Spatial Aggregation, Alarm Sparsity, and Event-Level Wildfire Capture: A Retrospective Evaluation in California

Wildfire monitoring systems increasingly rely on satellite-derived risk surfaces to support resource-constrained prioritization. However, less attention has been paid to how spatial aggregation interacts with alarm sparsity in shaping event-level wildfire capture. This study conducts a retrospective evaluation of percentile-based wildfire alarm regimes in California during the 2024 fire season. Using VIIRS-derived risk surfaces and MTBS burned-area perimeters, the analysis examines three aggregation scales (375, 1000, and 5000 m) under fixed alarm budgets (top 1%, top 5%, and top 10%). Event-level capture was evaluated by aggregating row-level capture values within each MTBS event, with the primary specification based on maximum event-level capture and a threshold of 0.02. Across 2078 unique wildfire events, the effect of spatial aggregation was conditional on alarm sparsity. Under the most restrictive budget (top 1%), scale effects were weak and non-monotonic. In contrast, under the top 5% and top 10%, the coarsest scale (5000 m) consistently produced the highest event-level threshold-exceedance rates. Robustness checks using mean event-level capture and a stricter threshold of 0.05 yielded qualitatively similar patterns under moderate alarm budgets. These findings indicate that the effect of spatial aggregation cannot be interpreted independently of alarm-budget design. Rather than treating spatial resolution as inherently beneficial or detrimental, the study shows that its implications depend on how event-level capture is evaluated under constrained alarm allocation.