Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar.

Area of Science:

• Geospatial Science
• Forestry
• Remote Sensing Technology

Background:

• Single photon lidar (SPL) is an emerging technology for characterizing forest structure and terrain.
• Large-area mapping requires efficient and accurate data acquisition methods.

Purpose of the Study:

• To evaluate the High Resolution Quantum Lidar System (HRQLS) for mapping forest structure and terrain in Garrett County, Maryland.
• To develop methods for filtering solar noise and deriving attributes from SPL point clouds.
• To validate SPL data against field measurements and existing discrete return lidar datasets.

Main Methods:

• Utilized the HRQLS instrument for a comprehensive lidar survey of Garrett County, USA.
• Developed novel algorithms for solar noise reduction in SPL data.
• Compared SPL-derived canopy and ground characteristics with ground-truth data and a discrete return lidar dataset.

Main Results:

• SPL data successfully characterized forest canopy structure and ground elevation.
• SPL-derived attributes showed similarity to discrete return lidar, with higher point density revealing more structural detail.
• Automated noise removal presented challenges, especially over high albedo surfaces, and instrument calibration is crucial.

Conclusions:

• High-resolution SPL is a viable and potentially cost-effective alternative to traditional lidar for large-area forest and terrain mapping.
• SPL data offers enhanced structural detail due to its high point density.
• Further research into automated noise filtering and calibration is recommended for optimal SPL application.