Probability Of Fire Detection Research Articles

The Role of UAV-IoT Networks in Future Wildfire Detection

The challenge of wildfire management and detection is recently gaining increased attention due to the increased severity and frequency of wildfires worldwide. Popular fire detection techniques, such as satellite imaging and remote camera-based sensing suffer from late detection and low reliability while early wildfire detection is a key to prevent massive fires. In this article, we propose a novel wildfire detection solution based on unmanned aerial vehicles assisted Internet of Things (UAV-IoT) networks. The main objective is to: 1) study the performance and reliability of the UAV-IoT networks for wildfire detection and 2) present a guideline to optimize the UAV-IoT network to improve fire detection probability under limited system cost budgets. We focus on optimizing the IoT devices’ density and the number of UAVs covering the forest area such that a lower bound on the wildfires detection probability is maximized within a limited time and system cost. At any time after the fire ignition, the IoT devices within a limited distance from the fire can detect it. These IoT devices can then report their measurements to nearby UAVs. Discrete-time Markov chain (DTMC) analysis is utilized to compute the fire detection and false-alarm probabilities. Numerical results suggest that given enough system cost, the UAV-IoT-based fire detection can offer a faster and more reliable wildfire detection solution than state-of-the-art satellite imaging techniques.

A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records

Satellite-based active fire data provide indispensable information for monitoring global fire activity and understanding its impacts on climate and air quality. Yet the limited spatiotemporal sampling capacities of current satellites result in considerable uncertainties in fire observation and emissions estimation. The mitigation of these uncertainties mainly relies on new remote-sensing technology. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R (GOES-R) Series observes fires across North and South Americas at an unprecedentedly spatiotemporal resolution of nominal 2 km every 5–15 min. This study evaluated the GOES-16 (the first GOES-R satellite) ABI active fire product using active fire data derived from the 30-m Landsat-8 and the 375-m and 750-m Visible Infrared Imaging Radiometer Suite (VIIRS), and ground-based burning data across the southeastern Conterminous United States (CONUS) during the 2018 peak fire season. Specifically, we characterized the overall fire detection performance of the ABI active fire detections, estimated omission and commission errors, and evaluated ABI fire radiative power (FRP). The results showed that the ABI fire detection probability and its omission and commission errors were highly related to fire size and temporal period. ABI detection probability was higher than 95% for the fire pixel that contained over 114 Landsat-8 (30 m) fire detections or 11 VIIRS (375 m) detections. During a period of ±8 h, ABI detected 19% and 29% more fires observed by Landsat-8 and 375-m VIIRS, respectively. Correspondingly, the omission error could reduce by up to 33%. Further, ABI was able to detect 6–22% and 31–42% more ground-recorded fires than VIIRS in Georgia and Florida States, respectively, but ABI still missed many very small fires because ABI was hard to detect fires smaller than ~34.5 MW. Additionally, compared with 750-m VIIRS FRP, ABI FRP was ~30–50% larger in individual fire events but was overall similar at a regional scale.

Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

Detection rates and biases of fire observations from MODIS and agency reports in the conterminous United States

With growing concern about the impacts of fires on ecosystems and economies, satellite products are increasingly being used to understand fire regimes. Concurrently, where available, agency records of fires have also been used to assess fire regimes. Yet, it remains unclear if these independent datasets measure the same fires, which raises concerns about the interpretation and benchmarking of models derived from these products. Here, we present a novel product intercomparison of the MODIS burned area and active fire products across the conterminous United States using nearly 250,000 agency reported wildfires as reference data to model consistencies and inconsistencies between all three datasets. We compared agency reported wildfires from the Fire Program Analysis fire occurrence database to the MODIS products to identify which fires were detected vs. omitted by MODIS products relative to agency fire records, and by agency fire records relative to MODIS. We created generalized linear models as a function of fire attributes (e.g. size) and environmental variables (e.g. cloud cover) to predict MODIS detection of agency wildfires, and anthropogenic variables (e.g. agriculture) to predict agency detection of MODIS fires. We modeled fire detection probability separately for MODIS burned area and active fire products, and for the eastern and western U.S. Overall, we found that MODIS product detection rates ranged from 3.5% to 23.4% of all documented agency wildfires >1 ha, and that likelihood of detection increased with fire size. Agency detection rates ranged from 23.5% to 48% of MODIS burned area and active fires. Under ideal conditions, the MODIS active fire product had a 50% probability of detecting a wildfire that grew to at least 10 ha (eastern U.S.) – 78 ha (western U.S.), while the burned area product had a 50% probability of detecting a wildfire that grew to at least 169 ha (eastern U.S.) – 234 ha (western U.S.). Cloud cover and leaf area index were significant predictors of MODIS fire detection, while state boundaries were significant predictors of agency fire detection. This analysis presents an important assessment of the fire attributes and ground conditions that influence MODIS fire detection relative to extensive and increasingly used ground-based wildfire records. The large discrepancy in records of fire occurrence between MODIS and agency fire datasets highlights the need for this type of analysis into the types of fires likely to be included in each database.

