Modelling bioclimate by means of Fourier analysis of NOAA‐AVHRR NDVI time series in Western Argentina
Modelling bioclimate by means of Fourier analysis of NOAA‐AVHRR NDVI time series in Western Argentina
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DOI：
10.1002/joc.1610
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发表时间：
2008-07
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影响因子：
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通讯作者：
M.M.G. Loyarte;M. Menenti;A. Diblasi
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文献类型：
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作者：
M.M.G. Loyarte;M. Menenti;A. Diblasi
本研究评估了气候与叶面物候的关系是否足够稳健，是否可以在观测稀疏的地区使用叶面物候测量来插值气候统计数据。采用归一化植被指数（NDVI）表示植被活动。作为叶面物候的度量，我们使用了通过快速傅里叶变换（FFT）对NDVI时间序列进行建模获得的参数，该参数应用于美国国家海洋和大气管理局（NOAA）每月先进的非常高分辨率辐射计（AVHRR） NDVI全球区域覆盖（GAC）图像的9年时间序列。FFT将该序列分解为平均信号和正弦分量。选取的FFT参数为1年期间的平均NDVI、振幅和相位。我们的具体目标是以两种互补的方式将降水量P /潜在蒸散量ETP与FFT参数联系起来。首先，将它们作为数值分类的属性，得到叶片等表型图，然后将这些分类与生物气候类型联系起来，生成生物气候图。二是以P/ETP为预测变量，FFT参数为预测变量，拟合多元线性回归模型。然后应用回归模型获得P/ETP比率图。后者给出了第二张生物气候图。叶面等候分类呈现南北向相值递减、幅度和平均NDVI值增大的趋势，反映了气候条件由偏热偏干向偏湿偏冷的转变。该模型解释了p /ETP比值92% （p值< 10−12）的空间变化。当使用单个FFT参数时，没有得到显著的关系。这三个参数为了解物候变化对气候变率的响应提供了补充信息。通过傅立叶分析总结的每月NDVI序列来模拟生物气候是扩展气候数据稀疏的适当工具。版权所有©2007英国皇家气象学会
This study assessed whether the relationship of climate with foliar phenology is sufficiently robust to use a measure of foliar phenology to interpolate climate statistics in areas where observations are sparse. The normalized difference vegetation index (NDVI) was used to represent vegetation activity. As a measure of foliar phenology, we used parameters obtained by modelling NDVI time series with a Fast Fourier Transform (FFT) applied to a 9‐year time series of monthly National Oceanographic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) NDVI global area coverage (GAC) images. The FFT decomposes the series into an average signal and to sinusoidal components. The selected FFT parameters were mean NDVI, and amplitude and phase for a 1‐year period. Our specific objective was to relate the ratio of precipitation, P, over potential evapotranspiration, ETP, to the FFT parameters in two complementary ways. The first was to use them as attributes in a numerical classification to obtain a map of foliar isophenology, and then associate these classes with bioclimatic types, thus generating a bioclimatic map. The second was to fit a multiple linear regression model with P/ETP as predicted variable and the FFT parameters as predictive variables. The regression model was then applied to obtain a map of the ratio P/ETP. The latter gave a second bioclimatic map. Foliar isophenology classes show a north–south decrease in phase value and increase in amplitude and mean NDVI values, thus reflecting the transition in climate conditions from hotter and drier to wetter and cooler. The model explains 92% (p‐value < 10−12) of the spatial variation in the P/ETP ratio. When using a single FFT parameter, no significant relationship was obtained. The three parameters provide complementary information to understand phenological variability in response to climate variability. Modelling bioclimate by means of monthly NDVI series summarized by Fourier analysis is an adequate tool to extend climate data where they are sparse. Copyright © 2007 Royal Meteorological Society