Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Abstract

In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the "top surface model" or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.