关键词:
algorithm classification
disaster prevention
hotspot automatic detection
infrared satellite data
reduction
thermal anomalies
thermal remote sensing
volcanic lava flows
volcano monitoring
摘要:
Volcano monitoring is essential for predicting volcanic eruptions and implementing early warning measures. Traditional ground-based monitoring methods cannot fully cover all volcanoes. Satellite remote sensing technology, with its advantages of global coverage and high temporal and spatial resolutions, is an important complement for near-real-time monitoring of volcanic activities, especially for the detection of lava flows and volcanic thermal anomalies. This study presents the current status of typical sensors used for infrared remote sensing of volcanic hotspots and summarizes the methodology for detecting volcanic hotspots by using satellite infrared data. First, the history of thermal infrared satellite data monitoring and satellite system development is summarized. Notably, various types of algorithms and satellite systems have been applied to make the monitoring of volcanic activities at the global scale efficient and accurate. Second, the development of volcanic hotspot identification algorithms is analyzed, and existing volcanic hotspot identification algorithms are classified into four categories in accordance with the different characteristics of the volcano used and its surrounding features (spatial/temporal). The four algorithm categories are spatial feature, temporal feature, comprehensive feature, and artificial intelligence algorithms. The spatial feature algorithms are categorized into fixed and dynamic threshold methods on the basis of different methods of threshold selection (fixed/dynamic threshold). On the basis of the classification above, we describe the current status of the volcanic hotspot identification algorithms and summarize their data, scope of application, and application limitations to provide a comprehensive classification and assessment for understanding and improving volcano hotspot detection technology. Such classification and assessment are crucial for the development of future volcano thermal remote sensing theories and technol