Suchergebnisse

Abstract. Satellite measurements of coseismic displacements are typically based on synthetic aperture radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very high-resolution satellites, or air photos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m-resolution visible and near-infrared frame images of approximately 20–30 km  ×  9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake, we observe surface displacements of up to almost 10 m and estimate matching accuracies from PlanetScope data between ±0.25 and ±0.7 pixels (∼ ±0.75 to ±2.0 m), depending on time interval and image product type. Thereby, the most optimistic accuracy estimate of ±0.25 pixels might actually be typical for the final, sun-synchronous, and near-polar-orbit PlanetScope constellation when unrectified data are used for matching. This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat 8 and Sentinel-2 data.

Abstract. Four large drainages from glacial lakes occurred during
2006–2014 in the western Teskey Range, Kyrgyzstan. These floods caused
extensive damage, killing people and livestock as well as destroying
property and crops. Using satellite data analysis and field surveys of this
area, we find that the water volume that drained at Kashkasuu glacial lake in
2006 was 194 000  m3, at western Zyndan lake in 2008 was
437 000 m3, at Jeruy lake in 2013 was 182 000 m3, and
at Karateke lake in 2014 was 123 000 m3. Due to their subsurface
outlet, we refer to these short-lived glacial lakes as the "tunnel-type", a
type that drastically grows and drains over a few months. From spring to
early summer, these lakes either appear, or in some cases, significantly
expand from an existing lake (but non-stationary), and then drain during
summer. Our field surveys show that the short-lived lakes form when an ice
tunnel through a debris landform gets blocked. The blocking is caused either
by the freezing of stored water inside the tunnel during winter or by the
collapse of ice and debris around the ice tunnel. The draining then occurs
through an opened ice tunnel during summer. The growth–drain cycle can
repeat when the ice-tunnel closure behaves like that of typical supraglacial
lakes on debris-covered glaciers. We argue here that the geomorphological
characteristics under which such short-lived glacial lakes appear are (i) a
debris landform containing ice (ice-cored moraine complex), (ii) a depression
with water supply on a debris landform as a potential lake basin, and (iii)
no visible surface outflow channel from the depression, indicating the
existence of an ice tunnel. Applying these characteristics, we examine 60
depressions (> 0.01 km2) in the study region and identify
here 53 of them that may become short-lived glacial lakes, with 34 of these
having a potential drainage exceeding 10 m3 s−1 at peak discharge.

Abstract. Landslides are a major geohazard that cause thousands of fatalities every year. Despite their importance, identifying unstable slopes and forecasting collapses remains a major challenge. In this study, we use the 7 February 2021 Chamoli rock–ice avalanche as a data-rich example to investigate the potential of remotely sensed datasets for the assessment of slope stability. We investigate imagery over the 3 decades preceding collapse and assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event digital elevation models (DEMs), which we use to evaluate elevation change over the same area. Both datasets show that the 26.9×106 m3 collapse block moved over 10 m horizontally and vertically in the 5 years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Despite observing a clear precursory signal, we find that the timing of the Chamoli rock–ice avalanche could likely not have been forecast from satellite data alone. Our results highlight the potential of remotely sensed imagery for assessing landslide hazard in remote areas, but that challenges remain for operational hazard monitoring.