Systematic Approach for Remote Sensing of Historical Conflict Landscapes with UAV-Based Laserscanning

Area of Science:

• Archaeological Prospection
• Geospatial Analysis
• Remote Sensing

Background:

• Drone-based LiDAR (Light Detection and Ranging) is increasingly vital for archaeological surveys and historical conflict landscape research.
• Aircraft-based LiDAR offers insufficient resolution for detailed small-scale analysis, necessitating high-resolution Unmanned Aerial Vehicle (UAV)-based LiDAR data.
• Existing studies often lack systematic approaches to UAV-LiDAR data acquisition and point cloud filtering.

Purpose of the Study:

• To systematically investigate factors influencing UAV-LiDAR data acquisition for detecting anthropogenic terrain anomalies.
• To optimize flight parameters, consider vegetation cover, and evaluate filter algorithms for improved ground point extraction.

Main Methods:

• Systematic variation of flight parameters: speed (e.g., 6 m/s) and altitude above ground (recommended 50-75 m).
• Comparison of data acquisition during different seasons and under varying vegetation cover (treeless, low-level vegetation, deciduous forest).
• Evaluation of three distinct filter algorithms for separating ground from non-ground points.

Main Results:

• Higher flight speeds (up to 6 m/s) are feasible for detecting anomalies in treeless terrain.
• Recommended flight altitude is 50-75 m; higher altitudes (100-120 m) risk missing small terrain features (<50 cm).
• The morphological filter is most suitable for low-level vegetation; filter choice is less critical in tree-covered areas during winter.

Conclusions:

• Systematic UAV-LiDAR data acquisition protocols are essential for accurate archaeological prospection.
• Flight parameters, seasonal timing, and vegetation type significantly impact data quality and anomaly detection.
• Appropriate filter selection, especially in vegetated areas, enhances the reliability of ground point extraction for historical landscape analysis.