Clustering analysis for the hydro-geomorphometric characterization of the George River watershed (Nunavik, Canada)
Notice bibliographique
Résumé
For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Fortunately, remote sensing approaches can provide considerable information about the structural properties of watersheds, which is useful for the indirect assessment of their hydrological characteristics and behavior. Our main objective is to produce a high-resolution territorial clustering based on key hydrologic landscape metrics for the entire 42 000 km2 George River watershed (GRW), located in Nunavik, northern Québec (Canada). This project is being conducted in partnership with the local Inuit communities of the GRW for the purpose of generating and sharing knowledge to anticipate the impact of climate and socio-environmental change in the GRW. Our clustering approach employs Unsupervised Geographic Object-Based Image Analysis (GeOBIA) applied to the entire GRW with the subwatersheds as our objects of analysis. The landscape metric datasets used to generate the input variables of our GeOBIA classification are raster layers with a 30m x 30m pixel resolution. Topographic metrics are derived from a Digital Elevation Model (DEM) and include elevation, slopes, aspect, drainage density and watershed elongation. Land cover spectral metrics comprised in our analysis are the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI) (Gao, 1996) and the Normalized Difference Water Index (NDWI) (McFeeters, 1996), which are all computed from a Landsat-8 cloud-free surface reflectance mosaic dating from 2015. Rasterized maps of surface deposit distribution and permafrost distribution, both produced by the Ministère des Forêts, de la Faune et des Parcs of Québec (MFFP), respectively constitute the surface and subsurface metrics of our GeOBIA. The clustering algorithm used in this Unsupervised GeOBIA is the Fuzzy C-Means (FCM) algorithm. The FCM algorithm provides the objects a set of membership coefficients corresponding to each cluster. The greatest membership coefficient is then used to attribute the distinct subwatersheds to a cluster of watersheds with similar hydro-geomorphometric characteristics. The classification returns a Fuzzy Partition Coefficient (FPC), which describes how well-partitioned our dataset is. The FPC can vary greatly depending on the number of clusters we want to produce. Thus, we find the optimal number of clusters by maximizing the FPC. Preliminary clustering results, computed only with topographic and land cover metrics, have identified two distinct watershed classes/clusters. In general, “Type 1” subwatersheds are clustered over the southern and northwestern portion of the GRW and are characterized by low to moderate elevation, high vegetation cover, high moisture and high surface water cover. Whereas “Type 2” subwatersheds located over the northeastern portion of the GRW are characterized by high elevation, low vegetation cover, low moisture and low surface water cover. These results will be refined with the use of additional metrics and will provide the detailed understanding necessary to assess how the hydrological regime of the river and its tributaries will respond to climate change, and how landscape change and human activities (e.g., planned mining development) may impact the water quality of the George River and its tributaries.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».