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

Matthias Forkel,Miguel Mahecha,Nuno Carvalhais,Jan Verbesselt,Markus Reichstein,Christopher Neigh

doi:10.3390/rs5052113

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 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.

Highlights

Climate change will likely change biome distributions, ecosystem productivity and forest carbon stocks [1]
To simulate surrogate time series, statistical distributions of Normalized Difference Vegetation Index (NDVI) mean, trend, seasonality, inter-annual and short-term variability were computed from observed NDVI time series in Alaska (Figure 2)
NDVI means occurred in the central Alaskan boreal forest and the lowest values in the northern

Summary

Introduction

Climate change will likely change biome distributions, ecosystem productivity and forest carbon stocks [1]. Such ecosystem changes can be detected and quantified using multi-temporal satellite observations of the land surface. Is a remotely-sensed measure of vegetation greenness and is related to structural properties of plants—like leaf area index [4] and green biomass [5]—and to properties of vegetation productivity—like absorbed photosynthetic active radiation and foliar nitrogen [5,6]. The NDVI from AVHRR (Advanced Very High Resolution Radiometer) satellite observations is the only global vegetation dataset which spans a time period of three decades and allows the quantification and attribution of ecosystem changes as a result of ecosystem dynamics and varying climate conditions. Annual mean or peak NDVI provides an integrated view on photosynthetic activity [7], the seasonal NDVI amplitude is related to the composition of evergreen and deciduous vegetation [8] and the length of the NDVI growing season can be related to phenological changes [9]

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