利用机器学习估算毛竹(Phyllostachys pubescens)林的固碳潜力并优化管理策略

Shaofeng Lv, Ning Yuan, Xiaobo Sun, Xin Chen, Yongjun Shi, Guomo Zhou, Lin Xu
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引用次数: 0

摘要

估算毛竹(Phyllostachys pubescens)林的固碳潜力和优化管理策略在提高质量和促进可持续发展方面发挥着关键作用。然而,目前尚缺乏基于固定样本精细调查的长期时间序列数据,模拟毛竹林固碳能力变化并筛选和优化最佳管理措施的方法。因此,本研究利用了浙江省从 2004 年至 2019 年的连续调查数据和固定样地的气候数据。通过比较四种不同的算法,即随机森林、支持向量机、XGBoost和BP神经网络,构建毛竹林地上碳储量模型。最终目标是确定最佳算法模型。此外,还考虑了未来碳储量的关键驱动参数,并预测了毛竹林未来的地上碳储量。然后根据这些预测结果制定最佳管理策略。结果表明,使用 XGBoost 算法构建的碳储量模型 R2 为 0.9895,均方根误差为 0.1059,性能最佳,被认为是最优算法模型。研究发现,对毛竹林植被碳储量影响最大的驱动参数是平均树龄、平均胸径和平均秆密度。在最佳管理措施下,1-3du竹子不采伐,4du竹子采伐30%,5du及以上竹子采伐80%。我们的预测表明,浙江省毛竹林的地上碳储量将在2046年达到36.25 ± 8.47 Tg C的峰值,并在2046年至2060年期间保持稳定。相反,退化不利于毛竹林固碳能力的长期保持,导致毛竹林地上碳储量在2033年达到峰值(29.50 ± 7.49 Tg C),随后持续下降。这项研究强调了估算固碳潜力和优化管理决策对提高和维持毛竹林固碳能力的重要影响。
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Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning
Estimating the carbon sequestration potential of Moso bamboo (Phyllostachys pubescens) forests and optimizing management strategies play pivotal roles in enhancing quality and promoting sustainable development. However, there is a lack of methods to simulate changes in carbon sequestration capacity in Moso bamboo forests and to screen and optimize the best management measures based on long-term time series data from fixed-sample fine surveys. Therefore, this study utilized continuous survey data and climate data from fixed sample plots in Zhejiang Province spanning from 2004 to 2019. By comparing four different algorithms, namely random forest, support vector machine, XGBoost, and BP neural network, to construct aboveground carbon stock models for Moso bamboo forests. The ultimate goal was to identify the optimal algorithmic model. Additionally, the key driving parameters for future carbon stocks were considered and future aboveground carbon stocks were predicted in Moso bamboo forests. Then formulated an optimal management strategy based on these predictions. The results indicated that the carbon stock model constructed using the XGBoost algorithm, with an R2 of 0.9895 and root mean square error of 0.1059, achieved the best performance and was considered the optimal algorithmic model. The most influential driving parameters for vegetation carbon stocks in Moso bamboo forests were found to be mean age, mean diameter at breast height, and mean culm density. Under optimal management measures, which involve no harvesting of 1–3 du bamboo, 30% harvesting of 4 du bamboo, and 80% harvesting of bamboo aged 5 du and above. Our predictions show that aboveground carbon stocks in Moso bamboo forests in Zhejiang Province will peak at 36.25 ± 8.47 Tg C in 2046 and remain stable from 2046 to 2060. Conversely, degradation is detrimental to the long-term maintenance of carbon sequestration capacity in Moso bamboo forests, resulting in a peak aboveground carbon stock of 29.50 ± 7.49 Tg C in 2033, followed by a continuous decline. This study underscores the significant influence of estimating carbon sequestration potential and optimizing management decisions on enhancing and sustaining the carbon sequestration capacity of Moso bamboo forests.
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