气候变化领域集成服务门户(CCPortal): Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

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DOI	10.1016/j.jag.2019.01.013
	Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery
	Melville B.; Fisher A.; Lucieer A.
发表日期	2019
ISSN	15698432
起始页码	14
结束页码	24
卷号	78
英文摘要	Vegetation cover is a key environmental variable often mapped from satellite and aerial imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) based on multispectral imagery acquired from an Unmanned Aerial System (UAS) has several applications, including the potential to revolutionise the collection of field data for calibration/validation of satellite products. In this study, abundance maps were derived using three methods, applied to data collected in a typical Australian rangeland environment. The first method used downscaling between Landsat FVC maps and UAS images with Random Forest regression to predict bare ground, photosynthetic vegetation and non-photosynthetic vegetation cover. The second method used spectral unmixing based on endmembers identified in the multispectral imagery. The third method used an object-based classification approach to label image segments. The accuracy of all UAS FVC and Landsat FVC products were assessed using 20 field plots (100 m diameter star transects), as well as from 138 ground photo plots. The classification method performed best for all cover fractions at the 100 m plot scale (12–13% RMSE), with the downscaling approach only able to accurately predict photosynthetic cover. The downscaling and unmixing generally over-predicted non-photosynthetic vegetation associated with Chenopod shrubs. When compared with the high-resolution photo plot data, the classification method performed the worst, while the downscaling and unmixing methods achieved reasonable accuracy for the photosynthetic component only (12–13% RMSE). Multispectral UAS imagery has great potential for mapping photosynthetic vegetation cover in rangelands at ultra-high resolution, though accurately separating non-photosynthetic vegetation and bare ground was only possible when the data was scaled-up to coarser resolutions. © 2019 Elsevier B.V.
英文关键词	Downscaling; Fractional vegetation cover; Spectral unmixing; Unmanned aerial systems
语种	英语
scopus关键词	downscaling; Landsat; multispectral image; spatial resolution; vegetation cover
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156493
作者单位	Faculty of Communication and Environment, Rhein-Waal University of Applied Sciences, Friederich-Heinrich Allee 25, Kamp-Lintfort, Germany; Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia; Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia; School of Technology, Environments and Design, Geography and Spatial Science Discipline, University of Tasmania, Private Bag 78, Hobart, Tasmania 7001, Australia
推荐引用方式 GB/T 7714	Melville B.,Fisher A.,Lucieer A.. Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery[J],2019,78.
APA	Melville B.,Fisher A.,&Lucieer A..(2019).Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery.International Journal of Applied Earth Observation and Geoinformation,78.
MLA	Melville B.,et al."Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery".International Journal of Applied Earth Observation and Geoinformation 78(2019).