The persistent threat of wildfires poses a significant challenge for mountainous regions with extensive forest cover. Traditional methods of patrol and monitoring, reliant on ground personnel, are often inadequate due to challenging terrain, limited visibility, and inefficiency. In our county, where forested land constitutes a major portion of the territory, we have pioneered a comprehensive solution centered on unmanned aerial vehicles and artificial intelligence. Our “Air-Space-Ground Integrated” intelligent prevention and control system represents a transformative approach to forest fire management, moving from reactive firefighting to proactive, intelligent governance.
Historically, our fire response suffered from fragmented information systems. Data from meteorology, forestry, and emergency management existed in silos, leading to delayed decision-making. The critical chain of fire detection, resource dispatch, and command transmission was broken. To address this, our first strategic pillar was the development of a unified command platform—the “One Map” system.
The “One Map” Intelligent Command Platform: Data-Driven Decision Core
This platform is far more than a geographic map; it is the central nervous system of our fire management operations. It integrates multi-source, real-time data onto a single, dynamic interface, enabling holistic situational awareness and command.
| Data Category | Specific Data Streams | Function |
|---|---|---|
| Environmental Monitoring | Real-time temperature, humidity, wind speed/direction, precipitation, fire danger rating; data from fire factor stations (surface temperature, vegetation moisture content, air humidity). | Continuous risk assessment and early warning. |
| Video Surveillance | Live feeds from 95+ fixed video monitoring points, 62 of which are equipped with AI behavioral analysis. | Visual verification, perimeter monitoring, and automated detection of risky activities. |
| Resource Inventory | Spatial distribution of firefighting equipment caches, material reserves, key risk areas (near power lines, nature reserves), and professional rescue teams. | Instant resource localization and optimized dispatch. |
| Communication & Alerting | Status of 54+ emergency broadcast terminals; integrated alert push system. | Direct, targeted communication to field personnel (rangers, first responders), bypassing bureaucratic delays. |
The core philosophy is data-driven decision-making. The platform can calculate a composite fire risk index ($FRI$) for any location, factoring in critical variables:
$$ FRI(t, x, y) = \alpha \cdot \left(1 – \frac{VM(x,y,t)}{100}\right) + \beta \cdot WS(x,y,t) + \gamma \cdot \left(\frac{ST(x,y,t)}{C}\right) $$
where:
- $VM(x,y,t)$ is the Vegetation Moisture content (%) at location $(x,y)$ and time $t$.
- $WS(x,y,t)$ is the Wind Speed.
- $ST(x,y,t)$ is the Surface Temperature.
- $\alpha, \beta, \gamma$ are weighting coefficients based on historical fire data.
- $C$ is a normalization constant.
When $VM$ falls below a threshold (e.g., 30%) or $FRI$ exceeds a critical level, the system automatically triggers alerts. It geographically targets the relevant emergency broadcast terminals and pushes instructions directly to the nearest personnel. This integration has drastically reduced our command latency. Previously, the process from detection to order issuance averaged 40 minutes; it is now streamlined to approximately 12 minutes, an efficiency improvement of nearly 70%.
Integrating the “Wireless End”: Dense Source Monitoring Network
The “golden period” for suppressing a nascent wildfire is often within the first 10 minutes. Ground patrols alone could only cover about 1% of our forest area daily, with an average detection time of 30-60 minutes. To achieve the goal of “striking early, striking small, striking decisively,” we built a dense, intelligent monitoring web.
We integrated systems from meteorology and environmental monitoring into our command platform, creating a “thousand-mile eye.” The 62 AI-enabled cameras are pivotal. Their algorithms are trained to recognize specific risk behaviors with high accuracy. The performance can be expressed as:
$$ Accuracy = \frac{TP + TN}{TP + TN + FP + FN} \approx 92\% $$
$$ False\ Alarm\ Rate = \frac{FP}{FP + TN} < 5\% $$
where $TP$ (True Positives) are correctly identified fire risks (e.g., open burning), $TN$ (True Negatives) are correctly ignored safe scenes, $FP$ (False Positives) are false alarms, and $FN$ (False Negatives) are missed incidents. This automated, 24/7 surveillance has reduced our average fire detection time by over 80%, fundamentally changing our capacity for early intervention.
Building the “Intelligent Governance Body”: The UAV Drone Fleet as the Mobile Force
While fixed sensors provide wide-area coverage, UAV drone mobility offers unmatched flexibility and precision. We have invested significantly in building a robust UAV drone ecosystem. Our fleet consists of 32 various types of UAV drone units, forming a “high-low altitude combination, fixed and mobile complement” patrol structure. We have trained over 80 certified UAV drone pilots to manage this fleet.
