Mountain Plains, SD
Mountain Plains, SD's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Mountain Plains lands at the 81st percentile for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — a figure built from its 219 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Mountain Plains's burn probability — fire likelihood with no building count factored in — sits at the 80th percentile nationally.
Mountain Plains's building exposure, zone by zone
Of Mountain Plains's 219 counted buildings, 100% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Mountain Plains against the rest of the country
Mountain Plains scores 81st nationally and 81st within South Dakota — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Mountain Plains ranks 5,969 for wildfire risk (1 is highest) and 24,176 by building count (1 is largest). Within South Dakota alone, it ranks 84 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Shopping for coverage in Mountain Plains
Mountain Plains's very high rating (81st percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Mountain Plains
Because Direct exposure dominates in Mountain Plains (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Mountain Plains's figures come from
Mountain Plains's 81st-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 Mountain Plains's dominant direct exposure actually means, with real examples from across the dataset.