Permafrost CCI Project - Mountain permafrost option

Permafrost cannot be directly detected from space, but many surface features of permafrost terrains and typical periglacial landforms are observable with a variety of EO sensors ranging from very high to medium resolution in various wavelengths. In addition, landscape dynamics associated with permafrost changes and geophysical variables relevant for characterising the state of permafrost, such as land surface temperature or snow-water equivalent, can be observed with space-based Earth Observation. Permafrost CCI Project will provide for different epochs consistent global maps of the parameters permafrost temperature and active layer thickness based on Earth Observation records ingested into a permafrost model scheme.

In periglacial mountain environments, the permafrost occurrence is patchy, and the preservation of permafrost is controlled by site-specific conditions. Three options initiated within CCN1 and CCN2 address the need for additional regional cases in cooperation with dedicated users in characterizing mountain permafrost as local indicator for climate change and direct impact on the society in mountainous areas. Started in October 2018, CCN1 is led by a Romanian team focusing on case studies in the Carpathians. The specific objective of CCN1 is to develop and deliver maps and products for mountain permafrost, such as (i) rock glacier inventories, (ii) kinematical time series of selected rock glaciers and (iii) a permafrost distribution model, primarily derived from satellite measurements. Started in September 2019, CCN2 consists of two options led by Swiss and Norwegian teams focusing on the investigation and definition of a new associated ECV Permafrost product related to rock glacier kinematics.

Early 2020, Rock Glacier Kinematics (RGK) has been proposed as a new product to the ECV Permafrost for the next GCOS implementation plan (IP). It would consist of a global dataset of surface velocity time series measured/computed on single rock glacier units. A proper rock glacier kinematics monitoring network, adapted to climate research needs, builds up a unique validation dataset of climate models for mountain regions, where direct permafrost (thermal state) measurements are very scarce or even lacking totally. The international Action Group Rock glacier inventories and kinematics, under the IPA (International Permafrost Association), supports this integration and CCN2 is working closely with this Action Group. Following the recommendations of this IPA Action Group, the overall goal of CCN2 is achieved through the development of two products: (i) regional rock glacier inventories and (ii) kinematical time series of selected rock glacier.


Collaborators: Aldo Bertone, Chloé Barboux, Cécile Pellet, Reynald Delaloye

Contact: reynald.delaloye[at]

Duration: 2018 - 2020

Study area: Globe


  • Illustrations

    Key regions investigated to provide standardized regional rock glacier inventories

  • Collaboration


    • Gamma Remote Sensing and Consulting AG (GAMMA), Switzerland
    • b.geos GmbH (B.GEOS), Austria
    • Norwegian Research Centre (NORCE), Norway
    • University of Oslo, Department of Geosciences (GUiO)
    • Alfred Wegener Institute Helmholtz Centre of Polar and Marine Research (AWI), Germany
    • Geography Unit of the Department of Geosciences of the University of Fribourg (UNIFR), Switzerland
    • Department of Physical Geography and Bolin Centre of Climate Research of Stockholm University (SU), Sweden
    • TERRASIGNA, Romania


    External partners

    • University of Bologna, Italy
    • Edytem - CNRS, France
    • University of Alaska Fairbanks, US
    • IANIGLA, Argentina
    • University of St Andrew, UK
    • University of Lausanne, Switzerland
  • Publications & Documentation

    A. Trofaier, S. Westermann &  A. Bartsch (2017) Progress in space-borne studies of permafrost for climate science: Towards a multi-ECV approachRemote Sensing of Environment, Volume 203, 15 December 2017, Pages 55-70. 


    Practical guidelines: rock glacier inventory using InSAR (kinematic approach) (current version)