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Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China

Vegetation plays an important role in atmospheric, hydrologic and biochemical cycles and is an important indicator of the impact of climate and human factors on the environment. In this paper, a method, which combines the empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) methods, is applied to monitor the phenological characteristcs and spatial distribution of vegetation phenology in the middle part of the Huai River region. Based on the variance and EOF curves, the EOF provides the number of phenology modes, information which is the basis for an accurate temporal unmixing model. The TUA describes the temporal vegetation phenological details and spatial distribution. Importantly, this approach does not require assumptions, prior information or pre-defined thresholds. The vegetation phenology curves derived from the MODIS EVI data using the combined EOF and TUA methods display much more detail than the curves from Landsat TM using spectral mixture analysis (SMA). Additionally, the vegetation phenology spatial distribution from MODIS EVI is consistent with the field survey data. The combination method of EOF and TUA can be used to monitor vegetation phenology spatiotemporal change in a large area from time series of MODIS EVI data.
- Columbia University United States
- China University of Geosciences China (People's Republic of)
- University of Chicago United States
- China University of Geosciences China (People's Republic of)
- Columbia University United States
vegetation phenology, 550, TJ807-830, TD194-195, Renewable energy sources, Huai River, empirical orthogonal function, Functions, GE1-350, Orthogonal, Functions, Orthogonal, spatiotemporal, Vegetation and climate, Environmental effects of industries and plants, Ecology, spatiotemporal; Huai River; empirical orthogonal function; temporal unmixing analysis; vegetation phenology, Vegetation monitoring, Environmental sciences, Sustainability, temporal unmixing analysis, FOS: Biological sciences, Land use--Planning, jel: jel:Q, jel: jel:Q0, jel: jel:Q2, jel: jel:Q3, jel: jel:Q5, jel: jel:O13, jel: jel:Q56
vegetation phenology, 550, TJ807-830, TD194-195, Renewable energy sources, Huai River, empirical orthogonal function, Functions, GE1-350, Orthogonal, Functions, Orthogonal, spatiotemporal, Vegetation and climate, Environmental effects of industries and plants, Ecology, spatiotemporal; Huai River; empirical orthogonal function; temporal unmixing analysis; vegetation phenology, Vegetation monitoring, Environmental sciences, Sustainability, temporal unmixing analysis, FOS: Biological sciences, Land use--Planning, jel: jel:Q, jel: jel:Q0, jel: jel:Q2, jel: jel:Q3, jel: jel:Q5, jel: jel:O13, jel: jel:Q56
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).11 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
