摘要:精确估算森林生物量对理解全球碳循环至关重要。已有的喀斯特森林地上生物量估算研究存在很高的不确定性，缺乏校准数据检验研究结果的精度。利用皆伐法，首次精确估算了我国西南贵州省中部喀斯特森林的地上生物量，并检验了已有生物量回归方程和平均标准木法对该喀斯特森林地上生物量的估算效果。该喀斯特森林的地上生物量为122.81 Mg/hm²，胸径（D）≥1 cm的木本植物、D<1 cm的木本植物和草本植物的地上生物量分别为120.00、2.56、0.24 Mg/hm²。D在10-30 cm范围内的植株（83.89 Mg/hm²）是地上生物量的主要贡献者。4个优势树种（云南鼠刺Itea yunnanensis、川钓樟Lindera pulcherrima、猴樟Cinnamomum bodinieri和化香树Platycarya strobilacea的地上生物量为103.03 Mg/hm²，占森林总地上生物量的83.89%。干（61.04 Mg/hm²）和枝（40.56 Mg/hm²）的生物量远高于皮（11.61 Mg/hm²）和叶（6.80 Mg/hm²）。在物种水平上，已有生物量回归方程（误差-56.10%-84.61%）和平均标准木法（误差-36.43%—-5.14%）对该喀斯特森林地上生物量的估算效果均较差。最后，建立了5个新的生物量回归方程。本研究可为我国西南喀斯特地区精确估算森林碳储量提供基础校验数据和方法指导。



Abstract:Accurate estimate of forest biomass is essential to understand the global carbon cycle. Existing studies on aboveground biomass (AGB) estimate conducted in karst forests present high uncertainties, and no benchmarking and validation data can be used to evaluate their accuracy. In the present study, the AGB of a karst forest in central Guizhou Province, southwestern China, was accurately estimated for the first time on the basis of a clear cutting plot. The performances of existing allometric models and average standard tree method were also tested. The accurate AGB of the karst forest was 122.81 Mg/hm². Living woody individuals with diameter at breast height (D) ≥ 1 cm and D < 1 cm and herbs were 120.00 Mg/hm², 2.56 Mg/hm², and 0.24 Mg/hm², respectively. Individuals within 10-30 cm (83.89 Mg/hm²) D classes were the major AGB contributors. The four dominant species (Itea yunnanensis, Lindera pulcherrima, Cinnamomum bodinieri and Platycarya strobilacea) with 103.03 Mg/hm² AGB accounted for 83.89% of the total forest AGB. Stem (61.04 Mg/hm²) and branch (40.56 Mg/hm²) exhibited much higher AGB than bark (11.61 Mg/hm²) and leaf (6.80 Mg/hm²). Both existing allometric models (bias: -56.10% to 84.61%) and average standard tree method (-36.43% to -5.14%) had low performances indicated by high bias when estimating AGB at species level. Finally, five new local allometric models were developed. These results provide benchmarking data and further guidance for accurate estimate of forest carbon stocks in karst geomorphologies in southwestern China.





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