Improving Water Table Interpolation Accuracy using High-resolution LiDAR-based Digital Elevation Models from Drone Surveys

Introduction:
Peatlands play a key role in storing carbon (C), as in their natural state they act as a C-sink by maintaining high water levels. When peatlands are drained for agricultural purposes, they are a significant source of greenhouse gas emissions. The water table's position relative to the soil surface significantly influences emissions. While current field-based methods to model hydrology are effective, they often lack scalability, highlighting the need for innovative approaches to accurately derive spatial water table levels. This study presents a scalable, high-resolution methodology for deriving Digital Elevation Models (DEMs) from Light Detection and Ranging (LiDAR) data and interpolating water level measurements to classify water level classes.
Therefore, we compared a publicly available DEM1 with a UAS (Unmanned Aerial System) LiDAR-DEM to quantify deviations from ground-truth elevation measurements. The primary objectives of the study were: a) to investigate the extent to which inaccuracies between the DEMs and ground-truth data can be quantified, and b) to evaluate the potential of UAS LiDAR-derived DEMs for deriving spatially distributed water levels using elevation data and gauge measurements.
Methods and Materials:
The study was conducted in the Hechtgrabenniederung near Rostock, Germany (54° 6′ N, 12° 7′ E). High-density LiDAR point clouds were generated using a DJI Matrice 300 drone, equipped with an L1 LiDAR and processed into a DEM with DJI Terra software. Water level time series were collected from an in-situ gauge measurement at a location within the study area. To evaluate accuracy, the publicly available DEM1 and the UAV LiDAR-derived DEM were validated against ground-truth elevation data points obtained through Real-Time Kinematic (RTK) measurements, with deviations quantified using statistical metrics. Finally, kriging was applied to interpolate water table levels from gauge measurements relative to the DEM, providing spatially resolved hydrological insights.
Preliminary result:
Preliminary results indicate that UAV LiDAR-derived DEMs offer greater accuracy and resolution compared to publicly available DEMs, especially in capturing heterogeneous topographic variations and temporal changes in peatland morphology resulting from deep drainage. The integration of kriging further refines the precision and spatial resolution of water table interpolations, enabling accurate derivation of water level classes. These results provide detailed insights into the temporal and spatial dynamics of peatland topography and water levels, particularly during transitional phases like post-rewetting.
Conclusion and Outlook:
The application of UAV LiDAR-derived DEMs for mapping peatland topography and water table levels has the potential to significantly improve accuracy and precision. This methodology demonstrates potential as a scalable technique for deriving hydrological parameters, effectively bridging the gap between field-based water table measurements and large-scale hydrological modeling. Future research will extend this approach to additional sites and leverage the more precise water level classes derived from LiDAR-DEM to advance the G-E-S-T approach (Gas-Emission-Type-Site), particularly during transitional phases such as post-rewetting, where vegetation is not adapted to the site conditions.