Yang Kun, Luo Kai, Zhang Jialong, Qiu Bo, Wang Feiping, Xiao Qinglin, Cao Jun, He Yunrun, Yang Jian
The Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Guangxi State-owned Gaofeng Forest Farm, Nanning, 530001, Guangxi, China.
Sci Rep. 2025 Jan 2;15(1):589. doi: 10.1038/s41598-024-84258-7.
Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored. In this study, a genetic algorithm (GA) was used to optimize the parameters of random forest (RF) to establish dynamic models to estimate the carbon sink intensity (CSI) of Pinus densata in Shangri-La and analyze the combined effects of multi-climatic factors on CSI. We found that (1) GA can effectively improve the estimation accuracy of RF, the R can be improved by up to 34.8%, and the optimal GA-RF model R is 0.83. (2) The CSI of Pinus densata in Shangri-La was 0.45-0.72 t C·hm from 1987 to 2017. (3) Precipitation has the most significant effect on CSI. The combined weak drive of precipitation, temperature, and surface solar radiation on CSI was the most dominant drive for Pinus densata CSI. These results indicate that dynamic models can be used for large-scale long-term estimation of carbon sink in highland forest, providing a feasible method. Clarifying the driving mechanism will provide a scientific basis for forest resource management.
准确估算森林碳汇并探索其气候驱动机制对于实现碳中和和可持续发展至关重要。较少有研究使用基于机器学习的动态模型来估算森林碳汇。香格里拉地区的气候驱动机制尚未得到探索。在本研究中，使用遗传算法（GA）优化随机森林（RF）的参数，以建立动态模型来估算香格里拉地区高山松的碳汇强度（CSI），并分析多种气候因素对CSI的综合影响。我们发现：（1）GA能够有效提高RF的估算精度，R最高可提高34.8%，最优GA-RF模型的R为0.83。（2）1987年至2017年期间，香格里拉地区高山松的CSI为0.45 - 0.72 t C·hm 。（3）降水对CSI的影响最为显著。降水、温度和地表太阳辐射对CSI的联合弱驱动是高山松CSI的最主要驱动因素。这些结果表明，动态模型可用于大规模长期估算高原森林的碳汇，提供了一种可行的方法。阐明驱动机制将为森林资源管理提供科学依据。
