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Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

文献类型：期刊论文

作者Zhu, Wanxue6,7; Sun, Zhigang1,6,7,8; Peng, Jinbang6,7; Huang, Yaohuan2,7; Li, Jing6; Zhang, Junqiang3,4; Yang, Bin4; Liao, Xiaohan2,5,7
刊名REMOTE SENSING
出版日期2019-11-02
卷号11期号:22页码:22
关键词unmanned aerial vehicle above-ground biomass LiDAR crop height machine learning multispectral data SfM point clouds
DOI10.3390/rs11222678
通讯作者Sun, Zhigang(sun.zhigang@igsnrr.ac.cn)
英文摘要Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.
WOS关键词LEAF-AREA INDEX ; CROP SURFACE MODELS ; WINTER-WHEAT ; VEGETATION INDEXES ; HYPERSPECTRAL IMAGERY ; CHLOROPHYLL CONTENT ; CANOPY HEIGHT ; UAV ; YIELD ; RESOLUTION
资助项目Key Projects of the Chinese Academy of Sciences[KFZD-SW-319] ; National Natural Science Foundation of China[31570472] ; National Natural Science Foundation of China[31870421] ; National Natural Science Foundation of China[41771388] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-STS-ZDTP-049] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040303] ; National Key Research and Development Program of China[2017YFC0503805]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000502284300083
出版者MDPI
资助机构Key Projects of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/131151]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Zhigang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
4.Yusense Informat Technol & Equipment Qingdao Ltd, Qingdao 266000, Shandong, Peoples R China
5.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Beijing 100101, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Wanxue,Sun, Zhigang,Peng, Jinbang,et al. Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales[J]. REMOTE SENSING,2019,11(22):22.
APA Zhu, Wanxue.,Sun, Zhigang.,Peng, Jinbang.,Huang, Yaohuan.,Li, Jing.,...&Liao, Xiaohan.(2019).Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales.REMOTE SENSING,11(22),22.
MLA Zhu, Wanxue,et al."Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales".REMOTE SENSING 11.22(2019):22.

入库方式： OAI收割

来源：地理科学与资源研究所

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