欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2024, Vol. 35 ›› Issue (1): 49-54.doi: 10.13287/j.1001-9332.202401.009

• 半干旱区风沙防控专栏 • 上一篇    下一篇

辽西北沙化土地植被生产力关键影响因素

刘洪顺1,2, 布仁仓1*, 王正文1, 常禹1, 熊在平1, 齐丽1,2, 高越1,2   

  1. 1中国科学院沈阳应用生态研究所, 沈阳 110016;
    2中国科学院大学, 北京 100049
  • 收稿日期:2023-08-10 接受日期:2023-12-15 出版日期:2024-01-18 发布日期:2024-03-21
  • 通讯作者: * E-mail: burc@iae.ac.cn
  • 作者简介:刘洪顺, 男, 1996年生, 博士研究生。主要从事沙地土壤和植被生产力研究。E-mail: liuhongshuning@163.com
  • 基金资助:
    辽宁省科技重大专项(2020JH1/10300006)

Critical influencing factors on vegetation productivity in sandy land of the Northwestern Liaoning Province,China

LIU Hongshun1,2, BU Rencang1*, WANG Zhengwen1, CHANG Yu1, XIONG Zaiping1, QI Li1,2, GAO Yue1,2   

  1. 1Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;
    2University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2023-08-10 Accepted:2023-12-15 Online:2024-01-18 Published:2024-03-21

摘要: 本研究基于土壤理化性质数据、地形数据、气候数据和植被自身特征,分别从区域尺度、像元尺度和样地尺度分析辽宁省西北部沙化土地的植被生产力,揭示影响沙化土地植被生产力的关键因素。在区域尺度,建立随机森林模型,探究地形因子、气候因子和植被自身特征对植被生产力的影响;在像元尺度,对植被覆盖度与气候因子进行相关性分析;在样地尺度,将234个样本的土壤理化性质与地形因子和植被自身特征相结合,采用随机森林模型计算各因子的重要值。结果表明: 当模型中只考虑土壤养分,不考虑其他因子时,土壤养分能解释24.8%的净初级生产力空间变化;当模型中增加地形因子时,模型可解释40%的净初级生产力空间变化;当模型中增加植被覆盖度和叶面积指数时,模型可解释72.8%的净初级生产力空间变化。综合分析,植被覆盖度、叶面积指数是影响研究区植被生产力最重要的因子,其次为地形因子,气候因子影响较小。

关键词: 沙化土地, 尺度, 植被结构, 土壤养分, 地形条件, 气候条件

Abstract: To reveal the key factors influencing vegetation productivity in sandy lands, we conducted a comprehensive analysis of vegetation productivity on regional scale, pixel scale, and plot scale of the sandy lands in northwes-tern Liaoning Province, based on soil physicochemical data, topographical data, climate data, and the intrinsic characteristics of vegetation. On the regional scale, we established a random forest model to explore the impact of topographical factors, climate factors, and vegetation characteristics on vegetation productivity. On the pixel scale, we performed a correlation analysis between vegetation cover and climate factors. On the plot scale, we combined the physicochemical properties of 234 soil samples with topographical factors and vegetation characteristics, and utilized the random forest model to calculate the importance values of each factor. The results showed that soil nutrients could explain 24.8% of the spatial variation in net primary productivity when other factors were excluded. When introducing topographical factors into the model, the model could explain 40% variation of net primary productivity. When further incorporating fractional vegetation coverage and leaf area index into the model, the model could explain 72.8% variation of net primary productivity. Our findings suggested that fractional vegetation coverage and leaf area index were the most influential factors affecting vegetation productivity in this area. Topographical factors ranked second, followed by climate factors, which had a relatively small impact.

Key words: sandy land, scale, vegetation structure, soil nutrient, topographic condition, climate condition