Forest biomass patterns across northeast China are strongly shaped by forest height

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• 作者：Xiangping Wang^a ; ^b ; ^{wangxiangping@bjfu.edu.cn} ; Shuai Ouyang^a ; Osbert Jianxin Sun^a ; Jingyun Fang^b
• 关键词：Forest biomass ; Forest (tree) height ; Belowground biomass ; Canopy-tree life form ; Northeast China
• 刊名：Forest Ecology and Management
• 出版年：2013
• 出版时间：1 April, 2013
• 年：2013
• 卷：293
• 期：Complete
• 页码：149-160
• 全文大小：928 K

文摘

The emergency of satellite-borne light detecting and ranging (LiDAR) technology in recent years have provided a promising way to monitor worldwide patterns of forest biomass and carbon sources/sinks. However, few studies have examined the roles of various abiotic and biotic factors in modulating the relationship between forest biomass and height at a large scale. This is important given the growing dependence on LiDAR derived forest height as a predictor of forest biomass. In this analysis, we used 529 plots across northeast China to examine this question, and to explore the method to estimate forest biomass from height. Our results showed that, while forest height and average tree height showed close relationships with stand biomass or mean biomass per stem (R² between 0.57 and 0.78), stand biomass could not be reliably predicted with two methods based on average tree height. In contrast, when the effects of climate and forest groups were included in the models, forest height could predict biomass patterns with a R² between 0.74 (belowground) and 0.91 (total biomass), which was comparable to the widely accepted biomass expansion factor method (R² between 0.72 and 0.98). We also showed that the ratio of both aboveground and belowground biomass to forest height (B/H ratio) was roughly similar at a large scale, suggesting that forest biomass patterns are strongly shaped by forest height. However, B/H ratio showed significant difference between deciduous and evergreen forests across northeast China. The life form of canopy trees was the major factor modulating the relationships between stand biomass and forest height, while climate, forest type and forest origin played a secondary role. Our results strongly support the use of LiDAR to monitor the large-sale patterns of both above and belowground forest biomass. Our analysis also found that the lack of forest height information in previous literatures has caused most of the biomass data could not be utilized to estimate biomass patterns from height, and we advocate future analyses to report forest height together with field-observed biomass.