13/07/2026
Chirumhanzu Fire Risk Project - Workflow & Map Output
Location: Chirumhanzu District, Midlands Province, Zimbabwe
Hey colleagues 👋
Sharing a personal GIS project, I just wrapped up: A Fire Risk Map for Chirumhanzu District. The goal was to identify high-risk areas to support fire prevention and planning ahead of the dry season.
Methodology adapted from: CUOSGwiki - Fire Risk Analysis using QGIS
🔗 https://dges.carleton.ca/CUOSGwiki/index.php/Fire_Risk_Analysis_using_QGIS.
I used QGIS for all processing and analysis.
1. Data Acquisition
• DEM 1-Arc Second - USGS EarthExplorer → for Slope & Aspect
• Landsat Bands - Band 4 Red & Band 5 NIR, USGS EarthExplorer → for NDVI
• Historical Fire Points - NASA FIRMS
• Population Data - 2022 gridded population, HDX OCHA Services
• Mitigation Layer - Rivers & Dams, digitized manually
2. Processing & Reclassification
(a) Vegetation – NDVI
Calculated NDVI using Raster Calculator: (NIR - Red) / (NIR + Red) Scaled x100 and reclassified:0-10 = 1 Low | 10-25 = 2 | 25-35 = 3 | 35-60 = 4 High
(b) Terrain - Slope & Aspect
Generated from DEM using GRASS r. slope.aspect Slope: 0-25° = Low Risk to >25° = High Risk Aspect: Reclassified by exposure. NW,SW, W = 1 Low to NE, SE, E = 4 High
Combined Slope & Aspect with Raster Calculator
(c) Fire History
FIRMS point data → Kernel Density Heatmap → Clipped to AOI → Reclassified 1-4
(d) Population / Vulnerability
Population raster reclassified: Low density = Low Risk, High density = High Risk
(e) Mitigation
Water Bodies Digitized rivers/dams → 25m buffer zones, 4 classes.
Areas closer to water = Lower Risk. Rasterized for analysis
3. Weighted Overlay Model
Grouped factors into 3 categories:
• Hazard: (0.5 _ NDVI) + (0.5 _ Slope_Aspect)
• Vulnerability: (0.65 _ Fire_History) + (0.35 _ Population)
• Mitigation: Water buffer raster
Final Fire Risk Index = Sum of Hazard + Vulnerability + Mitigation using GRASS r.series
Result reclassified into: Low, Moderate low, Moderate High, High Risk
4. Zonal Analysis for Planning
• Created a 5km x 5km vector grid over Chirumhanzu using Vector > Research Tools > Create Grid
• Clipped grid to district boundary
• Ran Zonal Statistics to get the mean fire risk per grid cell
• Symbolized by 'mean' value to highlight priority zones
5. Output
The final map highlights fire risk hotspots across Chirumhanzu district. This can help guide awareness campaigns, firebreak placement, and community preparedness.
Tools: QGIS, GRASS GIS, USGS, NASA FIRMS, HDX
idea for Facebook:
