高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

叶面滞尘量对大叶黄杨光谱特征影响研究

苏凯 于强 孙小婷 岳德鹏

苏凯, 于强, 孙小婷, 岳德鹏. 叶面滞尘量对大叶黄杨光谱特征影响研究[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200213
引用本文: 苏凯, 于强, 孙小婷, 岳德鹏. 叶面滞尘量对大叶黄杨光谱特征影响研究[J]. 北京林业大学学报. doi: 10.12171/j.1000-1522.20200213
Su Kai, Yu Qiang, Sun Xiaoting, Yue Depeng. Effects of dust retention on spectral characteristics of Euonymus japonicus[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200213
Citation: Su Kai, Yu Qiang, Sun Xiaoting, Yue Depeng. Effects of dust retention on spectral characteristics of Euonymus japonicus[J]. Journal of Beijing Forestry University. doi: 10.12171/j.1000-1522.20200213

叶面滞尘量对大叶黄杨光谱特征影响研究

doi: 10.12171/j.1000-1522.20200213
基金项目: 国家自然科学基金项目(41371189),中央高校基本科研业务费专项(BLX201806)
详细信息
    作者简介:

    苏凯,博士,讲师。主要研究方向:3S在资源环境中的应用、植被定量遥感。Email:sukai_lxy@gxu.edu.cn 地址:530004 广西南宁市大学东路100号广西大学林学院

    责任作者:

    岳德鹏,博士,教授。主要研究方向:3S在资源环境中的应用。Email:yuedepeng@126.com 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

  • 中图分类号: S771.8

Effects of dust retention on spectral characteristics of Euonymus japonicus

  • 摘要:   目的  叶面滞尘会影响植被光谱特征,削弱植被指数对植被的响应能力,影响反演评估的准确性。为探究叶面滞尘量对植被光谱响应特征及预测模型的影响,本研究以北京市常见常绿绿化树种大叶黄杨为研究对象展开研究。  方法  从封闭区域、半封闭区域、开放区域,采集叶片样本,并收集环境灰尘。通过室内控制试验,利用ASD FildSpec Handheld光谱仪测量不同滞尘量叶片的高光谱数据,选取5个特征波段,通过光谱角的方法研究了叶面滞尘量对叶片光谱特征的影响,以及滞尘量对叶面滞尘预测模型的精度和稳定性影响。  结果  随着叶面滞尘量的增加,植被光谱曲线特征逐渐减弱,灰尘的特征逐渐增强,但光谱曲线的总体变化趋势基本一致。当单位面积的滞尘量>120 g/m2时,光谱曲线的基本表现为灰尘的光谱特征。当叶面滞尘较少时,预测模型的模拟精度相对较高,随着滞尘量的增加,所有模拟预测模型的决定系数均减小;当叶面滞尘量>120 g/m2时,预测模型对叶面滞尘量的模拟预测能力将更差,并且均方根误差(RMSE)随着单位面积滞尘量的增加而增大,模拟预测模型的稳定性及预测精度逐渐降低。光谱角对滞尘叶片350 ~ 1 770 nm波段区间的光谱变化十分敏感,利用叶片光谱角检测滞尘程度不需要分区域讨论,只需与阈值做简单的比较,方法简便易行。  结论  本研究通过室内控制试验,研究叶面滞尘量对植被光谱响应特征,可为建立滞尘植被光谱反射物理模型提供参考与借鉴。

     

  • 图  1  采样点分布的位置

    Figure  1.  Distribution of sampling point

    图  2  室内滞尘量控制试验的主要过程(a)和光谱反射率测量原理图(b)

    Figure  2.  Main processes of control measurement on indoor dust retention (a) and measurement principles of spectral reflectance (b)

    图  3  不同滞尘量叶片光谱曲线的变化

    Figure  3.  Variation of leaf spectral curves of different dust retention

    图  4  经过一阶导数处理的光谱曲线

    Figure  4.  Spectral curves of leaves by first derivative treatment

    图  5  特征波段

    Figure  5.  Characteristic bands

    图  6  滞尘量与模型预测精度(a)和滞尘量对预测模型预测精度的影响(b)

    Figure  6.  Prediction model for different dust retention (a) and effect of dust detention on prediction accuracy of prediction model (b)

