Image acquisition effects on Unmanned Air Vehicle snow depth retrievals

Tekeli, Ahmet Emre; Dönmez, Senayi

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 minline-formula2. 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.



Tekeli, Ahmet Emre / Dönmez, Senayi: Image acquisition effects on Unmanned Air Vehicle snow depth retrievals. 2018. Copernicus Publications.


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Rechteinhaber: Ahmet Emre Tekeli

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