Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model |
| |
| Authors: | Kazukiyo Yamamoto Tomoaki Takahashi Yousuke Miyachi Naoto Kondo Shinichi Morita Motohiko Nakao Takashi Shibayama Yoshiyuki Takaichi Masashi Tsuzuku Naoaki Murate |
| |
| Institution: | (1) Laboratory of Forest Environment and Resources, Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan;(2) Japan Science and Technology Agency/CREST, Kawaguchi, Japan;(3) Forestry and Forest Products Research Institute (FFPRI), Tsukuba, Japan;(4) Fujitsu FIP Corporation, Tokyo, Japan;(5) The Norinchukin Bank, Tokyo, Japan;(6) PASCO, Tokyo, Japan;(7) Nakanihon Air Service, Aichi, Japan |
| |
| 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 (MTH
E) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTH
E and the observed mean tree height (MTH
O) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly
1-m accuracy. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
