苏伟,展郡鸽,张明政,吴代英,张蕊.基于机载LiDAR数据的农作物叶面积指数估算方法研究[J].农业机械学报,2016,47(3):272-277.
Su Wei,Zhan Junge,Zhang Mingzheng,Wu Daiying,Zhang Rui.Estimation Method of Crop Leaf Area Index Based on Airborne LiDAR Data[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):272-277.
摘要点击次数: 2952
全文下载次数: 2241
基于机载LiDAR数据的农作物叶面积指数估算方法研究   [下载全文]
Estimation Method of Crop Leaf Area Index Based on Airborne LiDAR Data   [Download Pdf][in English]
投稿时间:2015-09-25  
DOI:10.6041/j.issn.1000-1298.2016.03.038
中文关键词:  农作物  机载激光雷达  叶面积指数  Pearson相关性分析法  空间化
基金项目:国家自然科学基金项目(41371327)和北京高等学校青年英才计划项目(YETP0316)
作者单位
苏伟 中国农业大学 
展郡鸽 中国农业大学 
张明政 山东农业大学 
吴代英 中国农业大学 
张蕊 中国农业大学 
中文摘要:叶面积指数(LAI)是农作物长势监测及估产的重要参数,激光雷达能够提供精确的农作物冠层结构信息,可弥补光学遥感在提取冠层结构信息方面的不足。因此,本文旨在挖掘激光雷达所能提取的农作物垂直结构信息,并研究冠层结构参数与农作物叶面积指数之间的关系,从而估算整个研究区的叶面积指数。首先,基于机载激光雷达数据提取平均高度(Hmean)、最大高度(Hmax)、最小高度(Hmin)、高度百分位数(H25th、H50th、H75th、H90th)、激光穿透力指数(LPI)、回波点云密度、孔隙率(fgap)、叶倾角(MTA)等结构参数;然后,利用Pearson相关性分析法对以上参数与地面实测LAI进行相关性分析,并选择与LAI相关性高的参数;最后,对选择的敏感性参数进行回归分析,构建激光雷达参数与实测LAI的LiDAR-LAI估算模型,估算整个研究区的农作物冠层LAI。精度评价结果表明:预测LAI与实测LAI之间的相关系数为0.79,均方根误差为0.47,说明激光雷达所提取的农作物冠层结构参数可用于估算空间上连续、大面积的农作物LAI。
Su Wei  Zhan Junge  Zhang Mingzheng  Wu Daiying  Zhang Rui
China Agricultural University,China Agricultural University,Shandong Agricultural University,China Agricultural University and China Agricultural University
Key Words:crop  airborne LiDAR  leaf are index  Pearson correlation analysis  spatialization
Abstract:Leaf area index (LAI) is an important parameter in crop growth monitoring and crop yield estimation. However, optical remote sensing cannot extract the structural information. Light detection and ranging (LIDAR) can provide accurate crop structural information, so LiDAR can make up the shortage of optical remote sensing. Therefore, the purpose of this research is to study the vertical structure information of crops which can be extracted by LiDAR, analyze the correlation of LiDAR vertical metrics and LAI of crop, and estimate LAI of the whole study area. First, the metrics were extracted based on LiDAR data, including mean height above ground of all first returns (Hmean), maximum height above ground of all first returns (Hmax), minimum height above ground of all first returns (Hmin), the percentiles of the canopy height distributions(H25th, H50th, H75th, H90th), laser penetration index (LPI), density of points, porosity and leaf angle. Then, Pearson correlation analysis was used to filter LiDAR metrics which are better related to LAI measured data. Last, regression analysis of selected sensitive parameters was carried out on setting up LiDAR-LAI estimation model, and the LAI estimated result of the whole study area was calculated. The result shows that correlation coefficient between estimated LAI and field measured LAI is 0.79, and RMSE is 0.47. It shows that crop canopy structure parameters extracted by LiDAR can be used to estimate the spatial continuous and large area of LAI of crops.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

   下载PDF阅读器