Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional Vegetation Management operational practices have proven ineffective and are rapidly becoming obsolete due to the lack of frequent inspection of vegetation and environmental states. The rise of satellite imagery data and machine learning provides an opportunity to close the loop with continuous data-driven vegetation monitoring. This paper proposes an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm. The proposed satellite-based vegetation monitoring framework aims to reduce the cost and time of power line monitoring by partially replacing ground patrols and helicopter or drone inspection with satellite data analytics. It is implemented and demonstrated for a power distribution system operator (DSO) in the west of Norway. For further assessment, the satellite-based algorithm outcomes are compared with LiDAR survey data collected by helicopters. The results show the potential of the solution for reducing the monitoring costs for electric utilities.

Arctic landfast sea ice is widely utilized for transportation by local communities and industry, with trafficability largely governed by ice roughness. Here, we introduce an approach to evaluate ice roughness that can aid in routing of ice roads and assessment of spatial variability and long-term changes in trafficability. Drawing on synthetic aperture radar (SAR) polarimetry, SAR interferometry (InSAR), and other remote sensing techniques, we integrated approaches into the trafficability assessment that had rarely been applied over sea ice in the past. Analysis of aerial photogrammetry obtained through structure-from-motion helped verify cm-scale accuracy of X-band InSAR-derived ridge height and link L-band polarimetric classification to specific roughness regimes. Jointly, these approaches enable a km-scale evaluation of ridge topography and cm- to m-scale roughness—both critical for the assessment of trafficability. A trafficability index was derived from such SAR data in conjunction with analysis of ice trail routing and ice use near Utqiaġvik, Alaska. The index identifies areas of reduced trafficability, associated with pressure ridges or rubble ice, and served to delineate favorable trail routes for different modes of transportation, with potential uses ranging from ice road routing to emergency evacuation. Community outreach is needed to explore how this approach could assist different ice users in reducing risk, minimizing trail or ice construction efforts, and improving safety.

Arctic sea ice has undergone rapid changes during the last few decades, with negative implications for over-ice travel and on-ice operations, which benefit from services provided by the sea ice. A Parameter-based Trafficability Hierarchy (PATH) is presented here as a framework for developing quantitative assessment strategies that can guide planning and execution of operations on or near sea ice and quantify the impacts of recent changes on ice use. A PATH assessment has been completed for three case studies in Arctic Alaska. These cases, which correspond to a range of different icescapes and ice uses, identify and quantify different parameters linked to trafficability and safe operations. For ice road applications, PATH was used to determine an ice thickness compensation factor, a factor increasing the minimal thickness threshold for operations, to help translate sporadic auger ice thickness measurements along the Kotzebue–Kiana community ice road into an envelope for safe operations. A compensation factor as high as 1.5 was found to be necessary to ensure safety because of the high local thickness variability that is currently a concern for ice road operators. A PATH assessment of ice roughness for ice trail routing at Utqiaġvik draws on satellite remote sensing and is relevant for over-ice travel in general, including escape, evacuation, and rescue. We compared the routing of local snowmobile trails with Synthetic Aperture Radar (SAR) data products to identify specific ranges of ice conditions, roughness, and topography favored for ice trail construction. The same combination of data sources was used to identify potentially beneficial trail routes. Finally, an ice stability and safety assessment was completed for ice road construction and maintenance by industry near the Northstar Island oil production facility. We evaluated small-scale ice displacement data obtained from SAR interferometry to infer internal ice strain and stress and used these data in assessing the potential for fractures to reduce load-bearing capacity.