Between the Poles

Identifying areas of high risk vegetation encroachment on power lines from satellite imagery

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation. Vegetation encroachment initiated the massive North American Northeastern outage of 2003. With the growing incidence of extreme events managing vegetation near power lines is becoming increasingly crucial in tackling potential fire hazards, securing infrastructure increasing resilience. The traditional approach to identifying areas of high-risk vegetation encroachment involves helicopters and ground crews and is time consuming and costly. Drones have been used to reduce the cost of data collection, but it is still a costly process. Now an innovative startup is using satellite imagery together with deep learning to identify high risk areas of vegetation encroachment. By applying machine learning algorithms to high resolution satellite imagery, it is possible to distinguish vegetation from other objects, differentiate trees from grass and bushes and identify trees at risk of encroachment. Comparing a drone-based approach with the satellite imagery methodology reveals that the latter approach is highly accurate in identifying high risk areas and significantly less costly than a drone-based process.

Background

Power line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilience. They are generally performed by manned helicopters or by a ground crew. Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a power line (fall ins) or trees that are encroaching on power lines (grow ins). These inspections are mandated by NERC in North America and are not optional. Beyond visual line of sight (BVLOS) drones have reduced the cost of collecting line inspection data. Now a new company is reducing the cost of identifying trees at risk by analyzing high resolution satellite imagery to identify high-risk areas, thus reducing the total length of power line that requires more detailed inspection with drones, helicopters, or ground crews.

Evolving machine learning models at Spacept

A Swedish company Spacept has developed a vegetation management solution that applies machine learning models to multi-band satellite imagery to automatically identify areas of high risk for vegetation encroachment on above-ground transmission and distribution lines anywhere on the planet. Spacept develops its own in-house training data for machine learning models which enables follow-up ground inspections of areas of high risk to develop pruning and other response programs. The training data is constantly enhanced with feedback from new projects for continuous improvement of the models.

Satellite-based vegetation encroachment risk assessment

The key business benefit of Spacept’s satellite imagery-based solution is automating the process for identifying the high-risk areas that require ground-based inspection, whether helicopter, drone-based, or ground crew, thus significantly lowering the cost of vegetation encroachment inspections. Spacept’s satellite-based vegetation encroachment technology makes it possible for power grid operators to monitor vegetation encroachment over their entire infrastructure in a cost-effective manner. Spacept uses the most recent, highest resolution (0.50 – 0.30 cm) satellite imagery commercially available which satisfies the requirements for vegetation encroachment detection. Spacept's approach makes it possible to monitor hundreds of thousands of kilometers of power lines every year anywhere in the world.

Workflow

The workflow of identifying and downloading satellite imagery, applying machine learning for feature identification, and the assignment of risk categories to individual trees is currently a semi-automated process. The longer-term objective is to reduce manual intervention and achieve an end-to-end automated workflow. The current workflow proceeds from a geographical layout of the power line to be inspected in the form of a standard GIS file. The most recent available high resolution satellite images satisfying certain criteria are identified and downloaded from data providers. The criteria typically include panchromatic and multi-spectral bands, resolution of at least 50 cm, vegetation capture with leaf on, and minimal cloud coverage. The downloaded images are fed to the current vegetation model which automatically extracts features and distinguishes trees from other man-made and natural objects. One of the important capabilities of the model is that it is very efficient in differentiating trees from bushes. After the automated processing, the imagery and labelling are reviewed by experienced assessors who review each image and make corrections to the labelling for problem areas, for example, for cases of visually distinguishable bushes or edges of trees. After completion of the labelling step, the distance between each tree and the power line is calculated and a risk category is assigned using risk categories provided by the client. The final deliverable is a GIS file containing the location and the corresponding risk categories assigned to the trees.

In-field case study

To assess the potential business benefits of satellite-based analysis Spacept partnered with infrastructure health management platform Sterblue to apply their satellite-based power line vegetation management solution for thousands of kilometres of a French power grid operator grid. Spacept applied their algorithms to selected satellite imagery to generate a risk file for the targeted segment of the grid. Sterblue assigned emergency (risk) levels based on the distance from the power line using a classification of emergency levels provided by the grid operator. Sterblue then carried out in detail inspections of at-risk areas with its drone inspection fleet. A ground inspection team was sent to verify the locations of the trees physically and collect ground truth. A total of 283 locations were identified. The vegetation at each location was manually validated and was compared with the satellite predictions.

