利用无人机影像构建作物表面模型估测甘蔗LAI  

Estimation of leaf area index of sugarcane using crop surface model based on UAV image

作　　者：杨琦[1] 叶豪[1] 黄凯 查元源[1] 史良胜[1] 

机构地区：[1]武汉大学水资源与水电工程科学国家重点实验室,武汉430072 [2]广西壮族自治区水利科学研究院,南宁530023

出　　处：《农业工程学报》2017年第8期104-111,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：高等学校全国优秀博士学位论文作者专项资金(201248);广西水利厅科技项目(201615)

摘　　要：为探讨从作物表面模型(crop surface models,CSMs)中提取株高来估算糖料蔗叶面积指数(leaf area index,LAI)的可行性,该文采用无人机-RGB高清数码相机构成的低空遥感平台,以广西糖料蔗为研究对象,采集了糖料蔗全生育期的高清数码影像,分别在有无地面控制点条件下建立各生育期CSMs并提取株高。此外,该文利用高清数码影像计算了6种可见光植被指数并建立LAI估算模型,用以对比从CSMs提取的株高对LAI的估算效果。结果表明:全生育期CSMs提取的株高与实测株高显著相关(P<0.01),株高预测值与实测值高度拟合(R2=0.961 2,RMSE=0.215 2)。选取的6种可见光植被指数中,绿红植被指数对糖料蔗伸长末期以前的LAI的估测效果最好(R2=0.779 0,RMSE=0.556 1,MRE=0.168 0)。相同条件下,株高对LAI有更高的估测精度,其中CSMs提取的株高估测效果优于地面实测株高,预测模型R2=0.904 4,RMSE=0.366 2,MRE=0.124 3。研究表明,使用无人机拍摄RGB影像来提取株高并运用于糖料蔗重要生育期LAI的估算是可行的,CSMs提取的株高拥有较高的精度。该研究可为大区域进行精准快速的农情监测提供参考。The red-green-blue（RGB）digital camera on unmanned aerial vehicle（UAV）with the relatively low cost and near real-time image acquisition renders a remote sensing platform,which is an ideal tool for crop monitoring in precision agriculture.Some successful applications have been made in biomass and yield estimation.However,retrieval of leaf area index（LAI）using plant height information extracted by crop surface models（CSMs）has been paid very limited attention to.Therefore,the objective of this study was to demonstrate the feasibility of estimating LAI with CSMs-based plant height.The study was conducted in warm and wet southern China where the sugarcane was planted widely.In this study,we acquired RGB imaging data of sugarcane in whole growing stage（8 flights）by this platform.Afterward,42 ground control points（GCPs）were evenly distributed across the field due to the rugged terrain of the experimental area.The CSMs were built with the GCPs data and the UAV-based RGB image with very high resolution using the structure from motion（Sf M）algorithm,and then the plant height information derived from CSMs was applied to estimate the LAI of sugarcane.The estimated LAI values were validated using the ground measurement data,which were collected simultaneously with the image acquisition.To assess the accuracy of plant height extracted from the CSMs without geo-referencing by GCPs data,we also constructed the ground elevation model by inverse distance weighted（IDW）interpolation to obtain plant height.In addition,we applied 6 visible band vegetation indices including green-red vegetation index（GRVI）,normalized redness intensity（NRI）,normalized greenness intensity（NGI）,green leaf index（GLI）,atmospherically resistant vegetation index（ARVI）,and modified green-red vegetation index（MGRVI）from RGB image to predict the LAI,respectively.The performance of prediction models based on 6 vegetation indices was assessed by comparing with that based on plant height.The predicted plant hei

关 键 词：遥感 无人机 农作物 作物表面模型 糖料蔗 数码影像 株高 叶面积指数 

分 类 号：S566.1[农业科学—作物学] TP79[自动化与计算机技术—检测技术与自动化装置]