Estimating seasonal fractional green and dead vegetation cover in rehabilitated ecosystems using drone remote sensing
Bibliographic record
Abstract
Restoration of ecosystems affected by surface mining requires effective monitoring to evaluate rehabilitation success. Conventional approaches, including field surveys and satellite remote sensing, are often constrained by cost, spatial resolution, and temporal frequency, particularly when monitoring multiple small or fragmented sites. To address these limitations, this study uses Unmanned Aerial Vehicles (UAV) acquired visible imagery to estimate within-season variation in fractional green and dead vegetation cover across three sites at different rehabilitation stages in Southern Ontario, Canada. Using monthly UAV imagery collected between May and August 2023, and random forest regression models, we generated high-resolution maps of fractional green and dead vegetation cover for each site. Green vegetation cover was estimated with high accuracy (R 2 = 0.94–0.97; RMSE = 8–10 %) across the three sites, while dead vegetation cover was predicted with moderate to high accuracy (R 2 = 0.74–0.95; RMSE = 10–12 %). Mapping results revealed that site two (rehabilitated in 2006) showed limited recovery, with low green vegetation cover (24–47 % monthly average) and high dead vegetation cover (52–76 % monthly average), likely due to poor substrate and minimal follow-up. Site one (rehabilitated in 2017) demonstrated strong seasonal greening despite similar rehabilitated treatment (48–60 % monthly average) and moderate dead vegetation cover (40–51 % monthly average). Site three (rehabilitated in 2022) maintained high green cover (48–56 % monthly average) and low dead materials (10–15 % monthly average), suggesting early recovery, possibly supported by favorable conditions or better restoration inputs. These findings demonstrate the utility of UAV-derived visible imagery for cost-effective, fine-scale monitoring of vegetation dynamics in rehabilitated ecosystems.
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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 | 0.001 |
| 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 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".