The most important conclusion on the business benefit of the satellite imagery-based analysis is that it was able to identify all the trees that the subsequent ground inspection identified as at risk. In a real-world scenario, applying the satellite imagery analysis as a first step in a power line inspection would dramatically reduce the area and trees that would require ground-based inspection using helicopter or drone-based inspection. As a result of this process, Sterblue reduced its operating costs by 80%.

A more detailed analysis of the satellite data attempted to classify trees into detailed emergency levels U0 to U3. Comparing this analysis with Sterblue's classification was used to create a "confusion matrix" showing instances of agreement and disagreement between the two analyses. It was found that there was good agreement between the satellite and ground-based inspections and analyses.

Conclusion

Several companies have recognized that earth observation imagery is of limited value without the application of analytics to interpret the data. For example, Spacept is applying analytics to the huge amount of earth observation data to provide a valuable high risk vegetation encroachment detection service that is safer, faster, and less costly than ground-based approaches to transmission and distribution line vegetation monitoring.

Real time ground deformation monitoring from satellites to reduce risk of damage to utilities and pipelines

Several years ago I blogged previously about an application that used Sentinel-1 radar interferometry to monitor ground displacements with millimetre precision to look for subsidence which might be evidence of a leak in a municipal water system. Now access to data and processing algorithms are available to developers to create workflows to monitor ground deformation to reduce the risk of damage to underground Infrastructure including utilities, oil, gas and water transmission pipelines and associated industrial plants.

Ground movement represents a significant risk to linear structures such as utility, pipeline, and transportation networks as well as to large scale industrial and commercial structures.  Traditionally ground deformation is monitored by fixed high accuracy GNSS stations located at intervals and by periodic surveys along alignments.  Both of these are expensive and are limited by geospatial and temporal gaps. Satellites such as Sentinel-1 that use radar to image the earth are capable of radar interferometry (INSAR) which allows ground deformations on the order of cm and even mm to be detected.  The revisit times for these satellites are on the order of days and the radar antennas on these satellites allow very large areas to be monitored for deformations.  For example, Sentinel-1 is capable of detecting deformations of a few mm over an area of 42,500 km² with a 12 day revisit time. Furthermore INSAR data is available stretching back to the mid 1990's making it possible to monitor historical ground movement over quite long time periods.

UP42 provides the infrastructure of an enterprise software development and deployment platform enabling developers to create scale-able workflows using satellite imagery and customized algorithms. In a recent UP42 webinar (53 minutes in), Shawn Melamed and Sarah Thompson of CATALYST (PCI Geomatics) showed how innovative INSAR technology has made it possible to cost effectively continuously monitor ground deformation along thousands of km of  a linear structure such as a utility, pipeline, or transportation network.  The data can be sourced from any of the satellites capable of INSAR including Sentinel-1 (free) and TerraSAR-X (commercial).  An example is provided in the talk of the development of a UP42 workflow for detecting uneven land subsidence in California resulting from agricultural withdrawals of water from an aquifer that potentially could damage a power plant located in the area.  The code to perform this analysis is available as the Ground Displacement Block on the UP42 marketplace.

 

 

Canadian Underground Forum: Today is Day 1

We are starting at 10 AM EDT. You can see the program for April 29 and register here.

Today's speaker lineup

One of the early innovators in applying LiDAR to mapping underground pipelines, Joseph Hlady, President at Lux Modus in Calgary, Alberta will explain n his keynote the benefits that his technology has brought to the oil and gas pipeline industry. 

A number of jurisdictions around the world are developing digital twins at the urban, regional and even national level. In a lunch keynote Alex Shalash of UEVO will discuss creating a city subsurface infrastructure map which unifies fragmented information and produces an intelligent digital twin.

Our chief guest speaker is Jim Anspach, Chair of the ASCE 38 standards group, who will bring us up to date on two imminent ASCE standards.

