Date of Award

Winter 2019

Project Type

Thesis

Program or Major

Natural Resources

Degree Name

Master of Science

First Advisor

Scott V Ollinger

Second Advisor

Mark J Ducey

Third Advisor

Michael W Palace

Abstract

Automated individual tree crown delineation (ITCD) via remote sensing platforms offers a path forward to obtain wall-to-wall detailed tree inventory/information over large areas. While LiDAR-based ITCD methods have proven successful in conifer dominated forests, it remains unclear how well these methods can be applied broadly in deciduous broadleaf (hardwood) dominated forests. In this study, I applied five common automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to hardwood- dominated at the Harvard Forest in Petersham, MA, USA, and assess accuracy against manually delineation crowns. I then identified basic tree- and plot-level factors influencing the success of delineation techniques. My results showed that automated crown delineation shows promise in closed canopy mixed-species forests. There was relatively little difference between crown delineation methods (51-59% aggregated plot accuracy), and despite parameter tuning, none of the methods produce high accuracy across all plots (27 – 90% range in plot-level accuracy). I found that all methods delineate conifer species (mean 64%) better than hardwood species (mean 42%), and that accuracy of each method varied similarly across plots and was significantly related to plot-level conifer fraction. Further, while tree-level factors related to tree size (DBH, height and crown area) all strongly influenced the success of crown delineations, the influence of plot-level factors varied. Species evenness (relative species abundance) was the most important plot-level variable controlling crown delineation success, and as species evenness decreased, the probability of successful delineation increased. Evenness was likely important due to 1) its negative relationship to conifer fraction and 2) a relationship between evenness and increased canopy space filling efficiency. Overall, my work suggests that the ability to delineate crowns is not strongly driven by methodological differences, but instead driven by differences in functional group (conifer vs. hardwood) tree size and diversity and how crowns are displayed in relation to each other. While LiDAR-based ITCD methods are well suited for conifer dominated plots with distinct canopy structure, they remain less reliable in hardwood dominated plots. I suggest that future work focus on integrating phenology and spectral characteristics with existing LiDAR approaches to better delineate hardwood dominated stands.

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