17:15 〜 18:45
[HDS09-P05] Worldwide Disaster Monitoring Using Multiple Earth Observation Satellites' Imagery
キーワード： Synthetic aperture radar imagery, Optical imagery, Earthquake, Eruption, Flood
Contemporary remote sensing satellites, equipped with diverse sensors, capture high-resolution images, and generate extensive datasets. The scale and magnitude of this data have witnessed a substantial surge over the past decade, owing to significant advancements in sensor resolution and capabilities. The utilization of satellite imagery has gained popularity, particularly in disaster monitoring and response. Following a disaster, prioritizing rescue operations, and coordinating relief efforts becomes paramount to effective disaster response. Swift and efficient execution of these tasks is imperative, especially considering the often-limited resources in disaster-affected areas. Identifying areas of maximum damage promptly becomes crucial to optimize resource allocation for an effective disaster response. In the realm of remote sensing applications pertaining to the management of disaster risks, the utilization of multiple satellite-based mapping and monitoring is a critical procedure and a fundamental component of risk evaluation. This enables authorities and stakeholders to execute suitable disaster response and relief measures, aiming at achieving the reduction and mitigation of disaster risks as part of the emergency aid strategy during the initial phases.
The primary objective of this research is the application of multiple remote sensing datasets for disaster monitoring, including radar data from ALOS-2 and Sentinel-1, as well as optical sources such as WorldView and Sentinel-2. The research employs diverse methodologies, encompassing deep learning, synthetic aperture radar interferometry (InSAR), persistent scatterer (PS) technique, Small Baseline Subset (SBAS) method, and Coherent Change Detection (CCD) method. The goal is to detect, monitor, and map natural disasters worldwide, including earthquakes, such as those that occurred on Japan's Notohanto Island on January 1st, 2024, and in Northwest China on January 23, 2024, volcanic eruptions on islands on October 24, 2023, and floods in Libya on September 23, 2023.
The study successfully detected, monitored, and mapped various natural disasters worldwide. The research underscores the pivotal role of remote sensing in enhancing disaster response strategies and risk mitigation by providing timely and accurate information for prioritizing rescue operations and coordinating relief efforts.
The primary objective of this research is the application of multiple remote sensing datasets for disaster monitoring, including radar data from ALOS-2 and Sentinel-1, as well as optical sources such as WorldView and Sentinel-2. The research employs diverse methodologies, encompassing deep learning, synthetic aperture radar interferometry (InSAR), persistent scatterer (PS) technique, Small Baseline Subset (SBAS) method, and Coherent Change Detection (CCD) method. The goal is to detect, monitor, and map natural disasters worldwide, including earthquakes, such as those that occurred on Japan's Notohanto Island on January 1st, 2024, and in Northwest China on January 23, 2024, volcanic eruptions on islands on October 24, 2023, and floods in Libya on September 23, 2023.
The study successfully detected, monitored, and mapped various natural disasters worldwide. The research underscores the pivotal role of remote sensing in enhancing disaster response strategies and risk mitigation by providing timely and accurate information for prioritizing rescue operations and coordinating relief efforts.