Impact of rainfall anomalies on Fourier parameters of NDVI time series of northwestern Argentina

Abstract

This paper describes a method to detect the impact of rainfall anomalies on vegetation phenology, in terms of timing (phase) and greenness, by using Fourier series to fit a time series of Normalized Difference Vegetation Index (NDVI) observations. The study was conducted in the northern semiarid region of Argentina, where rainfall is the driving factor of vegetation phenology. A 9-year time series of monthly NDVI Global Area Coverage (GAC) images, obtained with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), was split into nine series of 12monthly images, each corresponding to a yearly growth cycle. A Fast Fourier Transform (FFT) algorithm was applied to each cycle, and derived parameters were analysed according to rainfall anomalies for irrigated and rainfed crops, grasslands and native forest. Derived Fourier parameters were: mean NDVI, amplitude and phase. Both negative and positive rainfall anomalies had a significant impact on the Fourier parameters. Amplitude and phase were the most sensitive parameters. Droughts modified the monomodal structure of the yearly cycle by decreasing the contribution of the 12-month periodic component and increasing the contribution of the 6-month component. The impact of drought on the Fourier parameters was highest for rainfed crops. Yearly values of Fourier parameters for grasslands and native forest were affected by prevailing hydrological conditions over the previous year.

Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000–2015

“…We used the least-squares linear regression model to detect temporal variations in NDVI, an indicator of grassland vegetation NPP, during the study period (i.e., [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015]. This method is commonly used to analyze the trends in similar types of data (Eklundh and Olsson, 2003;Stöckli and Vidale, 2004;González and Menenti, 2008;Beck et al, 2011). The per pixel-change trend was obtained by linearly fitting the variables of interest (NDVI or climatic variables) as a function of a time (in years):…”

Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area

“…Time series anomalies of the climatic variables were also computed. Time series anomalies have been previously employed for vegetation index time series analysis in relation to climatic variables [32,33]. It provided an intuitive way to identify intensity and duration of the canopy change/stress or different climatic variations.…”

“…It provided an intuitive way to identify intensity and duration of the canopy change/stress or different climatic variations. Various non-dimensional coefficients have been previously described to represent magnitude and timing of the anomalies of a time series, such as [33]: (1) coefficient D, that is the sum of negative anomalies for a given study period; and (2) coefficient S, that is the sum of positive anomalies for a given study period. Both coefficients were computed for time series of EVI and climatic variables for each year of the studied period.…”

Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index

“…The time series of daily NDVI at 1 km for the period of 1 January 2001-31 December 2020 were generated. The iHANTS (improved Harmonic Analysis of Time Series) method was updated from the original HANTS based on the Fourier transform, which is commonly used in remote sensing image processing to reconstruct the missing data and remove the noise [66][67][68][69][70][71][72]. The reconstructed daily NDVI did correctly capture the phenology response of each land cover type, as shown in Figure 2.…”