Near-real time monitoring of burned area at global scale based on deep learning
Recent advances in land surface reflectance modelling and machine learning techniques opened new opportunities for deriving burned area at a near-real time (NRT) basis and globally. Built from these recent advances, this paper describes a new and computationally efficient approach used by the Copern...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/39392 |
| Acceso en línea: | https://hdl.handle.net/10902/39392 |
| Access Level: | acceso abierto |
| Palabra clave: | Near-real time Monitoring Burned area Wildfire Terrestrial globe Sentinel-3 VIIRS |
| Sumario: | Recent advances in land surface reflectance modelling and machine learning techniques opened new opportunities for deriving burned area at a near-real time (NRT) basis and globally. Built from these recent advances, this paper describes a new and computationally efficient approach used by the Copernicus Land Monitoring Service (CLMS) to map burned area from Sentinel-3 OLCI&SLSTR reflectance data and VIIRS active fires at NRT within one day after each image acquisition, complemented by a non-time critical (NTC) product delivered several months after. A neural network is designed to predict fractional burned area on a per-pixel basis from time series of surface reflectance data. The neural network is used to generate time series of burned area detection maps, which are revised by active fire detections spatiotemporal densities to filter out areas likely to be related to land surface changes other than fires, such as agricultural practices or fast vegetation senescence. The algorithm was calibrated with data from 2019, and its quality was assessed with data from 2020, along with other global products, through an intercomparison analysis and a validation analysis using a stratified global random sampling of 30 m reference burned area data. The quality assessment included the NRT and NTC products presented here (CLMBA40nrt and CLMBA40ntc, respectively) and other global products available for 2020, the NTC product currently distributed by the CLMS (CLMBA31ntc), the MODIS-MCD64 Collection 6 (MCD64), the Sentinel-3 OLCI Climate Change Service C3SBA11 and the Sentinel-3 OLCI&SLSTR ESA’s Climate Change Initiative Fire Disturbance FIRECCIS311. The accuracies of CLMBA40ntc and CLMBA40nrt (Dice coefficient (DC) 65.0% and 56.5% respectively) are higher than CLMBA31ntc, MCD64 and C3SBA11 (DC ∼45%) and higher and similar, respectively, compared to FIRECCIS311 (DC 56.0%). The new algorithm described here allows for unprecedented accuracy and timeliness of global burned area estimates. |
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