气候变化领域集成服务门户(CCPortal): Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data

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DOI	10.1016/j.rse.2020.111856
	Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data
	Cooper S.; Okujeni A.; Jänicke C.; Clark M.; van der Linden S.; Hostert P.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	The next generation of spaceborne imaging spectrometers will enable hyperspectral analysis of vegetation cover across large spatial extents. Spectral unmixing provides a means to assess subpixel vegetation composition in such imagery. Here we implement a regression-based unmixing approach to generate fractional vegetation cover on a regional scale from a simulated Environmental Mapping and Analysis Program (EnMAP) satellite scene derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over the San Francisco Bay Area, California, USA, an area with a mixture of temperate and Mediterranean climate forests, woodlands and shrublands. A hierarchical classification scheme was implemented that considered fractional cover of vegetation as a whole (vegetation vs non-vegetation), vegetation life forms (woody vs non-woody vegetation; tree vs shrub vs grass), and tree leaf type (needleleaf vs broadleaf). A Gaussian Process Regression (GPR) model was trained using synthetically-mixed training data generated from an endmember library, and mapping accuracy was assessed using an independent validation dataset across four ecoregions. Our approach was able to effectively model landscape patterns at all levels of the class hierarchy. Site-wide map accuracy was highest when mapping generic vegetation fractions (MAE = 3.8%) and expectedly decreased at more complex hierarchy levels, with highest errors observed when separating tree and shrub fractions. Still, fraction estimates of needleleaf trees (MAE = 10.6%), broadleaf trees (MAE = 13.1%) and shrubs (MAE = 15.3%) were mapped with low overall error. Using Landsat imagery led to an average decrease in map accuracy of 1.9% when compared to hyperspectral image analysis and a maximum decrease of 3.5% when separating broadleaf and needleleaf trees across all sites. Further, a single regional model was shown to yield comparable results to multiple local ecoregion-based models, facilitating the analysis of large regions without creating a separate model for each region. Our results highlight the utility of regression-based approaches for quantitative vegetation mapping, which is of particular interest for future spaceborne imaging spectroscopy missions operating across large areas at moderate spatial resolution. © 2020 Elsevier Inc.
语种	英语
scopus关键词	Forestry; Hyperspectral imaging; Image analysis; Infrared spectrometers; Mapping; Regression analysis; Satellite imagery; Separation; Spectroscopy; Thermography (imaging); Airborne visible infrared imaging spectrometer; Fractional vegetation cover; Gaussian process regression; Hierarchical classification; Hyperspectral analysis; San Francisco Bay area , California; Spaceborne imaging spectrometers; Vegetation composition; Vegetation; AVIRIS; image analysis; imaging method; regression analysis; satellite altimetry; satellite data; simulation; spectral analysis; vegetation cover; California; San Francisco Bay; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179287
作者单位	Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany; Center for Interdisciplinary Geospatial Analysis, Sonoma State University, Rohnert Park, CA 94928, United States; Institute for Geography and Geology, Universität Greifswald, Domstraße 11, Greifswald, 17489, Germany; Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany
推荐引用方式 GB/T 7714	Cooper S.,Okujeni A.,Jänicke C.,et al. Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data[J],2020,246.
APA	Cooper S.,Okujeni A.,Jänicke C.,Clark M.,van der Linden S.,&Hostert P..(2020).Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data.Remote Sensing of Environment,246.
MLA	Cooper S.,et al."Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data".Remote Sensing of Environment 246(2020).