    图  7  4组光谱角

    Figure  7.  Spectral angles calculated at four spectral regions

    表  1  光谱特征参数和光谱角

    Table  1.   Spectral characteristic parameters and spectral angles

    项目 Item 光谱特征参数 Spectral characteristic parameter光谱角 Spectral angle/nm波段间隔 Wavelength interval/nm
    紫谷 Purple valley 382 ~ 500 nm 的最小值
    Minimum value of 382−500 nm
    350 ~ 716 4
    绿峰 Green peak 500 ~ 600 nm一阶导数的最大值
    Maximum value of the first derivative among 500−600 nm
    716 ~ 975 3
    红边 Red edge 670 ~ 760 nm一阶导数的最大值
    Maximum value of the first derivative among 670−760 nm
    976 ~ 1 265 17
    黄边 Yellow edge 550 ~ 582 nm一阶导数的最大值
    Maximum value of the first derivative among 550−582 nm
    1 266 ~ 1 770 3
    蓝边 Blue edge 490 ~ 530 nm一阶导数的最大值
    Maximum value of the first derivative among 490−530 nm
    下载: 导出CSV

    表  2  叶面滞尘量的光谱模型

    Table  2.   Spectral models for leaf dust retention

    光谱参数 Spectral parameter滞尘量回归模型 Regression model of dust retentionR2回归类型 Regression type
    紫谷 Purple valley y = 0.274x2 − 0.007 5x + 0.817 9 0.110 2 二次多项式 Quadratic polynomial
    绿峰 Green peak y = −0.236x2 + 0.098 5x + 0.105 6 0.101 2 二次多项式 Quadratic polynomial
    红边 Red edge y = 6.109x2 − 2.648 5x + 0.202 6 0.472 3 二次多项式 Quadratic polynomial
    黄边 Yellow edge y = −0.423x2 − 0.007 3x + 0.769 7 0.095 2 二次多项式 Quadratic polynomial
    蓝边 Blue edge y = −0.325x2 + 0.008 7x + 0.980 2 0.125 2 二次多项式 Quadratic polynomial
    NDVI y = 7.128x2 − 2.915 9x + 0.345 1 0.673 5 二次多项式 Quadratic polynomial
    NDPI y = 8.248x2 − 2.586 4x + 0.654 5 0.726 5 二次多项式 Quadratic polynomial
    EVI y = −7.677x2 + 3.692 4x + 0.379 24 0.564 1 二次多项式 Quadratic polynomial
    下载: 导出CSV
  • [1] 史贵涛, 陈振楼, 许世远, 等. 上海城市公园土壤及灰尘中重金属污染特征[J]. 环境科学, 2007, 28(2):238−242. doi: 10.3321/j.issn:1001-0742.2007.02.019

    Shi G T, Chen Z L, Xu S Y, et al. Characteristics of heavy metal pollution in soil and dust of urban parks in Shanghai[J]. Environmental Science, 2007, 28(2): 238−242. doi: 10.3321/j.issn:1001-0742.2007.02.019
    [2] Das R, Das S N, Misra V N. Chemical composition of rainwater and dustfall at Bhubaneswar in the east coast of India[J]. Atmospheric Environment, 2005, 39(32): 5908−5916. doi: 10.1016/j.atmosenv.2005.06.030
    [3] Wang L, Hu S, Ma M, et al. Magnetic characteristics of atmospheric dustfall in a subtropical monsoon climate zone of China and its environmental implications: a case study of Nanjing[J]. Atmospheric Environment, 2019, 212: 231−238. doi: 10.1016/j.atmosenv.2019.05.039
    [4] 李新荣, 张景光, 王新平, 等. 干旱沙漠区土壤微生物结皮及其对固沙植被影响的研究[J]. 植物学报, 2000, 42(9):965−970.

    Li X R, Zhang J G, Wang X P, et al. Study on soil microbioyic crust and its influences on sand-fixing vegetation in arid desert region[J]. Journal of Integrative Plant Biology, 2000, 42(9): 965−970.
    [5] 王晓磊, 王成. 城市森林调控空气颗粒物功能研究进展[J]. 生态学报, 2014, 34(8):1910−1921.

