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

## TL;DR

This study shows how LiDAR data can be used to estimate tree growth in forests, improving forest management.

## Contribution

A new methodology was developed to assess tree growth using publicly available LiDAR data and inventory data.

## Key findings

- A moderate correlation (Pearson coefficient of 0.6) was found between LiDAR and inventory height increments.
- Using LiDAR during the growing season can reduce errors in height estimation.
- Airborne LiDAR data can effectively support and improve forest inventory and management.

## Abstract

The use of LiDAR in estimating tree growth is a current and practical research topic that is important from both an ecological and forest management perspective. The aim of this study was to assess the possibility of applying publicly available LiDAR data to assess the growth of forest stands. This study focused on forests in northern Poland, where pine trees dominate, but deciduous trees such as alders and birches are also partially present. The research used generally available point clouds from airborne LiDAR data from the years 2013 and 2022 with an average density of 4 pts/m2 and an accuracy of 0.15–0.25 m. Inventory data were obtained for the same dates. A methodology was developed to determine height increments from these data, and 216 corresponding tree stands were compared. The Pearson correlation coefficient was 0.6, showing a moderate correlation between height increments determined from LiDAR and inventory data. Performing LiDAR measurements during the growing season could minimize errors in determining stand heights and increase the correction between airborne laser scanning data and inventory data. Our experiment confirms that it is possible to improve forest inventory and forest management using airborne LiDAR data.