Development of a three dimensional structure analysis method of deciduous broadleaf forest using airborne LiDAR data

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K06123
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 40010:Forest science-related
• Research Institution: Gifu University
• Principal Investigator: Awaya Yoshio 岐阜大学, 流域圏科学研究センター, 教授 (90353565)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: LiDAR / 落葉広葉樹林 / 葉面積指数 / Beer-Lambert則 / 吸光係数 / 階層別

Outline of Final Research Achievements

A leaf area index (LAI) estimation method for deciduous broadleaf forest was developed based on the 3D structure of canopies using an airborne laser scanning data obtained in August, 2011 in the Namai river basin, Gifu prefecture. Vertical leaf area distribution per plot was computed using tree measurement records and models which estimate leaf amount and its vertical distribution. Extinction coefficients of the Beer-Lambert law for three normalized canopy height layers (0-33%,34-66%,67-100%) was estimated using the computed leaf area in the three layer. LAI was estimated using the extinction coefficients for the three layers and the Beer-Lambert law. Saturation in LAI estimation was reduced greatly and accuracy was improved much better than traditional methods by treating canopy as one layer.

Free Research Field

森林科学

Academic Significance and Societal Importance of the Research Achievements

樹木の葉は光合成により大気中の二酸化炭素から炭素を固定して木部に貯蔵する。樹木が固定する炭素量を正確に推定するには葉量（葉面積指数：LAI）を正確にマッピングする必要があるが、LAI推定に広く利用されているBeer-Lambert則ではLAIが４を超える場合には推定値が飽和して高精度の推定は困難だった。本研究では林冠を３層（葉層、中間層、幹層）に分けてLAIの推定精度を大幅に改善できた。森林の炭素固定能を評価する基礎データであるLAIの推定精度が向上することにより林の炭素固定能の評価精度が向上し、ひいては温暖化に対する適応策の改善に貢献すると期待される。