Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry
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 mu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/5/1027 |
| _version_ | 1797999312526901248 |
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| author | Theodora Lendzioch Jakub Langhammer Michal Jenicek |
| author_facet | Theodora Lendzioch Jakub Langhammer Michal Jenicek |
| author_sort | Theodora Lendzioch |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:01:41Z |
| format | Article |
| id | doaj.art-281fa134c10d44b09b1c07575cc5a557 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:01:41Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-281fa134c10d44b09b1c07575cc5a5572022-12-22T04:28:30ZengMDPI AGSensors1424-82202019-02-01195102710.3390/s19051027s19051027Estimating Snow Depth and Leaf Area Index Based on UAV Digital PhotogrammetryTheodora Lendzioch0Jakub Langhammer1Michal Jenicek2Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech RepublicDepartment of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech RepublicDepartment of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech RepublicThis 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.https://www.mdpi.com/1424-8220/19/5/1027UAVforestdisturbancesnow depthleaf area indexcanopy closure |
| spellingShingle | Theodora Lendzioch Jakub Langhammer Michal Jenicek Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry Sensors UAV forest disturbance snow depth leaf area index canopy closure |
| title | Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry |
| title_full | Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry |
| title_fullStr | Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry |
| title_full_unstemmed | Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry |
| title_short | Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry |
| title_sort | estimating snow depth and leaf area index based on uav digital photogrammetry |
| topic | UAV forest disturbance snow depth leaf area index canopy closure |
| url | https://www.mdpi.com/1424-8220/19/5/1027 |
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