured estimates for a vegetation clump

Summary

Introduction

Semi-arid regions are characterized by lower above ground biomass (AGB) and low fractional cover of vegetation compared to temperate and tropical regions [1,2,3]. Coupled with high intra- and inter-annual variability in rainfall, carbon dynamics in semi-arid biomes are highly variable at both short and long timescales. In spite of water limitations, these ecosystems have been shown to contribute significantly to the global carbon sink when precipitation is high [4]. Given the widely distributed nature of these biomes, quantifying the total carbon stored in them is a critical step towards further describing the relationship between structural properties and functional processes; a relationship that governs regional and landscape-scale carbon dynamics. The ability to accurately assess structural properties, such as AGB, at ecosystem and landscape scales is an essential precursor to monitoring carbon dynamics in semi-arid biomes and extends a critical component to global and national-scale climate change mitigation strategies, such as REDD+

Methods

Results

Discussion

Conclusion