Thermal anomalies detected by Earth observation satellites have been widely used to identify active fires. Fire Radiative Power (FRP) can be estimated from the radiance at medium wave infrared (3-5 μm) wavelengths, measured by multiple polar-orbiting and geostationary satellite sensors, and represents the instantaneous radiative energy that is released from actively burning fires. FRP has been used to support mapping of burned scars, by identifying core areas, and estimating trace gas and aerosol rate of emissions, hence strengthen monitoring of wildfires activities and their impact on environment and ecosystems.
Algorithms to operationally generate FRP products from Earth observation satellite acquisitions in near real-time account for background window statistics, corrections, adjustments and tests to eliminate false alarms, in order to distinguishing fire pixels from non-fire pixels. Nevertheless, a high percentage of thermal anomalies are wrongly classified as possible fire pixels. Source of misclassification could likely be the presence of real thermal anomalies of Earth surface, corresponding to pixels exhibiting significantly higher released radiative energy than background window area (e.g. industrial areas, solar photovoltaic plant).
This research study aims at presenting a methodological approach to identify thermal anomalies hotspots, misclassified as fire pixels. FRP products over Italy National territory, generated for the period 2022-2023 from SLSTR, MODIS and VIIRS satellite sensors and distributed by Copernicus, EUMETSAT and NASA FIRMS, have been collected and analysed.
A total of about 75000 FRP fire pixels have been first spatially and temporally intersected with EFFIS Burned Areas Database, distributed under the Copernicus Emergency Management Service, in order to identify misclassified fire pixels. Later, zonal statistics has been performed in order to evaluate fractional land cover within each fire pixel. Thermal anomalies hotspots misclassified as fire pixels have been identified using a cluster analysis in order to partition a data set into discrete subsets, based on defined distance measures like the spatial distance of the pixel centroids, the temporal frequencies of the pixels and fractional land cover of selected classes.
Results demonstrate that misclassified large surfaces, like industrial areas, can be identified from both spatial and temporal patterns, while other FRP false alarms are smaller in size and exhibit uneven temporal frequencies. Limitations of the proposed approach are discussed, recommending possible future improvements.
Results show the capability of the presented approach to identify thermal anomalies hotspots, misclassified as fire pixels, in order to generate static masks for FRP products post-processing, improving the capacity of FRP products in providing prompt and accurate information for operational services addressing the monitoring of wildfires and their impact on environment and ecosystems.