Abstract:Large-scale restoration in drylands is widely promoted to address land degradation and biodiversity loss, yet many efforts rely on long-term irrigation, limiting sustainability in water-scarce regions. A key challenge is identifying locations where native vegetation can persist without intensive management while minimizing costly field campaigns. A scalable pre-screening framework is presented that integrates climate and remote sensing data to enable cost-efficient site selection in arid environments using Saudi Arabia as a case study. A Climate Suitability Score (CSS), derived from machine learning models trained on expert-curated reference sites, captures complex climatic dependencies on vegetation persistence. Using multi-year ERA5-Land data for Saudi Arabia, national-scale prediction maps are generated and combined with vegetation indices to identify areas where climate is favorable, but vegetation remains underdeveloped. Multi-criteria screening reduces candidates to thirteen priority locations. Climatically analogous intact ecosystems provide benchmarks for restoration targets and indicate that an average 2.5 fold increase in vegetation coverage is a realistic target for restoration efforts. Overall, this approach narrows the search space, reduces costs, and supports resilient ecosystem recovery planning in water-limited regions.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.04206 [cs.LG] |
| (or arXiv:2605.04206v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04206 arXiv-issued DOI via DataCite (pending registration) |
From: Jonathan Klein [view email]
[v1]
Tue, 5 May 2026 18:46:53 UTC (17,216 KB)
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