Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Abstract

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends OPEN ACCESSRemote Sens. 2013, 5 2114 is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

“…Uma abordagem frequente é estabelecer tendências por meio de regressão linear do NDVI, calculado em períodos anuais ou sazonais (Jong & Bruin, 2012;Forkel et al, 2013). A regressão linear é um teste paramétrico que pode apresentar, entretanto, dificuldades na caracterização de tendências, referentes à capacidade de se determinar o quanto o coeficiente de declividade da reta ajustada difere significativamente de zero.…”

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

“…There are several methods described in the literature for time series analysis of satellite imagery [12,13,20]. The simplest and most common method is the ordinary least-squares (OLS) regression, where the main assumption is that the land surface is changing linearly and gradually over time [44].…”

“…In comparison to the full-resolution time series, the use of annually aggregated data reduces the number of observations, which results in the underestimation of the trend significance. Nevertheless, annual aggregation decreases the risk of detecting false positive trends [12]. Furthermore, the aggregation is an effective method to account for autocorrelation: dependence between successive observations in a time series [50].…”

“…The GIMMS NDVI3g dataset is an improved 8 km NDVI dataset produced by the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments that extend from July 1981 to December 2013 [12,30].…”

Reaching the human scale: A spatial and temporal downscaling approach to the archaeological implications of paleoclimate data

“…For temporal downscaling, the mean, trend, seasonal, and interannual values from 287 modern data for the study area are used to generate monthly values with a modified version of 288 the greenbrown package (Forkel et al, 2013;Forkel and Wutzler, 2015) in R. 289 R code for the procedures detailed below, with reference to the data sources described 290 in Section 3.1 and Table 2, is available in the supplementary online material. 291…”