Global remote sensing reveals vegetation clustering as a physical footprint of shifting aridity trends in drylands

TL;DR

This study uses global remote sensing data to empirically validate that vegetation spatial patterns serve as physical indicators of aridity trends, distinguishing between degradation and recovery in drylands.

Contribution

It provides the first large-scale empirical evidence linking vegetation pattern morphology to historical aridity changes, validating theoretical predictions.

Findings

Increasing aridity correlates with periodic vegetation patterns.

Decreasing aridity leads to scale-free clustering of vegetation.

Vegetation patterns can diagnose ecosystem degradation or recovery.

Abstract

Due to climatic changes, excessive grazing, and deforestation, semi-arid and arid ecosystems are vulnerable to desertification and land degradation. As aridity increases, vegetation cover often self-organizes into spatial patterns before collapsing to bare soil. While recent theoretical work has established that spatially heterogeneous yet isotropic environments induce a smooth hysteresis loop -- yielding either periodic (hexagonal) patterns during degradation or disordered (clustered) patterns during recovery -- empirical validation of this physical footprint at a global scale has been lacking. Here, we present an extensive empirical validation using remote sensing across eight distinct global ecosystems, coupled with historical bio-climatic databases. We demonstrate that the spatial morphology of vegetation patches acts as a direct physical footprint of the ecosystem's historical aridity trend. Our results show that ecosystems experiencing increasing aridity display periodic arrays with a defined wavelength, whereas those recovering under decreasing aridity exhibit scale-free clustering. This framework provides a non-destructive, robust satellite-based indicator for diagnosing whether a dryland ecosystem is on a degradation or recovery pathway.