Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

	Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system
	Li, Wang 1; Niu, Zheng 1; Chen, Hanyue 1; Li, Dong 1; Wu, Mingquan 1; Zhao, Wei 1
刊名	Ecological Indicators
	2016
卷号	67 页码:637-648
关键词	HURRICANE BOUNDARY-LAYER COLD-AIR OUTBREAK ROLL VORTICES CELLULAR CONVECTION SAR IMAGES SATELLITE SPEED OCEAN SIMULATION RETRIEVAL
通讯作者	Li, Wang (lwwhdz@sina.com)
英文摘要	Canopy height (Hcanopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate Hcanopyand AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize Hcanopyand AGB were predicted by a multiple stepwise linear (SWL) regression model (R2= 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R2= 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for Hcanopy, and 0.05 kg/m2for AGB. (3) The SfM point metrics, especially the mean point height (Hmean) greatly contributed to the estimation model of maize Hcanopyand AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management. © 2016 Elsevier Ltd. All rights reserved.
学科主题	Biodiversity & Conservation; Environmental Sciences & Ecology
类目[WOS]	Biodiversity Conservation ; Environmental Sciences
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20161402195882
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39198]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. College of Resource and Environmental Science, Fujian Agriculture and Forestry University, Fuzhou, China 3. Airborne Remote Sensing Center, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Li, Wang,Niu, Zheng,Chen, Hanyue,et al. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system[J]. Ecological Indicators,2016,67:637-648.
APA	Li, Wang,Niu, Zheng,Chen, Hanyue,Li, Dong,Wu, Mingquan,&Zhao, Wei.(2016).Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system.Ecological Indicators,67,637-648.
MLA	Li, Wang,et al."Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system".Ecological Indicators 67(2016):637-648.