Rob Martindale, Program Manager - Utilities/Railroads at the Colorado Department of Transportation, will present an overview on how a mix of legislation and regulation has enabled Colorado to revolutionize the capture and sharing of accurate underground utility location data. Page Tucker, President & CEO at ProStar which implemented the mobile and cloud solution adopted by the Colorado DoT will speak about developing a digital twin of the underground.

One of the remarkably innovative technologies applied to mapping underground infrastructure was developed by 4M-Analytics. Harel Dan, Head of GIS and Remote Sensing, and Ophir Wainer, Director of North America Business Development, will explain how 4M-Analytics uses satellite and aerial imagery to map underground infrastructure.

Government has a crucial role to play in locating and mapping underground infrastructure. Capping the first day of CUF Prashant Shukle, who was responsible for mapping and earth observation in the Canadian government for many years, will moderate a panel discussion on the role of government in underground infrastructure with panelists from the State of Colorado DoT, the U.K. Government's Geospatial Commission, Public Services and Procurement Canada, and Infrastructure Ontario.

Canadian Underground Forum begins tomorrow

You can see the program for April 29 and 30 and register here.

Speaker lineup

One of the early innovators in applying LiDAR to mapping underground pipelines, Joseph Hlady, President at Lux Modus in Calgary, Alberta will explain n his keynote the benefits that his technology has brought to the oil and gas pipeline industry. Another early innovator is Otto Ballintijn, CEO of Reduct who has developed inertial or gyro technology for mapping underground pipe networks. In his keynote he will explain the evolution of the technology including the concept, operational method, and data output of gyro mapping technology. A number of jurisdictions around the world are developing digital twins at the urban, regional and even national level. Alex Shalash of UEVO will discuss creating a city subsurface infrastructure map which unifies fragmented information and produces an intelligent digital twin. Danny Petrecca of Locusview, an early leader in capturing and managing underground data in the gas industry, will review why as-builting still remains paper-based and discuss a digital construction solution that stitches together existing technologies to create the reliable digital twin that utilities need in the age of the smart grid.

Our chief guest speaker is Jim Anspach, Chair of the ASCE 38 standards group, who will bring us up to date on the imminent 38-21 utility as-built standard. Carsten Roensdorf and Neil Brammall will introduce the Open Geospatial Consortium's work in progress, the MUDDI standard for exchanging location data about underground infrastructure. Lawrence Arcand, who has led Subsurface Utility Engineering (SUE) investigations on a national level since 2003, will provide an overview of the Canadian standard CSA S250:20 Mapping of underground utility infrastructure.

Rob Martindale, Program Manager - Utilities/Railroads at the Colorado Department of Transportation, will present an overview on how a mix of legislation and regulation has enabled Colorado to revolutionize the capture and sharing of accurate underground utility location data. Page Tucker, President & CEO at ProStar which implemented the mobile and cloud solution adopted by the Colorado DoT will speak about how the partnership between Colorado DoT and Prostar made this successful. One of the remarkable new technologies applied to mapping underground infrastructure was developed by 4M-Analytics. Harel Dan, Head of GIS and Remote Sensing, and Ophir Wainer, Director of North America Business Development, will explain how 4M-Analytics uses satellite and aerial imagery to map underground infrastructure. Leica Geosystems' IDS Georadar was one of the early innovators in ground penetrating radar. Simon Pedley, Detection Ambassador at Leica Geosystems will talk about some of the innovations in hardware and software that Leica Geosystems has developed in the last couple of years.

Government has a crucial role to play in locating and mapping underground infrastructure. Gord Reynolds and Alon Gat will discuss how Infrastructure Ontario partnered with Metrolinx and the Ministry of Transportation to establish the Utility Coordination Program (UCP), a new initiative which oversees the development of utility relocation standards and the supporting process to improve the location, treatment and relocation of utility infrastructure. Gordon McElravy, of buildingSMART Canada, will discuss buildingSMART's efforts in implementing the Municipal Infrastructure Council Program to encourage building information model (BIM) initiatives at the municipal level. Capping the first day of CUF Prashant Shukle, who was responsible for mapping and earth observation in the Canadian government for many years, will moderate a panel discussion on the role of government in underground infrastructure with panelists from the State of Colorado, the U.K. Government's Geospatial Commission, Public Services and Procurement Canada, and Infrastructure Ontario.

