Thaw subsidence can damage the infrastructure including buildings, roads and airfields founded on ice- rich permafrost, increase their maintenance costs, change the landscape and influence the sustainable development in the northern region. Information about the ground movements is important for making decisions on various geotechnical approaches to reduce impacts of permafrost degradation. However, field measurements of ground movements and long term monitoring using traditional field survey may be logistically expensive in vast and remote Northern Canada and Alaska, USA. The ability to measure surface displacements, identify the areas being impacted, and provide information of seasonal timing using remote sensing techniques would improve the knowledge and expertise of those involved in infrastructure engineering and management where permafrost is degrading. Traditional Interferometric Synthetic Aperture Radar (InSAR) measurements of deformation do not consider the effects of seasonal freeze-thaw, thus may not effectively reveal the long term trend of ground movements in permafrost region. In this paper we propose to quantitatively evaluate the seasonal ground movements resulted from on-going seasonal freezing and thawing, and estimate long term deformation of linear infrastructure in permafrost area using InSAR technique. The proposed approach has been tested on Alaska Highway built on permafrost at Beaver Creek, Yukon, Canada using Radarsat 2 data acquired during 2013-2015. Results indicate that there was long term deformation at a rate of five cm/year, in addition to an average of magnitude of vertical movement of 4 cm between winter heaving and summer thawing during annual climate cycles.
Well sites, including both well pads and exploratory core holes, are small polygonal landscape disturbance features approximately one half to one hectare (0.5-1 ha) in area, resulting from oil and gas exploration activities. Automatic extraction and monitoring of such small features using remote-sensing technology at regional scales has always been desirable for wildlife habitat monitoring and environmental planning and modelling. Due to the vast disturbances of well sites in a province like Alberta, Canada, high-resolution imagery is not practical for well site extraction. For operational purposes, mid-resolution and cost-effective satellite imagery such as Landsat is the choice. However, automatic well site extraction using midresolution satellite imagery is a challenging task. Wells are typically less than three pixels in width and length in a Landsat multispectral image. Furthermore, the spectral contrast between the well site pixels and the surrounding areas is low due to vegetation regrowth and the spectral complexity of the surrounding environment. This article presents a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines both pixel-and objectbased image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. The method was applied to Landsat-5 TM images acquired over Fort McMurray, Alberta, Canada. For accuracy assessment, four regions of interest were selected and the results of the proposed automatic method were evaluated against visual inspection of the Landsat-8 pan-sharpened image. The method results in a total average correctness, completeness, and quality measures of about 80, 96, and 77%, respectively over the four sites. In addition, the method is very fast as an entire Landsat scene is processed in less than 10 minutes. The method is an operational approach for automatic detection of well sites over the entire province and can dramatically reduce the labour cost of manual digitization for monitoring and updating well site maps.
With Radarsat-1 presently in operation and Radarsat-2 approved, Canada is starting to develop synthetic aperture radar (SAR) applications that require imagery on an operational schedule.Sea ice surveillance is now a proven near-real-time application, and new marine and coastal roles for SAR imagery are emerging. Although some image quality and calibration issues remain to be addressed, ship detection and coastal wind field retrieval are now in demonstration phases, with significant participation from the Canadian private sector.
Mapping linear disturbances, including pipelines, roads, and seismic lines created by resource exploration, traditionally relies on very high-resolution remote sensing data, which usually limits results to small operational areas. With increased availability of low-cost medium-resolution satellite data, complete information of linear disturbances may be monitored and reconstructed from processing time series images from more than 30 years archival data. In this study, we propose a novel approach to incorporate spectral, spatial, and temporal information for mapping and characterizing linear disturbances based on time series Landsat imagery. The mapping process involves 4 steps: line detection based on a multiscale directional template, line updating based on reappearance frequency, line connection using the Hough transform, and linear disturbance characterization. The proposed method was tested and evaluated over 4 sites in Alberta, Canada, with various linear densities for detecting and reconstructing linear disturbances from 1984-2013 using time series Landsat imagery. The results obtained by processing time series Landsat imagery have shown improved accuracy in detecting linear disturbances over that from single or multiple Landsat images. It is concluded that the strategy of integrating information from time series imagery has the potential to lead to improved operational mapping of linear disturbances. Résumé. La cartographie des perturbations linéaires, y compris les pipelines, les routes et les lignes sismiques créées par l'exploration des ressources, repose traditionnellement sur des données de télédétection de très haute résolution, qui limitent habituellement les résultatsà de petites zones opérationnelles. Avec la disponibilité accrue de données satellitairesà moyenne résolutionà faible coût, des informations complètes sur les perturbations linéaires peuventêtre suivies et reconstruitesà partir du traitement des images de séries temporelles de plus de 30 ans de données d'archives. Dans cet article, nous avons proposé une approche novatrice pour intégrer des informations spectrales, spatiales et temporelles pour la cartographie et la caractérisation des perturbations linéaires basée sur des séries temporelles d'imagesLandsat. Le processus de cartographie a comporté 4étapes : la détection de la ligne basée sur un modèle directionnel multi-échelle, la miseà jour de ligne basée sur la fréquence de réapparition, la connexion de ligneà l'aide de transformées de Hough et de la caractérisation de perturbations linéaires. La méthode proposée á eté testée etévaluée sur quatre sites en Alberta, Canada, avec différentes densités linéaires pour la détection et la reconstruction de perturbations linéaire de 1984à 2013 en utilisant des séries temporelles d'images Landsat. Les résultats ont montré une meilleure précision dans la détection de perturbations linéaires en traitant des séries temporelles d'images Landsat par rapportà celle obtenueà partir des images Landsat uniques ou multiples. On conclut que la stratégie...
A remote sensing technique, based on processing satellite altimeter data, for iceberg detection was validated and implemented for operational iceberg monitoring. Algorithms for altimeter data preprocessing and analysis were developed to efficiently detect icebergs and eliminate false detections caused by signal noise and the presence of small islands. Results of iceberg detection for application in ship navigation are demonstrated.
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