Aerial Imagery as a Tool for Monitoring Urban Tree Retention: Applications, Strengths, and Challenges for Backyard Tree Planting Programs
Bibliographic record
Abstract
Abstract Background Urban tree planting initiatives are a popular way to increase municipal tree presence. Initiatives on private land, including backyard tree planting programs, are essential because most available planting space across cities is on private property. Therefore, understanding the success of these programs, including long-term tree retention rates, is crucial for determining future urban forest characteristics and associated ecosystem services. However, few studies evaluate the outcome of backyard planting programs, primarily because of barriers like limited organizational resources and the inaccessibility of trees planted in backyards. To address this issue, our study examined the feasibility of using publicly available aerial imagery to assess long-term retention of trees planted through a backyard tree planting program in Toronto, Ontario. Methods Using 20 years of leaf-off imagery and hand-drawn planting maps, a sample of 2,654 trees was assessed for feasibility of location digitization, presence-absence classification in 2022, and 5-year survivorship. Results We successfully digitized 1,865 (70%) of these trees, but the remaining 30% could not be mapped due to insufficient location information. Of those digitized, we could confidently determine if 1,533 trees (82%) were present or absent in 2022. The status of the remaining 18% of trees was unclear, often due to image obstruction or quality. We were able to determine presence/absence 5 years after planting for 81% of trees in the subset examined. Conclusions Ultimately, using aerial imagery could be a time- and cost-effective approach to long-term, ongoing urban tree monitoring, though challenges associated with image availability and quality should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".