Shelterbelt Agroforestry Systems Inventory and Removal Analyzed by Object-based Classification of Satellite Data in Saskatchewan, Canada
Bibliographic record
Abstract
Shelterbelt agroforestry systems inventory is challenging given their narrow linear feature and extensive distribution. The objective of this study was to evaluate the capability of Sentinel-2A Multispectral Instrument (MSI) and Sentinel-1B Synthetic Aperture Radar (SAR) imagery in (i) delineating shelterbelt tree rows on managed agricultural land in Saskatchewan, Canada, and (ii) detecting shelterbelt removal during the period 2008–2016. Contrast split segmentation for the normalized difference vegetation index and Gaussian filter (line filter) data sets from SAR were used to delineate feature borders. Several feature variables from the spectral bands of MSI were used as inputs for an object-based classification using the random forest classifier. A resulting land cover map, including the linear features of existing shelterbelts, was created with an overall accuracy of 80% and kappa value of 0.69. Shelterbelt change detection analysis using the land cover map (2016) and a legacy shelterbelt inventory map (2008) showed that 354 km of shelterbelts were removed within the study area (1,400 km2), accounting for 29.8% of the total shelterbelt length present in 2008. Our results demonstrated that the combination of Sentinel SAR and MSI imagery can provide sufficient information for mapping future shelterbelt planting, as well as allow the detection of shelterbelt removal.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".