Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 17-23, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
22 Jun 2016
Suzanne Brunke1, Guy Aubé2, Serge Legaré3, and Claude Auger4 1MDA, 13800 Commerce Parkway, Richmond, BC, Canada V6V 2J3
2Canadian Space Agency, 6767 route de l'Aéroport, Saint-Hubert, Québec, Canada J3Y 8Y9
3Quebec Ministry of Public Security, 2525 Boulevard Laurier, 6e étage B, Quebec G1V 2L2
4Public Security Canada, 800 Rue Du Square Victoria, Montréal, Quebec H4Z 1B7
Keywords: Disaster, Train Derailment, Remediation, NDVI, Optical, SAR, Change Detection Abstract. On July 6, 2013 a train owned by Montréal, Maine & Atlantic Railway (MMA) Company derailed in Lac-Mégantic, Quebec, Canada triggering the explosion of the tankers carrying crude oil. Several buildings in the downtown core were destroyed. The Sûreté du Québec confirmed the death of 47 people in the disaster. Through the Canadian Space Agency (CSA) Rapid Information Products and Services (RIPS) program, MDA developed value-added products that allowed stakeholders and all levels of government (municipal, provincial and federal) to get an accurate picture of the disaster. The goal of this RIPS Project was to identify the contribution that remote sensing technology can provide to disasters such as the train derailment and explosion at Lac-Mégantic through response and remediation monitoring. Through monitoring and analysis, the Lac-Mégantic train derailment response and remediation demonstrated how Earth observation data can be used for situational awareness in a disaster and in documenting the remediation process. Both high resolution optical and RADARSAT-2 SAR image products were acquired and analyzed over the disaster remediation period as each had a role in monitoring. High resolution optical imagery provided a very clear picture of the current state of remediation efforts, however it can be difficult to acquire due to cloud cover and weather conditions. The RADARSAT-2 SAR images can be acquired in all weather conditions at any time of day making it ideal for mission critical information gathering. MDA’s automated change detection processing enabled rapid delivery of advanced information products.
Conference paper (PDF, 2527 KB)

Citation: Brunke, S., Aubé, G., Legaré, S., and Auger, C.: ANALYSIS AND REMEDIATION OF THE 2013 LAC-MÉGANTIC TRAIN DERAILMENT, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 17-23,, 2016.

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