Abstract
Abstract. Advancements in technology have facilitated new opportunities in aerial photogrammetry; one of these is the use of unmanned aerial vehicles (UAVs) to estimate snow depth (SD). Here, a multi-rotor type UAV is used for SD retrievals over an area of 172 000 m2. Photos with 80 % forward and 60 % side overlaps were taken by UAV on two different (snow-covered and snow-free) days. SD estimations were obtained from the difference between 3-D stereo digital surface models (DSMs) produced for both days. Manual SD measurements were performed on the ground concurrent with UAV flights. The current study is unique in that the SD retrievals were derived using two different image acquisition modes. In the first, images were taken as UAV was continuously flying and in the second UAV had small stops and kept its position in air fixed as the photos were taken. Root mean square error of UAV derived SDs is calculated as 2.43 cm in continuous and 1.79 cm in fixed acquisitions. The results support the hypothesis, based on theoretical considerations, that fixed-position image acquisitions using multi-rotor platforms should enable more accurate SD estimates. It is further seen that, as SDs increased, the errors in SD calculations are reduced.
Highlights
Accurate estimation of water potential within the basin is important for optimum management of water resources
Before snow depth (SD) calculations, the geolocation (x, y, z) accuracies of digital surface models (DSMs) were assessed by comparing with ground control points (GCPs) measurements performed onsite
SD was determined for all locations using the imagery collected for unmanned aerial vehicles (UAVs) in-flight, it was not possible to determine SD in all cases Proc
Summary
Accurate estimation of water potential within the basin is important for optimum management of water resources. To this end, timely and accurate measurements of the rainfall and snowfall, which are major fresh water inputs into the basin, are needed. Reliable estimation of snow depth (SD) and snow water equivalent (SWE) which are indicators of water potential of the basin are very important for hydrological modelling, flood forecasting, avalanche mitigation and disaster management (Vander Jagt et al, 2015). Snow pillows and depth sensors improve the temporal resolutions of SD and SWE data obtained from the field. They provide the most reliable information about SD and SWE. Getting data from mountainous regions, where the main snowfall occurs, is limited due to safety and logistics
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