ISPRS journal of photogrammetry and remote sensing2025,Vol.230Issue(Dec.) :147-169.DOI:10.1016/j.isprsjprs.2025.09.005

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

Dias C.R.G. Neves A.K. Silva J.M.N. Pereira J.M.C. Ribeiro N.S.
ISPRS journal of photogrammetry and remote sensing2025,Vol.230Issue(Dec.) :147-169.DOI:10.1016/j.isprsjprs.2025.09.005

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

Dias C.R.G. 1Neves A.K. 2Silva J.M.N. 3Pereira J.M.C. 3Ribeiro N.S.4
扫码查看

作者信息

  • 1. Forest Research Centre School of Agriculture University of Lisbon Tapada da AjudaForest Research Centre School of Agriculture University of Lisbon Tapada da Ajuda||Forestry Department Northwest Regional Delegation Agricultural Research Institute of Mozambique||
  • 2. Forest Research Centre Associate Laboratory TERRA School of Agriculture University of Lisbon Tapada da Ajuda||GRAS Global Risk Assessment Services GmbH
  • 3. Forest Research Centre Associate Laboratory TERRA School of Agriculture University of Lisbon Tapada da Ajuda
  • 4. Department of Forest Engineering Faculty of Agronomy and Forest Engineering UEM Campus Universitario
  • 折叠

Abstract

© 2025 The Author(s)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.

Key words

Deep learning/Fire cycle/Landsat imagery/Miombo woodlands/Remote sensing/Seasonal fire patterns

引用本文复制引用

出版年

2025
ISPRS journal of photogrammetry and remote sensing

ISPRS journal of photogrammetry and remote sensing

ISSN:0924-2716
参考文献量125
段落导航相关论文