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Article

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

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Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA
2
Department of Geography, University of New Mexico, Albuquerque, NM 87131, USA
3
Applied Research Laboratories, University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete and Prasad S. Thenkabail
Remote Sens. 2016, 8(6), 453; https://doi.org/10.3390/rs8060453
Received: 15 March 2016 / Revised: 29 April 2016 / Accepted: 18 May 2016 / Published: 27 May 2016
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. View Full-Text
Keywords: LiDAR; biomass; semi-arid; juniper; woodland; segmentation; canopy delineation LiDAR; biomass; semi-arid; juniper; woodland; segmentation; canopy delineation
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MDPI and ACS Style

Krofcheck, D.J.; Litvak, M.E.; Lippitt, C.D.; Neuenschwander, A. Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries. Remote Sens. 2016, 8, 453. https://doi.org/10.3390/rs8060453

AMA Style

Krofcheck DJ, Litvak ME, Lippitt CD, Neuenschwander A. Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries. Remote Sensing. 2016; 8(6):453. https://doi.org/10.3390/rs8060453

Chicago/Turabian Style

Krofcheck, Dan J., Marcy E. Litvak, Christopher D. Lippitt, and Amy Neuenschwander. 2016. "Woody Biomass Estimation in a Southwestern U.S. Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries" Remote Sensing 8, no. 6: 453. https://doi.org/10.3390/rs8060453

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