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不同地形校正方法对刺槐林遥感提取的影响

刘时城 温仲明 陶宇 朱朵菊 张静 曾鸿文

刘时城, 温仲明, 陶宇, 朱朵菊, 张静, 曾鸿文. 不同地形校正方法对刺槐林遥感提取的影响[J]. 北京林业大学学报, 2017, 39(5): 25-33. doi: 10.13332/j.1000-1522.20160309
引用本文: 刘时城, 温仲明, 陶宇, 朱朵菊, 张静, 曾鸿文. 不同地形校正方法对刺槐林遥感提取的影响[J]. 北京林业大学学报, 2017, 39(5): 25-33. doi: 10.13332/j.1000-1522.20160309
LIU Shi-cheng, WEN Zhong-ming, TAO Yu, ZHU Duo-ju, ZHANG Jing, ZENG Hong-wen. Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution[J]. Journal of Beijing Forestry University, 2017, 39(5): 25-33. doi: 10.13332/j.1000-1522.20160309
Citation: LIU Shi-cheng, WEN Zhong-ming, TAO Yu, ZHU Duo-ju, ZHANG Jing, ZENG Hong-wen. Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution[J]. Journal of Beijing Forestry University, 2017, 39(5): 25-33. doi: 10.13332/j.1000-1522.20160309

不同地形校正方法对刺槐林遥感提取的影响

doi: 10.13332/j.1000-1522.20160309
基金项目: 

国家自然科学基金项目 41271297

详细信息
    作者简介:

    刘时城。主要研究方向:3S在水土保持中的应用。Email:xinongtudou@foxmail.com   地址:712100 陕西省咸阳市杨凌区西农路26号西北农林科技大学水土保持研究所

    责任作者:

    温仲明,博士,副研究员。主要研究方向:水土保持与生态恢复、功能生态学。Email:zmwen@ms.iswc.ac.cn   地址:同上

  • 中图分类号: S771

Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution

  • 摘要: 刺槐是黄土丘陵区生态环境建设的主要乔木树种之一,而地形阴影是影响刺槐林遥感提取精度的重要因素。为研究不同地形校正方法对刺槐林分布信息提取的影响,以黄土丘陵区安塞县的刺槐人工林为例, 使用Cosine、SCS、Minnaert、C、SCS+C 5种校正方法对该地区2015年7月份的Landsat8 OLI影像进行地形校正,并采用基于样本、面向对象提取的方法对人工刺槐林的分布信息进行提取。最后对地形校正前后的影像进行视觉比较和回归分析,并对提取结果进行精度评估,从而比较不同地形校正方法对刺槐人工林分布信息提取的影响。结果表明:1) 5种地形校正方法削弱了遥感影像上地形阴影的视觉效果, 其中Cosine、SCS校正存在过度校正的现象。2) 5种地形校正方法使得各波段辐射亮度值的均值和方差较之前发生变化,且SCS+C校正符合预期效果。3) Minnaert、SCS+C及C校正降低了太阳入射角的余弦值cosi与影像各波段的辐射亮度值间的回归直线斜率m的绝对值及相关系数r的绝对值, Cosine、SCS校正使两参数在部分波段上变大。4) 5种地形校正方法都不同程度地降低刺槐提取的漏分误差,但Cosine校正后用户精度降低了2.47%;Minnaert、SCS+C及C校正均提高了用户者精度和生产者精度,以C校正的精度最高, 生产者精度提高了16.66%,用户精度提高了2.82%。5) 5种地形校正方法均提高了Kappa系数值,以C校正最高,Kappa系数值为0.76。本研究说明刺槐林遥感提取有必要进行地形校正,且应结合研究区的情况选择地形校正方法,这为黄土丘陵区刺槐信息提取时合适地形校正方法的选取提供了重要的依据。

     

  • 图  1  研究区位置

    Figure  1.  Location of research area

    图  2  相同区域的校正前后影像对比

    a.未校正;b. Cosine校正;c. SCS校正;d. Minnaert校正;e. C校正;f. SCS+C校正。

    Figure  2.  Comparison of uncorrected images and corrected ones

    a, uncorrected; b, Cosine correction; c, SCS correction; d, Minnaert correction; e, C correction; f, SCS+C correction.

