Water distribution based on SAR and optical data to improve hazard mapping.

Climate projections foresee intense precipitation and long-term drought events is increasing with consequent rapid changes in surface water bodies in a short period. In areas with drastic hydrological changes, achieving accurate and rapid mapping of these phenomena in combination with hydrologic variability characteristics is a key of effective emergency management and disaster risk reduction plans. This study presents an automatic method for mapping drought and flood hazards, particularly in regions with significant hydrological changes. We use Sentinel-1/2 and Landsat data to extract surface water and classify permanent and seasonal water bodies in historical periods, which serve as the basis for identifying flood or drought areas. The water extraction method combines index-based analysis for optical data and the region-Otsu method for radar data, ensuring accurate identification of water. The effectiveness of this approach is demonstrated through comparisons with existing products in Poyang Lake (China), the Po River Plain (Italy), and the Indus River Plain (Pakistan). Findings show a high similarity between the two, and our results can provide more specific details. Our method is particularly well-suited for areas with fluctuating hydrological conditions, can also map quickly without optical data. By effectively identifying areas affected by drought and flood hazards while mitigating errors from natural hydrological dynamics, this methodology contributes valuable insights to enhance emergency management and disaster risk reduction plans.

Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster mapping.

Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost. The increasing availability of satellite imagery and other remote sensing data has triggered research activities on development of algorithms for detection and monitoring of natural events. Here, we introduce an unsupervised subspace learning-based methodology that uses multi-temporal and multi-spectral satellite images to identify regions damaged by natural disasters. It first performs region delineation, matching, and fusion. Next, it applies subspace learning in the joint regional space to produce a change map. It identifies the damaged regions by estimating probabilistic subspace distances and rejecting the non-disaster changes. We evaluated the performance of our method on seven disaster datasets including four wildfire events, two flooding events, and a earthquake/tsunami event. We validated our results by calculating the dice similarity coefficient (DSC), and accuracy of classification between our disaster maps and ground-truth data. Our method produced average DSC values of 0.833 and 0.736, for wildfires and floods, respectively, and overall DSC of 0.855 for the tsunami event. The evaluation results support the applicability of our method to multiple types of natural disasters.

Internal Disaster Mapping of Asan Medical Center

PURPOSE: Hospitals should be prepared for out-of-hospital (external) and in-hospital (internal) disasters. Mapping is vital for disaster management. Disaster situations can be quickly and comprehensibly understood through the use of disaster mapping. The purpose of this article is to design an internal disaster map for the Asan Medical Center (AMC). We expect this mapping to be helpful in making an internal disaster plan and in controlling an internal disaster. METHODS: Internal disasters were classified into three categories according to the disaster size and the response level, and the three categories were AMC Code I, II, and III. The triage was divided into 6 groups. AMC and its surrounding area were latticed at the interval of 50 meters. The essential elements of the internal disaster plan were drawn on the latticed map by using the procedures in the internal disaster plan. RESULTS: Internal disaster maps were designed for Code II and III internal disaster. Primary and secondary control lines enclosed the hospital area and the disaster site. A command post, a treatment area with a triage area, and a transport area were positioned in each disaster code map. Evacuation roots and traffic flow were drawn. Evacuation maps for intensive-care-unit and high-floor patients were designed in light of their limited mobility. Disaster ID cards and parking cards were also made. CONCLUSION: The internal disaster mapping of AMC will allow emergency medical personnel to visualize and verify their disaster plan and to quickly access critical information on the disaster situation. Also, it can be used for interagency communication.