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 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 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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结合遥感和3-PG模型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的碳循环和评估森林碳汇潜力至关重要。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，从而显着提高模拟森林生长和碳储量动态的能力。然而，利用 PBM 大规模模拟落叶松碳储量分布仍然有限。在本研究中，我们在孟家岗林场 (MFF) 应用了参数化 3-PG（预测生长的生理原理）模型，对人工林的生物量和碳储量进行了大范围的预测。该模型用于模拟平均胸径（DBH）和总生物量，随后利用包括样地数据、森林管理库存数据和机载激光扫描数据在内的广泛观测数据进行验证。结果表明，3-PG模型对林分和区域尺度胸径和总生物量的预测具有较高的准确度，决定系数在0.78~0.88之间。基于总生物量的估算，我们成功制作了MFF种植园碳储量图，空间分辨率为20米，有助于提供相关管理建议。这些结果表明，3-PG模型与遥感数据的结合可以很好地预测区域乃至更大尺度的生物量和碳储量。此外，这种整合有利于森林固碳能力的评估和森林管理计划的制定。