Fuel-Specific Aggregation of Active Fire Detections for Rapid Mapping of Forest Fire Perimeters in Mexico

Context and Background. Active fires have the potential to provide early estimates of fire perimeters, but there is a lack of information about the best active fire aggregation distances and how they can vary between fuel types, particularly in large areas of study under diverse climatic conditions. Objectives. The current study aimed at analyzing the effect of aggregation distances for mapping fire perimeters from active fires for contrasting fuel types and regions in Mexico. Materials and Methods. Detections of MODIS and VIIRS active fires from the period 2012–2018 were used to obtain perimeters of aggregated active fires (AGAF) at four aggregation distances (750, 1000, 1125, and 1500 m). AGAF perimeters were compared against MODIS MCD64A1 burned area for a total of 24 fuel types and regions covering all the forest area of Mexico. Results/findings. Optimum aggregation distances varied between fuel types and regions, with the longest aggregation distances observed for the most arid regions and fuel types dominated by shrubs and grasslands. Lowest aggregation distances were obtained in the regions and fuel types with the densest forest canopy and more humid climate. Purpose/Novelty. To our best knowledge, this study is the first to analyze the effect of fuel type on the optimum aggregation distance for mapping fire perimeters directly from aggregated active fires. The methodology presented here can be used operationally in Mexico and elsewhere, by accounting for fuel-specific aggregation distances, for improving rapid estimates of fire perimeters. These early fire perimeters could be potentially available in near-real time (at every satellite pass with a 12 h latency) in operational fire monitoring GIS systems to support rapid assessment of fire progression and fire suppression planning.

Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.

Smart fire monitoring system for a city: design and development

All Fire Alarm Systems essentially operate on the same fundamental principle and framework. It provides audible and visual alarm signals. An alarm is raised if a sensor detects smoke or heat, warns people that there may be a fire, and evacuate the premises immediately. Consequently, concerned individuals may call the fire department to mitigate the emergency at which, at this point of time, the firefighter's Emergency Respond Time (ERT) is vital. ERT is one of the contributing factors to how much fire will consume lives and, or properties. In worst cases, an entire building turns into ashes, lives taken because of a slow ERT. Then there are these casual "false-alarms" caused by the false-positive readings of the installed Fire Alarm Systems. These false alarms cause mass panic and consume firefighters' valuable time and resources. The instances mentioned above are a few reasons why there is a need to implement an efficient city-wide fire alarm system. This study focused on the design, development and testing of a wide-area Smart Fire Monitoring System comprised of major parts, the devices and the system software. Specifically, this study aimed to develop a smart fire alarm device using the Agile Prototyping Methodology (APM) and employ Agile Software Development Methodology (ASDM) for the development of the Smart Fire Monitoring System Software (SFMSS). For the development of the smart fire alarm device, the proponents assembled necessary sensors, electrical components, microcontrollers, and other electrical modules to create a working smart fire alarm device that abled to detect smoke; detect temperature spikes; detect the existence of fire; broadcast a GPS coordinates to the fire department; transmit and receive data through RF signals using long-range radio frequency (RF) module and SMS technology; and, be functional and operational under Low Power mode. Concomitant with this device is its system software, SFMSS, to manage and process the broadcasted data. SFMSS is a centralized system developed for the fire department to monitor the city for a fire outbreak. SFMSS continuously communicates with the fire alarm devices to autonomously monitor fire presence via radio frequency and process SMS notifications containing GPS coordinates, convert them into a readable address, and plot it in a city map in the event of a fire emergency. The proponents conducted a Stress and Reliability Test on the smart fire alarm device and Product Evaluation for Quality, Efficiency, and Usability with the Bureau of Fire Protection Region 6 for the entirety of the system.

Studying recent hydroclimatic variability in Madagascar despite deficient measurement networks: use of CHIRPS and GRACE data at the scale of the Mahajunga province

Given the lack of in situ hydroclimatic measurements and networks in Madagascar, the GRACE (2003–2016) spatial gravimetry data, combined with other satellite data such as CHIRPS rainfall estimates or fire monitoring using GFED products, make it possible to establish an interannual assessment of certain climatic and environmental covariations at the northwest scale of the country. The results show a negative trend in continental rainfall and water content, especially after 2007, but also a time lag in the linear variations and trends of the Water Equivalent Height as well as the number of detected fires (variable indirectly measuring the pressure of deforestation by slash and burn agriculture).

Research on Large Space Fire Monitoring Based on Image Processing

Fire in large space buildings has the characteristics of rapid spread and easy to ignite, which makes it difficult to extinguish fire. Therefore, the research on fire safety prevention technology of large space buildings has strong practical significance and practical value. Fire image automatic monitoring technology is based on the advantages of image processing and the high-speed operation of computer language. This paper proposes a fire monitoring technology which uses the information of flame image to analyze and judge fire. It combines computer graphics, digital image processing and computer vision technology, and one new image-based fire detection and processing technology is studied from the aspects of flame morphology and flame color.

Computer Vision Method for Forest Fires Detection Based on RGB Images Obtained by Unmanned Motor Glider

The article proposes a method (algorithm) of forest fire detection by means of RGB images obtained by using an unmanned aerial vehicle (motor glider). It includes several stages associated with background detection and subtraction and recognition of fire areas by means of RGB colour space. The proposed method was tested using images of forest fires. It is proposed to use unmanned aerial vehicles capable to monitor large areas continuously for several hours. The results of calculations are shown, which demonstrate that the proposed method allows us to detect areas of images occupied by forest fires and may be used in automatic forest fire monitoring systems.