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林业科学 ›› 2025, Vol. 61 ›› Issue (11): 92-101.doi: 10.11707/j.1001-7488.LYKX20240772

• 研究论文 • 上一篇    下一篇

基于哑变量的中国东北天然次生林立地质量评价

雷晶晶,王映霓,廖丹,包雨鑫,杨可馨,王娟*()   

  1. 北京林业大学 森林资源与生态系统过程北京市重点实验室 北京 100083
  • 收稿日期:2024-12-17 修回日期:2025-06-16 出版日期:2025-11-25 发布日期:2025-12-11
  • 通讯作者: 王娟 E-mail:wangjuan@bjfu.edu.cn
  • 基金资助:
    国家重点研发计划项目子课题(2022YFD2201004-04)。

Site Quality Evaluation of Natural Secondary Forest in Northeast China Based on Dummy Variables

Jingjing Lei,Yingni Wang,Dan Liao,Yuxin Bao,Kexin Yang,Juan Wang*()   

  1. Key Laboratory for Forest Resources & Ecosystem Processes of Beijing, Beijing Forestry University Beijing 100083
  • Received:2024-12-17 Revised:2025-06-16 Online:2025-11-25 Published:2025-12-11
  • Contact: Juan Wang E-mail:wangjuan@bjfu.edu.cn

摘要:

目的: 以包括大兴安岭、小兴安岭和长白山脉在内的整个东北地区天然次生林为研究对象,将其划分成大兴安岭、小兴安岭、张广才岭、长白山和辽东山区5个区域,以“区域”为哑变量对东北天然次生林进行立地质量评价,对比分析不同区域立地质量分布格局的差异,为东北天然次生林立地质量的精准评价和林分质量的精准提升提供一定依据。方法: 选取东北地区456块0.1 hm2永久性圆形样地为数据来源,依据Assmann标准筛选出优势木并考虑样地全部树种断面积加权的优势木高为基础建模数据,在7个常用的理论生长方程中选择出最佳导向曲线,采用广义代数差分法(GADA)推导立地形模型,将“区域”作为哑变量构建出最终的哑变量模型,选取决定系数(R2)、均方根误差(RMSE)和平均绝对误差(MAE)评价模型拟合优度。基于模型得到各区域的立地形值并对其进行分析比较,探讨东北天然次生林立地生产力的分布格局。结果: 采用GADA法推导的立地形模型的R2为0.335,将“区域”作为哑变量后各区域的R2显著提高,RMSE和MAE均有所下降。东北天然次生林立地质量空间分布呈现出显著异质性,优(A)和良(B)等级立地主要集中分布于大兴安岭和长白山主体区域。大兴安岭优(A)等级立地占比超过90%,立地质量等级占比自差(D)至优(A)呈递增趋势;小兴安岭中(C)、差(D)等级立地占比达80%以上,南部立地质量显著优于北部;张广才岭优(A)、良(B)等级立地仅占35.6%,且靠近长白山的区域立地质量更优;长白山优(A)、良(B)等级立地占比超过60%,整体立地质量呈上升趋势;辽东山区中(C)、差(D)等级立地占主导地位,空间分布呈现与长白山邻近区域质量提升的特征,不同区域立地质量分布也存在显著差异。结论: 在广义代数差分法基础上建立的以“区域”为哑变量的立地形模型,其拟合精度和预测能力显著增强。基于该模型对东北天然次生林进行立地质量评价,不同区域的立地质量分布格局存在显著差异。本研究可为实现森林资源可持续发展和生态保护提供一定的理论支持,未来研究应综合考虑环境因素,以深入理解森林生态系统的动态变化,为森林管理和保护策略提供指导。

关键词: 立地形, 立地质量评价, 哑变量, 异龄混交林, 广义代数差分法

Abstract:

Objective: The entire northeast region, including the Daxing’an Mountains, Xiaoxing’an Mountains, and Changbai Mountains, was taken as the research area, and the region is divided into five parts: the Daxing’an Mountains, Xiaoxing’an Mountains, Zhangguangcai Mountains, Changbai Mountains, and the Liaodong Mountainous area. The “region” was used as a dummy variable to evaluate the site quality of natural secondary forests in northeast China. The differences in the distribution patterns of site quality across different areas were compared and analyzed, in order to provide a scientific basis for forest management and planning. Method: In this study, 456 research plots with an area of 0.1 hm2 in northeast China were selected as the data source. Dominant trees were screened using Assmann’s criteria, and the height of dominant trees weighted by the cross-sectional area of all tree species in the plot was used as the basic modeling data. The optimal guide curve was selected from seven commonly used theoretical growth equations. The generalized algebraic difference approach (GADA) was used to derive the site form model, and the “region” was used as a dummy variable to construct a final dummy variable model. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to assess the goodness of fit of the model. Finally, the site form values of each region were obtained according to the model and analyzed and compared to explore the distribution pattern of forest site productivity in northeast China. Result: The research results showed that R2 of the site form model derived by the GADA was 0.307. However, after using “region” as a dummy variable, the R2 values of each region increased significantly, and both the RMSE and MAE of the model decreased. The spatial distribution of forest site quality in northeast China showed significant heterogeneity, which is characterized as follows: highest site quality level (A) and high-intermediate site quality level (B) were mainly concentrated in the main areas of the Daxing’an Mountains and the Changbai Mountains. The proportion of the highest quality level (A) sites in the Daxing’an Mountains exceeded 90%, and the proportion of site quality levels increased from D to A. The proportion of C and D level sites in the Xiaoxing’an Mountains was over 80%, and the site quality in the south was significantly better than that in the north. A and B level sites in Zhangguangcai Mountains accounted for only 35.6%, and the site quality in the areas closer to the Changbai Mountains was better. In the Changbai Mountains, the proportion of A and B level sites exceeded 60%, and the overall site quality showed an upward trend. In the Liaodong Mountainous area, C and D level sites were dominant, and the spatial distribution showed the characteristic of improved quality in the areas adjacent to the Changbai Mountains. It can be seen that there are also significant differences in the distribution of site quality in different regions. Conclusion: This study has established a dummy variable site form model based on the GADA. The introduction of dummy variables enhances the model’s performance. Based on this, an assessment of the site quality of natural secondary forests in northeast China is conducted. The results of the site quality assessment indicate significant differences in site quality across different regions. This study provides theoretical support for achieving sustainable development of forest resources and ecological protection. In addition, this study also emphasizes that future research should comprehensively consider environmental factors in order to gain a deeper understanding of the dynamic changes in forest ecosystems and provide guidance for forest management and conservation strategies.

Key words: site form, site quality assessment, dummy variables, uneven-aged mixed forest, generalized algebraic difference approach (GADA)

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