Addressing the complexity in non-linear evolution of vegetation phenological change with time-series of remote sensing images

Earth observation based monitoring of change in vegetation phenology and productivity is an important and widely used approach to quantify degradation of ecosystems due to climatic or human influences. Most satellite based studies apply linear or polynomial regression methods for trend detections. In this paper it is argued that natural systems hardly react to human or natural influences in a linear or a polynomial manner. At shorter time-scales of few decades natural systems fluctuate to a certain extent in a non-systematic manner without necessarily changing equilibrium. Finding a systematic model that describes this behavior on large spatial scales is certainly a difficult challenge. Furthermore, the manner vegetation phenology reacts to climate and to socio-economic changes is also dependent on the land cover type and on the bioclimatic region. In addition to this, traditional parametric methods require the fulfillment of several statistical criteria. In case these criteria are violated confidence intervals and significance tests of the models may be biased, even misleading. This paper proposes an alternative approach termed the Steadiness to traditional trend analysis methods. Steadiness combines the direction or tendency of the change and the net change of the time-series over a selected time period. It is a non-parametric approach which can be used without violation of statistical criteria, it can be applied on short time-series as well and results are not dependent on the significance test or on thresholds. To demonstrate differences, a time-series of satellite derived Season Length images for 24 years is analyzed for the entire European continent using linear regression and the Steadiness approach. Spatial and temporal change patterns and sensitivity to pre-processing algorithms are compared between the two methods. We show that linear regression limits the possibilities of assessing fluctuating ecosystem changes whereas the non-parametric Steadiness index more consistently confirms the fluctuating phenological change patterns. (C) 2012 Elsevier Ltd. All rights reserved.