摘要
【目的】快速准确提取冬小麦返青期植被覆盖度信息。【方法】利用无人机获取田间冬小麦可见光图像,提取图像中4种常见可见光植被指数;在像元二分法原理的基础上,分别构建基于差异植被指数(Visible-band difference vegetation index,VDVI)、过绿指数(Excess green,EXG)、归一化绿蓝差异指数(Normalized green-blue difference index,NGBDI)和归一化绿红差异指数(Normalized green-red difference index,NGRDI)的植被覆盖度提取模型,采用支持向量机(Support vector machine,SVM)监督分类结果作为真值对各模型进行精度验证。【结果】4种模型中,利用VDVI植被覆盖度提取模型获取的植被覆盖度精度最高,提取效果较好。与监督分类结果对比,4种植被覆盖度提取模型的提取误差(EF)分别为3.36%、15.68%、8.74%和15.46%,R2分别为0.9461、0.9344、0.6953和0.7460,均方根误差(RMSE)分别为0.0219、0.0595、0.0420和0.0559。【结论】采用可见光植被指数结合像元二分法构建植被覆盖度提取模型实现了冬小麦返青期植被覆盖度准确快速提取,为植被覆盖度提取提供了一种新途径,可为无人机遥感监测提供参考。
【Objective】To quickly and accurately extract vegetation coverage information of winter wheat in turning green period.【Method】The field visible light images of winter wheat were obtained by UAV,and four common visible light vegetation indices were extracted.According to the principle of pixel dichotomy model,the vegetation coverage extraction models were established based on visible-band difference vegetation index(VDVI),excess green(EXG),normalized green-blue difference index(NGBDI)and normalized green-red difference index(NGRDI)respectively.The accuracies of four models were verified using the support vector machine(SVM)supervised classification results as the truth values.【Result】The VDVI vegetation coverage extraction model had the highest accuracy and the best extraction effect to extract vegetation coverage among the four models.Compared with the supervised classification results,the extraction errors(EF)of four vegetation coverage extraction models were 3.36%,15.68%,8.74% and 15.46% respectively,the values of R2 were 0.9461,0.9344,0.6953 and 0.7460 respectively,and the values of root mean square error(RMSE)were 0.0219,0.0595,0.0420 and 0.0559 respectively.【Conclusion】The vegetation coverage extraction model based on visible vegetation index and pixel dichotomy has realized accurate and rapid extraction of vegetation coverage of winter wheat in turning green period,which provides a new way to extract vegetation coverage and a reference for UAV remote sensing monitoring vegetation coverage information.
作者
孟沌超
赵静
兰玉彬
鲁力群
杨焕波
李志铭
闫春雨
MENG Dunchao;ZHAO Jing;LAN Yubin;LU Liqun;YANG Huanbo;LI Zhiming;YAN Chunyu(School of Agricultural and Food Engineering,Shandong University of Technology,Zibo 255000,China;International Precision Agriculture Aviation Application Technology Research Center,Shandong University of Technology,Zibo 255000,China;College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255000,China)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2020年第3期126-132,共7页
Journal of South China Agricultural University
基金
山东省引进顶尖人才“一事一议”专项经费资助项目(鲁政办字[2018]27号)
中央引导地方科技发展专项资金资助项目(2017-01—2019-12)。
关键词
无人机遥感
冬小麦
像元二分法
可见光植被指数
植被覆盖度
UAV remote sensing
winter wheat
pixel dichotomy
visible vegetation index
vegetation coverage
