Forest Fire Finder – DOAS application to long-range forest fire detection

Abstract

Abstract. Fires are an important factor in shaping Earth's ecosystems. Plant and animal life, in almost every land habitat, are at least partially dependent on the effects of fire. However, their destructive force, which has often proven uncontrollable, is one of our greatest concerns, effectively resulting in several policies in the most important industrialised regions of the globe.This paper aims to comprehensively characterise the Forest Fire Finder (FFF), a forest fire detection system based mainly upon a spectroscopic technique called differential optical absorption spectroscopy (DOAS). The system is designed and configured with the goal of detecting higher-thanthe-horizon smoke columns by measuring and comparing scattered sunlight spectra. The article covers hardware and software, as well as their interactions and specific algorithms for day mode operation. An analysis of data retrieved from several installations deployed in the course of the last 5 years is also presented.Finally, this paper features a discussion on the most prominent future improvements planned for the system, as well as its ramifications and adaptations, such as a thermal imaging system for short-range fire seeking or environmental quality control.

Development of the information system for monitoring time changes in forest plantations based on the analysis of space images

“…Existing systems [23][24][25][26][27][28] do not have the functionality to identify dependences between the data it operates on and do not predict them. The information system using correlation and regression analysis identifies factors that negatively affect forest plantations.…”

“…The Fire Finder mobile application makes it possible to find data on forest fires in America; they are displayed on a satellite map that can track their progress. The information is provided from government sources: IncіWeb and Cal Fire [26].…”

Intelligent Air Pollution Sensors Calibration for Extreme Events and Drifts Monitoring

“…Furthermore, as the deployment of sensor networks in smart cities has recently increased, the drifts of calibration models have become challenging during their in-field operation time. The drifts result from various reasons including clean air policies e.g., traffic, changes in humans consumption patterns such as fuel and gas [22], or temporal effects such as forest fires and volcano eruptions [23]. To detect the drifts, the methods in the state of art use statistical difference in distributions of air pollution measurements [12].…”

Application of Wireless Sensor Networks to Prevent the Spread of Forest Fires

“…Based on the change in atmospheric air composition, the system recognizes the type of smoke particles depending on their source of origin (organic/industrial). The radius of action of the system is 15 km [11] 5…”