Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques
Bibliographic record
Abstract
The importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any field campaigns. We performed a Random Forest (RF) classification to map the spatial distribution of native trees in different scenarios. The optimal model showed an overall accuracy and kappa of 0.99 and 0.98, respectively. We mapped 40,500 km2 of tree cover, including native tree cover (approximately 29,565 km2 ≈10.5%), excluding plantations, regional and provincial parks, and water bodies in the Canadian prairie region of Saskatchewan. According to our results, the highest numbers of native trees were found in the eastern and northwestern parts of the study area – cluster “BLK_1” and the “Black” soil zone, with total cover of 5,388 and 13,233 km2, respectively. The lowest numbers of native trees were found in the southwest side of the study area – cluster “BRN_6” and the “Brown” soil zone, with total cover of 2.38 and 979.5 km2, respectively. This research is important as detecting and quantifying native trees is an integral part of studies on carbon sequestration, economics, and effective management strategies.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".