引用本文:高 林,杨贵军,李红军,李振海,冯海宽,王 磊,董锦绘,贺 鹏.基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报,2016,24(9):1254-1264
GAO Lin,YANG Guijun,LI Hongjun,LI Zhenhai,FENG Haikuan,WANG Lei,DONG Jinhui,HE Peng.Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging[J].Chinese Journal of Eco-Agriculture,2016,24(9):1254-1264
DOI:10.13930/j.cnki.cjea.151237
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基于无人机数码影像的冬小麦叶面积指数探测研究
高 林1, 杨贵军2, 李红军3, 李振海2, 冯海宽2, 王 磊2, 董锦绘2, 贺 鹏2
1.1. 北京农业信息技术研究中心 北京 100097 2. 国家农业信息化工程技术研究中心 北京 100097 3. 农业部农业信息技术重点实验室 北京 100097 4. 北京市农业物联网工程技术研究中心 北京 100097 5. 南京大学地理与海洋科学学院 南京 210023;2.1. 北京农业信息技术研究中心 北京 100097 2. 国家农业信息化工程技术研究中心 北京 100097 3. 农业部农业信息技术重点实验室 北京 100097 4. 北京市农业物联网工程技术研究中心 北京 100097;3.中国科学院遗传与发育生物学研究所农业资源研究中心 石家庄 050022
摘要:  叶面积指数(LAI)是评价作物长势的重要农学参数之一, 利用遥感技术准确估测作物叶面积指数(LAI)对精准农业意义重大。目前, 数码相机与无人机系统组成的高性价比遥感监测系统在农业研究中已取得一些成果, 但利用无人机数码影像开展作物LAI估测研究还少有尝试。为论证利用无人机数码影像估测冬小麦LAI的可行性, 本文以获取到的3个关键生育期(孕穗期、开花期和灌浆期)冬小麦无人机数码影像为数据源, 利用数字图像转换原理构建出10种数字图像特征参数, 并系统地分析了3个生育期内两个冬小麦品种在4种氮水平下的LAI与数字图像特征参数之间的关联性。结果表明, 在LAI随生育期发生变化的同时, 10种数字图像特征参数中R/(R+G+B)和本文提出的基于无人机数码影像红、绿、蓝通道DN值以及可见光大气阻抗植被指数(VARI)计算原理构建的数字图像特征参数UAV-based VARIRGB也有规律性变化, 说明冬小麦的施氮差异不仅对LAI有影响, 也对某些数字图像特征参数有一定影响; 在不同条件(品种、氮营养水平以及生育期)下的数字图像特征参数与LAI的相关性分析中, R/(R+G+B)和UAV-based VARIRGB与LAI显著相关。进而, 研究评价了R/(R+G+B)和UAV-based VARIRGB构建的LAI估测模型, 最终确定UAV-based VARIRGB为估测冬小麦LAI的最佳参数指标。结果表明UAV-based VARIRGB指数模型估测的LAI与实测LAI拟合性较好(R2=0.71, RMSE=0.8, P<0.01)。本研究证明将无人机数码影像应用于冬小麦LAI探测是可行的, 这也为高性价比无人机遥感系统的精准农业应用增添了新成果和经验。
关键词:  无人机 遥感 数码影像 冬小麦 叶面积指数 数字图像特征参数
中图分类号:
基金项目:国家高技术研究发展计划(863计划)项目(2013AA102303)、北京市自然科学基金项目(4141001)和河北省科技计划项目(14227423D)资助
Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging
GAO Lin1, YANG Guijun2, LI Hongjun3, LI Zhenhai2, FENG Haikuan2, WANG Lei2, DONG Jinhui2, HE Peng2
1.1. Beijing Research Center for Agricultural Information Technology, Beijing 100097, China 2. National Engineering Research Center for Agricultural Information Technology, Beijing 100097, China 3. Key Laboratory of Agro-informatics, Ministry of Agriculture, Beijing 100097, China 4. Beijing Engineering Research Center of Agricultural Internet Transactions, Beijing 100097, China 5. School of Geographical and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;2.1. Beijing Research Center for Agricultural Information Technology, Beijing 100097, China 2. National Engineering Research Center for Agricultural Information Technology, Beijing 100097, China 3. Key Laboratory of Agro-informatics, Ministry of Agriculture, Beijing 100097, China 4. Beijing Engineering Research Center of Agricultural Internet Transactions, Beijing 100097, China;3.Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
Abstract:  Leaf area index (LAI) is an important agronomic parameter used in evaluating crop growth characteristics. The accurate estimation of LAI based on remote sensing technology is critical for precision agriculture. The current cost-effective unmanned aerial vehicle (UAV) of agricultural remote sensing monitoring system, which was established based on a multi-rotor UAV with a digital camera mounted on its platform, has led to significant achievements in agricultural research. However, there has been little research on retrieving crop LAI based on UAV digital imagery. To demonstrate the feasibility of using UAV digital imagery to estimate winter wheat LAI, we used this cost-effective UAV system to monitor agricultural operation in the study area. Then many UAV digital images (also known as RGB images) used as the study data source recorded at three critical growth stages — booting, anthesis and filling stages of winter wheat. We calculated ten characteristic parameters from the RGB images based on digital imaging conversion principle. Furthermore, we systematically analyzed the relationship between LAI at the three growth stages of the two winter wheat varieties with the four nitrogen levels and characteristic parameters of RGB images. It was indicated that among the ten characteristic parameters, R/(R+G+B) and UAV-based VARIRGB (visible atmospherically resistant index based on UAV RGB image, which was calculated in this paper based on DN in the red, green and blue channels of UAV digital images and the calculation principle of VARI) regularly changed with LAI of winter wheat. The change occurred regularly and simultaneously for the three growth stages. It showed that different nitrogen levels in winter wheat not only influenced LAI, but also influenced some characteristic parameters of digital images. Meanwhile, the study also indicated that R/(R+G+B) and UAV-based VARIRGB were more significantly correlated with LAI under different conditions, including variety, nitrogen level and growth stage among the ten characteristic parameters. Then two comprehensive evaluation of LAI inversion models between LAI and R/(R+G+B) and UAV-based VARIRGB were established. The evaluation demonstrated that UAV-based VARIRGB was the best parameter which optimally retrieved LAI of winter wheat. LAI estimated by the exponential model of UAV-based VARIRGB strongly matched with measured LAI, with R2 = 0.71, RMSE = 0.8 and at 0.01 significance level. Therefore, the results showed that the application of UAV digital imagery in retrieving winter wheat LAI was feasible. The study also enriched the achievements and experience of using cost-effective UAV remote sensing monitoring system in precision agriculture.
Keyword:  Unmanned aerial vehicle (UAV)  Remote sensing  Digital imagery  Winter wheat  Leaf area index (LAI)  Characteristic parameters of digital image
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