北京山区不同植被恢复类型土壤质量综合评价

李鹏; 齐实; 张林; 胡俊; 唐颖; 逯进生; 王翔宇; 赖金林; 廖瑞恩; 张岱; 张岩

doi:10.13870/j.cnki.stbcxb.2024.01.025

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北京山区不同植被恢复类型土壤质量综合评价

更新时间：2025-03-12

- 北京山区不同植被恢复类型土壤质量综合评价
- Comprehensive Evaluation of Soil Quality of Different Vegetation Restoration Types in Mountainous Areas of Beijing
- 水土保持学报 2024年38卷第1期页码：337-346
- 作者机构：
  
  1. 北京林业大学水土保持学院,北京,100083
  2. 北京市园林绿化局,北京,100013
  3. 北京市林业工作总站,北京,100029
- 作者简介：
  
  [ "李鹏(1998—),男,在读硕士研究生,主要从事水土保持研究。E-mail:934529036@qq.com" ]
- 基金信息：
  
  国家自然科学基金项目(32271964);北京市京津风沙源治理二期工程项目(2020-SYZ-01-17JC05)
- DOI：10.13870/j.cnki.stbcxb.2024.01.025
  中图分类号：
- 纸质出版：2024
- 稿件说明：
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李鹏, 齐实, 张林, 等. 北京山区不同植被恢复类型土壤质量综合评价[J]. 水土保持学报, 2024,38(1):337-346.

LI Peng, QI Shi, ZHANG Lin, et al. Comprehensive Evaluation of Soil Quality of Different Vegetation Restoration Types in Mountainous Areas of Beijing[J]. 2024, 38(1): 337-346.
李鹏, 齐实, 张林, 等. 北京山区不同植被恢复类型土壤质量综合评价[J]. 水土保持学报, 2024,38(1):337-346. DOI： 10.13870/j.cnki.stbcxb.2024.01.025.

LI Peng, QI Shi, ZHANG Lin, et al. Comprehensive Evaluation of Soil Quality of Different Vegetation Restoration Types in Mountainous Areas of Beijing[J]. 2024, 38(1): 337-346. DOI： 10.13870/j.cnki.stbcxb.2024.01.025.

摘要

[目的] 综合评价北京山区不同植被恢复类型土壤质量

并进一步确定影响土壤质量的关键因素

为该地区植被恢复与重建提供数据支撑。[方法] 以立地条件相近的侧柏纯林、油松纯林、侧柏油松混交林、侧柏针阔混交林、油松针阔混交林、落叶阔叶混交林和无林地(对照)为研究对象

测定14个土壤理化指标作为土壤质量评价的总数据集(TDS)

采用主成分分析法(PCA)和Pearson相关性分析建立土壤质量最小数据集(MDS)

利用线性(L)和非线性(NL)2种评分方法计算土壤质量指数(SQI)和一般线性模型(GLM)确定影响土壤质量的关键因素。[结果] 植被恢复后相较于无林地

土壤容重、砂粒含量下降

而有机质、全氮、全钾、速效氮、速效钾等土壤养分含量增加。筛选出的研究区土壤质量评价MDS指标为全氮、砂粒、全钾、pH、有效含水量。4种方法(SQI-LT、SQI-NLT、SQI-LM、SQI-NLM)下

不同植被恢复类型的SQI值排序均为落叶阔叶混交林＞侧柏针阔混交林＞油松纯林＞油松针阔混交林＞侧柏油松混交林＞侧柏纯林＞无林地

植被恢复后土壤质量显著提升。SQI-NLM的土壤质量评价方法在北京山区具有更好的适用性。相较于无林地

其他植被恢复类型的SQI-NLM分别提高64%

48%

45%

36%

33%

27%。GLM模型解释了土壤质量指数总变异的85.24%

植被类型对土壤质量指数的解释比例最大(45.09%)。[结论] 选择适宜的植被恢复类型是改善区域土壤质量的关键。未来实施植被恢复时

树种选择上优先考虑阔叶树种。造林配置方式的选择应取决于树种而定

如侧柏纯林中引入本土阔叶树种形成侧柏针阔混交林或选择油松纯林是最佳造林模式。

Abstract

[Objective] This study is aimed at comprehensively evaluating the soil quality of different vegetation restoration types in the mountainous areas of Beijing

and further identifying the key factors affecting soil quality

so as to provide data support for vegetation restoration and reconstruction in the region. [Methods] The study utilized various vegetation types

including Platycladus orientalis pure forest

Pinus tabulaeformis pure forest

P. orientalis-P. tabulaeformis mixed forest

P. orientalis coniferous and broadleaved mixed forest

P. tabulaeformis coniferous and broadleaved mixed forest

deciduous broad-leaved mixed forest

and non-forest land (CK)

with similar stand conditions

as research objects. Fourteen soil physical and chemical indicators were measured to establish the total data set (TDS) for evaluating soil quality. Principal component analysis (PCA) and Pearson correlation analysis were employed to determine the minimum data set (MDS) for soil quality evaluation. Two scoring methods

linear (L) and non-linear (NL)

were used to calculate the soil quality index (SQI) and a general linear model (GLM) was employed to identify key factors influencing soil quality. [Results] The bulk density and sand content decreased

while the content of soil nutrients such as organic matter

total nitrogen

total potassium

available nitrogen

and available potassium increased after the vegetation restoration compared with the non-forest land. The screened MDS indicators for soil quality evaluation in the study area were total nitrogen (TN)

sand content

total potassium (TK)

and available water capacity (AWC). Under the four methods (SQI-LT

SQI-NLT

SQI-LM

and SQI-NLM)

the SQI values of different vegetation restoration types were ranked as deciduous broadleaf mixed forest > P. orientalis coniferous and broadleaved mixed forest > P. tabulaeformis pure forest > P. tabulaeformis coniferous and broadleaved mixed forest > P. orientalis-P. tabulaeformis mixed forest > P. orientalis pure forest > non-forest land

and the soil quality significantly improved after vegetation restoration. The soil quality evaluation method of SQI-NLM exhibited better applicability in the mountainous areas of Beijing. Compared with non-forested land

the SQI-NLM of other vegetation restoration types improved by 64%

48%

45%

36%

33% and 27%

respectively. The GLM model accounted for 85.24% of the total variation in the soil quality index

with vegetation type explaining the largest proportion of the soil quality index (45.09%). [Conclusion] The selection of suitable vegetation restoration types is crucial for improving regional soil quality. In future vegetation restoration efforts

priority should be given to broad-leaved species in tree species selection. Additionally

the choice of silvicultural configuration should depend on the tree species

such as introducing native broad-leaved species into Platycladus orientalis pure forest to form a Platycladus orientalis coniferous and broadleaved mixed forest

or selecting Pinus tabulaeformis pure forest as the optimal silvicultural model.

关键词

Keywords

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