铀污染下的商陆叶片反射光谱特征与铀含量关系研究
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摘要
通过室内盆栽试验,利用微分技术处理叶片反射光谱数据,研究铀污染下商陆叶片中的铀含量在不同光谱波段与原始光谱反射率、一阶导数光谱的相关关系,找到商陆铀污染诊断的敏感波段范围和最优光谱特征参数,并以相关性较好的敏感波段及光谱特征参数为自变量,与商陆叶片铀含量建立对应的估测拟合模型。如果以该模型为基础创建铀含量的冠层光谱模型,则有可能实现通过遥感影像监测叶片中的铀含量。实验结果表明:当商陆叶片中的铀含量为5.94～71.74 mg·kg~(-1)时,叶片中铀含量与一阶导数光谱数据的相关性较原始光谱数据好,在749～766 nm区间内存在较好的相关性和光谱响应;根据上述相关性分析,选择14个光谱特征参数,计算他们与商陆叶片铀含量的相关系数,其中蓝边面积、红边位置、红边面积与蓝边面积的比值及红边面积与蓝边面积的归一化值与叶片铀含量的相关系数达到了0.05显著检验水平;选取一阶导数光谱中相关系数最高的波段757, 758, 760和761 nm处的值和上述相关性最高的4个光谱特征参数,与叶片铀含量建立多种形式的估测拟合模型,通过对拟合模型的精度检验,发现以红边面积与蓝边面积的比值、 757和760 nm处反射率的一阶导数为自变量的拟合模型的预测效果较好,其中拟合效果最优的模型是以757 nm波段处反射率的一阶导数为自变量的三次函数模型,模型预测精度达到了89.8%。
A pot cultivation experiment was carried out to investigate the relationship between uranium contents in leaves of Phytolacca acinosa Roxb. and original spectral datum and first derivative spectral datum with derivative technique, and to seek out the sensitive wavelengths and spectral characteristics of Phytolacca acinosa Roxb. under uranium pollution. Then by choosing sensitive bands and the best correlated spectrum characteristic parameters, uranium estimation models were constructed. The results showed that when the U contents in leaves were 5.94～71.74 mg·kg~(-1), they correlated closely with the first derivative reflectance in the range of 749～766 nm. The chosen 14 spectral characteristic parameters were used to calculate the correlation coefficients with uranium contents in leaves, and correlations of the blue edge area, the red edge position, the ratio of red edge area to blue edge area and the normalized values of red edge area and blue edge area were significantat the 0.05 level. The selected wavelengths of 757, 758, 760, 761 nm and the above-mentioned 4 best spectral characteristic parameters were used to establish the uranium estimation models, and precision tests proved that the uranium estimation models established bythe ratio of red edge area to blue edge area, first derivative reflectanceat 757 and 760 nm achieved better test results, among them, the best model was the cubic function model using first derivative reflectance at 757 nm as a variable and the prediction accuracy of it was up to 89.8%.
A pot cultivation experiment was carried out to investigate the relationship between uranium contents in leaves of Phytolacca acinosa Roxb. and original spectral datum and first derivative spectral datum with derivative technique, and to seek out the sensitive wavelengths and spectral characteristics of Phytolacca acinosa Roxb. under uranium pollution. Then by choosing sensitive bands and the best correlated spectrum characteristic parameters, uranium estimation models were constructed. The results showed that when the U contents in leaves were 5.94～71.74 mg·kg~(-1), they correlated closely with the first derivative reflectance in the range of 749～766 nm. The chosen 14 spectral characteristic parameters were used to calculate the correlation coefficients with uranium contents in leaves, and correlations of the blue edge area, the red edge position, the ratio of red edge area to blue edge area and the normalized values of red edge area and blue edge area were significantat the 0.05 level. The selected wavelengths of 757, 758, 760, 761 nm and the above-mentioned 4 best spectral characteristic parameters were used to establish the uranium estimation models, and precision tests proved that the uranium estimation models established bythe ratio of red edge area to blue edge area, first derivative reflectanceat 757 and 760 nm achieved better test results, among them, the best model was the cubic function model using first derivative reflectance at 757 nm as a variable and the prediction accuracy of it was up to 89.8%.
引文
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[3] GU Yan-wen, LI Shuai, GAO Wei, et al(顾艳文, 李帅, 高伟, 等). Acta Ecologica Sinica(生态学报), 2015, 35(13): 4445.
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[6] WU Lian-xi, LI Jia-zhen(吴连喜, 李佳珍). Chinese Agricultural Science Bulletin(中国农学通报), 2016, 32(18): 96.
[7] ZENG Feng, TANG Yong-jin(曾峰, 唐永金). Chinese Journal of Environmental Engineering(环境工程学报), 2016, 8(7): 3075.
[8] HUANG De-juan, XU Wei-dong, LUO Ming-biao, et al(黄德娟, 徐卫东, 罗明标, 等). Environmental Science & Technology(环境科学与技术), 2011, 34(3): 29.
[9] WANG Jia, LUO Xue-gang, SHI Yan(王佳, 罗学刚, 石岩). Acta Scientiae Circumstantiae(环境科学学报), 2014, 34(8): 2094.
[10] LI Qing-ting, YANG Feng-jie, ZHANG Bing, et al(李庆亭, 杨锋杰, 张兵, 等). Journal of Remote Sensing(遥感学报), 2008, 12(2): 284.
[11] WANG Hui, ZENG Lu-sheng, SUN Yong-hong, et al(王慧, 曾路生, 孙永红, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(2): 171.
[12] GUO Hui, YANG Ke-ming, ZHANG Wen-wen, et al(郭辉, 杨可明, 张文文, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(1): 212.
[13] PU Rui-liang, GONG Peng(浦瑞良, 宫鹏). Hyperspectral Remote Sensing and Its Application(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2000. 53.
[14] ZHANG Yong-he, CHEN Wen-hui, GUO Qiao-ying, et al(张永贺, 陈文惠, 郭乔影, 等). Acta Ecologica Sinica(生态学报), 2013, (3): 876.
[15] GONG Zhao-ning, ZHAO Ya-li, ZHAO Wen-ji, et al(宫兆宁, 赵雅莉, 赵文吉, 等). Acta Ecologica Sinica(生态学报), 2014(20): 5736.