Abstract
Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km2 snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, QΔD) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the QΔD and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of QΔD and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and QΔD; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and QΔD; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible.
Highlights
Snowfall in most regions of Korea occurs in the form of shallow snow, a term referring to snow with depth of less than 20 cm
NMAD validated based on 21 in situ measurements is 1.78 cm, which is significantly lower than other previous studies; (2) This study analyzed how the accuracy of the estimated snow depth varies with regard to varying conditions of photography, such as altitude, photograph overlap ratio, time of survey, and condition of Ground Control Points (GCPs)
RMSE = Σin=1 ∆Di 2 /n where Dobs,i and DU AV,i represent the observed in situ snow depth and the snow depth estimated from the Unmanned Aerial Vehicles (UAVs) photogrammetry at the ith measurement location, respectively, and
Summary
Snowfall in most regions of Korea occurs in the form of shallow snow, a term referring to snow with depth of less than 20 cm. Shallow snowpacks increase surface albedo, causing the depth of frozen soil to increase [1]. While efforts have been made to estimate the spatial variability of shallow snow depths [11,12,13], most publicly available snow depth data are provided in the format of a set of sparsely placed point measurements or of low-resolution raster data [14] that are not accurate and precise enough to be used as input for hydrological models aimed at estimating snowmelts, groundwater recharge, and soil erosion. The techniques of photogrammetry [32,33] can construct three-dimensional models of snow fields from multiple photographs taken from UAV, so it has been recently applied to estimate the snow depth [34,35,36].
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