An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps

Kofler, Christian, Steger, Stefan, Mair, Volkmar, Zebisch, Marc, Comiti, Francesco and Schneiderbauer, Stefan, (2019). An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps. Geomorphology, 350(106887), 1-16

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    Author Kofler, Christian
    Steger, Stefan
    Mair, Volkmar
    Zebisch, Marc
    Comiti, Francesco
    Schneiderbauer, Stefan
    Title An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps
    Appearing in Geomorphology
    Volume 350
    Issue No. 106887
    Publication Date 2019-10-28
    Place of Publication Amsterdam
    Publisher Elsevier B.V.
    Start page 1
    End page 16
    Language eng
    Abstract Ice presence in rock glaciers is a topic that is likely to gain importance in the future due to the expected decrease in water supply from glaciers and the increase of mass movements originating in periglacial areas. This makes it important to have at ones disposal inventories with complete information on the state of rock glaciers. This study presents a method to overcome incomplete information on the status of rock glaciers (i.e. intact vs. relict) recorded in regional scale inventories. The proposed data-driven modelling framework can be used to estimate the likelihood that rock glaciers contain frozen material. Potential predictor variables related to topography, environmental controls or the rock glacier appearance were derived from a digital terrain model (DTM), satellite data and gathered from existing data sets. An initial exploratory data analysis supported the heuristic selection of predictor variables. Three classification algorithms, namely logistic regression (GLM), support vector machine (SVM) and random forest (RF), were trained on the basis of the available information on the status of rock glaciers within the territory of South Tyrol (Eastern Italian Alps). The resulting classification rules led to assign a binary label – intact or relict – to 235 unclassified rock glaciers present in the inventory. All models were validated quantitatively on spatially-independent test samples (spatial cross validation) and achieved highly satisfactory performance scores. Hereby, the less flexible statistically-based classifier (GLM) performed slightly better than the more flexible machine learning algorithms (SVM and RF). Spatial permutation-based variable importance assessment revealed that elevation and vegetation cover (based on NDVI) were the most relevant predictors. For more than 80% of the unclassified rock glaciers, all of the three models agreed on the spatially predicted rock glacier status. Only for a minor portion (12.3%), one model differed from the remaining two.
    Keyword Machine learning
    Probabilistic modelling
    Classification algorithms
    Rock glacier inventory
    Rock glacier activity
    Copyright Holder The Authors
    Copyright Year 2019
    Copyright type Creative commons
    DOI 10.1016/j.geomorph.2019.106887
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