干旱区生态环境知识资源中心(Arid): 基于连续小波变换的蒺藜植被覆盖度预测

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	基于连续小波变换的蒺藜植被覆盖度预测
其他题名	Prediction on the Vegetation Coverage of Tribulus terrestris L.Based on the Continuous Wavelet Transform
	刘昕; 杨光; 林群; 张龙英; 陈昊宇; 王宁; 刘峰; 刘晨
来源期刊	中国农业科技导报
ISSN	1008-0864
出版年	2021
卷号	23 期号:10 页码:107-116
中文摘要	植被覆盖度是评价土地是否荒漠化最有效的指标之一,也是植被监测的重要指标。通过高光谱估算植被覆盖度,可以为植被监测提供重要依据。以半干旱区托克托县的固沙植被蒺藜(Tribulus terrestris L.)为研究对象,分析了不同植被覆盖度光谱曲线特征的变化情况;提取两波段原始光谱植被指数并与植被覆盖度之间的相关性,选取最优波段组合;利用连续小波变换(continuous wavelet transform,CWT)对植被光谱反射率进行不同尺度分解,提取出不同分解尺度的最优波段;采用偏最小二乘法(partial least square,PLSR)和支持向量机(support vector machine,SVM)两种方法,以不同自变量建立植被覆盖度估算模型。结果表明:①原始光谱植被指数与植被覆盖度呈显著相关,相关系数均在0.55以上,最优波段组合为DI(2 260 nm,2 210 nm)、RI(1 410 nm,660 nm)、NI(1 470 nm,670 nm)、RDVI(1 770 nm,670 nm)、MSR(1 410 nm,660 nm);②小波系数也与植被覆盖度之间有良好的相关性,原始光谱中1~ 10尺度对应的相关系数均在0.72以上,在波段630 nm处第6分解尺度中,相关性最大为0.788 9;一阶微分光谱提取的小波系数与植被覆盖度的最大相关性为0.806 9;二阶微分光谱中1 ~ 10尺度小波系数与植被覆盖度的相关性均在0.6以上,其中最大相关性为0.781 8;③以原始光谱植被指数与不同导数变换的CWT提取的小波系数为自变量建立的模型中,输入量为二阶微分小波系数的PLSR模型精度最高,模型最稳定,R~2为0.905 9,RMSE为0.035 6,这表明经过CWT算法处理后,可以提高光谱的特征信息,为植被覆盖度的估算反演提供技术方法。
英文摘要	The vegetation coverage is one of the most effective indicators to evaluate the desertification of land,and also is an important indicator for monitoring vegetation.Hyperspectral estimation of vegetation coverage can provide an important basis for vegetation monitoring.Tribulus terrestris L.fixed sand in the semi arid zone Togtoh County was as the research object,the spectral curve features of different vegetation coverages were analyzed;the correlation between the original spectral vegetation index of two wave bands and the vegetation coverage was extracted,and the optimal wave band combination was selected.The spectral reflectance ratio of vegetation was decomposed at the different scales by the continuous wavelet transform(CWT),and the optimal wave bands at the different decomposition scales were extracted.The vegetation coverage estimation models were established using the partial least square method(PLSR)and support vector machine(SVM)with the different independent variables.The results showed that:① there was significant correlation between the original spectrum vegetation index and vegetation coverage with all the correlation coefficients above 0.55,and the optimal band combinations were DI(2 260 and 2 210 nm),RI(1 410 and 660 nm),NI(1 470 and 670 nm),RDVI(1 770 and 670 nm)and MSR(1 410 and 660 nm),respectively.② There was also good correlation between the wavelet coefficient and vegetation coverage,which the correlation coefficients in the 1 ~ 10 scales of original spectral were high than 0.72 with the maximum correlation 0.788 9 in the sixth decomposition scale at the band 630 nm.The maximum correlation between the wavelet coefficient extracted from the first-order differential spectrum and the vegetation coverage was 0.806 9,and the correlations between the wavelet coefficient of 1 ~ 10 scale extracted from the second-order differential spectrum and the vegetation coverage were above 0.6 with the maximum correlation 0.781 8.③Among the models established by using the original spectral vegetation index and wavelet coefficient extracted by CWT as the independent variables,the PLSR model input the second-order differential wavelet coefficient showed best precision and stability,which R~2 was 0.905 9 and RMSE was 0.035 6,respectively.These results showed that the CWT algorithm could improve spectral characteristic information,which provided a technical method for the estimated inversion of vegetation coverage.
中文关键词	植被覆盖度 ; 蒺藜 ; 连续小波变换 ; 偏最小二乘法 ; 支持向量机
英文关键词	vegetation coverage Tribulus terrestris L. continuous wavelet transform partial least square method support vector machine
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:7075941
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/378084
作者单位	刘昕, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 杨光, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 张龙英, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 陈昊宇, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 王宁, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 刘峰, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 刘晨, 内蒙古农业大学沙漠治理学院, 呼和浩特, 内蒙古 010018, 中国.; 林群, 山西大学计算机与信息技术学院, 太原, 山西 030006, 中国.
推荐引用方式 GB/T 7714	刘昕,杨光,林群,等. 基于连续小波变换的蒺藜植被覆盖度预测[J],2021,23(10):107-116.
APA	刘昕.,杨光.,林群.,张龙英.,陈昊宇.,...&刘晨.(2021).基于连续小波变换的蒺藜植被覆盖度预测.中国农业科技导报,23(10),107-116.
MLA	刘昕,et al."基于连续小波变换的蒺藜植被覆盖度预测".中国农业科技导报 23.10(2021):107-116.