Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L. plantations

胸径 激光扫描 均方误差 随机森林 树(集合论) 竞赛(生物学) 林业 松属 统计 森林健康 播种 遥感 体积热力学 数学 估计 环境科学 计算机科学 地理 生态学 激光器 农学 植物 生物 物理 机器学习 光学 数学分析 经济 量子力学 管理
作者
Matthew J. Sumnall,Timothy J. Albaugh,David R. Carter,Rachel L. Cook,W. Cully Hession,Otávio Camargo Campoe,Rafael Rubilar,Randolph H. Wynne,Valerie A. Thomas
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:44 (1): 217-247 被引量:7
标识
DOI:10.1080/01431161.2022.2161853
摘要

The competitive neighbourhood surrounding an individual tree can have a significant influence on its diameter at breast height (DBH) and individual tree stem volume (SV). Distance dependent competition index metrics are rarely recorded in traditional field campaigns because they are laborious to collect and are spatially limited. Remote sensing data could overcome these limitations while providing estimation of forest attributes over a large area. We used unoccupied aerial vehicle laser scanning data to delineate individual tree crowns (ITCs) and calculated crown size and distance-dependent competition indices to estimate DBH and SV. We contrasted two methods: (i) Random Forest (RF) and (ii) backwards-stepwise, linear multiple regression (LMR). We utilized an existing experiment in Pinus taeda L. plantations including multiple planting densities, genotypes and silvicultural levels. While the tree planting density did affect the correct delineation of ITCs, between 61% and 99% (mean 86%) were correctly linked to the planting location. The most accurate RF and LMR models all included metrics related to ITC size and competitive neighbourhood. The DBH estimates from RF and LMR were similar: RMSE 3.05 and 3.13 cm (R2 0.64 and 0.62), respectively. Estimates of SV from RF were slightly better than for LMR: RMSE 0.06 and 0.07 m3 (R2 0.77 and 0.70), respectively. Our results provide evidence that ITC size and competition index metrics may improve DBH and SV estimation accuracy when analysing laser-scanning data. The ability to provide accurate, and near-complete, forest inventories holds a great deal of potential for forest management planning.
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