Modelling bioclimate by means of Fourier analysis of NOAA‐AVHRR NDVI time series in Western Argentina

Abstract

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.

Spatio-temporal variability in remotely sensed land surface temperature, and its relationship with physiographic variables in the Russian Altay Mountains

“…Assessment of the spatio-temporal variability of the Fourier components reflected the importance of the average (k=0) and annual signal (k=8) in the original time series. These periodics correspond to the relevant signals found by several authors in NDVI time series (Lhermitte et al, 2008;Loyarte et al, 2008), although this significance is closely related to the local climatic conditions, as well as typical vegetation phenologies. As such, the FFT method was succesfully applied to analyze spatial variability in average and annual climatologies.…”

Assessing growing season beginning and end dates and their relation to climate in Taiwan using satellite data

“…Remotely sensed data, on the other hand, cover a much broader area and so have been widely used in assessing environmental changes at landscape to global scales. A number of vegetation indices, such as normalized difference vegetation index (NDVI; Beck et al 2006, Loyarte et al 2008, enhanced vegetation index (EV; Xiao et al 2006, Evrendilek andGulbeyaz 2008) and wide dynamic range vegetation index (WDRVI; Gitelson 2004, Vina et al 2004, have been used to determine satellite-image-derived phenological patterns.…”

Optimal Estimate of Global Biome—Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method

“…Polynomial or inter-annual harmonic components: If HANTS is applied to annual NDVI time series, the estimated Fourier components can only explain variations over periods shorter than a year while real signals may contain trends or inter-annual variations [12]. Earlier studies on Fourier analysis of NDVI time series were focused on investigate inter-annual variation of vegetation regulated by climate using multi-annual data records [30,[46][47][48].…”

“…HANTS has been widely applied to analyze long time series of terrestrial remote sensing observables for almost 30 years, either for gap-filling or extracting accurate harmonic components for further analysis [34,44,46,47]. The algorithm has been implemented using different programming languages and platforms, such as Fortran, ENVI/IDL, Matlab, Python, R, etc.…”

“…The amplitudes and phases of the periodic components of NDVI signals are quantitative phenological metrics of vegetation vigor across timescales and have been frequently applied in land cover change detection or vegetation-climate interaction analysis [34,[46][47][48]. The main purpose of HANTS is the quantitative analysis of observable signals in the frequency domain.…”