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Unmanned Aerial Vehicle Applications in Cryospheric Sciences

Special Issue Information

Dear Colleagues,

The large diffusion, in civilian uses, of Unmanned Aerial Vehicles (UAVs), also known as drones, represent the latest revolution in the survey of the Earth [Kelleher et al. 2018, Williams et al. 2016] and new opportunities in high-resolution mapping. From a technological point of view, UAVs may have different configurations with different designs (e.g., multi-rotors, fixed-wing, and single or swarm of drones), settings (e.g., overlaps, flight height), payloads, and sensors (e.g., RGB or CIR camera, laser scanning, and GPS stand alone or differential). UAVs constitute a cheap source of high-resolution aerial images, which, processed with photogrammetric methods, allow one to produce digital surface models and orthophotos. Thus, UAVs provide incredible opportunities for developing geophysical applications and improving our knowledge of phenomena under investigation.

In this Special Issue, we focus our attention on UAVs applications in cryospheric sciences, related to the following:

  • The assessment of snow and ice dynamics;
  • The investigation of glacier changes and modifications in terms of both mass and shape;
  • The development of new UAV settings for reducing the number GCPs in field campaigns;
  • The comparison between surveys operated with UAVs and other sensors and instruments available in the literature.   

References

  1. Kelleher, C.; Scholz, C.A.; Condon, L. and Reardon, M. Drones in geoscience research: The sky is the only limit. Eos, 2018, 99,doi:10.1029/2018EO092269.
  2. Williams, G. D., et al. Drones in a cold climate, Eos, 2016, 97,doi: 10.1029/2016EO043673.

Prof. Dr. Carlo De Michele
Prof. Dr. Riccardo Barzaghi
Prof. Dr. Alberto Cina
Prof. Dr. Livio Pinto
Prof. Dr. Marco Piras
Guest Editors

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Keywords

  • Unmanned aerial vehicle
  • Digital photogrammetry
  • Geomatics
  • 3D model
  • Applications
  • Snow
  • Ice
  • Glacier
  • Boreal forest
  • Change detection
  • Climate change

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Remote Sens. - ISSN 2072-4292