Doria · Field Intelligence Module 03

Fire Monitoring
& Climate Threats

Doria integrates NASA FIRMS satellite fire data with on-the-ground patrol intelligence — giving reserve commanders real-time situational awareness of active fires, predictive burn risk zones, and coordinated multi-asset suppression capabilities before a blaze becomes catastrophic.

Intro Spread Detection Impact Approach 03
Introduction
4.3M
hectares of protected African savanna and forest burned in uncontrolled wildfires annually
72hr
average detection-to-response lag using traditional ground-patrol fire reporting systems
$2.4B
estimated annual ecological and economic damage caused by unmanaged wildfire in conservation zones

Wildfire is among the most immediate and indiscriminate threats to protected ecosystems. Driven by prolonged drought, illegal agricultural burning, and shifting climate patterns, uncontrolled fires can devastate thousands of hectares of critical habitat within hours — far outpacing the response capacity of ground-based ranger teams relying on visual observation and radio communication.

Doria's Fire Monitoring module integrates NASA FIRMS (Fire Information for Resource Management System) satellite detections directly into the operational dashboard — overlaying active fire perimeters, wind vector data, and historical burn patterns onto live reserve maps. Commanders can immediately see fire proximity to wildlife corridors, community boundaries, and watering holes — and dispatch multi-asset response teams in a coordinated, intelligence-led suppression operation.

* Fire extent data sourced from NASA FIRMS MODIS & VIIRS active fire products; economic impact estimates from UNEP and GFW fire analytics.
• How Doria Responds

Four pillars of fire intelligence
and climate threat management

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NASA FIRMS Real-Time Integration
Active fire detections from MODIS and VIIRS satellite instruments are pulled directly into the Doria map every 10 minutes — giving command teams precise coordinates, fire radiative power scores, and perimeter geometry the moment a detection is confirmed.
FIRMS Data
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Wind Vector & Spread Modelling
Real-time wind speed and direction data is overlaid onto active fire detections to project burn perimeter spread trajectories — identifying which wildlife corridors, water sources, and ranger posts are at risk in the next 6, 12, and 24-hour windows.
Spread Model
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Multi-Asset Suppression Dispatch
Commanders coordinate aerial water bombers, 4x4 firebreaks, and ground ranger teams from a single live tactical map — assigning sectors, tracking asset positions, and communicating via encrypted in-system messaging without radio chaos.
Asset Dispatch
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Seasonal Risk Profiling
Historical fire occurrence data, combined with vegetation dryness indices and rainfall deficit records, feeds a seasonal risk model — flagging zones of elevated ignition probability weeks in advance so patrol deployments can be preemptively repositioned.
Risk Profile
The Crisis

Blind to
the Burn

Swipe to explore the four fire threats →

Detection & Response Failures
Hours lost before mobilisation
Climate-Driven Escalation
Worsening seasons, unchanged tools
01
Ground Detection Is Too Slow
The majority of protected areas in Sub-Saharan Africa still rely on visual ranger observation as the primary fire detection mechanism. In dense bush or remote terrain, a fire can burn for 12–36 hours before a ground team encounters it — by which time suppression is impossible and evacuation is the only option.
02
Uncoordinated Multi-Asset Response
When a fire is detected, the response commonly involves multiple agencies — national parks, neighbouring reserves, aviation contractors — each operating on separate radio channels with no shared operational picture. Asset conflicts, redundant coverage, and uncovered flanks are routine, directly worsening outcomes.
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03
Illegal Agricultural Burning
A significant proportion of large-scale bush fires in East and Southern Africa originate from uncontrolled agricultural burns along reserve boundaries — set deliberately or negligently by farming communities. Without spatial attribution tools, accountability and preventive engagement with those communities is near impossible to implement systematically.
04
Escalating Climate Conditions
Fire seasons across Sub-Saharan Africa are measurably lengthening. The window between the end of one fire season and the onset of the next — historically a period of vegetation recovery and fire risk reduction — is shrinking, leaving ecosystems with less resilience and reserve managers with no additional tools to address a fundamentally worsening threat environment.
How it Works

The Fire
Response

Swipe to explore the fire response lifecycle →

Detection & Triage
Satellite to commander in minutes
Suppression & Recovery
Coordinated action and post-burn audit
1
Satellite Detection & Alerting
A new FIRMS fire detection within a monitored reserve boundary triggers an automatic Doria alert — pushed to the duty commander's dashboard within minutes of the satellite overpass. The alert includes coordinates, fire radiative power, and a preliminary wind-adjusted spread projection mapped directly onto the reserve.
2
Threat Assessment & Asset Tasking
The commander reviews the active fire layer against wildlife corridors, water sources, ranger post locations, and community boundaries. Using Doria's live map, they task specific assets — aerial units, ground vehicles, ranger bikes — and issue sector assignments with real-time tracking of each team's position.
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3
Coordinated Suppression Operation
All field assets operate from the same shared live map. Pilots relay updated fire perimeter observations directly into the system; ground teams mark completed firebreaks. The commander sees the full suppression operation unfold in real time — and can redirect assets dynamically as fire behaviour changes.
4
Post-Burn Audit & Risk Update
Once contained, the burned area is captured as a spatial record — updating the vegetation layer, logging ignition source attribution, and feeding burn history into the seasonal risk model. Each incident makes the next prediction more precise, building a compounding intelligence asset over time.