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Tree height explains stand volume of closed-canopy stands: Evidence from forest inventory data of China

Xu, Yanli, Li, Chao, Sun, Zhichao, Jiang, Lichun, Fang, Jingyun
Forest ecology and management 2019 v.438 pp. 51-56
biomass, canopy, carbon footprint, environmental factors, forest inventory, forest management, forest types, lidar, models, remote sensing, temperate forests, tree height, China
The use of tree height to estimate forest volume allows the calculation of forest biomass at large scales, and this calculation is of great importance in forest management and carbon accounting for temperate forests. Additionally, LiDAR (Light Detecting and Ranging) shows an increasing dependence on tree height in large-scale forest volume or biomass estimation and carbon accounting. However, the extent to which tree height determines forest volume remains controversial. Additionally, how the relationship between stand volume and tree height is modulated by canopy density has seldom been quantified at large scales across different forest types. This relationship may be important for improving large-scale forest volume or biomass estimations based on LiDAR technology. In this study, based on national forestry inventory data from China containing 34,130 forest plots, we examined the effect of canopy density on the relationship between tree height and stand volume across different forest types. The results showed forest height alone explained 72.9% of variation in stand volume across China and was far more powerful than canopy density (R2 = 49.7%). However, the relationship between stand volume and forest height was significantly affected by canopy density. When the effect of canopy density was included in the model, stand volume could be well predicted from forest height, with an R2 of 0.863. In closed-canopy stands, tree height could explain 91.5% of stand volume at a canopy density of 1.0. In conclusion, we provided the first national-scale model for estimating stand volume from forest height and canopy density across China with a large number of field-measured plots. The results were a robust predicted combination for forest volume, regardless of biotic and abiotic factors, in national scales across different forest types in China. Therefore, they can be utilized by LiDAR or other remote-sensing studies in the future.