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毛乌素沙地油蒿群落叶面积指数动态及模拟

张财 查天山 贾昕 刘鹏 李成

张财, 查天山, 贾昕, 刘鹏, 李成. 毛乌素沙地油蒿群落叶面积指数动态及模拟[J]. 北京林业大学学报, 2018, 40(3): 75-83. doi: 10.13332/j.1000-1522.20170298
引用本文: 张财, 查天山, 贾昕, 刘鹏, 李成. 毛乌素沙地油蒿群落叶面积指数动态及模拟[J]. 北京林业大学学报, 2018, 40(3): 75-83. doi: 10.13332/j.1000-1522.20170298
Zhang Cai, Zha Tianshan, Jia Xin, Liu Peng, Li Cheng. Dynamics and simulation of leaf area index for Artemisia ordosica community in the Mu Us Desert of northwestern China[J]. Journal of Beijing Forestry University, 2018, 40(3): 75-83. doi: 10.13332/j.1000-1522.20170298
Citation: Zhang Cai, Zha Tianshan, Jia Xin, Liu Peng, Li Cheng. Dynamics and simulation of leaf area index for Artemisia ordosica community in the Mu Us Desert of northwestern China[J]. Journal of Beijing Forestry University, 2018, 40(3): 75-83. doi: 10.13332/j.1000-1522.20170298

毛乌素沙地油蒿群落叶面积指数动态及模拟

doi: 10.13332/j.1000-1522.20170298
基金项目: 

国家自然科学基金项目 31670708

国家自然科学基金项目 31670710

中央高校基本科研业务费专项 2015ZCQ-SB-02

详细信息
    作者简介:

    张财。主要研究方向:荒漠植被冠层结构动态。Email:czhangbjfu@163.com 地址:100083北京市海淀区清华东路35号北京林业大学水土保持学院

    责任作者:

    贾昕,博士,副教授。主要研究方向:干旱半干旱区生态系统碳水循环。Email:xinjia@bjfu.edu.cn 地址:同上

  • 中图分类号: S717.19+3

Dynamics and simulation of leaf area index for Artemisia ordosica community in the Mu Us Desert of northwestern China

  • 摘要: 目的本研究以毛乌素沙地灌木油蒿群落为研究对象,研究如何快速、准确、连续地获取油蒿群落LAI,获取长期连续的LAI数据对研究生态系统过程与环境变化间的相互关系具有重要意义。方法本文采用原位连续监测和野外试验的方法,于2013和2014年每年4—10月使用LAI-2000冠层分析仪定期测定油蒿群落LAI,同时利用太阳辐射传感器和光量子传感器测得的辐射数据计算地面观测NDVI(NDVIground)并获取同期遥感MODIS NDVI(NDVIMODIS)。用LAI数据分析油蒿叶面积群落指数的季节动态,并与归一化积温、NDVIground和NDVIMODIS分别建立经验模型,得到最优油蒿群落LAI模拟模型。结果油蒿群落LAI在整个生长季内的变化与物候具有一致性,整体上随时间呈现单峰型变化趋势,4—8月末油蒿持续生长,LAI稳定增大,8月份达到峰值,2013年为1.09m2/m2,2014年为1.33m2/m2。9月初至9月中旬油蒿冠层结构趋于稳定,LAI变化较小;9月下旬油蒿叶片开始枯黄掉落,LAI迅速下降。经验证,利用NDVIground模拟荒漠地区油蒿群落LAI效果最优(R2=0.76, P < 0.01)。结论在荒漠地区简单测量NDVI并使用本文研究模型,可以快速、无破坏地获取长期连续可靠的LAI数据,研究结果对理解和预测荒漠生态系统对全球气候变化的响应具有重要意义。

     

  • 图  1  LAI测量样地及测量点布设

    Figure  1.  Deployment of LAI sampling quadrats and measurement points

    图  2  毛乌素沙地油蒿叶面积指数及归一化植被指数的季节动态

    NDVIground.地面观测NDVI Ground-based NDVI; NDVIMODIS.遥感MODIS NDVI Remote sensing MODIS NDVI;LE.展叶期Leaf expansion stage; FB.开花结果期Flowering and bearing stage; LC.变色期Leaf coloration stage; DP.凋落期Defoliation stage