Development of a mathematical model of information serial redundancy of management information systems of the aircraft fire alarm

The issues of improving the effectiveness of information systems by means of serial information redundancy due to an increase in the reliability of control of dangerous flight situations on board aircrafts are considered. The use of microprocessor equipment taking into account the physical principles of connection of sensors to determine dangerous situations on board aircrafts is proposed. The method for serial switching of detectors (with memory), considering a priori information according to the Bayes' method is proposed. The mathematical and graphical dependencies of the a priori probability of fire detection on the sensor quality with the given values of a posteriori probability and the number of repeated requests α=f(P 1 , g, k) are obtained. It is found that, in order to implement serial information redundancy, the following requirements shall be taken into account: high technical reliability of a particular information source; relatively large permissible information aging time; short correlation time of random technical faults, transient failures, fluctuation noise, etc. If the a priori probability a of a controlled phenomenon is low, the probabilities p 1 and p 2 change slowly with increasing k, and the probability p 3 (false alarm) can be quite high, in comparison with the probability of non-detection p 2 . If the probability a is sufficiently high, the probability p 1 effectively increases with increasing k, and the probability of non-detection p 2 will be greater than the probability of false alarm p 3 .

Fire Detection System Using Arduino Sensor

Recently various types of disaster monitoring system using smart-phones are under active studying. In this paper, we propose a system that automatically performs the disaster and fire detection. Additionally we implement the Arduino-based smart image sensor system in the web platform. When a fire is detected, an SMS is sent to the Fire and Disaster Management Agency. In order to improve fire detection probability, we proposed a smart Arduino fire detection sensor simulation which searches the smart sensor inference algorithm using fuzzy rules.

On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS) is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.

A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery

The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.

Onboard sensor orientation analysis with kriging method

We propose in this paper a method to analyse the efficiency of the direction pointed by an infrared sensor dedicated to forest fire detection on board of a space vehicle. The objective is to use these sensors to detect forest fires on a defined territory with a specified probability. One way to verify that the system stands this specification is to test each possible configuration (pointing direction of the sensor and number of space vehicles) and estimate the corresponding probability of forest fire detection. However, when the coverage territory is wide or when the number of vehicles is important, it is hardly possible to simulate every configuration in a reasonable amount of time. We propose to use kriging method for the prediction of forest fire detection probability for all possible configurations.

Modelling the effects of distance on the probability of fire detection from lookouts

In the management of forest fires, early detection and fast response are known to be the two major actions that limit both fire loss and fire-associated costs. There are several inter-related factors that are crucial in producing an efficient fire detection system: the strategic placement and networking of lookout towers, the knowledge of the fire detection radius for lookout observers at a given location and the ability to produce visibility maps. This study proposes a new methodology in the field of forest fire management, using the widely accepted Fire Detection Function Model to evaluate the effect of distance and other variables on the probability that an object is detected by an observer. In spite of the known variability, the model seems robust when applied to a wide variety of situations, and the results obtained for the effective detection radius (13.4 km for poor conditions and 20.6 km for good conditions) are in general agreement with those proposed by other authors. We encourage the application of the new approach in the evaluation or planning of lookout networks, in addition to other integrated systems used in fire detection.

An approach for the compensation of the background variability in fire detection systems

Background (BG) variability is an important issue in fire detection. Due to atmospheric situations in general a high correlation can be assumed between the BG infrared radiation for spatially near zones. This correlation can be even greater if similar forest and soil-surface conditions exist in these zones. In this paper we present an approach for fire detection in ground systems. This approach takes into account the temporal and spatial BG behavior. We show that if we use differences of sensor outputs of BG zones with highly correlated surface temperature and reflectance then the false alarm probability is reduced for a given threshold. We also show that for a fixed false alarm probability the use of differences between sensors instead of a single sensor provides higher fire detection probability.

Evaluation of global fire detection algorithms using simulated AVHRR infrared data

This paper provides a comparison of selected algorithms that have been proposed for global active fire monitoring using data from the NOAA Advanced Very High Resolution Radiometer (AVHRR). A simple theoretical model was used to generate scenes of AVHRR infrared channel 3 and channel 4 data containing fires of various sizes and temperatures in a wide range of terrestrial biomes and climates. Three active fire detection algorithms were applied to the simulated AVHRR images and their performance was characterized in terms of probability of fire detection and false alarm as functions of fire temperature and area, solar and viewing geometry, visibility, season and biome. Additional comparisons were made using AVHRR imagery. Results indicate that while each algorithm has a comparable probability of detecting large (1000m2) fires in most biomes, substantial differences exist in their ability to detect small fires, their tolerance of smoke and neighbouring fires, the number of false alarms, and their overall suitability for global application. An improved automatic algorithm is finally presented. It offers enhanced active fire detection with comparable or reduced false alarm rates in most biomes.