Abstract
This article presents unmanned aerial system (UAS)-based photogrammetry as an efficient method for the estimation of snow-field parameters, including snow depth, volume, and snow-covered area. Unlike similar studies employing UASs, this method benefits from the rapid development of compact, high-accuracy global navigation satellite system (GNSS) receivers. Our custom-built, multi-sensor system for UAS photogrammetry facilitates attaining centimeter- to decimeter-level object accuracy without deploying ground control points; this technique is generally known as direct georeferencing. The method was demonstrated at Mapa Republiky, a snow field located in the Krkonose, a mountain range in the Czech Republic. The location has attracted the interest of scientists due to its specific characteristics; multiple approaches to snow-field parameter estimation have thus been employed in that area to date. According to the results achieved within this study, the proposed method can be considered the optimum solution since it not only attains superior density and spatial object accuracy (approximately one decimeter) but also significantly reduces the data collection time and, above all, eliminates field work to markedly reduce the health risks associated with avalanches.
Highlights
Environmental mapping embodies a relevant target field for unmanned aerial system (UAS)-based photogrammetry
This is somewhat unusual, considering the fact that the area is located on a south-facing slope exposed to sunlight during most of the day; the reason rests in the amount of snow contained within the field, where the snow depth (SD) significantly exceeds the average value in the given location
The global navigation satellite system (GNSS)/INS logging was triggered with the camera shutter, and the number of records contained in the log file equaled that of images captured
Summary
Environmental mapping embodies a relevant target field for unmanned aerial system (UAS)-based photogrammetry. The basic parameter rests in determining the presence of snow, namely the snow-covered area (SCA). Such information is of interest mainly for the investigation of trends in large areas (or, more concretely, at the level of regions and countries) and finds use in climate and hydrological research. The snow field (representing the object, with a color approaching white) in our study area is clearly separable from the snow-free terrain (or the background, covered with vegetation) via thresholding since the two classes are characterized by different levels in both red-green-blue (RGB) channels and the grayscale interpretation. Once the snow has been separated from the background, the snow field, Mapa Republiky, must be separated from the snow cover in its vicinity This step was performed using the bwlabel function, which labels connected components in the binary image. All the approaches are discussed in the final sections of this article
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