| Type | Technology | Description |
|---|---|---|
| Vision-Based Ground Sensing Technologies | Visible Light Range Cameras | Operate within the visible spectrum, integrated into ground IoT systems for wildfire detection. |
| Vision-Based Ground Sensing Technologies | Infrared Range Cameras | Capture thermal images for improved night visibility, each pixel represents temperature or heat intensity. |
| Vision-Based Ground Sensing Technologies | Ultraviolet Range Cameras | Specialize in detecting UV radiation, a potential sign of fire. |
| Environmental Monitoring Technologies | Weather Monitoring Sensors | Monitor atmospheric conditions (temperature, humidity, wind speed) indicating high fire risk. |
| Environmental Monitoring Technologies | Gas/Smoke Sensors | Detect presence of gases and smoke particles in the air, common indicators of a fire. |
Reference 1:
1- Raging bushfires worldwide jeopardize lives, infrastructure, and ecosystems, while prolonged exposure to their smoke threatens human health. Current approaches, hampered by inadequate detection technology, struggle during extreme conditions. Urgent innovation is crucial for effective technologies to swiftly detect and extinguish fires, especially anticipating future catastrophic seasons.
2- An AI-driven bushfire detection system, developed in collaboration with the Minderoo Foundation, ACT Rural Fire Service, BushfireLive, Insight Robotics, and the ANU-Optus Bushfire Research Centre, utilizes tower cameras. Deployed on ACT fire towers, it leverages imaging and deep learning advancements, showing promising results with higher true positive rates. Additionally, a drone network is being explored for swift identification and verification of bushfire ignitions caused by lightning during dry thunderstorms. Drones offer enhanced detection, close-up sensor feedback, and tactical support, potentially serving as communication hubs for mobile firefighting services.
Reference 2:
1- Regional Factors:
– Consider climatic conditions, natural fire regimes, and topography.
– Assess tree flammability for recurrent fire risks.
2-Landscape Design:
– Use mixed land use to limit fire spread.
– Implement buffers for community protection.
– Strategically fragment plantations to mitigate widespread destruction.
3- Compartment Management:
– Reduce compartment size to control fire spread.
– Arrange compartments with less flammable land uses.
– Apply fuel treatments like thinning and pruning.
4- On-Site Readiness:
– Ensure firefighting equipment accessibility during harvesting.
5- Technology for Detection and Suppression:
– Use lightning strike modeling, drones, and advanced assets for rapid fire detection.
6- Post-Fire Management:
– Minimize environmental impact from post-fire logging.
– Seize opportunities for plantation redesign.
7 – Multi-Scaled Plans:
– Develop detailed plans for fire risk and management at different scales.
Regularly update plans for sustainability and certification.
1- Sensor nodes are the simplest and least expensive technologies for detecting wildfires. By consuming less energy, they can provide better coverage and monitoring frequency over longer periods. The simplicity of design reduces the need for storage solutions. This makes it a more robust industrial solution, at least for continuous monitoring and early detection.
2- Sensor Type:
- Temperature: Measured using thermistors and RTDs to detect ambient temperature changes indicating fire presence or risk.
- Humidity: Assessed with resistive and thermal conductivity sensors to determine air moisture, influencing fire likelihood.
- Air Pressure: Monitored using piezoresistive sensors to detect pressure changes related to fire-affecting weather conditions.
- Gas Concentration: Detected through gas sensors to identify fire-indicative gases like carbon monoxide and dioxide.
- Smoke Detection: Utilized smoke sensors to identify the presence of smoke particles.
- Wind Speed and Direction: Monitors wind characteristics to assess how they might affect fire spread.
- Soil Moisture: Measures soil moisture content to gauge fire risk due to dry conditions.
- Light Intensity: Tracks changes in light conditions that could be influenced by smoke or fire.
- Sound: A sound spectrum analysis can be used to detect fires. This approach uses a wireless acoustic detection system to differentiate between crown and surface fires.
Standard household sensors:
- Smoke Detection: Detects smoke particles, a primary indicator of fire.
- Carbon Monoxide Detection: Measures levels of carbon monoxide, a dangerous gas produced by incomplete combustion.
- Temperature Monitoring: Monitors significant changes in temperature, which could indicate a fire.
- Absolute Temperature Sensors: These sensors detect the specific temperature in an environment, alarming when it reaches a high level indicative of a fire.
- Rate-of-Rise Temperature Sensors: These sensors trigger an alarm when they detect a rapid increase in temperature over a short period, indicating the possible start of a fire.
- Gas Leak Detection: Identifies the presence of combustible gases like natural gas or propane, indicating potential leaks.
3- Sensor Process
4- Centralize
5- Sensor Location:
Sensors can be installed in buildings by standards provided by organizations such as the National Fire Protection Association (NFPA) and the International Residential Code (IRC). Various factors determine these guidelines, including the type of building (commercial or residential), the number and type of rooms, and their specific use (like bedrooms, kitchens, etc.). But in the wild area, there is no standard!!
5.1 Sampling
Poisson-disk sampling is a technique for spacing out points (like sensors) evenly but randomly, ensuring that each point is at a certain minimum distance from the others. This is useful for arranging sensors so they cover an area efficiently without overlapping in their detection ranges, providing thorough and uniform coverage.
Multi-Class Disk Distributions refer to arrangements or patterns of circles (disks) from multiple categories or types in a two-dimensional space. A layout using this concept illustrates how different objects interact and relate spatially, each represented by a disk. Using the term “multi-class” indicates you don’t have one homogeneous group of objects but multiple distinct categories, each with unique properties. Disk placement and potential overlap are often considered when synthesizing these distributions.
Forest Segment
Australia’s forests have been divided into eight different “types.” Among them, the lower density and flatter terrain of acacia forests could make sensor deployments for bushfire detection more effective. Forests of Acacia grow in arid and semi-arid regions, generally away from coasts,
Reference
- Title: An Integrated System to Protect Australia from Catastrophic Bushfires
Authors: Yebra et al.
Year: 2021
Link: Yebra et al. 2021 - Title: Better Managing Fire in Flammable Tree Plantations
Authors: Lindenmayer et al.
Year: 2023
Journal: Forest Ecology and Management - Title: IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges, and Opportunities
Authors: Chan, Chiu Chun; Alvi, Sheeraz A; Zhou, Xiangyun; Durrani, Salman; Wilson, Nicholas; Yebra, Marta
Year: 2023
Journal: arXiv preprint arXiv:2312.10919 - Finally, at least according to a very recent survey 2022, the sample-based detection method we are willing to suggest seems to introduce a new category of models in this context.
- A Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
- Accurate Synthesis of Multi-Class Disk Distributions
