Forest biomass patterns across northeast China are strongly shaped by forest height
Forest biomass patterns across northeast China are strongly shaped by forest height
复制标题
DOI：
10.1016/j.foreco.2013.01.001
复制
发表时间：
2013-04
影响因子：
3.7
通讯作者：
Xiangping Wang;S. Ouyang;O. Sun;Jingyun Fang
中科院分区：
文献类型：
--
作者：
Xiangping Wang;S. Ouyang;O. Sun;Jingyun Fang
近年来，星载激光雷达（LiDAR）技术的出现为监测全球森林生物量和碳源/汇格局提供了一种很有前途的方法。然而，很少有研究探讨了各种非生物和生物因子在调节森林生物量和高度之间的关系在大尺度上的作用。这一点很重要，因为人们越来越依赖LiDAR导出的森林高度作为森林生物量的预测因子。本文以东北地区529块样地为研究对象，探讨了从高度估算森林生物量的方法。结果表明，林高和平均树高与林分生物量或平均单茎生物量关系密切（R2在0.57 ~ 0.78之间），但基于平均树高的两种方法都不能可靠地预测林分生物量。与此相反，当气候和森林集团的影响，在模型中，森林高度可以预测生物量格局的R2在0.74（地下）和0.91（总生物量）之间，这是广泛接受的生物量扩展因子方法（R2在0.72和0.98之间）。我们还发现，地上和地下生物量与森林高度的比值（B/H比值）在大尺度上大致相似，表明森林生物量格局强烈地受森林高度的影响。然而，B/H比值在中国东北的落叶林和万年青林之间表现出显著差异。林分生物量与林高的关系主要受林分生活型的影响，其次是气候、林分类型和森林起源。我们的研究结果强烈支持使用激光雷达来监测地上和地下森林生物量的大规模销售模式。我们的分析还发现，森林高度信息的缺乏，在以前的文献中，导致大部分的生物量数据不能被用来估计生物量格局从高度，我们建议未来的分析报告森林高度与实地观察的生物量。
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 529plots 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 (R2between 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 R2between 0.74 (belowground) and 0.91 (total biomass), which was comparable to the widely accepted biomass expansion factor method (R2between 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.