Modelling and mapping permafrost at high spatial resolution using Landsat and Radarsat-2 images in Northern Ontario, Canada: Part 2 – regional mapping
Notice bibliographique
Résumé
AbstractPermafrost occurs mainly at high latitudes and is currently subjected to widespread thawing due to global warming, which has significant ecological and socio-economic impacts. One way to map permafrost condition and its change at high spatial resolution is to calculate soil temperatures using a process-based permafrost model, such as the Northern Ecosystem Soil Temperature (NEST) model, based on remote-sensing data. In a previous paper (Ou et al. 2015), we showed that the NEST model can be used to model soil temperatures, frozen depth, and active layer thickness in a warm and discontinuous permafrost area that is located in the Hudson Bay Lowlands in northern Ontario, Canada. In this article, we applied the model over the whole study area to map the distribution of permafrost conditions and their changes from the 1960s to the 2000s at high spatial resolution. Some of the model inputs were estimated based on land-cover and surficial material maps derived from Landsat-5 TM and Radarsat-2 SAR C-HH and C-HV images. By comparison to field observations, we were able to achieve an average mapping accuracy of 99% for both the land-cover and surficial material maps, when both Landsat and Radarsat-2 images were used. In addition, all the 49 field-observed treed and shrub permafrost landform sites found in the study area were correctly identified on the classified image. This spatial modelling study shows that permafrost currently underlies about 9.4% of the land in this area, and varies significantly among land-cover types. With the increases in mean annual air temperature (1.9°C) and precipitation (2.4%) from the 1960s to the 2000s, the model results show a deepening of active layer by 20.5%. However, the permafrost has persisted in most of the area due to the protection of thick peat. The modelled permafrost extent reduced only from 9.8% to 9.4%. AcknowledgementsWe would like to thank Chris Charron of Ontario Ministry of Environment and Climate Change and Elyn Humphreys of Carleton University for providing processed flux tower data. Dan McKenny of Canadian Forest Service provides the climate grid data. We also thank Mark Crofts and Benoît Hamel of Ontario Ministry of Natural Resources and Forestry as well as De Beers Canada for their kind support during our Victor Diamond Mine field work. We also gratefully acknowledge the access to the high speed ACENET computer system.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe study was funded by the Canadian Space Agency 'Grant and Contribution' programme.
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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 ».