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Sustainability 2015, 7(3), 2841-2857; doi:10.3390/su7032841

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

1,2
and
1,*
1
School of land Science and Technology, China University of Geosciences, Beijing 100083, China
2
Lamont Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 21 November 2014 / Revised: 3 March 2015 / Accepted: 4 March 2015 / Published: 9 March 2015
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Abstract

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. View Full-Text
Keywords: spatiotemporal; Huai River; empirical orthogonal function; temporal unmixing analysis; vegetation phenology spatiotemporal; Huai River; empirical orthogonal function; temporal unmixing analysis; vegetation phenology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xu, D.; Fu, M. Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China. Sustainability 2015, 7, 2841-2857.

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