In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest

作者：Yunsheng Wang, Jiri Pyörälä, Xinlian Liang, Matti Lehtomäki, Antero Kukko, Xiaowei Yu, Harri Kaartinen, Juha Hyyppä · 发表于：Remote Sensing of Environment · 年份：2019 · DOI：10.1016/j.rse.2019.111309 · 被引用次数：102 · 研究领域：Remote Sensing and LiDAR Applications、Forest ecology and management、Forest Ecology and Biodiversity Studies

Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing int...