Monitoring success: remote sensing and artificial intelligence for tracking success of individual seedlings at a revegetation trial in northern Canada
Bibliographic record
Abstract
Progressive reclamation and reclamation trials are vital to mine closure success, providing opportunities to refine and validate reclamation prescriptions. Comprehensive monitoring is essential for evaluating the relative success of treatments over time. The research objective was to identify newly planted tree seedlings, classify them by species, and develop a robust indicator of seedling health using high-spatial-resolution multispectral imagery, object-based image analysis, and machine learning. In July 2023, a 2 hectare revegetation trial established on a mined waste rock dump was planted with aspen (Populus tremuloides), lodgepole pine (Pinus contorta), and white spruce (Picea glauca). Multispectral imagery was acquired via remotely piloted aircraft system (RPAS) in August 2023, and 30 permanent sampling plots (PSPs) were established across the trial, where each seedling’s position and species were recorded (n = 1,545). The multispectral imagery was filtered using a vegetation index to isolate individual seedlings across the trial, and a Random Forest model was used to classify seedlings by species. Subsequent RPAS imagery and vigour rankings for seedlings within PSPs were collected in August 2024 and analysed using a similar approach to classify survival (dead or alive) and vigour (0–5) or generate seedling size percentiles as indicators of health. Species and survival were classified with an average F1-score of 0.94 and 0.98, respectively. Vigour classifications for aspen, lodgepole pine, and white spruce yielded average F1-scores of 0.82, 0.86, and 0.83, respectively. Increases in size percentiles coincided with increases in field-assessed vigour, demonstrating a clear relationship between the two. This study resulted in the precise geotagging of 4,942 aspen, 2,524 white spruce, and 2,819 lodgepole pine across the reclamation trial, demonstrating the ability to monitor the progression of individual seedling performance immediately after planting. This approach improves the efficiency of monitoring planting programs, enabling deeper insights into the long-term effects of different treatments on seedlings.
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.
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 | 0.001 | 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".