Each UAV drone is a versatile platform equipped with modular payloads:
- High-resolution visible-light cameras for daytime detail.
- Infrared thermal imaging sensors for night operations and spotting heat signatures through smoke.
- Multispectral sensors to assess vegetation health and moisture stress.
- Loudspeakers for issuing warnings to people on the ground.
- Emergency supply delivery pods to transport critical gear to remote fire lines or stranded personnel.
This UAV drone-based solution enables full-process control. The operational improvement is quantifiable. The area coverage efficiency of a UAV drone ($A_{UAV}$) versus a ground patrol ($A_{Ground}$) over time $T$ is exponentially greater:
$$ \frac{A_{UAV}(T)}{A_{Ground}(T)} = \frac{v_{UAV} \cdot w_{UAV} \cdot T}{v_{Ground} \cdot w_{Ground} \cdot T} \gg 1 $$
where $v$ is patrol speed and $w$ is effective observation width. The UAV drone‘s speed and vantage point make its observation width orders of magnitude larger.
| Mission Phase | UAV Drone Role & Payload | Outcome |
|---|---|---|
| Prevention & Patrol | Automated route patrols with visible/thermal cameras; AI analysis of feeds for smoke or unauthorized activity. | Early detection, deterrence of risky behavior, data collection for risk modeling. |
| Early Fire Response | Rapid deployment to GPS coordinates of alarm; live thermal video feed to confirm and locate fire perimeter; loudspeaker warnings. | Instantaneous visual confirmation, accurate fire sizing, initial public warning. |
| Active Firefighting Support | Thermal mapping of fire edge and hotspots; guiding ground crews; assessing access routes; dropping emergency supplies. | Enhanced firefighter safety, tactical intelligence, efficient resource use. |
| Post-Fire Assessment | High-resolution mapping of burn scar; multispectral analysis for severity; monitoring for re-ignition. | Accurate damage assessment, informed rehabilitation planning, continued surveillance. |
The “Air-Space-Ground” Integrated Perception Platform: The Brain for Fusion
The individual components—the One Map, the video network, and the UAV drone fleet—are powerful, but their integration creates a true “intelligent governance body.” We built a dedicated platform that leverages county-level computing power to perform multi-modal data fusion and AI analysis.
This platform creates a closed-loop, automated workflow: Perception → Decision → Execution.
- Perception: Data flows in from fixed cameras (detecting smoke via AI), environmental sensors (spiking risk indices), and patrol UAV drone feeds.
- Decision: The platform’s AI fuses these data streams. It corroborates alerts, pinpoints the incident location with high accuracy, and assesses the threat level. It then automatically queries the resource database on the “One Map” and generates an initial response plan.
- Execution: The system simultaneously:
- Dispatches the nearest high-endurance UAV drone for detailed reconnaissance.
- Sends targeted alerts to the specific rangers and town-level response teams in the affected area.
- Provides the command center with a consolidated view, including the live UAV drone feed, resource locations, and risk overlays.
The entire process, from initial AI detection to field verification and initial response, has been documented in as little as 15 minutes for small incidents. This system embodies the concept of automated, precision governance for low-altitude airspace and emergency coordination.
| Key Performance Indicator (KPI) | Traditional Model | Integrated UAV & AI System | Improvement |
|---|---|---|---|
| Average Detection Time | 30 – 60 minutes | < 10 minutes | > 80% reduction |
| Command & Dispatch Latency | ~40 minutes | ~12 minutes | ~70% reduction |
| Daily Patrol Coverage (Forest Area) | ~1.02% | Near-total coverage via combined assets | Massive increase |
| Night/Obscured Condition Monitoring | Very Limited | Fully Operational (Thermal UAV drone) | 24/7 capability enabled |
| Resource Dispatch Accuracy | Manual, error-prone | Automated, GIS-precise | Optimized efficiency |
Conclusion: A Replicable Model for Mountainous Regions
Our journey demonstrates that the integration of UAV drone technology and AI is not merely an incremental improvement but a paradigm shift in forest fire management. The “Air-Space-Ground” integrated system addresses the core pain points of late detection, slow response, and inefficient coordination. By building an intelligent platform that unifies data, automates analysis, and mobilizes a flexible UAV drone fleet, we have constructed a resilient and responsive governance framework.
The UAV drone serves as the critical linchpin in this model—the versatile, mobile sensor and tool that bridges the gap between static data systems and the dynamic reality on the ground. The formula for success lies in this integration: Centralized Data Intelligence + Ubiquitous UAV drone Mobility = Proactive Resilience. This model, born from the practical needs of a forested mountainous county, offers a viable and scalable “solution package” for similar regions worldwide seeking to harness technology for safeguarding their natural heritage and communities.