干旱区生态环境知识资源中心(Arid): UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert

Arid

DOI	10.1016/j.ecolind.2022.109155
	UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert
	Wang, Ning; Guo, Yuchuan; Wei, Xuan; Zhou, Mingtong; Wang, Huijing; Bai, Yunbao
通讯作者	Guo, YC
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2022
卷号	141
英文摘要	Natural vegetation is an important indicator for the maintenance of symbiosis in an oasis in extremely arid zones. Unmanned aerial vehicles have advantages of high resolution and multiple wavebands to obtain details of sparse vegetation cover. So far, studies on the selection of machine learning methods are relatively limited and usually focus on only a few selected methods. In this study, the natural vegetation of the Dariyabui Oasis in the hinterland of the Taklamakan Desert in China was mapped using 2,550 samples of data and 14 visible and multispectral vegetation indices as model variables. Six machine learning methods were used to construct fractional vegetation cover (FVC) predictive regression models. Coefficient of determination (R2), root-mean-square error (RMSE), and mean-absolute error (MAE) were used to evaluate the models. The regression models were divided into four components: visible (RF: R2 = 0.65, RMSE = 0.59 %, MAE = 0.41 %), multispectral (RF: R2 = 0.71, RMSE = 0.54 %, MAE = 0.36 %), visible and multispectral (RF: R2 = 0.69, RMSE = 0.55 %, MAE = 0.37 %), and the product of visible and multispectral vegetation indices (RF: R2 = 0.68, RMSE = 0.57 %, MAE = 0.39 %). Besides, the visible vegetation index results were validated using different years and different aerial height data. The results show that these four regression models can effectively obtain the FVC of sparse vegetation of the desert. This study applied the Random Forest model, which was selected based on a comparison of other models, to predict the status of desert vegetation cover based on spectral data to provide information for its conservation and management.
英文关键词	Machine learning Optimal model Taklimakan desert UAV Vegetation coverage Vegetation indices
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000856282700004
WOS关键词	UNMANNED AERIAL VEHICLES ; MODIS DATA ; BIOMASS ; WATER ; LANDSAT ; HEIGHT ; MODELS ; IMAGES ; YIELD
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392325
推荐引用方式 GB/T 7714	Wang, Ning,Guo, Yuchuan,Wei, Xuan,et al. UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert[J],2022,141.
APA	Wang, Ning,Guo, Yuchuan,Wei, Xuan,Zhou, Mingtong,Wang, Huijing,&Bai, Yunbao.(2022).UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert.ECOLOGICAL INDICATORS,141.
MLA	Wang, Ning,et al."UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert".ECOLOGICAL INDICATORS 141(2022).