Forest biomass is strongly shaped by forest height across boreal to tropical forests in China

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Forest height is a major factor shaping geographic biomass patterns, and there is a growing dependence on forest height derived from satellite light detecting and ranging (LiDAR) to monitor large-scale biomass patterns. However, how the relationship between forest biomass and height is modulated by climate and biotic factors has seldom been quantified at broad scales and across various forest biomes, which may be crucial for improving broad-scale biomass estimations based on satellite LiDAR.


We used 1263 plots, from boreal to tropical forest biomes across China, to examine the effects of climatic (energy and water availability) and biotic factors (forest biome, leaf form and leaf phenology) on biomass–height relationship, and to develop the models to estimate biomass from forest height in China.

Important Findings

(i) Forest height alone explained 62% of variation in forest biomass across China and was far more powerful than climate and other biotic factors. (ii) However, the relationship between biomass and forest height were significantly affected by climate, forest biome, leaf phenology (evergreen vs. deciduous) and leaf form (needleleaf vs. broadleaf). Among which, the effect of climate was stronger than other factors. The intercept of biomass–height relationship was more affected by precipitation while the slope more affected by energy availability. (iii) When the effects of climate and biotic factors were considered in the models, geographic biomass patterns could be well predicted from forest height with an r2 between 0.63 and 0.78 (for each forest biome and for all biomes together). For most biomes, forest biomass could be well predicted with simple models including only forest height and climate. (iv) We provided the first broad-scale models to estimate biomass from forest height across China, which can be utilized by future LiDAR studies. (v) Our results suggest that the effect of climate and biotic factors should be carefully considered in models estimating broad-scale forest biomass patterns with satellite LiDAR.

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