    表  1  5种地形校正方法

    Table  1.   Existing models for topographic correction

    序号
    No.
    类别
    Type
    校正模型
    Correction model
    公式
    Formula
    参考文献
    Reference
    1 朗伯体反射率模型
    Lambertian reflectance model
    Cosine [10]
    2 SCS [11]
    3 C [10]
    4 SCS+C [12]
    5 非朗伯体反射率模型
    Non-Lambertian reflectance model
    Minnaert [13]
    注:L为校正前影像的辐射亮度值;Lm为校正后影像的辐射亮度值;θ为太阳天顶角;i为太阳入射角;α为坡度角;c为经验参数;k为Minnaert常数;c值和k值的确定方法参见文献[4]。Notes: L is the radiance value of the uncorrected image; Lm is the radiance value of the corrected image; θ is the solar zenith angle; i is solar incident angle; α is the topographic slope angle; c is the wavelength-dependent adjustment coefficient; k is the Minnaert value; c and k are calculated according to the reference[4].
    下载: 导出CSV

    表  2  地形校正前后各波段辐射亮度值的均值与方差

    Table  2.   Mean and variance of each bands' radiance in corrected and uncorrected images

    校正方法
    Correction method
    参数
    Parameter
    波段1
    Band 1
    波段2
    Band 2
    波段3
    Band 3
    波段4
    Band 4
    波段5
    Band 5
    波段6
    Band 6
    未校正Uncorrected 平均值Mean 60.94 55.74 45.27 73.88 15.55 3.73
    方差Variance 84.29 156.49 267.17 144.15 10.79 1.56
    Cosine 平均值Mean 64.99 59.29 47.99 78.72 16.51 3.95
    方差Variance 140.16 194.40 297.63 221.85 12.47 1.71
    SCS 平均值Mean 61.47 56.11 45.46 74.41 15.61 3.74
    方差Variance 113.37 170.65 271.14 173.28 10.64 1.53
    Minnaert 平均值Mean 63.41 59.70 50.47 77.62 16.92 4.19
    方差Variance 92.73 174.69 322.24 154.05 11.68 1.89
    C 平均值Mean 62.00 57.32 47.21 75.65 16.15 3.91
    方差Variance 83.38 157.01 279.26 143.89 10.61 1.63
    SCS+C 平均值Mean 60.94 55.75 45.31 73.87 15.55 3.73
    方差Variance 82.64 152.91 264.05 137.19 9.94 1.51
    下载: 导出CSV

    表  3  地形校正前后cosi与各波段的辐射亮度值的回归参数

    Table  3.   Regression model parameters between all bands' radiance and cosi of corrected and uncorrected images

    校正方法
    Correction method
    回归参数
    Regression model parameter
    波段1
    Band 1
    波段2
    Band 2
    波段3
    Band 3
    波段4
    Band 4
    波段5
    Band 5
    波段6
    Band 6
    未校正Uncorrected m 18.27 27.46 31.44 37.54 10.71 2.98
    r 0.18 0.20 0.18 0.28 0.31 0.22
    Cosine m -55.71 -36.71 -14.68 -62.17 -7.08 -0.82
    r -0.45 -0.26 -0.08 -0.40 -0.20 -0.06
    SCS m -40.17 -23.90 -6.13 -42.33 -3.77 -0.15
    r -0.35 -0.17 -0.03 -0.30 -0.11 -0.01
    Minnaert m -9.04 -8.03 -7.53 -3.61 -1.45 -0.60
    r -0.08 -0.06 -0.04 -0.03 -0.04 -0.04
    C m -2.41 -3.18 -4.54 -1.36 -1.60 -0.58
    r -0.02 -0.02 -0.02 -0.01 -0.05 -0.04
    SCS+C m 0.32 1.06 0.58 4.09 0.24 -0.04
    r 0.00 0.01 0.00 0.03 0.01 0.00
    注:m表示斜率,r表示相关系数。Notes: m means slope, r means correlation coefficient.
    下载: 导出CSV

    表  4  校正前后的刺槐提取结果的精度比较

    Table  4.   Comparison in the extracted results' accuracy of R. pseudoacacia with corrected and uncorrected images

    校正方法
    Correction method
    生产者精度
    Producer’s accuracy/%
    漏分误差
    Omission/%
    用户精度
    User’s accuracy/%
    错分误差
    Commission/%
    Kappa系数
    Kappa coefficient
    未校正Uncorrected 66.67 33.33 83.72 16.28 0.61
    Cosine 72.22 27.78 81.25 18.75 0.64
    SCS 77.78 22.22 85.71 14.29 0.71
    Minnaert 81.48 18.52 86.27 13.73 0.75
    C 83.33 16.67 86.54 13.46 0.76
    SCS+C 83.33 16.67 84.90 15.10 0.71
    下载: 导出CSV
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  • 收稿日期:  2016-09-29
  • 修回日期:  2017-01-16
  • 刊出日期:  2017-05-01

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