黑龙江省红松人工林林分乔木层可加性碳储量模型

辛士冬; 姜立春; 穆林

doi:10.12302/j.issn.1000-2006.202007007

辛士冬, 姜立春, 穆林

南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (1) : 115-121.

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (1) : 115-121. DOI: 10.12302/j.issn.1000-2006.202007007

研究论文

黑龙江省红松人工林林分乔木层可加性碳储量模型

辛士冬 ¹ ,
姜立春 ¹^,^* ,
穆林 ²

作者信息 +

Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China

XIN Shidong ¹ ,
JIANG Lichun ¹^,^* ,
MU Lin ²

Author information +

文章历史 +

摘要

【目的】大尺度森林碳储量的估算备受关注,而构建林分乔木层碳储量模型是一种评估森林碳储量快捷且准确的方式。【方法】以黑龙江省(东京城、林口、帽儿山、孟家岗)207块红松人工林样地数据为研究对象,选择聚合法、平差法、分解法作为构建林分碳储量模型的可加性方法,以加权回归来消除碳储量模型的异方差。采用留一交叉验证法(leave-one-out cross validation, LOOCV)对3种可加性方法的碳储量模型进行评价。【结果】基于3种可加性方法林分碳储量模型拟合结果之间存在略微的差异。聚合法的总体预测能力略优于平差法和分解法,具体预测精度排序为聚合法>平差法>分解法。当预测林分总碳储量时,3种可加性方法在不同林分断面积区间的预测能力表现并不一致。【结论】基于聚合法的林分碳储量模型更适合于黑龙江省红松人工林的碳储量预测,但当预测红松人工林的林分总碳储量时,应根据林分断面积区间选择合适的可加性方法。

Abstract

【Objective】 The estimation of large-scale forest carbon storage has attracted much attention, and the establishment of forest tree layer carbon storage model is an effective method to evaluate forest carbon storage. 【Method】 A total of 207 plots in a Korean pine plantation in Heilongjiang Province (Dongjingcheng, Linkou, Maoershan, Mengjiagang) were studied for modeling stand carbon storage, and choose the aggregation method, adjustment method, disaggregation method as the additivity method of establishing the stand carbon storage model, the research used the weighted regression to eliminate the heteroscedasticity of the carbon storage model, and adopted the leave-one-out cross validation method to evaluate the carbon stock model based on three additivity methods. 【Result】 There were slight differences between the fitting results of the forest carbon storage model based on the three additivity methods. The overall prediction ability of the aggregation method was slightly better than the adjustment method and the disaggregation method, and the specific prediction precision was ranked as the aggregation method > adjustment method > disaggregation method. When predicting stand total carbon storage, the predictability of the three additivity methods in different stand basal area intervals was not consistent. 【Conclusion】 The stand carbon storage model based on aggregation method was more suitable for the prediction of carbon storage of Korean pine plantation in Heilongjiang Province. However, when predicting the total carbon storage of Korean pine plantations, the appropriate additive method should be selected according to the stand basal area interval.

导出引用

辛士冬, 姜立春, 穆林. 黑龙江省红松人工林林分乔木层可加性碳储量模型[J]. 南京林业大学学报（自然科学版）. 2022, 46(1): 115-121 https://doi.org/10.12302/j.issn.1000-2006.202007007

XIN Shidong, JIANG Lichun, MU Lin. Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(1): 115-121 https://doi.org/10.12302/j.issn.1000-2006.202007007

中图分类号： S758.5

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