【目的】在我国“双碳”战略与森林资源监测数字化转型的背景下,传统森林蓄积量监测方法存在成本高、时效性差、小样本下精度不足等问题。为支持市级“年度出数”的业务需求,本研究提出一种基于数据同化算法的年度森林蓄积量更新方法,通过融合不同林分类型的最优广义代数差分生长方程与抽样技术,以提升估计效率与精度。【方法】基于黑河市1995—2015年5期连续清查数据,选取434块固定样地,采用不放回抽样模拟“每年调查1/5样地”的轮换调查机制,构建混合估计模型,并与简单随机抽样进行对比,以评估其估计精度与方差控制能力;进一步设置1/7样本量的试验,检验模型在小样本下的鲁棒性。【结果】混合估计模型的性能优于传统方法:在1/5样本下,其总体蓄积量估计值更接近真值,估计标准误(0.8~4.2 m³/hm²)显著低于简单随机抽样(1.8~4.7 m³/hm²);即使在抽样量缩减至1/7(相当于抽样强度降低约28.6%)的情况下,其标准误(1.9~4.5 m³/hm²)仍低于简单随机抽样(2.7~5.6 m³/hm²),表现出良好的小样本适应性与收敛稳定性。【结论】该模型通过融合广义代数差分方程与统计抽样,在显著降低抽样强度的同时,仍能有效控制估计误差,兼具较高精度与强稳健性,为基于小样本的森林资源年度动态监测提供可靠方法,并为市级尺度年度监测提供可行的技术路径;所采用的数据同化框架降低了对初始参数的敏感性,适用于复杂生态系统的时序建模;研究结果可为森林碳汇计量与国际履约提供数据基础,并具备向湿地、草原等自然资源动态监测推广的潜力。

【Objective】In the context of China’s “Dual Carbon” strategy and the digital transformation of forest resource monitoring, traditional forest stock volume monitoring methods still face challenges such as high cost, low timeliness, and insufficient accuracy under small-sample conditions. To address these issues and meet the municipal demand for “Annual data release”, this study proposes an annual forest stock volume updating method based on a data assimilation algorithm, integrating optimal generalized differential stand growth equations for different forest types with sampling techniques.【Method】Using five consecutive forest inventory datasets from Heihe City collected between 1995 and 2015, we selected 434 permanent sample plots. A non-replacement sampling scheme was employed to simulate the operational rotation mechanism of surveying 1/5 of the plots each year. A hybrid estimator model was then constructed, and its performance was compared with that of simple random sampling (SRS) in terms of estimation accuracy and variance control. In addition, a 1/7 sampling experiment was designed to further evaluate the robustness of the method under reduced sampling intensity. 【Result】The hybrid estimator model outperformed the traditional method under both sampling scenarios. Under the 1/5 sampling scheme, the estimated total stock volume obtained by the mixed estimator was closer to the true value, with a standard error of 0.8-4.2 m³/hm², which was substantially lower than that of SRS (1.8-4.7 m³/hm²). Even when the sampling proportion was reduced to 1/7, corresponding to an approximately 28.6% decrease in sampling intensity, the standard error of the hybrid estimation model remained lower (1.9-4.5 m³/hm²) than that of simple random sampling (2.7-5.6 m³/hm²). These findings indicate that the hybrid estimation model has good adaptability and convergence performance under small-sample conditions. 【Conclusion】By integrating theoretical growth equations with statistical sampling, the proposed model effectively controls estimation error while substantially reducing sampling intensity, thereby maintaining high precision and strong robustness. It provides a reliable solution for annual dynamic monitoring of forest resources based on small samples and offers a practical technical pathway for municipal-level annual monitoring. Moreover, its data assimilation framework avoids sensitivity to initial parameter values and is well suited for time-series modeling of complex ecosystems. The method can provide an important data foundation for carbon sink accounting and international compliance, and it also has the potential to be extended to the dynamic monitoring of other natural resources, such as wetlands and grasslands.