摘要
为探究无人机多源遥感影像估算玉米叶面积指数(Leaf area index,LAI)垂直分布,在田间设置了密度和播期试验,在7个生育时期利用无人机采集了可见光、多光谱和热红外影像并同步获取玉米LAI垂直分布数据。同时,为合理制定无人机飞行任务,分析了不同飞行高度和不同太阳高度角下获取的无人机影像对估算玉米LAI的影响。基于无人机影像提取的与玉米LAI相关性较高的植被指数、纹理信息和冠层温度等特征,利用7种机器学习方法分别构建了玉米冠层不同高度LAI估算模型,从中选取鲁棒性强的2个模型用于分析在不同飞行高度和不同太阳高度角下估算LAI的差异。研究结果表明,MLPR和RFR模型对玉米LAI估算鲁棒性最强,全生育期下模型rRMSE为11.31%(MLPR)和11.42%(RFR)。玉米冠层LAI垂直分布估算误差,所有模型的平均rRMSE分别为9.1%(LAI-1)、14.19%(LAI-2)、18.62%(LAI-3)、23.29%(LAI-4)和26.7%(LAI-5)。对于玉米穗位叶及以下部位的LAI估算误差均在20%以下,得到了较好精度。同时,在不同飞行高度和太阳高度角试验中可以得出,当飞行高度为30 m时LAI估算精度最高,R^(2)为0.73,rRMSE为10.97%,在09:00—10:00观测的玉米LAI估算精度最高。无人机多源遥感影像数据可以准确估算玉米冠层LAI垂直分布,及时掌握玉米功能叶片LAI长势差异,可为玉米品种筛选提供辅助。
Maize leaf area index(LAI)displays a significant vertical distribution gradient.However,there is currently a limited amount of research focused on directly estimating the vertical distribution patterns of maize LAI from images.Designing an appropriate unmanned aerial vehicle(UAV)detection scheme can contribute to improving the accuracy of maize LAI estimation.Thus different maize varieties,and density and disease were used,and sowing experiments were carried out in the field to collect data on the vertical distribution of maize LAI.UAVs equipped with RGB,multi-spectral(MS),and thermal infrared(TIR)cameras were used to capture visible,multi-spectral,and thermal infrared images.Seven sets of UAV image data were collected during the reproductive growth stage of maize.To validate the effects of different UAV flight altitudes and solar zenith angles on maize LAI estimation,two completely controlled experiments with different flight altitudes were conducted,resulting in a total of 10 sets of UAV image data.Additionally,UAV image data were collected at each hour from 08:00 to 18:00 on a single day,resulting in 11 sets of image data,to discuss the robustness of the maize LAI estimation model under different flight experiments.A multi-source remote sensing image dataset was constructed to provide image feature variables highly correlated with maize LAI.Eight texture information categories were generated based on gray-level co-occurrence matrix from the original image texture features.In the end,51,43,and 9 image features were obtained from RGB,MS,and TIR image data sources,respectively.Seven machine learning models,including GBDT,LightGBM,MLPR,PLSR,RFR,SVR,and XGBoost,were selected to estimate the vertical distribution of maize LAI.These models were applied to estimate LAI vertical distribution data at different maize growth stages.Two models with the strongest robustness were selected to verify the optimal observation time and flight altitude under different drone flight heights and sun elevation angles.The research resu
作者
刘帅兵
金秀良
冯海宽
聂臣巍
白怡
余汛
LIU Shuaibing;JIN Xiuliang;FENG Haikuan;NIE Chenwei;BAI Yi;YU Xun(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;Institute of Crop Science,Chinese Academy of Agricultural Sciences,Beijing 100081,China;National Nanfan Research Institute(Sanya),Chinese Academy of Agricultural Sciences,Sanya 572024,China;Information Technology Research Center,Beijing Academy of Agriculture and Forest Sciences,Beijing 100097,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第5期181-193,287,共14页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2016YFD0300602)
国家自然科学基金项目(42071426、51922072、51779161、51009101)
海南省崖州湾种子实验室项目(JBGS+B21HJ0221)
中国农业科学院南繁研究院南繁专项(YJTCY01、YBXM01)。
关键词
玉米
叶面积指数
无人机多源遥感
垂直分布
飞行试验
maize
leaf area index
UAV multi-source remote sensing
vertical distribution
flight test