    Figure  2.  Seasonal variations of LAI and NDVI in Artemisia ordosica in Mu Us Desert

    图  3  2013年使用不同方法建立的油蒿群落叶面积指数模拟模型

    Figure  3.  LAI simulation models of Artemisia ordosica community established by different methods in 2013

    图  4  2014年不同方法油蒿群落叶面积指数模拟值与实测值的比较和相对误差

    a、b为积温模拟法;c、d为地面观测NDVI模拟法;e、f为遥感反演法。

    Figure  4.  Comparison and relative error between simulated and measured LAI of Artemisia ordosica community in 2014

    a and b, normalized effective accumulated temperature; c and d, ground-based NDVI; e and f, remote sensing retrieval.

    表  1  2013和2014年油蒿群落物候期

    Table  1.   Phenophases of Artemisia ordosica community in 2013 and 2014

    年份
    Year
    展叶期
    Leaf expansion period
    开花结果期
    Flowering and bearing period
    变色期
    Leaf coloration period
    凋落期
    Defoliation period
    2013 94~145 170~248 235~287 242~287
    2014 102~147 171~247 232~291 238~291
    数据为一年中的第几天。Note: data is the ordinal date in a year.
    下载: 导出CSV

    表  2  不同方法模拟油蒿LAI的结果

    Table  2.   Simulated results of Artemisia ordosica LAI by different methods

    模拟方法Simulation method 拟合方程Model expression R2 RMSE AIC
    积温模拟法Simulation by NEAT LAI=0.63NEAT+0.47 0.83 0.09 -103.90
    LAI=0.54exp(0.72NEAT) 0.77 0.10 -97.56
    LAI=0.27ln(NEAT)+1.04 0.93 0.07 -123.77
    LAI=1.07NEAT0.36 0.91 0.07 -118.80
    LAI=1.01/(1+exp(0.97-6.03NEAT)) 0.95 0.05 -132.36
    LAI=1.00/(1+exp(0.82-4.76NEAT-2.82NEAT2)) 0.95 0.05 -132.65
    地面观测NDVI模拟法
    Simulation by NDVIground
    LAI=4.32NDVIground-0.92 0.74 0.11 -90.07
    LAI=0.10exp(5.10NDVIground) 0.69 0.12 -86.02
    LAI=1.69ln(NDVIground)+2.37 0.76 0.11 -91.37
    LAI=5.38NDVIground2.08 0.72 0.12 -88.03
    遥感反演法
    Simulation by remote sensing retrieval
    LAI=5.87NDVIMODIS-0.34 0.82 0.09 -103.05
    LAI=0.20exp(7.07NDVIMODIS) 0.77 0.11 -97.35
    LAI=1.10ln(NDVIMODIS)+2.62 0.84 0.09 -106.04
    LAI=7.78NDVIMODIS1.39 0.81 0.10 -101.45
    注:所有模型均通过0.01水平的显著性检验。Note: all models were examined by a significance test of 0.01 level.
    下载: 导出CSV

    表  3  不同植被基于NDVI的LAI估算模型

    Table  3.   LAI estimation models based on NDVI for different vegetation species

    植被
    Vegetation species
    模型方程
    Model expression
    R2
    小麦Triticum aestivum LAI=exp(2.77-0.52/NDVI) 0.96
    玉米Zea mays LAI=3.20NDVI+0.53 0.65
    水稻Oryza sativa LAI=1.06NDVI+1.05 0.99
    杨树Populus L. LAI=0.48exp(2.89NDVI) 0.51
    草地Grassland LAI=1.42NDVI-0.09 0.62
    LAI=0.13exp(NDVI/0.31) 0.59
    下载: 导出CSV
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  • 收稿日期:  2017-08-22
  • 修回日期:  2017-12-15
  • 刊出日期:  2018-03-01

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