主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (9): 39-45.

• • 上一篇    下一篇

基于Sentinel-2时序的森林火烧迹地提取与植被恢复评估

石宽1,2, 张嘉欣1,2, 高敏1,2, 齐方忠1,2, 张佳男3, 武英达1,2, 孟盛旺4, 白夜1,2   

  1. （1.中国消防救援学院，北京 102202；2.森林草原火灾风险防控应急管理部重点实验室，北京 102202；3.黑龙江省庆安国有林场管理局，黑龙江 庆安 152400；4.中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室 千烟洲试验站，北京 100101）
  • 收稿日期:2026-01-05 修回日期:2026-03-01 出版日期:2026-09-15 发布日期:2026-09-15
  • 作者简介:石宽，中国消防救援学院，讲师，主要从事森林火灾监测预警及森林灭火指挥与技战术研究，北京市昌平区南口镇南雁路4号25号楼，102202。
  • 基金资助:
    森林草原火灾风险防控应急管理部重点实验室2025年度开放课题（FGFRP202501）

Identification of burned areas and assessment of the vegetation recovery process based on Sentinel-2 time-series images

Shi Kuan1,2, Zhang Jiaxin1,2, Gao Min1,2, Qi Fangzhong1,2, Zhang Jianan3, Wu Yingda1,2, Meng Shengwang4, Bai Ye1,2   

  1. (1.    China Fire and Rescue Institute, Beijing 102202, China; 2. Key Laboratory of Forest and Grassland Fire Risk Prevention, Ministry of Emergency Management, Beijing 102202, China; 3. Qing'an State-owned Forest Farm Administration Bureau of Heilongjiang Province, Qing'an Heilongjiang 152400, China; 4. Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
  • Received:2026-01-05 Revised:2026-03-01 Online:2026-09-15 Published:2026-09-15

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摘要： 森林火灾是干扰生态系统平衡的重要因子，及时准确地监测火烧迹地范围与评估植被恢复对生态修复至关重要。本研究以北京丫髻山2019年“3·30”森林火灾为例，基于多时相Sentinel-2影像，综合运用差分指数阈值法（dNDVI, dBAI, dNBR）与监督分类法（最大似然MLC、支持向量机SVM、随机森林RF）进行火烧迹地提取，并利用增强型植被指数（EVI）评估了2018―2025年的植被恢复动态。结果表明：随机森林算法的提取精度最高（总体精度89.72%，Kappa系数0.79）；研究区火烧迹地总面积为91.1 hm2，其中轻度火烧面积占80.8%，中度火烧面积占14.8%，重度火烧面积占4.4%，且主要集中分布于景区周边及油松密林区，生态风险突出。EVI时序分析显示，灾后植被呈现快速恢复态势，至2021年大部分区域EVI已恢复至灾前水平，并于2022年后进入平稳阶段，但此恢复主要反映了林下灌草层的覆盖，乔木群落的长期恢复仍需持续监测。本研究验证了随机森林方法在复杂山区火烧迹地提取中的有效性，研究结果可为区域灾后生态修复与森林可持续经营提供科学依据。

关键词: 火烧迹地, 植被指数, 随机森林, 植被恢复

Abstract: Forest fires are a critical disturbance factor affecting ecosystem balance, timely and accurate monitoring of burned area extent and assessment of vegetation recovery essential for ecological restoration are important. In this study, we investigated the "3·30" forest fire that occurred on Yaji Mountain, Beijing, in 2019 using multi-temporal Sentinel-2 imagery. We comprehensively applied differential index thresholding methods (dNDVI, dBAI, and dNBR) and supervised classification approaches (Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF)) to extract burned areas, and employed the Enhanced Vegetation Index (EVI) to evaluate vegetation recovery dynamics during 2018―2025. The results showed that the Random Forest algorithm achieved the highest extraction accuracy, with an overall accuracy of 89.72% and a Kappa coefficient of 0.79. The total burned area in the study region was 91.1 hm2, of which low-severity burns accounted for 80.8%, while moderate-severity burns accounted for 14.8%，while high-severity burns accounted for 4.4% and were mainly distributed around the scenic area and within dense Pinus tabuliformis forests, indicating notable ecological risks. Time-series EVI analysis revealed that post-fire vegetation exhibited a rapid recovery trend; by 2021, EVI values in most areas had recovered to pre-fire levels, and the recovery process entered a relatively stable stage after 2022. However, this recovery mainly reflected the increased coverage of understory shrubs and grasses, whereas the long-term recovery of tree communities requires continuous monitoring. This study demonstrated the effectiveness of the Random Forest method for burned area extraction in complex mountainous environments, and the findings provide a scientific basis for post-fire ecological restoration and sustainable forest management.

Key words: burned area, vegetation index, random forest, vegetation restoration