About the project
Landscape fires impact the carbon cycle and alter atmospheric composition. This project will exploit the improved imaging characteristics of the Meteosat Third Generation (MTG) satellite to characterise fire activity and estimate fire emissions, in particular exploring how its very high temporal resolution can be harnessed to improve current algorithmic approaches.
Earth Observation is vital to capture the strong spatio-temporal dynamics of landscape fire activity. Thermal observations can detect actively burning fires and estimate their emissions using fire radiative power (FRP) retrievals [1,2]. Meteosat Third Generation, launched in December 2022, will provide active fire data at an unprecedented spatial and temporal scale across Africa, Europe and South America.
A limitation of EO active fire products is the omission of small fires which contribute significantly to continental burned area and fire emissions. The omissions result from the sensors coarse spatial resolution and the reduced sensitivity of the fire detection algorithm. This project will utilize MTG data to develop a fire detection algorithm that exploits MTGs high temporal frequency – focusing on improving the detection of small\low intensity fires using optimal estimation and machine learning methods . To assess the degree of improvement, these data will be compared to independent datasets – such as Sentinal-2 data which can be used to quantify the presence or absence of a burned area related to the active fires being detected. The newly derived active fire dataset will be used to estimate continental scale fire emissions, with the results compared to independent emissions inventories. The project will assess the improvement in emissions estimation that comes from these developments, comparing them to those from alternative algorithms that do not use multi-temporal methods.
