Programs
- M. Tech. in Automotive Engineering -Postgraduate
- Master of Physician Associate (M.PA) – (Medicine, Surgery) 2 Year -Postgraduate
Publication Type : Conference Proceedings
Publisher : IEEE
Url : https://doi.org/10.1109/GCAT59970.2023.10353465
Keywords : Wildfires;Image resolution;Instruments;Observers;Global warming;Climate change;Wildfire;Meteorology;Climate Change;Glitter Belt;Kavaratti;FLT8m;Flying Leaflet;Flying Leaf
Campus : Coimbatore
School : School of Engineering
Department : Aerospace
Year : 2023
Abstract : Wildfires cause massive trauma and damage. They are getting more intense as Global Warming increases. Swift detection and intervention allow most fires to be extinguished while they are still confined to a few trees, rather than millions of acres. Fires that start inside remote valleys at night, are not detectable by ground-based observers until daylight makes smoke plumes visible. Detection from Space faces must eliminate false positives with sub-pixel resolution, despite long time between successive orbits. High Altitude Long Endurance stratospheric platforms can improve detection accuracy and speed by orders of magnitude. The “Flying Leaf” (FL) swarms of the Glitter Belt architecture, designed for global meteorological mapping, are applied here at reduced scale to detect and diagnose wildfires, eliminate false positives, and guide responders. The approach is dovetailed into existing concept development. A near-term 8m span biplane concept enables construction of FL swarms that endure above controlled airspace through a 12-hour night, with an instrument payload. Work on sensing thermal radiation from re-entering spacecraft is adapted for wildfire detection. A Virtual Aperture formed by an FL Swarm offers orders of magnitude improvement in resolution. We consider design parameters and distil lessons relevant to wildfire suppression.
Cite this Research Publication : Narayanan Komerath, Satyajit Meti, Nandeesh Hiremath, Vishwa Karthi, Rajesh Senthil Kumar, Wildfire Detection Using HALE Meteorology Vehicles, [source], IEEE, 2023, https://doi.org/10.1109/GCAT59970.2023.10353465