FIRE-BYOL: A Real-Time Grassland Active-Fire Detection Algorithm Fusing VIIRS Fire Products and Himawari-8/9 Data

Abstract

Frequent grassland fires on the Mongolian Plateau endanger the regional environment, human safety, and property, creating a demand for near-real-time active-fire detection with high spatiotemporal resolution. While remote sensing serves as the primary detection method, current fire products struggle to balance high temporal and spatial resolutions for immediate monitoring. Furthermore, although deep learning models may sometimes outperform traditional threshold-based algorithms in generalization, their heavy reliance on extensive, manually annotated datasets severely restricts their application in small-sample, data-scarce scenarios. To address the dual challenges of near-real-time detection and limited sample availability, this study proposes FIRE-BYOL, an active-fire detection framework integrating BYOL self-supervised learning with supervised fine-tuning. Leveraging high-frequency multispectral AHI data from Himawari-8/9 as the primary input, the framework effectively trains on a small sample of high-confidence labels generated from VIIRS fire products. Robust feature extraction is achieved through self-supervised pre-training on large volumes of unlabeled data to mitigate label dependency, followed by supervised fine-tuning for precise active-fire classification. Our experimental results demonstrate that FIRE-BYOL excels under small-sample conditions, achieving F1 scores of 0.8746 during the day and 0.9764 at night. Outperforming Random Forest, XGBoost, FireCNN, and the WLF product, the model exhibits exceptional near-real-time monitoring capabilities for early-stage and entire fire events. By delivering accurate detection at a 10 min temporal and 2 km spatial resolution, FIRE-BYOL offers a highly effective, data-efficient technical solution for high-frequency regional fire monitoring on the Mongolian Plateau and similar environments.