How Low Can You Go? Implications of Spatial Aggregation for the Estimation of Ecosystem Resilience

Spatial and temporal aggregations are common when preparing remote sensing data for analysis. Aggregations often serve to enhance the underlying signal of interest while suppressing noise, and can improve estimations of mean states and long-term trends in data. However, aggregating means that the highest-resolution parts of a signal can no longer be resolved, and rapid or fine-scale fluctuations are removed, potentially biasing analyses that rely on these parts of the signal. Further, data aggregation often goes along with gap-filling, which can further dilute the signals of interest.

In this work, we examine the impact of spatial aggregation on estimates of vegetation resilience by comparing MODIS vegetation data sets at a range of spatial resolutions (native 250 m – 25 km). We first use synthetic data to investigate various de-seasoning and de-trending schemes and their responsiveness to gaps in the underlying data. Based on these insights, we calculate two estimates of vegetation resilience at the global scale and at multiple spatial resolutions to determine the optimal level of spatial aggregation for MODIS data, considering the tradeoffs between fine-scale (gappy, noisy) and aggregated (continuous, smooth) vegetation data in terms of resilience estimation. Our results provide best practices for the aggregation, deseasoning, detrending, and analysis of vegetation resilience at the global scale.