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Urban UAV logistics planning optimizes depot locations for delivery distance while treating airspace capacity as a fixed constraint. We show that this decoupling is not merely a modeling simplification — it produces a structural error that cost monitoring cannot detect in time. When depot configurations begin diverging under airspace capacity pressure, the window for low-cost infrastructure correction closes before any cost signal appears.The central finding is a lead–lag relationship between structural and cost responses: at demand levels where total cost differences remain negligible, depot layouts already diverge substantially under airspace capacity pressure. Cost is a lagging indicator, not a planning trigger.To close this gap, we derive the Ω index, a closed-form demand threshold with three city-level parameters — capacity gap ΔC (from building-footprint data via a FAR volumetric method), depot distance differential Δd (from road-network geometry), and mean OD delivery distance d (from weighted building-centroid sampling) — separating a regime where distance-based planning is analytically sufficient from one where airspace capacity must be internalized into the location decision. Applied to Dongli District, Tianjin, the threshold lies above current UAV density but within a foreseeable planning horizon given observed growth rates in Chinese pilot cities. The depot relocation distance Δreloc provides a leading structural indicator, detectable well before any cost signal emerges. The Ω index gives planners a computable early-warning trigger for airspace-integrated infrastructure review — before congestion, not after.