Galena, MD
Galena, MD's wildfire risk, in USFS's own numbers
USFS scores Galena at the 26th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 340 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Galena at the 25th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Galena's building exposure, zone by zone
52.4% of Galena's 340 buildings sit in USFS's Direct exposure zone, roughly 178 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 47.7% are Minimal.
How Galena compares
Galena's risk sits at a similar level relative to Maryland (37th percentile statewide) as it does nationally (26th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Galena ranks 23,381 for wildfire risk (1 is highest) and 20,573 by building count (1 is largest). Within Maryland alone, it ranks 332 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Shopping for coverage in Galena
At the 26th national percentile, Galena rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
With 52.4% of Galena in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Galena's figures come from
Galena's 26th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Galena's dominant direct exposure actually means, with real examples from across the dataset.