苏伟,展郡鸽,李静,马鸿元,吴代英,张蕊.基于地基激光雷达的叶倾角分布升尺度方法研究[J].农业机械学报,2016,47(9):180-185.
Su Wei,Zhan Junge,Li Jing,Ma Hongyuan,Wu Daiying,Zhang Rui.Upscaling Leaf Angle Distribution Using Terrestrial Laser Scanning Technique[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):180-185.
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基于地基激光雷达的叶倾角分布升尺度方法研究   [下载全文]
Upscaling Leaf Angle Distribution Using Terrestrial Laser Scanning Technique   [Download Pdf][in English]
投稿时间:2016-01-28  
DOI:10.6041/j.issn.1000-1298.2016.09.026
中文关键词:  地基激光雷达  叶倾角分布  主成分分析  BP神经网络  升尺度
基金项目:国家自然科学基金项目(41371327)
作者单位
苏伟 中国农业大学 
展郡鸽 中国农业大学 
李静 环境保护部卫星环境应用中心 
马鸿元 中国农业大学 
吴代英 中国农业大学 
张蕊 中国农业大学 
中文摘要:地基激光雷达因其具有穿透力强,能够提取植被冠层三维结构信息的优势,是提取植被叶倾角分布(Leaf angle distribution, LAD)的理想数据源,因此将地基激光雷达数据与遥感影像结合获取大尺度叶倾角分布结果颇具潜力。以河北省保定市北部4个县为研究区,利用10个玉米样地的地基激光雷达数据提取叶倾角分布结果,使用主成分正变换提取玉米实测叶倾角分布数据中信息量最大的前3个主成分,再利用神经网络模型对所提取的主成分与Landsat8反射率数据结合建立关系模型,然后将训练好的模型应用于整个研究区进行升尺度转换,最后通过主成分逆变换,得到升尺度后平均叶倾角(Mean tilt angle, MTA)结果。对升尺度后LAD与实测LAD及升尺度后MTA与实测MTA进行交叉验证,结果表明,升尺度MTA与实测MTA的验证精度(R2)为0.7862,均方根误差(RMSE)为3.04°。该结果表明,使用提取主成分方法建立光谱数据与叶倾角分布的关系模型从而达到升尺度转换的目的具有可行性,模拟精度较高,且误差较小。
Su Wei  Zhan Junge  Li Jing  Ma Hongyuan  Wu Daiying  Zhang Rui
China Agricultural University,China Agricultural University,Satellite Environment Center, Ministry of Environmental Protection,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:terrestrial laser scanning  leaf angle distribution  principal components analysis  back propagation neural network  upscaling
Abstract:Leaf angle distribution (LAD) can be used to describe the canopy structure of vegetation completely, such as crops, trees and grass. It’s one of the important parameters to quantitative description of vegetation canopy structure. At the present, there are few studies used the spectral data to inverse LAD, and results of the most existing studies of mean leaf tilt angle and leaf angle distribution were the locational inversion. Therefore, this study set the study site in five counties of Baoding City, Hebei Province, using terrestrial laser scanning (TLS) to acquire the leaf angle distribution data of maize. Combining the Landsat8 remote sensing data, firstly, the principle component analysis was taken to extract the principle information of measured leaf angle distribution of maize. Secondly, the back propagation artificial neural network was taken to model the relationship of principal information and spectral data. Then, the model was used in the whole study area to accomplish the upscaling transform. Finally, the upscaled mean tilt angel (MTA) was calculated based on the predicted LAD by principal component inverse transformation, in order to quantitate the leaf angle data. The cross validation result showed that the accuracy (R2) between upscaled MTA and measured MTA was 0.7862, and the mean square root error (RMSE) was 3.04°. Consequently, it shows that this method can realize the aim of LAD upscaling.

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.

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