顾纯僖, 欧光龙, 刘畅, 等. 思茅松森林碳储量动态预测研究[J]. 西南林业大学学报(自然科学), 2024, 44(6): 1–8. DOI: 10.11929/j.swfu.202309043
引用本文: 顾纯僖, 欧光龙, 刘畅, 等. 思茅松森林碳储量动态预测研究[J]. 西南林业大学学报(自然科学), 2024, 44(6): 1–8. DOI: 10.11929/j.swfu.202309043
Gu Chunxi, Ou Guanglong, Liu Chang, Xu Jiannan, Wang Hezhi, Yang Xi. Dynamic Prediction of Carbon Storage in Pinus kesiya var. langbianensis Forest[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202309043
Citation: Gu Chunxi, Ou Guanglong, Liu Chang, Xu Jiannan, Wang Hezhi, Yang Xi. Dynamic Prediction of Carbon Storage in Pinus kesiya var. langbianensis Forest[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202309043

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思茅松森林碳储量动态预测研究

Dynamic Prediction of Carbon Storage in Pinus kesiya var. langbianensis Forest

  • 摘要: 以3次云南森林调查的81块思茅松永久样地数据结合遥感,经过自变量筛选相关性显著的遥感因子作为思茅松林碳储量反演建模备选变量,建立K–NN、RF、PLSR 3种模型优选出思茅松天然林碳储量遥感估算最佳模型,并用碳储量估算值拟建立多种预测模型对2030年思茅松天然林碳储量进行预测。结果表明:随机森林模型决定系数(R2)最高为0.83;均方根误差最小为5.55;预估精度最高为83.2%;AICc值最小为162;具有最优的拟合效果,可用于思茅松天然林碳储量的估算。本研究发现,随海拔升高,思茅松天然林碳储量呈“单峰”变化,碳储量主要集中分布海拔1500~2000 m。根据幂函数模型和多项式回归模型的预测结果,表明多项式回归模型精度优于幂函数模型,其R2和RMSE分别为0.960和0.054。思茅松森林碳储量预测结果显示2030年思茅松森林碳储量将到达2961.204 t,较2000年实现增汇42.095 t/hm2。本研究为更好地把握森林碳汇的变化规律提供了新的解决方案。

     

    Abstract: The 81 permanent sample plots of Pinus kesiya var. Langbianensis from three Yunnan forest surveys were combined with remote sensing, and the remote sensing factors with significant correlation were selected as the alternative variables for inverse modeling of carbon stock in Pinus kesiya var. langbianensis forests after independent variable screening, and three models, K-NN, RF, and PLSR, were established to optimize the best model for remote sensing of carbon stock estimation of natural forests of Pinus kesiya var. langbianensis, and multiple prediction models were proposed to predict the carbon stock in Pinus kesiya var. langbianensis natural forests in 2030 with the carbon stock estimation. A variety of models were developed to predict the carbon stock of Pinus kesiya var. langbianensis natural forest in 2030. The results showed that the coefficient of determination (R2) of the random forest model was the highest at 0.83, the root mean square error was the smallest at 5.55, and the prediction accuracy was the highest at 83.2%. In this study, it was found that the carbon stock of Pinus kesiya var. langbianensis natural forest showed a "single peak" change with the increase of elevation, and the carbon stock was mainly concentrated in the distribution of elevation 1 500-2 000 m. According to the prediction results of the power function model and the polynomial regression model, it was shown that the accuracy of the polynomial regression model was better than that of the power function model, and the R2 and RMSE of the model were 0.960 and 0.054, respectively. The prediction result of Pinus kesiya var. langbianensis forest carbon stock shows that the carbon stock of Pinus kesiya var. langbianensis forest will reach 2 961.204 t in 2030, which is 42.095 t/hm2 compared with that of 2 000, and this study provides a new solution for better grasping the change law of forest carbon sink.

     

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