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Open AccessArticle

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech Republic
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 1027;
Received: 17 December 2018 / Revised: 20 February 2019 / Accepted: 23 February 2019 / Published: 28 February 2019
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Earth and Environmental Sciences)
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. View Full-Text
Keywords: UAV; forest; disturbance; snow depth; leaf area index; canopy closure UAV; forest; disturbance; snow depth; leaf area index; canopy closure
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MDPI and ACS Style

Lendzioch, T.; Langhammer, J.; Jenicek, M. Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry. Sensors 2019, 19, 1027.

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