低空遥感平台下可见光与多光谱传感器在水稻纹枯病病害评估中的效果对比研究  

Comparison between the Effects of Visible Light and Multispectral Sensor Based on Low-Altitude Remote Sensing Platform in the Evaluation of Rice Sheath Blight

作　　者：赵晓阳 张建[1,2] 张东彦 周新根[4] 刘小辉 谢静 ZHAO Xiao-yang;ZHANG Jian;ZHANG Dong-yan;ZHOU Xin-gen;LIU Xiao-hui;XIE Jing(College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China;Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture and Rural Affairs, Wuhan 430070, China;Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China;Texas A&M AgriLife Research and Extension Center, Beaumont, TX 77713, USA;College of Science, Huazhong Agricultural University, Wuhan 430070, China)

机构地区：[1]华中农业大学资源与环境学院,湖北武汉430070 [2]农业部长江中下游耕地保育重点实验室,湖北武汉430070 [3]安徽大学安徽省农业生态大数据工程实验室,安徽合肥230601 [4]Texas A&M AgriLife Research and Extension Center [5]华中农业大学理学院,湖北武汉430070

出　　处：《光谱学与光谱分析》2019年第4期1192-1198,共7页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31501222;41201364;41771463);中央高校基本科研业务费专项(2018JC012;2017JC038;2015BQ026;2014JC008);国家大学生创新训练项目(201610504017)资助

摘　　要：高效无损地评估农作物病害等级,对于实际农业生产和研究都具有重要意义。研究探讨了基于低空无人机遥感平台进行水稻纹枯病病害等级评估的可行性,分析可见光与多光谱传感器的光谱响应差异及其对感病水稻光谱反射率获取的影响,并定量对比两种传感器的病害监测效果。实验研究区由67个不同品种的水稻小区组成,每块小区均分为相接的纹枯病接种区和侵染区。以大疆精灵Phantom 3 Advanced小型消费级无人机作为搭载平台,分别搭载该无人机系统自带的可见光传感器和MicasenseRedEdge^(TM)多光谱传感器获取遥感影像。同时,通过植保专家现场调查的方式识别病害等级,并利用Trimble公司的手持式NDVI测量仪获取实测NDVI值。基于影像拼接、波段叠合、辐射校正后的预处理结果,对可见光图像的接种区和侵染区共134个小区计算七种可见光植被指数,即NDI(normalized difference index), ExG(excess green), ExR(excess red), ExG-ExR, B~*, G~*, R~*,多光谱图像除上述可见光指数外再计算NDVI(normalized difference vegetation index), RVI(ratio vegetation index)和NDWI(normalized difference water Index)三种多光谱植被指数。将计算得到的图像植被指数与地面实测NDVI进行相关性分析,以选取两种传感器的最优图像植被指数建立水稻纹枯病病害等级反演模型。相关性分析结果表明,基于多光谱传感器计算的图像NDVI与实测NDVI拟合度最高,接种区R^2为0.914, RMSE为0.024,侵染区R^2为0.863, RMSE为0.024。对于可见光传感器, NDI与实测NDVI的相关性最好,接种区R^2为0.875, RMSE为0.011,侵染区R^2为0.703, RMSE为0.014。比较两种传感器两种区域的同一图像植被指数与实测NDVI的一致性,除B~*外, NDI, ExR, ExG-ExR, G~*, ExG, R~*与实测NDVI基本属于高度相关,在病害严重的接种区,两种传感器对水稻纹枯病的监测效果相近,但在病害相对较轻的侵染区,多光谱传�Efficient and non-destructive assessment of crop disease grade is of great significance to the practical agricultural production and research. In this study, the feasibility of low-altitude UAV(Unmanned Aerial Vehicle) remote sensing platform for the disease grade assessment of rice Sheath Blight(ShB) was discussed. Then the spectral response differences of visible light sensor and multispectral sensor and their effects on the spectral reflectance acquisition of rice with ShB were analyzed. And rice ShB monitoring effects of two kinds of sensors were compared quantitively. The study area consisted of 67 rice plots with different varieties, each of which was divided into inoculation zone and infection zone. The drone was Phantom 3 Advanced, a small consumer-grade UAV made by DJI-Innovations company, and the payloads were the self-contained visible light sensor and Micasense RedEdgeTM multispectral sensor to acquire remote sensing images respectively. At the same time, the rice ShB disease grades were investigated by manual expert recognition and measured NDVI was obtained with Trimble’s GreenSeeker? Handheld Crop Sensor. Remote sensing images were preprocessed by image mosaic, layer stacking and radiometric calibration. A total of 134 plots in inoculation and infection zones of visible light image were used to calculate seven kinds of visible light vegetation indices, namely NDI(Normalized Difference Index), ExG(Excess Green), ExR(Excess Red), ExG-ExR, B*, G* and R*. Besides the above seven kinds of visible light vegetation indices, multispectral image was calculated by three kinds of multispectral vegetation indices additionally, namely NDVI(Normalized Difference Vegetation Index), RVI(Ratio Vegetation Index) and NDWI(Normalized Difference Water Index). The correlation between the image-based vegetation index and ground-based NDVI was analyzed, and the optimal image-based vegetation indices of the visible light and multispectral sensor were selected to establish the disease grade inversion model of rice ShB. Th

关 键 词：多光谱传感器 可见光传感器 低空遥感 水稻纹枯病 病害等级评估 植被指数 

分 类 号：S127[农业科学—农业基础科学]