The goal of the DACC project is to improve the monitoring of wildfires and the estimate of their emissions using uncrewed aerial systems (UAS), with a focus on the Amazon region.
Uncontrolled wildfires destroy vegetation and wildlife habitats, produce CO2 and other harmful emissions in vast quantities, and are a hazard to human health. In the Amazon and several other regions, anthropogenic activities and climate change are increasing the scale, intensity and frequency of fire activity, fuelling a harmful feedback loop.
Satellite observations are vital for monitoring wildfires and estimating their impact in terms of fuel consumption and smoke emissions. However, satellite systems are constrained by cloud cover and their spatial and temporal resolutions: geostationary satellites acquire imagery every 10-15 minutes but have a resolution of 2-4 km; polar-orbit satellites have higher spatial resolution (200-1000 m) but provide data every 6-12 hours. Consequently, small/low intensity and/or short-lived fires, as well as fires burning below clouds are not detected and thereby underestimating the extent of fire activity. Moreover, emission measurements currently rely on coarse resolution biomass (fuel load) estimates in fire affected areas and, combined, these effects result in relatively large uncertainty of regional smoke emissions.
Small UAS can improve the wildfire monitoring capabilities and reduce the emission measurement uncertainty by providing a responsive system with higher spatial and temporal resolution, as well as providing a more accurate estimate of the local biomass. UAS can perform targeted measurements that are inaccessible to satellites, such as smoke plume height and local wind speed and direction, that can be used to improve the smoke dispersion models and potentially predict the direction and rate of fire spread. Additionally, UAS can provide data for validation & calibration of the models used by satellites to improve their larger scale estimates.
This research will develop an optimal UAS survey system to complement satellite observations by investigating the best combination of sensors, aircraft, operational strategies, automated data postprocessing and modelling.
Initial tests of the UAS survey system will be performed in the UK, possibly exploiting local pre-known agricultural burn events. The main study region will be the area surrounding the Xixuaú community in the Amazon forest. It is expected that at least one field trip will be performed during the PhD to perform in situ measurements. Access and logistics will be facilitated by the Amazon Charitable Trust.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and will work with partners at the Amazon Charitable Trust (ACT). Specific training will include:
- UAS system integration and design of bespoke solutions
- UAS flight operations (planning, safety cases, command and control)
- Automated sensor data processing
- Remote sensing of fires
- Fire ecology
The candidate will become a member of the SotonUAV research team (www.sotonuav.uk) and will participate in UAS flight tests and fieldwork related to landscape fire activity, both in the UK and in the Amazon (with the support of the ACT and the Turner-Kirk UAV Research Support Programme).
Please see https://inspire-dtp.ac.uk/how-apply for details.
- Roberts, G., & Wooster, M. (2021). Global impact of landscape fire emissions on surface level PM2.5 concentrations, air quality exposure and population mortality. Atmospheric Environment, 252, [118210]. https://doi.org/10.1016/j.atmosenv.2021.118210
- Schumann, B., Ferraro, M., Surendra, A., Scanlan, J. P., & Fangohr, H. (2014). Better design decisions through operational modeling during the early design phases. Journal of Aerospace Information Systems, 11(4), 195-210. https://doi.org/10.2514/1.I010149
- Sudhakar S., Vijayakumar V., Sathiya Kumar C., Priya V., Logesh Ravi, Subramaniyaswamy V., Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires, Computer Communications 149, 2020, 1-16 https://doi.org/10.1016/j.comcom.2019.10.007
