Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, Harbin 150040, China.
Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, Harbin 150040, China.
Sci Total Environ. 2024 Feb 10;911:168726. doi: 10.1016/j.scitotenv.2023.168726. Epub 2023 Nov 23.
Planted forests play a crucial role in addressing global climate change and are also valued globally for their numerous ecosystem services. Therefore, it is essential to understand how biotic and abiotic factors affect the carbon sequestration potential. This study focuses on quantifying the effects of 26 different variables on the carbon sequestration potential of Larix spp. plantations in northeast China, utilizing the random forest algorithm (RF). To eliminate the age-related tendency of stand carbon stock, a novel carbon sequestration index (CSI) was defined, which measures the ratio of actual to predicted stand carbon stocks for a stand at a specific age. The results indicated that the developed RF model explained approximately 64.75 % of the variations of CSI. Among the four categories of variables analyzed, stand variables (35.73 %) contributed significantly more than terrain variables (3.31 %), soil variables (3.68 %), and climate variables (9.06 %). The partial dependence analysis revealed that the Larix spp. plantations had a potential maximum carbon stock of approximately 73.34 t·ha. This potential was associated with certain attributes, including a stand mean diameter of 15 cm, a stand density of 1700 trees·ha, a stand basal area of 30 m·ha, and a crown density of 0.7, respectively. These findings provide insightful information for plantation management to improve stand carbon stocks in northeast China with attempting to mitigate climate change.
人工林在应对全球气候变化方面发挥着至关重要的作用,其众多的生态系统服务功能在全球范围内也受到重视。因此,了解生物和非生物因素如何影响碳固存潜力至关重要。本研究利用随机森林算法(RF),重点定量研究了 26 个不同变量对中国东北落叶松人工林碳固存潜力的影响。为了消除林分碳储量与林龄相关的趋势,定义了一个新的碳固存指数(CSI),该指数衡量了特定林龄林分实际和预测碳储量的比值。结果表明,所开发的 RF 模型解释了 CSI 变化的约 64.75%。在所分析的四类变量中,林分变量(35.73%)的贡献明显大于地形变量(3.31%)、土壤变量(3.68%)和气候变量(9.06%)。偏依赖分析表明,落叶松人工林的潜在最大碳储量约为 73.34 t·ha-1。这一潜力与某些属性有关,包括林分平均直径为 15 cm、林分密度为 1700 株·ha-1、林分基面积为 30 m·ha-1和冠密度为 0.7。这些发现为人工林管理提供了有价值的信息,有助于提高中国东北的林分碳储量,从而尝试缓解气候变化。