Terrestrial Radar Interferometry and Structure-from-Motion Data from Nevado del Ruiz, Colombia for Improved Hazard Assessment and Volcano Monitoring

Abstract:

Ground-based remote sensing geodesy has huge potential for volcano monitoring and improved modelling of volcanic hazards. Terrestrial Radar Interferometers (TRI) can rapidly and accurately create DEMs and repeat occupation of sites allows measurement of deformation. Structure-from-Motion (SfM) photogrammetry can be used to construct DEMs and SfM surveys can be carried out with relatively accessible equipment. TRI and SfM techniques are highly complimentary: The upper slopes of a volcano may be cloud covered, but can be imaged by TRI, whereas lower canyons may be in radar shadow, but can be imaged with SfM. Both methods are also complimentary to satellite observations (e.g. SRTM, ASTER), offering some advantages in terms of coverage and resolution. We present the acquisition of two new geodetic datasets at Nevado del Ruiz, Colombia (NRV). NRV is a large glacierised volcano that erupted in 1985, generating a glacier-derived lahar that killed over 23,000 people in the city of Armero and 2,000 people in the town of Chinchina. NRV is the most active volcano in Colombia and since 2012 has generated small eruptions (with no casualties) and constant gas and ash emissions. In early 2015, we collected data from several sites close to the crater of NRV and around the Azufrado drainage (the site of previous debris avalanches and lahars). The TRI was operated from three sites, while drone- and ground-based cameras ventured into the canyons to fill in radar shadow gaps. These data have three primary uses: 1) generation of high-precision DEMs for lahar modelling and visualisation of previous events, 2) imaging of summit glacier motion, and 3) establishing a baseline for long-term deformation studies. We discuss ground-based remote sensing geodetic data from high-tech (TRI) to low-tech (SfM) methods and show the importance of combining these complimentary datasets to improve DEMs for hazard modelling and volcano monitoring.