Development of broadleaved forest measurement technology at the individual tree level using UAV-based laser and multispectral data

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K15870
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 40010:Forest science-related
• Research Institution: Shinshu University
• Principal Investigator: DENG SONGQIU 信州大学, 先鋭領域融合研究群山岳科学研究拠点, 特任助教 (00772477)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 森林計測 / リモートセンシング / ドローンレーザ / 多波長画像 / 広葉樹単木解析 / 樹種分類

Outline of Final Research Achievements

A highly accurate method of individual broadleaved tree detection was developed using UAV laser scanning data with high point density collected during leaf-off periods. Then, we established a tree species classification method at the individual tree level by combining the information of detected trees and high-resolution UAV multispectral imagery. The single tree detection and correction rates for all trees are 94.6% and 89.6%, whereas they are 96.6% and 94.3% for upper trees, respectively. Additionally, the accuracy of tree species classification on upper trees is between 73%~86%. Furthermore, we have developed a method for estimating forest volume at the stand level using the single-tree information of different species at the large scale. These research results were registered as patents and published on international English journals with open access, and our oral presentations at the specialized conferences were highly acclaimed.

Free Research Field

森林計測学

Academic Significance and Societal Importance of the Research Achievements

本研究は、ドローンレーザデータとUAV多波長画像の組み合わせから樹種別の立木位置、樹高、胸高直径と材積を算出することができ、林層構造が複雑な広葉樹林にも適用できる森林資源解析技術を開発し、広葉樹資源の有効活用に貢献できる。本研究で開発した広葉樹林の樹種別資源量を高精度に把握し、森林調査をせずに毎木の森林資源量が客観的かつ広域的に把握できることから、コストの削減効果が大きい。解析精度が実用化レベル以上に達成したため、日本の林業成長産業化に貢献できる。また、日本の森林だけではなく、広葉樹林の広い中国や東南アジアなど諸外国の森林にも応用可能なことから、国際共同研究にも貢献できる。