    Wang X L, Wang C. Research status and prospects on functions of urban forests in regulating the air particulate matter[J]. Acta Ecologica Sinica, 2014, 34(8): 1910−1921.
    [6] 周健, 吴宇, 张林菁, 等. 保定市区常见灌木滞尘能力研究[J]. 林业与生态科学, 2019, 34(1):114−120.

    Zhou J, Wu Y, Zhang L J, et al. The study of dust-retention ability of some common shrubs in Baoding[J]. Forestry and Ecological Sciences, 2019, 34(1): 114−120.
    [7] 韩轶, 李吉跃, 郭连生, 等. 居住小区生态型绿地模式的研究[J]. 北京林业大学学报, 2002, 24(4):102−106. doi: 10.3321/j.issn:1000-1522.2002.04.023

    Han Y, Li J Y, Guo L S, et al. Patterns of green area in residential districts[J]. Journal of Beijing Forestry University, 2002, 24(4): 102−106. doi: 10.3321/j.issn:1000-1522.2002.04.023
    [8] Zheng J G. Study on the dust-retention capacity resulting from greenbelt of the main roads in Xuchang[J]. Advanced Materials Research, 2013, 726-731: 1805−1808. doi: 10.4028/www.scientific.net/AMR.726-731.1805
    [9] 彭杰, 向红英, 王家强, 等. 叶面降尘的高光谱定量遥感模型[J]. 红外与毫米波学报, 2015, 35(5):1365−1369.

    Peng J, Xiang H Y, Wang J Q, et al. Quantitative model of foliar dustfall contentusing hyperspectral remote sensing[J]. Spectroscopy and Spectral Analysis, 2015, 35(5): 1365−1369.
    [10] Freer-Smith P H, Holloway S, Goodman A. The uptake of particulates by an urban woodland: site description and particulate composition[J]. Environmental Pollution, 1997, 95(1): 27−35. doi: 10.1016/S0269-7491(96)00119-4
    [11] 郑西平, 张启翔. 北京城市园林绿化植物应用现状与展望[J]. 中国园林, 2011, 27(5):81−85. doi: 10.3969/j.issn.1000-6664.2011.05.024

    Zhang X P, Zhang Q X. Status and prospects of urban landscape plants’ application in Beijing[J]. Chinese Landscape Architecture, 2011, 27(5): 81−85. doi: 10.3969/j.issn.1000-6664.2011.05.024
    [12] Lei C, Li Q M. Ectomycorrhizal communities associated with Tilia amurensis trees in natural versus urban forests of Heilongjiang in northeast China[J]. Journal of Forestry Research, 2016(2): 401−406.
    [13] Lee J, Lee D. Nature experience, recreation activity and health benefits of visitors in mountain and urban forests in Vienna, Zurich and Freiburg[J]. Journal of Mountain Science, 2015(6): 1551−1561.
    [14] Shen Z X, Cao J J, Li X X, et al. Chemical characteristics of aerosol particles (PM2.5) at a site of Horqin Sand-Land in northeast China[J]. Journal of Environmental Sciences, 2006, 18(4): 704.
    [15] 赵娟娟, 欧阳志云, 郑华, 等. 城市植物分层随机抽样调查方案设计的方法探讨[J]. 生态学杂志, 2009,28(7):1430−1436.

    Zao J J, Ouyang Z Y, Zheng H, et al. Proposed procedure in designing and planning stratified random selection investigation of urban vegetation[J]. Chinese Journal of Ecology, 2009,28(7): 1430−1436.
    [16] 熊佑清, 李崇涛, 刘晓辉. 大叶黄杨的抗寒性及其应用研究[J]. 中国园林, 2004, 20(4):36−38. doi: 10.3969/j.issn.1000-6664.2004.04.013

    Xiong Y Q, Li C T, Liu X H. A study of the cold resistance of Euonymus japonicus and its application[J]. Chinese Landscape Architecture, 2004, 20(4): 36−38. doi: 10.3969/j.issn.1000-6664.2004.04.013
    [17] 苏凯, 于强, 胡雅慧, 刘智丽, 王朋冲, 张启斌, 朱济友, 牛腾, 裴燕如, 岳德鹏. 基于光谱特征的北京市冬季城市森林滞尘分布反演研究[J]. 光谱学与光谱分析, 2020, 40(6):1696−1702.

