Global remote sensing reveals vegetation clustering as a physical footprint of shifting aridity trends in drylands
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| Format: | Preprint |
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2026
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| _version_ | 1866908990233903104 |
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| author | Pinto-Ramos, David Clerc, Marcel Gabriel Makhoute, Abdelkader Tlidi, Mustapha |
| author_facet | Pinto-Ramos, David Clerc, Marcel Gabriel Makhoute, Abdelkader Tlidi, Mustapha |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22122 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Global remote sensing reveals vegetation clustering as a physical footprint of shifting aridity trends in drylands Pinto-Ramos, David Clerc, Marcel Gabriel Makhoute, Abdelkader Tlidi, Mustapha Populations and Evolution Pattern Formation and Solitons Biological Physics 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. |
| title | Global remote sensing reveals vegetation clustering as a physical footprint of shifting aridity trends in drylands |
| topic | Populations and Evolution Pattern Formation and Solitons Biological Physics |
| url | https://arxiv.org/abs/2604.22122 |