Zhou Bao, Gao Sha, Yin Ying, Zhong Yanling
College of Electronic and Information Engineering, West Anhui University, Luan, 237000, China.
School of land and resources engineering, Kunming University of Science and Technology, Kunming, 650093, China.
Sci Rep. 2024 Dec 28;14(1):31076. doi: 10.1038/s41598-024-81976-w.
The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions. Utilizing the Gini coefficient, the study assesses the importance of spectral and texture features in the RF, culminating in the selection of an optimal feature combination for the construction of a bespoke RF model tailored for active fire detection. The research utilized texture features based on the Grey Level Co-occurrence Matrix (GLCM), demonstrating their significant contribution to enhancing the accuracy of fire detection using the RF model. Our analysis reveals that GLCM-based texture features, which form 40% of the model's final feature set, are crucial for improving detection accuracy. The optimized RF model demonstrates a marked superiority in identifying active fires, achieving an overall accuracy of 86.1%. The study results demonstrate that the bespoke RF model is suitable for detecting active fire across various land cover environments in China.
人类活动引发的一系列野火活动已成为大气污染的重要来源，对公众健康和财产安全构成了相当大的风险。本研究利用哨兵 - 2 卫星数据，采用包括光谱指数法、阈值法和随机森林（RF）模型等多种方法进行活跃火点检测。该研究涵盖了中国不同地区的多种土地覆盖类型。利用基尼系数，该研究评估了光谱和纹理特征在随机森林中的重要性，最终选择了最优特征组合，以构建一个针对活跃火点检测量身定制的随机森林模型。该研究利用基于灰度共生矩阵（GLCM）的纹理特征，证明了它们对提高随机森林模型火灾检测准确性的显著贡献。我们的分析表明，基于 GLCM 的纹理特征占模型最终特征集的 40%，对提高检测准确性至关重要。优化后的随机森林模型在识别活跃火点方面表现出明显优势，总体准确率达到 86.1%。研究结果表明，定制的随机森林模型适用于检测中国各种土地覆盖环境中的活跃火点。
