Urban Tree Canopy Assessment Using Geospatial Technologies: A Case Study of the Town of Lincoln, Ontario
Bibliographic record
Abstract
Urban trees provide important benefits to communities, from mitigating stormwater to improved air quality. Municipalities across Ontario encounter a decline in their urban tree canopy (UTC). UTC assessment is essential for the management of urban trees, especially in the context of climate change. However, quantifying the canopy remains a challenge, given that tree crowns are difficult to assess from the ground. Geospatial technologies provide a suitable alternative to costly, ground-based assessments. Still, they typically require a significant investment in resources, including technical expertise and equipment. For many small- and medium-sized municipalities facing the realities of climate change, these investments are cost-prohibitive. This study aimed to assess the UTC within the Town of Lincoln, Ontario, using geospatial technologies. The first objective was to estimate canopy cover and distribution using image classification as the main approach. The second objective was to assess the proficiency of a low-cost method based on image interpretation (i.e., i-Tree Canopy) to calculate canopy cover compared to the main approach. The third objective was to examine the possibility of using the canopy goal designated by the Niagara Official Plan as a standard canopy goal. This research study produced three main results. First, the image classification indicated that the tree canopy covers 21% of the Town. Second, this study demonstrated that the results from the main approach are similar to those obtained from i-Tree Canopy. Given the similarity between these approaches, this study concluded that the lower-cost i-Tree Canopy method could be combined with other methods to prepare accurate and affordable canopy assessments for resource-limited municipalities. Finally, this study concluded that canopy goals should account for local Urban Tree Canopy Assessment Using Geospatial Technologies differences based on geographic location. This study makes a valuable contribution to the literature as it informs management of canopy resources in communities with limited resources. Outcomes from this study can also better inform tree-canopy goals and policies with a cost-effective method that requires minimal expertise. The ability to conduct UTC assessment in smaller communities is critical in mitigating the impacts of climate change facing most of these communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".