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Characterization and quantification of forest secondary structure using airborne LiDAR

University of British Columbia

Knowledge of forest structure can be used to guide sustainable forest management decisions. Currently, Airborne Laser Scanning (ALS) has been well established as an effective tool to delineate and characterize canopy structure of forested biomes. However, the use of ALS to characterize forest secondary structure is less well developed. Secondary structure consists of suppressed sub-canopy trees, short-stature vegetation and coarse-woody-debris (CWD). I utilized discrete return ALS to develop methodologies which characterize two secondary structural units, sub-canopy trees and CWD, within natural forest stands in central British Columbia. I first segmented the forest vertically into canopy versus sub-canopy and computed a suite of ALS metrics to develop predictive models of sub-canopy stand attributes. Calibrated against 28 ground plots, models were developed using stepwise regression resulting in the strongest predictors being a combination of height, structure and cover-based metrics. Two sets of models were developed, one with the canopy removed and another with it retained. The sub-canopy set of models resulted in stronger cross-validated R-squared values for volume and basal area and as a result the sub-canopy volume model was used to map sub-canopy volume over the entire study area. The second structural unit, CWD, is a meaningful contributor to forest carbon levels and biodiversity. In this work I detail a novel methodology that isolates CWD returns from large diameter logs (>30cm) using a refined grounding algorithm, a mixture of height and pulse-based filters and linear pattern recognition to transform returns into measurable vectorized shapes. Height values are extracted directly from the point cloud to calculate volume for detected shapes. This approach is then demonstrated by successfully mapping CWD and estimates of volume as well as providing an assessment of individual log and plot-level attributes that influence successful detection. I compared plot volume totals calculated from ALS-derived CWD against field measured CWD and found a strong correlation. Lastly this methodology was applied over a larger region to quantify CWD volume differentials between stands. These methodologies demonstrate the capability to generate a secondary structure inventory that can highlight locations for selective logging, model fire susceptibility and carbon sequestration, and quantify wildlife habitat.

Text

2020-09-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75901

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/