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 | W.X.Zhu; Z.G.Sun; J.B.Peng; Y.H.Huang; J.Li; J.Q.Zhang |
| 刊名 | Remote Sensing
 |
| 出版日期 | 2019 |
| 卷号 | 11期号:22页码:22 |
| 关键词 | unmanned aerial vehicle,above-ground biomass,LiDAR,crop height,machine learning,canopy height,multispectral data,SfM point clouds,leaf-area index,crop surface models,winter-wheat, |
| DOI | 10.3390/rs11222678 |
| 英文摘要 | Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth;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,;an Alpha Series AL3-32 Light Detection;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),;support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation,;that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP,;SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM;RF methods performed slightly more robustly. This study is expected to optimize UAV systems;algorithms for specific agronomic applications. |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/62718]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | W.X.Zhu,Z.G.Sun,J.B.Peng,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 | W.X.Zhu,Z.G.Sun,J.B.Peng,Y.H.Huang,J.Li,&J.Q.Zhang.(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 | W.X.Zhu,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
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