A landsat-based burned area atlas (2000–2023) for the Niassa Special Reserve, Mozambique using U-Net deep learning

Abstract

Savanna burning plays a key ecological role in miombo woodlands, influencing vegetation regeneration, biodiversity, and ecosystem structure. This study provides a comprehensive fire atlas and spatiotemporal assessment of fire activity from 2000 to 2023, in the Niassa Special Reserve (NSR), northern Mozambique, a key protected area is sub-Saharan Africa. Using medium-resolution satellite imagery and a Deep Learning classification approach (U-Net), we mapped annual burned areas and analysed spatial and temporal patterns of burning, including recurrence and seasonality. The results indicate a mean fire return interval of 2.8 years, with distinct differences between the Early Dry Season (EDS) and Late Dry Season (LDS): fire recurrence was as frequent as 1.9 years in the LDS, while EDS intervals extended up to 30 years. Fire activity was most intense in central and eastern lowlands, while higher elevations such as Mount Mecula showed lower fire occurrence. The classification model demonstrated strong performance, with Dice Coefficients ranging from 91.4 % to 94.6 %. The resulting atlas offers valuable insights for adaptive fire management, biodiversity conservation, and climate resilience in the NSR and similar savanna ecosystems.