Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We examine vineyard wildfire impacts and exposure during the electrically initiated 2019 Kincade Fire in Sonoma County, California, using an open, event-anchored geospatial framework spanning 4,581 vineyard fields (8,813.2 ha), wildland vegetation, surveyed structures, roads, overhead smoke, and post-fire greenness. Sentinel-2 spectral response, OpenET evapotranspiration, gridMET fire weather, USDA soils, USGS terrain, NOAA smoke polygons, an ignition-date OpenStreetMap network, and three-dimensional data inventories were analyzed at their native decision scales. Vineyard pixels displayed substantially lower descriptive dNBR than wildland pixels inside the perimeter (means 0.130 and 0.337, respectively). Yet this landscape contrast did not identify a universal vineyard firebreak effect. A segment-clustered boundary model yielded a small negative contrast at 100 m (tau = −0.0166) but changed across bandwidths, failed slope continuity, disappeared in a 100 m donut specification, and produced a large wrong-signed placebo estimate. A 250 m spatial generalized additive model showed a negative vineyard-fraction association: after conditioning on location, terrain, and water use, the joint-smooth coefficient was −0.111, and residual Moran’s I was 0.611. Beyond direct spectral impact, all mapped vineyards intersected overhead smoke on at least 1 day (mean 7.78 potential smoke-days per field), 34.2% of road-network nodes were dead ends, and vineyards within the perimeter showed a larger greenness deficit through 2021 (recovery ratios 0.815 inside and 0.854 outside). Lower immediate spectral change did not establish vine survival or recovery of agricultural function. The study offers a reproducible urban–rural informatics template that separates descriptive contrasts, conditional associations, exposure indicators, and recovery evidence for decision-making in working landscapes.
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Farajpoor et al. (2026) studied this question.
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