UAV Photogrammetry today already enjoys a largely automated and efficient data processing pipeline. However, the goal of dispensing with Ground Control Points looks closer, as dual-frequency GNSS receivers are put on board. This paper reports on the accuracy in object space obtained by GNSS-supported orientation of four photogrammetric blocks, acquired by a senseFly eBee RTK and all flown according to the same flight plan at 80 m above ground over a test field. Differential corrections were sent to the eBee from a nearby ground station. Block orientation has been performed with three software packages: PhotoScan, Pix4D and MicMac. The influence on the checkpoint errors of the precision given to the projection centers has been studied: in most cases, values in Z are critical. Without GCP, the RTK solution consistently achieves a RMSE of about 2-3 cm on the horizontal coordinates of checkpoints. In elevation, the RMSE varies from flight to flight, from 2 to 10 cm. Using at least one GCP, with all packages and all test flights, the geocoding accuracy of GNSS-supported orientation is almost as good as that of a traditional GCP orientation in XY and only slightly worse in Z.
High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only.
ABSTRACT. In order to account for the effects of debris cover in model scenarios of the response of glaciers to climate change and water resource planning, it is important to know the distribution and thickness of supraglacial debris and to monitor its change over time. Previous attempts to map surface debris thickness using thermal band remote sensing have relied upon time-specific empirical relationships between surface temperature and thickness, limiting their general applicability. In this paper, we develop a physically based model that utilizes Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal band remotely sensed imagery and is based on a solution of the energy balance at the debris surface. The model is used to estimate debris thickness on Miage glacier, Italy, and is validated using field debris-thickness measurements and a previously published debristhickness map. The temporal transferability of the model is demonstrated through successful application to a separate ASTER image from a different year using reanalysis meteorological input data. This model has the potential to be used for regional-scale supraglacial debris-thickness mapping and monitoring for debris up to at least 0.50 m thickness, but improved understanding of the spatial patterns of air temperature, aerodynamic roughness length and thermal properties across debriscovered glaciers is needed.
ABSTRACT. Distributed energy-balance melt models have rarely been applied to glaciers with extensive supraglacial debris cover. This paper describes the development of a distributed melt model and its application to the debris-covered Miage glacier, western Italian Alps, over two summer seasons. Subdebris melt rates are calculated using an existing debris energy-balance model (DEB-Model), and melt rates for clean ice, snow and partially debris-covered ice are calculated using standard energy-balance equations. Simulated sub-debris melt rates compare well to ablation stake observations. Melt rates are highest, and most sensitive to air temperature, on areas of dirty, crevassed ice on the middle glacier. Here melt rates are highly spatially variable because the debris thickness and surface type varies markedly. Melt rates are lowest, and least sensitive to air temperature, beneath the thickest debris on the lower glacier. Debris delays and attenuates the melt signal compared to clean ice, with peak melt occurring later in the day with increasing debris thickness. The continuously debris-covered zone consistently provides $30% of total melt throughout the ablation season, with the proportion increasing during cold weather. Sensitivity experiments show that an increase in debris thickness of 0.035 m would offset 18C of atmospheric warming.
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