Urban Wildfire, Mapped: A GIS Workflow Cities Can Use Right Now

Wildfire isn’t just a wildland issue anymore. In many metros, fires are crossing into neighborhoods and the wildland–urban interface (WUI). To plan defensible space, vegetation management, and building codes, cities need more than statewide summaries—they need city-specific evidence about where fires are intersecting urban land, how that’s changing through time, and which areas are most likely to burn again.

Below is a repeatable GIS workflow I use to track urban-intersecting wildfires across the U.S. (1985–2024). It turns open satellite data into policy-ready maps and metrics your team can use in weeks, not months.

What decisions this supports

• Prioritize fuel reduction and canopy management along high-risk corridors

• Update setback rules and fire-resistant materials guidance

• Target community outreach and grant applications

• Monitor whether risk is spreading or concentrating over time

Data stack (all open)

• MTBS (Monitoring Trends in Burn Severity): fire perimeters + burn severity rasters

• LCMS: annual land-cover maps to derive urban/developed classes

• Population density (≥ 150 people/km² threshold to flag urban fabric)

• Landsat (30 m): long-run context for NDVI, impervious, pre/post conditions

• Sentinel-2 (10 m): finer-scale analysis for 2017–present large events

• Optional: SRTM (slope/elevation), road/structure layers, SPEI or drought/weather indices

Tools: Google Earth Engine (data + time series), ArcGIS Pro/QGIS (spatial ops, cartography), Python for modeling/QA.

The workflow (step-by-step)

1) Define “urban” every year

• Build an annual urban mask by combining LCMS developed classes with population density ≥150/km².

• Why it matters: urban boundaries shift; a static mask will bias counts and areas.

2) Extract urban-intersecting fire pixels

• Intersect the annual urban mask with MTBS perimeters and severity rasters.

• Output: a per-fire table with urban area burned, severity mix, and year.

3) Separate counts from area & severity

• Time series for (a) number of urban fires, (b) total urban area burned, (c) severity distribution.

• Insight: it’s common to see flat counts but rising area burned—meaning bigger/meaner events.

4) Map recurrence (burn frequency per 30 m pixel)

• Accumulate per-pixel burn counts 1985–present (e.g., 1×, 2×, 3×…).

• Flag corridors where repeat burns concentrate; these are priority maintenance zones.

5) Model likelihood and severity drivers

• Response variables: burned vs. unburned (logit), burn severity (ordinal/continuous).

• Predictors: impervious %, canopy %, slope, distance to roads/structures, antecedent NDVI, drought index, last-burn year.

• Output: short list of actionable drivers (e.g., “low canopy + high impervious near roads” → higher severity odds).

6) Add Sentinel-2 detail for recent large events

• For 2017–present fires, repeat Step 5 at 10 m to see parcel-scale patterns and to ground recommendations for setbacks, species, and maintenance.

7) Package as policy-ready deliverables

• Maps: counts vs. area trends; severity mix; recurrence heat map

• Ranked list: top tracts/blocks/segments for mitigation

• Brief (8–10 pp): plain-English findings, drivers, and specific recommendations

• Reproducible code: GEE/Python so your team can rerun annually

Core metrics I recommend (and why)

• Urban fire count (by year) – communication metric; easy for councils to grasp

• Urban area burned (ha/yr) – better for resourcing; captures growing footprint

• Severity distribution – links directly to materials and vegetation guidance

• Recurrence (%) – justifies ongoing maintenance vs. one-off treatment

• Distance-to-assets analysis – proximity to housing, schools, critical infrastructure

Common pitfalls (and how to avoid them)

• Static urban masks → misclassification. Always use annual land cover + population thresholds.

• Only tracking counts → misses growth in burned area and severity. Plot both.

• No recurrence metric → underestimates risk in corridors that burn every decade.

• Black-box models → hard to act on. Keep predictors interpretable; publish QA and assumptions.

Example findings you can expect

• Counts flat; area up: no increase in the number of urban fires, but significant rise in total urban area burned, indicating larger footprints/more severe events.

• Recurrence is rare but real: e.g., 1× 96.3%, 2× 3.4%, 3× 0.23%, 4× 0.03%, 5× 0.02% of 30-m pixels over 1985–2024.

• Driver patterns: high impervious + low canopy + road adjacency often coincides with higher likelihood/severity; slopes and drought history modulate risk.

What a 2-week pilot looks like

• Week 1: Build annual urban mask; intersect MTBS; compute trends (counts, area, severity).

• Week 2: Recurrence map; Landsat/Sentinel driver modeling; priority corridors + 10-page brief; deliver code + GeoTIFF/GeoPackage.

How to use this with your team

• Align the priority list with capital budgets and vegetation maintenance crews.

• Update building/materials guidance for neighborhoods in high-severity zones.

• Use recurrence and proximity metrics to sequence fuel breaks, inspections, outreach.

• Rerun annually to track progress and adjust.

About the author

Remote sensing & urban climate consultant (PhD; 9+ years’ GIS experience). I help cities, NGOs, and research teams turn satellite data into policy-ready wildfire, heat, and equity maps with reproducible pipelines.

Want this for your city?

I can scope a 1–2 week pilot that delivers the trends, recurrence maps, and a ranked mitigation plan for your AOI.