The potential of UAV photogrammetry for hydro-glaciological forecasts

ISSN:0374-005

The potential of UAV photogrammetry for hydro-glaciological forecasts

In recent decades, glaciers worldwide have lost a significant part of their volume due to increasing atmospheric temperatures. In the future, these large storages of fresh water resources will continue to shrink. Glacio-hydrological models predict that glacier retreat will first induce an increased release of melt water until a certain peak is reached, followed by a decrease in runoff due to a reduction in glacier size and volume. For some glaciers, this peak has already occurred. By 2100, many regions of the world will likely lose more than 75% of their current glacier volume. Declining glacier volumes, in turn, will lead to a smaller contributions of glacier meltwater to streamflow, resulting in direct consequences for agriculture, drinking water and hydropower for instance. The anticipated trend in future glacier runoff is very similar across glacio-hydrological models. However, different sources of errors in the models themselves and in the model input, lead to large uncertainties in the predictions. This thesis, composed of three main studies, examines the uncertainties linked to the models’ input, namely the meteorological forecasts and the glacier-related datasets (i.e. Digital Surface Models (DSMs), ice thickness or snow height distribution). The ultimate goal is to help improving the next generation of runoff forecasts. The first study is related to the model meteorological inputs. The aim was to assess how the skill (i.e. the quality) of temperature and precipitation forecasts are influencing the skill of the resulting runoff predictions. A synthetic experiment was set up in which meteorological forecasts ranging over a spectrum of different skills were created. The meteorological forecasts were fed into a glacio-hydrological model, and the skill of the resulting runoff forecasts was assessed. The same experiment was performed for catchments with different degrees of glacierization. The results show that temperature and precipitation have an influence on runoff that depends on the catchment’s degree of glacierization, with the importance of accurate precipitation forecasts increasing at the expenses of accurate temperature projections with a decreasing degree of glacierization. The second and third studies are related to the models' glacier-related inputs. The aim was to investigate the potential of Unmanned Aerial Vehicle (UAV) photogrammetry to derive datasets including high-resolution ortho-images, DSMs of the glacier surface, and ice-flow velocity fields. First, the focus was set on assessing the accuracy of the DSMs, and how the number of Ground Control Points (GCPs), used to geo-reference the DSMs, affects them. To answer this question, several UAV surveys were performed on three alpine glaciers during different seasons. The results show that not only the number but also the distribution of GCPs affects the accuracy of UAV-derived DSMs. Once the recipe to derive accurate datasets was found, the ortho-images and DSMs were used to derive surface velocity fields, which is the subject of the third study. The goal was to develop a new procedure that derives (a) robust surface displacements and (b) an estimate of the result’s accuracy. The procedure consists of generating many different surface displacements based on various filters and matching functions, and to stack the so-obtained ensemble of results into a final surface velocity field. This procedure was embedded in a new tool that works in a semi-automated way. The tool was tested on three different types of glaciers, namely a calving glacier, an alpine glacier and a rock glacier. The experiments showed that it successfully generated robust velocity fields for all test sites and that the vector fields cover a large spatial extent. This thesis demonstrates that in high mountain areas, UAV photogrammetry allows to obtain high-resolution datasets with centimeter to decimeter accuracy. The potential of UAV photogrammetry to derive insightful snow and glacier-related datasets is high, but the method is currently limited to small catchments of several square kilometers, due to short flying time and geo-referencing requirements. Technical advancements and innovation in the field are fast, and UAV photogrammetry might become a valuable tool for surveying larger areas in the near future

Skill transfer from meteorological to runoff forecasts in glacierized catchments

Runoff predictions are affected by several uncertainties. Among the most important ones is the uncertainty in meteorological forcing. We investigated the skill propagation of meteorological to runoff forecasts in an idealized experiment using synthetic data. Meteorological forecasts with different skill were produced with a weather generator and fed into two different hydrological models. The experiments were repeated for two glacierized catchments of different sizes and morphological characteristics, and for scenarios of different glacier coverage. The results show that for catchments with high glacierization (>50%), the runoff forecast skill is more dependent on the skill of the temperature forecasts than the one for precipitation. This is because snow and ice melt are strongly controlled by temperature. The influence of the temperature forecast skill diminishes with decreasing glacierization, while the opposite is true for precipitation. Precipitation starts to dominate the runoff skill when the catchment’s glacierization drops below 30%, or when the total contribution of ice and snow melt is less than about 60%. The skill difference between meteorological forecasts and runoff predictions proved to be independent from the lead time, and all results were similar for both the considered hydrological models. Our results indicate that long-range meteorological forecasts, which are typically more skillful in predicting temperature than precipitation, hold particular promise for applications in snow- and glacier-dominated catchments.ISSN:2157-758

Accuracy assessment of digital surface models from Unmanned Aerial Vehicles' imagery on glaciers

The use of Unmanned Aerial Vehicles (UAV) for photogrammetric surveying has recently gained enormous popularity. Images taken from UAVs are used for generating Digital Surface Models (DSMs) and orthorectified images. In the glaciological context, these can serve for quantifying ice volume change or glacier motion. This study focuses on the accuracy of UAV-derived DSMs. In particular, we analyze the influence of the number and disposition of Ground Control Points (GCPs) needed for georeferencing the derived products. A total of 1321 different DSMs were generated from eight surveys distributed on three glaciers in the Swiss Alps during winter, summer and autumn. The vertical and horizontal accuracy was assessed by cross-validation with thousands of validation points measured with a Global Positioning System. Our results show that the accuracy increases asymptotically with increasing number of GCPs until a certain density of GCPs is reached. We call this the optimal GCP density. The results indicate that DSMs built with this optimal GCP density have a vertical (horizontal) accuracy ranging between 0.10 and 0.25 m (0.03 and 0.09 m) across all datasets. In addition, the impact of the GCP distribution on the DSM accuracy was investigated. The local accuracy of a DSM decreases when increasing the distance to the closest GCP, typically at a rate of 0.09 m per 100-m distance. The impact of the glacier’s surface texture (ice or snow) was also addressed. The results show that besides cases with a surface covered by fresh snow, the surface texture does not significantly influence the DSM accuracy.ISSN:2072-429

Low-altitude UAV-borne remote sensing in dunes environment: Shoreline monitoring and coastal resilience

UAV systems, fitted with either active or passive surveying sensors, can provide land-related measures and quantitative information with low costs and high resolution in both space and time. Such surveying systems can be quite valuable in defining geometrical and descriptive parameters in coastal systems, especially dune ecosystems. The present work is based on a survey of the dune system at the mouth of the Fiume Morto Nuovo in the San Rossore Estate (Pisa) and focuses on comparing LiDAR with UAV- and airplane-borne photogrammetry, as well as the respective 2D and 3D cartographic output, in order to assess topography changes along a stretch of coastline and to check their possible use in defining some ecological resilience features on coastal dune systems. Processing of survey data generates a Digital Surface Model (DSM) or Digital Terrain Model (DTM) and an orthophotograph, checked for accuracy and image resolution. Comparison of these products against those available in public access cartographical databases highlights differences and respective strengths