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    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, 2021, 43(11): 40-49. 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, 2021, 43(11): 40-49. DOI: 10.12171/j.1000-1522.20200213
    Su Kai, Yu Qiang, Sun Xiaoting, Yue Depeng. Effects of leaf dust retention on spectral characteristics of Euonymus japonicus[J]. Journal of Beijing Forestry University, 2021, 43(11): 40-49. DOI: 10.12171/j.1000-1522.20200213
    Citation: Su Kai, Yu Qiang, Sun Xiaoting, Yue Depeng. Effects of leaf dust retention on spectral characteristics of Euonymus japonicus[J]. Journal of Beijing Forestry University, 2021, 43(11): 40-49. 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

    Effects of leaf 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,本文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量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。当叶面滞尘量 > 120 g/m2时,光谱曲线的基本表现为灰尘的光谱特征。当叶面滞尘量较少时,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)随着叶片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。

       

      Abstract:
        Objective  Leaf dust retention will affect the spectral characteristics of vegetation, which will weaken the response ability of vegetation index to vegetation and affect the accuracy of inversion evaluation. In order to explore the influence of leaf dust retention on vegetation spectral response characteristics and prediction models, this study took the common greening tree species Euonymus japonicus in Beijing as the research object.
        Method  Leaf samples were collected from closed area, semi closed area and open area, and environmental dust was collected. Hyperspectral data from different dust-retaining leaves were measured using an ASD FildSpec Handheld spectrometer through indoor control experiments. Five characteristic bands and spectral angles were used to study the influence of leaf dust retention on the spectral characteristics of leaves, and the influence of dust retention on the accuracy and stability of the prediction model for leaf dust retention was also studied.
        Result  The characteristics of vegetation spectral curves were gradually weakened and the characteristics of dust were gradually enhanced with the increase of leaf dust retention, but the overall trend of spectral curves was basically the same. However, when the leaf dust retention was greater than 120 g/m2, the spectral curve basically showed the spectral characteristics of dust. When the leaf dust retention was less, the simulation accuracy of the prediction model was relatively high, but with the increase of dust retention, the determinant coefficients of all prediction models decreased; when the leaf dust retention was greater than 120 g/m2, the prediction ability of all prediction models for leaf dust retention will be worse. And the root mean square error (RMSE) increased with the increase of leaf dust retention per unit area, and the stability and prediction accuracy of the prediction model were gradually reduced. The spectral angle was very sensitive to the spectral changes of dust-retaining leaves in the range of 350−1 770 nm. It is not necessary to discuss the degree of dust-retaining using the spectral angle of leaves in different regions, while simple comparison with the threshold was need to be done.
        Conclusion  In this study, the response characteristics of leaf dust retention to vegetation spectrum were studied through indoor control experiments, which could provide basic theory and data support for establishing physical model of spectral reflection of dust retention vegetation.

       

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