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Article

Burned Area Mapping and Fire Severity Assessment of Forest–Grassland Ecosystems Using Time-Series Landsat Imagery (1985–2023): A Case Study of Daxing’anling Region, China

1
College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
2
Key Laboratory of Mongolian Plateau’s Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot 010022, China
3
College of Resources and Environmental Economics, Inner Mongolia University of Finance and Economics, Hohhot 010051, China
*
Author to whom correspondence should be addressed.
Fire 2025, 8(9), 337; https://doi.org/10.3390/fire8090337 (registering DOI)
Submission received: 20 July 2025 / Revised: 17 August 2025 / Accepted: 22 August 2025 / Published: 23 August 2025
(This article belongs to the Section Fire Science Models, Remote Sensing, and Data)

Abstract

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km2). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km2 after 2010 and reaching a minimum of 92.8 km2 in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions.
Keywords: burned area; fire severity; image compositing; time-series Landsat; Daxing’anling burned area; fire severity; image compositing; time-series Landsat; Daxing’anling

Share and Cite

MDPI and ACS Style

Chen, L.; Wei, B.; Jia, X.; Liu, M.; Zhao, Y. Burned Area Mapping and Fire Severity Assessment of Forest–Grassland Ecosystems Using Time-Series Landsat Imagery (1985–2023): A Case Study of Daxing’anling Region, China. Fire 2025, 8, 337. https://doi.org/10.3390/fire8090337

AMA Style

Chen L, Wei B, Jia X, Liu M, Zhao Y. Burned Area Mapping and Fire Severity Assessment of Forest–Grassland Ecosystems Using Time-Series Landsat Imagery (1985–2023): A Case Study of Daxing’anling Region, China. Fire. 2025; 8(9):337. https://doi.org/10.3390/fire8090337

Chicago/Turabian Style

Chen, Lulu, Baocheng Wei, Xu Jia, Mengna Liu, and Yiming Zhao. 2025. "Burned Area Mapping and Fire Severity Assessment of Forest–Grassland Ecosystems Using Time-Series Landsat Imagery (1985–2023): A Case Study of Daxing’anling Region, China" Fire 8, no. 9: 337. https://doi.org/10.3390/fire8090337

APA Style

Chen, L., Wei, B., Jia, X., Liu, M., & Zhao, Y. (2025). Burned Area Mapping and Fire Severity Assessment of Forest–Grassland Ecosystems Using Time-Series Landsat Imagery (1985–2023): A Case Study of Daxing’anling Region, China. Fire, 8(9), 337. https://doi.org/10.3390/fire8090337

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