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Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

机译:基于经验正交函数和时间分解分析的植被覆盖度下降检测

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摘要

Vegetation plays an important role in the energy exchange of the land surface, biogeochemical cycles, and hydrological cycles. MODIS (MODerate-resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) is considered as a quantitative indicator for examining dynamic vegetation changes. This paper applied a new method of integrated empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) to detect the vegetation decreasing cover in Jiangsu Province of China. The empirical orthogonal function (EOF) statistical results provide vegetation decreasing/increasing trend as prior information for temporal unmixing analysis. Temporal unmixing analysis (TUA) results could reveal the dominant spatial distribution of decreasing vegetation. The results showed that decreasing vegetation areas in Jiangsu are distributed in the suburbs and newly constructed areas. For validation, the vegetation's decreasing cover is revealed by linear spectral mixture from Landsat data in three selected cities. Vegetation decreasing areas pixels are also calculated from land use maps in 2000 and 2010. The accuracy of integrated empirical orthogonal function and temporal unmixing analysis method is about 83.14%. This method can be applied to detect vegetation change in large rapidly urbanizing areas.
机译:植被在土地表面的能量交换,生物地球化学循环和水文循环中起着重要作用。 MODIS(中等分辨率成像光谱仪)EVI(增强植被指数)被认为是检查动态植被变化的定量指标。本文应用经验正交函数积分(EOF)和时间分解分析(TUA)的新方法来检测江苏省的植被减少量。经验正交函数(EOF)统计结果提供了植被减少/增加的趋势,作为时间上的非混合分析的先验信息。时间拆解分析(TUA)结果可以揭示植被减少的主要空间分布。结果表明,江苏植被减少的地区分布在郊区和新建区。为了验证,植被的递减覆盖由三个选定城市的Landsat数据中的线性光谱混合揭示。从2000年和2010年的土地利用图还可以计算出植被减少面积像素。经验正交函数和时间分解分析方法的综合精度约为83.14%。该方法可用于检测大型快速城市化地区的植被变化。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第2017期|5032091.1-5032091.10|共10页
  • 作者单位

    Shanghai Normal Univ, Urban Dev Res Inst, Shanghai 200234, Peoples R China;

    East China Normal Univ, Sch Geog Sci, Shanghai 200062, Peoples R China;

    Columbia Univ, CIESIN, 61 Route 9W,POB 1000, Palisades, NY 10964 USA;

    Shanghai Normal Univ, Tourism Coll, Shanghai 200234, Peoples R China;

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