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机构地区:[1]中国科学院城市环境研究所 城市环境与健康重点实验室,厦门361021 [2]中国科学院大学,北京100049 [3]福建省永丰国有林场,漳州363602
出 处:《林业科学》2014年第2期92-98,共7页Scientia Silvae Sinicae
基 金:国家林业公益性行业科研专项(201304205);国家自然科学基金青年科学基金项目(31200363);国家科技支撑计划(2011BAC09B04);福建省重点项目(2011Y0052)
摘 要:森林生物量受多重因素的综合影响,准确估算区域森林生物量可以为森林可持续经营管理提供依据。以福建省南靖县为研究区,基于森林资源清查数据和野外实测数据,利用缺省的和本地化的生物量模型分别得到区域桉树林生物量的估算值,然后利用BRT(boosted regression trees)方法解析区域生物量估算差异的来源及其相对贡献率。结果表明:利用缺省的生物量模型推算的桉树林生物量比本地化的生物量模型推算的高估了20.88%。林分条件是估算森林生物量的主导因素,3个变量(林龄、平均胸径和林分密度)对森林生物量估算差异的贡献率达70.94%,其中林龄是导致生物量估算差异的最主要因素(54.92%),并且生物量估算差异随林龄增长而逐渐减小。环境因素(地形和土壤)对森林生物量估算的影响较小,二者对生物量估算差异的贡献率为29.06%,其中,海拔和土层厚度分别是地形和土壤因素中造成森林生物量估算差异的最大因素。Sustainable forest management on regional scales attributed to accurate biomass estimates, which were influenced by multiple biotic and abiotic factors. Combining forest inventory data with yield sampling, we calculated biomass accumulation of Eucalypt plantation in Nanjing, a county in Fujian, and presented spatial distribution of biomass differences between values using local biomass model gained in this study and default biomass model in many existing references. The source of differences and their relative contribution rates were analyzed using BRT (boosted regression trees) method. The result indicated that biomass accumulation calculated using default biomass model was overestimated by 20.88%, and the largest error source ascribed to stand conditions which accounting for 70.94% of biomass differences. Among all variables, stand age was dominant due to contributing 54.92% of biomass differences which declined along with stand age growth. In addition, abiotic factors had less effect on biomass differences with 29.06% ; besides, elevation and soil depth were, respectively, the key variables among topography and soil factors.
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