Brief communication: An autonomous UAV for catchment-wide monitoring of a debris flow torrent

Debris flows threaten communities in mountain regions worldwide. Combining modern photogrammetric processing with autonomous unoccupied aerial vehicle (UAV) flights at sub-weekly intervals allows mapping of sediment dynamics in a debris flow catchment. This provides important information for sediment disposition that pre-conditions the catchment for debris flow occurrence. At the Illgraben debris flow catchment in Switzerland, our autonomous UAV launched nearly 50 times in the snow-free periods in 2019-2021 with typical flight intervals of 2-4 d, producing 350-400 images every flight. The observed terrain changes resulting from debris flows exhibit preferred locations of erosion and deposition, including memory effects as previously deposited material is preferentially removed during subsequent debris flows. Such data are critical for the validation of geomorphological process models. Given the remote terrain, the mapped short-term erosion and deposition structures are difficult to obtain with conventional measurements. The proposed method thus fills an observational gap, which ground-based monitoring and satellite-based remote sensing cannot fill as a result of limited access, reaction time, spatial resolution, or involved costs.ISSN:1561-8633ISSN:1684-998

Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV

Mapping the underlying density field, including non-visible dark matter, using weak gravitational lensing measurements is now a standard tool in cosmology. Due to its importance to the science results of current and upcoming surveys, the quality of the convergence reconstruction methods should be well understood. We compare three methods: Kaiser–Squires (KS), Wiener filter, and Glimpse. Kaiser–Squires is a direct inversion, not accounting for survey masks or noise. The Wiener filter is well-motivated for Gaussian density fields in a Bayesian framework. Glimpse uses sparsity, aiming to reconstruct non-linearities in the density field. We compare these methods with several tests using public Dark Energy Survey (DES) Science Verification (SV) data and realistic DES simulations. The Wiener filter and Glimpse offer substantial improvements over smoothed Kaiser–Squires with a range of metrics. Both the Wiener filter and Glimpse convergence reconstructions show a 12 per cent improvement in Pearson correlation with the underlying truth from simulations. To compare the mapping methods’ abilities to find mass peaks, we measure the difference between peak counts from simulated ΛCDM shear catalogues and catalogues with no mass fluctuations (a standard data vector when inferring cosmology from peak statistics); the maximum signal-to-noise of these peak statistics is increased by a factor of 3.5 for the Wiener filter and 9 for Glimpse. With simulations, we measure the reconstruction of the harmonic phases; the phase residuals’ concentration is improved 17 per cent by Glimpse and 18 per cent by the Wiener filter. The correlationbetween reconstructions from data and foreground redMaPPer clusters is increased 18 per cent by the Wiener filter and 32 per cent by Glimpse.ISSN:0035-8711ISSN:1365-2966ISSN:1365-871