Abstract
This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.
Highlights
Snow is an essential component of the hydrological cycle [1]
Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring leaf area index (LAI) was underestimated by using Unmanned Aerial Vehicle (UAV) photogrammetry method
Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics
Summary
Snow is an essential component of the hydrological cycle [1] It serves as a reliable water resource and the dynamics of snow cover play a vital function in rebalancing the global energy and water budget [2]. Environmental agents interact with snow in complex ways. Repeatedly affected by by bark beetle outbreaks. The theregion regionis is repeatedly affected bark beetle outbreaks. Recent large-scale beetle infestation is occurring in consequence of windstorms in Bavarian National. Park Park in 1984 that bark beetle infestation is occurring in consequence of windstorms in Bavarian. In the extensive forest disturbance is reaching the boundary part of part the mountain range. Result, the extensive forest disturbance is reaching the boundary of the mountain. National in 1984 started the bark beetlebeetle outbreak and were accelerated since Kyrill windstorm in 2007.inIn result, that started the bark outbreak and heavily were heavily accelerated since Kyrill windstorm
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