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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».