Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

Abstract

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

Impacts of Climate on Spatiotemporal Variations in Vegetation NDVI from 1982–2015 in Inner Mongolia, China

“…Spatiotemporal variations in vegetation growth can affect the terrestrial carbon cycle and other biochemical processes [5], thus explaining why the study of its dynamics is an emerging issue in the field of environmental analysis. Vegetation changes are constantly affected by differences in factors such as precipitation, temperature, and human activities [6,7].…”

“…The primary vegetation includes grass, trees, shrubs, and cropland. In the past few decades, it has experienced dramatic climate change, which has been more pronounced there than in most other areas in China [7,[24][25][26][27]. For this reason, many remote sensing data archives have been processed to examine various aspects of the vegetation cover change dynamics [28][29][30][31], such as habitat maps [32], the degradation of grassland [33,34], phenology patterns [21], and the driving forces of vegetation changes [35][36][37].…”

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

“…Many vegetation indices (VIs) have been developed to monitor vegetation conditions over large spatial ranges, such as the Normalized Difference Vegetation Index (NDVI) [24], enhanced vegetation index (EVI) [25], and ratio vegetation index (RVI) [26]. NDVI has been proven useful for meaningful comparisons of seasonal and interannual changes in vegetation growth and activity, and it has been widely used in related studies to reflect the vegetation response to drought [27][28][29][30][31][32][33][34]. However, anomalies of growing seasons highlighted by the NDVI profile over the years can be attributed to several reasons, not only to extreme weather events like droughts, but also to agricultural practice; i.e., crop rotations or different planting times (winter crops versus summer crops).…”

Vegetation dynamics and responses to climate anomalies in East Africa

“…of vegetation dynamics. In addition, results are likely to differ significantly depending on the time series length as well the data sources (De Keersmaecker et al, 2017). However, further analysis is required to disentangle human and climatic induced causes of these variations.…”

“…Although the maximum LAI-SPEI correlation is characterised by variations in the SPEI timescales in different vegetation types, we used a three-month timescale to assess the short-term vegetation response. Following De Keersmaecker et al, (2015 and2017), three response metrics were used to described the short-term vegetation response: (i) variance metric (the standard deviation of the LAI anomaly time series); (ii) resistance metric (the association between the LAI anomaly and SPEI time series); and (iii) resilience metric (the 10 auto-correlation at lag one of the LAI anomaly).…”