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Updated: Jan 28, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry.

Theodora Lendzioch1, Jakub Langhammer2, Michal Jenicek3

  • 1Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Albertov 6, 128 43 Prague, Czech Republic. theodora.lendzioch@natur.cuni.cz.

Sensors (Basel, Switzerland)
|March 3, 2019
PubMed
Summary

This study introduces Unmanned Aerial Vehicle (UAV) imaging to simultaneously measure snow depth and Leaf Area Index (LAI). This novel approach simplifies snow dynamics assessment in national parks.

Keywords:
UAVcanopy closuredisturbanceforestleaf area indexsnow depth

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Area of Science:

  • Environmental Monitoring
  • Remote Sensing
  • Forestry

Background:

  • Accurate snow depth and Leaf Area Index (LAI) are crucial for understanding snow dynamics and their impact on ecosystems.
  • Traditional methods for measuring snow depth and LAI can be labor-intensive and spatially limited.
  • Unmanned Aerial Vehicle (UAV) technology offers potential for more efficient and detailed environmental assessments.

Purpose of the Study:

  • To develop and validate a novel approach using UAV imaging for the simultaneous assessment of snow depth and winter Leaf Area Index (LAI).
  • To evaluate the accuracy of UAV-derived snow depth and LAI compared to ground-based measurements and conventional instruments.
  • To explore the utility of UAV photogrammetry in characterizing forest canopy properties relevant to snowpack studies.

Main Methods:

  • High-resolution digital surface models (DSMs) from multi-temporal UAV imagery were used to derive snow depth by differencing snow-free and snow-covered terrain.
  • Downward-looking UAV images were analyzed using the snow background to estimate winter Leaf Area Index (LAI) in a Norway spruce forest.
  • UAV-derived snow depth and LAI were validated against manual snow probes, a denser network of snow measurements, and conventional instruments like the LAI-2200 and digital hemispherical photography (DHP).

Main Results:

  • UAV-based snow depth estimation achieved a Root Mean Square Error (RMSE) of 0.08-0.15 m when compared to ground control points (GCPs).
  • Comparative analysis with a denser manual snow depth network yielded RMSEs between 0.16 m and 0.32 m.
  • UAV-derived winter LAI estimates were comparable to those obtained from the LAI-2200 plant canopy analyzer and DHP, though spring LAI was underestimated and snow depth overestimated by UAV photogrammetry.

Conclusions:

  • UAV imaging provides a viable and efficient method for conjointly assessing snow depth and winter LAI.
  • The accuracy of UAV-based DSMs is influenced by canopy density and ground properties.
  • This combined UAV approach holds significant potential for future snow dynamics studies, simplifying data acquisition and analysis.