Stand Height Increment from Two-Epoch Aerial Laser Scanning Data and Inventory Data

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Estimating tree growth is crucial for ecological and forest management.
• Airborne Light Detection and Ranging (LiDAR) offers a practical approach for forest assessment.
• Publicly available LiDAR data presents an opportunity for widespread forest monitoring.

Purpose of the Study:

• To evaluate the feasibility of using publicly available LiDAR data for assessing forest stand growth.
• To compare height increments derived from LiDAR data with traditional inventory data.

Main Methods:

• Utilized airborne LiDAR point clouds from 2013 and 2022 (4 pts/m², 0.15-0.25 m accuracy) for forests in northern Poland.
• Collected corresponding forest inventory data for the same years.
• Developed a methodology to calculate height increments from LiDAR data and compared results for 216 tree stands.

Main Results:

• A moderate positive correlation (Pearson's r = 0.6) was found between height increments estimated by LiDAR and inventory data.
• The study identified potential for error reduction by conducting LiDAR measurements during the growing season.

Conclusions:

• Airborne LiDAR data can be effectively applied to estimate forest stand growth.
• This technology holds significant potential for enhancing forest inventory and management practices.