Data-driven mapping of the potential mountain permafrost distribution

Nicola Deluigi; Christophe Lambiel; Mikhail Kanevski

doi:10.1016/j.scitotenv.2017.02.041

Data-driven mapping of the potential mountain permafrost distribution

Sci Total Environ. 2017 Jul 15:590-591:370-380. doi: 10.1016/j.scitotenv.2017.02.041. Epub 2017 Mar 8.

Authors

Nicola Deluigi¹, Christophe Lambiel², Mikhail Kanevski²

Affiliations

¹ Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland. Electronic address: nicola.deluigi@unil.ch.
² Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland.

PMID: 28284636
DOI: 10.1016/j.scitotenv.2017.02.041

Abstract

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km² area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping.

Keywords: Classification algorithms; Machine learning; Mapping; Mountain permafrost.