Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials. Recent studies have shown that integrating process-based models (PBMs) with remote sensing data can enhance simulations from stand to regional scales, significantly improving the ability to simulate forest growth and carbon stock dynamics. However, the utilization of PBMs for large-scale simulation of larch carbon storage distribution is still limited. In this study, we applied the parameterized 3-PG (Physiological Principles Predicting Growth) model across the Mengjiagang Forest Farm (MFF) to make broad-scale predictions of the biomass and carbon stocks of Larix olgensis plantation. The model was used to simulate average diameter at breast height (DBH) and total biomass, which were later validated with a wide range of observation data including sample plot data, forest management inventory data, and airborne laser scanning data. The results showed that the 3-PG model had relatively high accuracy for predicting both DBH and total biomass at stand and regional scale, with determination coefficients ranging from 0.78 to 0.88. Based on the estimation of total biomass, we successfully produced a carbon stock map of the Larix olgensis plantation in MFF with a spatial resolution of 20 ​m, which helps with relevant management advice. These findings indicate that the integration of 3-PG model and remote sensing data can well predict the biomass and carbon stock at regional and even larger scales. In addition, this integration facilitates the evaluation of forest carbon sequestration capacity and the development of forest management plans.

中文翻译：

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将遥感与 3-PG 模型相结合模拟长白落叶松人工林生物量和碳储量


准确估计生物量及其时间动态对于监测森林生态系统中的碳循环和评估森林碳封存潜力至关重要。最近的研究表明，将基于过程的模型 （PBM） 与遥感数据相结合可以增强从林分到区域尺度的模拟，显著提高模拟森林生长和碳储量动态的能力。然而，PBMs 在落叶松碳储量分布的大规模模拟中的应用仍然有限。在这项研究中，我们在整个孟家港林场 （MFF） 中应用参数化 3-PG （生理原理预测生长） 模型，对长白落叶松人工林的生物量和碳储量进行了广泛的预测。该模型用于模拟胸高平均直径 （DBH） 和总生物量，随后通过广泛的观测数据进行了验证，包括样本地数据、森林管理清查数据和机载激光扫描数据。结果表明，3-PG 模型在林分和区域尺度上预测胸径和总生物量的准确性相对较高，决定系数范围为 0.78 至 0.88。基于对总生物量的估计，我们成功地制作了 MFF 长白落叶松人工林的碳储量图，空间分辨率为 20 m，这有助于提供相关的管理建议。这些发现表明，3-PG 模型与遥感数据的整合可以很好地预测区域甚至更大尺度的生物量和碳储量。此外，这种整合还有助于评估森林碳汇能力和制定森林管理计划。