Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

Yanyan Gao,Libo Dang,Yunjia Wang,Ming Hao,Yuecheng Guo

doi:10.3390/ijgi10070449

Abstract

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

Highlights

Underground coal fires are usually caused by human factors, spontaneous combustion, forest fires, and natural hazards [1]
These false temperature anomalies were caused by solar radiation, topographic undulation, types of surface features, etc. Compared with these traditional methods, the improved active contour model achieved better results and eliminated many false alarms. This method can greatly reduce the workload of field verification and is of great significance in the field of coal fire detection
The range of the Miquan coal fire area collected by the unmanned aerial vehicle (UAV) was in area 5 of the Landsat-8 image

Summary

Introduction

Underground coal fires are usually caused by human factors, spontaneous combustion, forest fires, and natural hazards [1]. In China, underground coal fires, mainly caused by human factors, are mostly distributed in in Inner Mongolia Autonomous Region [7], Xinjiang Uygur Autonomous Region [8], Ningxia Hui Autonomous Region [9], and Shanxi Province [10]. Coal fires cause significant harm to the ecological environment and humans. Underground coal fires can release a lot of poisonous and harmful gases, such as SO2, NO, CO, and CH4 [11]. Coal fires have caused great harm to the ecological environment and human. Traditional geophysical and geochemical methods were used to detect coal fires. These methods have high detection accuracy, they are time consuming, labor intensive, inefficient, and dangerous [8]. Coal fire detection using a manual survey method is a challenging task, but this can be overcome with the help of remote sensing technology [17]

Methods

Results

Conclusion