scholarly journals ANALYSIS AND REMEDIATION OF THE 2013 LAC-MÉGANTIC TRAIN DERAILMENT

Author(s):  
Suzanne Brunke ◽  
Guy Aubé ◽  
Serge Legaré ◽  
Claude Auger

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.

2021 ◽  
Vol 13 (7) ◽  
pp. 1310
Author(s):  
Gabriele Bitelli ◽  
Emanuele Mandanici

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]


2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Abhishek V. Potnis ◽  
Surya S. Durbha ◽  
Rajat C. Shinde

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.


2021 ◽  
Author(s):  
Andreas Zuefle ◽  
Konrad Wessels ◽  
Dieter Pfoser

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Boheng Duan ◽  
Weimin Zhang ◽  
Haijin Dai

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing.


2021 ◽  
Author(s):  
Anna Iglseder ◽  
Markus Immitzer ◽  
Christoph Bauerhansl ◽  
Hannes Hoffert-Hösl ◽  
Klaus Kramer ◽  
...  

<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>


2020 ◽  
Author(s):  
Gary Watmough ◽  
Amy Campbell ◽  
Charlotte Marcinko ◽  
Cheryl Palm ◽  
Jens-Christian Svenning

<p>Planning for disaster responses and targeting interventions to mitigate future problems requires frequent, up-to-date data on social, economic and ecosystem conditions. Monitoring socioeconomic conditions using household survey data requires national census enumeration combined with annual sample surveys on consumption and socioeconomic trends, the cost of which is prohibitive. We examine the role that Earth Observation (EO) data could have in mapping poverty in rural areas by exploring two questions; (i) can household wealth be predicted from RS data? (ii) What role can EO data play in future geographic targeting of resources? Here, we demonstrate that satellite data can predict the poorest households in a landscape in Kenya with 62% accuracy. When using a multi-level approach, a 10% increase in accuracy was achieved compared to previously used single-level methods which do not consider how landscapes are utilised in as much detail. EO derived data on buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead and the length of growing season were important predictor variables. A multi-level approach to link RS and household data allows more accurate mapping of homestead characteristics, local land uses and agricultural productivity. High-resolution EO data could provide a limited but significant contribution to geographic targeting of resources, especially when sudden changes occur that require targeted responses. The increasing availability of high-resolution satellite data and volunteered geographic data means this method can be modified and upscaled to larger scales in the future.</p><p> </p>


Polar Record ◽  
2007 ◽  
Vol 43 (2) ◽  
pp. 165-167
Author(s):  
Daniel Clavet ◽  
Alexandre Beaulieu

Between 2003 and 2006, the Centre for Topographic Information in Sherbrooke (CTI), Québec, under the Canadian Space Agency (CSA) Government Related Initiatives Programme (GRIP), conducted a project (Cartonord project) aimed at new base mapping at a scale of 1:50,000 for unmapped areas of northern Canada using earth observation data.


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