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UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning

文献类型：期刊论文

作者Zhu, Wanxue; Rezaei, Ehsan Eyshi; Nouri, Hamideh; Sun, Zhigang; Li, Jing; Yu, Danyang; Siebert, Stefan
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2023
卷号16页码:7471-7485
ISSN号1939-1404
关键词Agriculture image resolution image texture analysis remote sensing spectral analysis
DOI10.1109/JSTARS.2023.3302571
产权排序1
文献子类Article
英文摘要Optical unmanned aerial vehicle (UAV) remote sensing is widely prevalent to estimate crop aboveground biomass (AGB). Nevertheless, limited knowledge of the UAV flight height (mainly characterized by different image numbers and spatial resolutions) influences the crop AGB estimation accuracy across diverse sensing datasets and machine-/deep-learning models. This article assessed the impacts of flight height and integration of multiscale sensing information on wheat AGB estimation. The multispectral UAV flight missions with 30, 60, 90, and 120 m heights were conducted at the wheat grain filling phase in 2018 and 2019. To estimate AGB, we used the UAV-based crop surface model (CSM), spectral, texture indices, and their combinations along with a deep convolutional neural network (DCNN with AlexNet architecture), random forest, and support vector machine models. Results showed the CSM and textures exhibit sensitivity to flight height, with estimation accuracy declining by 48% and 41%, respectively, as the flight height increased from 30 to 120 m. Spectral indices displayed lesser sensitivity with accuracy decrease of 25%. Integrating data from different heights exhibited better performances in texture and spectral indices while reducing performance when CSM was input. The DCNN performed best particularly at high spatial image scales, whereas more sensitive to flight height, as the AGB estimation accuracy decreased by 30% and 47% from 30 to 120 m for machine learning and DCNN, respectively. Integrating texture and spectral information derived from images with moderate spatial resolutions (4-6 cm), and the integration of multiscale textures, are optimal for grain-filling wheat AGB estimation.
WOS关键词LEAF-AREA INDEX ; UNMANNED AERIAL VEHICLE ; SUPPORT VECTOR MACHINE ; CROP SURFACE MODELS ; IMAGE TEXTURE ; WORLDVIEW-2 IMAGERY ; CANOPY STRUCTURE ; FOREST ; CHLOROPHYLL ; REFLECTANCE
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001050527700011
源URL[http://ir.igsnrr.ac.cn/handle/311030/194617]  
专题禹城站农业生态系统研究中心_外文论文
作者单位1.Institute of Geographic Sciences & Natural Resources Research, CAS
2.University of Gottingen
3.Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
4.University of Chinese Academy of Sciences, CAS
5.University of Twente
6.Chinese Academy of Sciences
推荐引用方式
GB/T 7714		 Zhu, Wanxue,Rezaei, Ehsan Eyshi,Nouri, Hamideh,et al. UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2023,16:7471-7485.
APA Zhu, Wanxue.,Rezaei, Ehsan Eyshi.,Nouri, Hamideh.,Sun, Zhigang.,Li, Jing.,...&Siebert, Stefan.(2023).UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,16,7471-7485.
MLA Zhu, Wanxue,et al."UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16(2023):7471-7485.

入库方式： OAI收割

来源：地理科学与资源研究所

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