Estimating seasonal fractional green and dead vegetation cover in rehabilitated ecosystems using drone remote sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it