    Su K, Yu Q, Hu Y H, et al. Inversion research on dust distribution of urban forests in Beijing in winter based on spectral characteristics[J]. Spectroscopy and Spectral Analysis, 2020, 40(6): 1696−1702.
    [18] 郑有飞, Olfert O, Brandt S, 等. 高光谱遥感在农作物长势监测中的应用[J]. 气象与环境科学, 2007, 30(1):10−16. doi: 10.3969/j.issn.1673-7148.2007.01.003

    Zheng Y F, Olfert O, Brandt S, et al. Monitoring growth vigour of crop using hyperspectral remote sensing data[J]. Meteorological and Environmental Sciences, 2007, 30(1): 10−16. doi: 10.3969/j.issn.1673-7148.2007.01.003
    [19] 谭倩, 赵永超, 童庆禧, 等. 植被光谱维特征提取模型[J]. 遥感信息, 2001(1):14−18. doi: 10.3969/j.issn.1000-3177.2001.01.003

    Tan Q, Zhao Y C, Tong Q X, et al. Vegetation spectral dimension feature extraction model[J]. Remote Sensing Information, 2001(1): 14−18. doi: 10.3969/j.issn.1000-3177.2001.01.003
    [20] Dennison P E, Halligan K Q, Roberts D A. A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper[J]. Remote Sensing of Environment, 2004, 93(3): 359−367. doi: 10.1016/j.rse.2004.07.013
    [21] 李燕, 杨可明, 荣坤鹏, 等. 重金属铜胁迫下玉米的光谱特征及监测研究[J]. 光谱学与光谱分析, 2019, 39(9):2823−2828.

    Li Y, Yang K M, Rong K P, et al. Spectral characteristics and identification research of corn under copper stress[J]. Spectroscopy and Spectral Analysis, 2019, 39(9): 2823−2828.
    [22] 黄爽. 烃类微渗漏胁迫植被光谱模型研究[D]. 吉林: 吉林大学, 2015.

    Huang S. Study on spectral model of plants stressed by hydrocarbon microseepage[D]. Jilin: Jilin University, 2015.
    [23] Jiao C X, Zheng G H, Shang G, et al. Coastal soil clay content estimation using reflectance spectroscopy[J]. Editorial Office of Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(5): 137−141.
    [24] 朱济友, 于强, 刘晓希, 等. 叶片滞尘对大叶黄杨光谱特征的影响及其滞尘量预测研究[J]. 光谱学与光谱分析, 2020, 40(2):191−196.

    Zhu J Y, Yu Q, Liu X X, et al. Effect of leaf dust retention on spectral characteristics of Euonymus japonicus and its dust retention prediction[J]. Spectroscopy and Spectral Analysis, 2020, 40(2): 191−196.
    [25] Filippa G, Cremonese E, Migliavacca M, et al. NDVI derived from near-infrared-enabled digital cameras: applicability across different plant functional types[J]. Agricultural and Forest Meterorology, 2018, 249: 275−285. doi: 10.1016/j.agrformet.2017.11.003
    [26] Zaitunah A, Samsuri, Ahmad A G, et al. Normalized difference vegetation index (NDVI) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia[J/OL]. Iop Conference Series: Earth and Environmental Science, 2018, 126: 012112 [2020−10−15]. https://iopscience.iop.org/article/10.1088/1755-1315/126/1/012112/meta.
    [27] Malthus T J, Dekker A G. First derivative indices for the remote sensing of inland water quality using high spectral resolution reflectance[J]. Environment International, 1995, 21(2): 221−232. doi: 10.1016/0160-4120(95)00012-7
    [28] 段敏杰, 李新宇, 赵松婷, 等. 不同滞尘环境下植物叶片高光谱特征变化研究[J]. 西南林业大学学报(自然科学), 2020, 40(3):88−94.

    Duan M J, Li X Y, Zhao S T, et al. Study on changes of hyperspectral characteristics of plant leaves onder different dust retention conditions[J]. Journal of Southwest Forestry University (Natural Sciences), 2020, 40(3): 88−94.
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  87
  • HTML全文浏览量:  26
  • PDF下载量:  22
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-07-07
  • 修回日期:  2020-12-26
  • 网络出版日期:  2021-10-11

目录

    /

    返回文章
    返回