Estimation of land-use/land-cover changes associated with energy footprints and other disturbance agents in the Upper Peace Region of Alberta Canada from 1985 to 2015 using Landsat data
Bibliographic record
Abstract
Quantification of Land‐Use and Land‐Cover (LULC) changes associated with energy extraction footprints plays an important role in reclamation performance assessment, compliance monitoring, and sustainable land management practices. For regulatory planning and decision making purposes, it is crucial to understand the types and representation of disturbance present on the landscape related to energy footprints (i.e., oil, gas, and coal mining activities), non-energy footprints (i.e., other infrastructure), cutblocks, and wildfire. In this study, Landsat multispectral datasets were analysed using advanced classification and unmixing algorithms to calculate LULC changes associated with these disturbance agents from 1985 to 2015 in the Upper Peace Region, one of the major oil and gas exploration sites in Alberta, Canada. Based on 5-year intervals throughout this 30-year study, seven epochal LULC maps and a cumulative land disturbance map were produced with 82 % and 89 % overall accuracies, respectively, based on extensive and multisource, independent validation. Assessment of multiple disturbance regimes and time-series trajectories with high accuracies in this complex environment was vital to document and quantify disturbance, vegetation re-establishment, succession, and reclamation. Results indicate that more than 60 % forest loss (coniferous and deciduous) occurred due to harvesting and less than 20 % forest loss occurred due to energy and non‐energy footprints. An assessment of the current state showed 40–60 % of cutblocks and burned areas returned as immature forest and wetland and 15–20 % recovered to forest, accounting for successional processes that take time for the re-establishment of trees following disturbance. Out of total energy footprints, 8 % recovered as shrubland and 16 % recovered as forest, with almost no sign of vegetation recovery evident in non‐energy footprints (e.g., urban and long term industrial land use). The highest area of land disturbance (3412 km2) was over coniferous forest with 5-year disturbance areas of 55 km2, 65 km2, 70 km2, and 295 km2, associated with, respectively, wildfire, energy development, non‐energy footprints, and harvesting. This type of LULC investigation informs land‐use threshold management for energy footprints by identifying and partitioning key disturbance agents into land use. Knowledge of change type and land use enables insights on long term implications of the changes present and serves to guide sustainable development, environmental protection, and informing forward looking land use future state simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| 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".