Using Campus-wide LiDAR to Map and Characterize Understory at the University British Columbia Vancouver Campus
Bibliographic record
Abstract
In response to the pressing need to address biodiversity loss and mitigate the impacts of climate change, recent studies have explored the role of understory vegetation in urban green spaces. Despite its importance, understory vegetation is often ignored in current inventories of urban green spaces. The University of British Columbia (UBC) seeks to characterize understory vegetation on campus and apply strategies for mitigating the climate change effect in specific hot spot areas. This study investigated the understory community of the UBC Vancouver campus by (1) mapping the height and height variation of the understory plant in the northern part of the UBC Vancouver campus by extracting campus-wide LiDAR data, and (2) generating complexity analysis near Saltwater residences based on field observation and height data retrieved from the LiDAR point cloud. Four metrics were identified to characterize understory structure complexity, including understory coverage, height variation, understory species diversity, and species rarity. Results indicate that over 50% of the understory in the study area was less than 0.8 meters in height. While understory plants grown near buildings showed relatively higher height variability (ranging from 0.35 to 0.69m), the distribution of understory height was not dependent on spatial location, and there was no clear pattern of similarity in the structural complexity between neighboring areas on the UBC campus. More species with a higher ecological value such as the Tall Oregon grape could be planted near saltwater residences to better combat climate change. Overall, this study demonstrates the potential of LiDAR to model and visualize understory vegetation structure and provides a methodology for conducting its structural analysis on the UBC Vancouver campus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".