A 3D base map for the globe

At the GeoIgnite virtual event Ryan Hamilton, Senior Manager 3D Products, at Maxar described a remarkable project Globe in 3D to create a 3D model for the earth's entire landmass using the petabytes of 50 cm imagery that Digital Globe (and now Maxar) has collected and is collecting from its constellation of satellites.  Multiview stereoscopy technology enables Maxar to create a 3D model for every point (pixel) on earth by combining multiple views of the same point taken at different times and with different camera locations.  The result is a textured 3D model that has better than 3 meter absolute positional accuracy, avoids the blind spots that result from a single camera view, and averages out errors that are often found in individual images.

In 2015 Digital Globe and Saab formed a partnership which created Vricon to integrate 3D technology with Digital Globe's imagery to enable the creation of an accurately georeferenced 3D model of the earth's landmass without using control points. (Maxar completed the acquisition of Vricon last year.) The process is compute intensive and is still underway. The result will be a 3D TIN model with textures on all sides for the entire landmass of the planet. The Globe in 3D is available in Vricon format, Esri i3S or Cesium 3D Tiles.

A number of derivative products are also available for specific use cases including digital terrain and digital surface models, textured buildings, classification layers identifying different land uses and vegetation, point clouds, 2D ortho images, 3D vector representations of buildings, vegetation and transportation infrastructure with coverage in dense urban, suburban and rural areas. A limitation resulting from the algorithm that combines images captured at different times is that it is not possible to assign a specific date to  the 3D model at any location. But Maxar does provide tools to enable change detection over longer periods of time.

The 3D model can be used for many use cases including estimating cut and fill for a road, railway or other linear structure, estimating tree height for vegetation management, planning 5G telecom networks and others.  But perhaps it's biggest achievement could be to provide a shared 3D base map for the entire planet. To assist in realizing this vision Maxar provides precision 3D registration (P3DR) tools to allow other data to be corrected (conflated) to the Maxar base map. P3DR is a stand-alone software solution that automatically georegisters imagery from any source to Maxar's Globe in 3D base map.

Making Landsat data free and open generated billions of dollars of economic benefits

Open data has been gathering momentum among governments. First enunciated in the United States, the principle that citizens should not be required to pay twice for government data, free and open access to government geospatial data has been adopted by many governments including US Federal, Canada, Japan, South Africa, Brazil, California state and counties, and by the City of Vancouver and other cities.  In 2013 G8 leaders (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, United States, and the European Union) signed a charter on open data.  The Ordnance Survey in the UK began releasing some of its data with open licensing as part of its OpenData initiative in 2010, but it wasn't until 2018 that the OS Mastermap was made openly available for the public and businesses to use. But being able to access prepared maps and other derived material is just a first step, governments need to provide access to raw geospatial data in commonly used Web-friendly formats. In the U.S. Data.gov was launched in late 2009.  In 2013, President Obama issued an Executive Order making open and machine readable the new default for federal government information in the United States.

A key transition occurred in 2008 when the U.S. Geological Survey announced a policy of open data. Prior to this Landsat images were made available on a cost recovery basis, costing hundreds to thousands of dollars per scene. But in January 2008 Barbara Ryan, the Associate Director for Geography at the U.S. Geological Survey, and Michael Freilich, NASA’s Director of the Earth Science Division, signed off on a Landsat Data Distribution Policy that made Landsat images free to the public. The USGS announced the free-and-open data policy on April 21, 2008. In a recent talk at the Geospatial Knowledge Infrastructure Summit Barbara Ryan described the impact of that decision. Landsat downloads jumped from 53 scenes per day to 5,700 scenes per day. The economic benefit derived from the Landsat data was estimated at US $1.7 billion domestically, $400 million internationally for a global total of $2.1 billion In 2011.

Satellite able to measure methane emissions from individual facilities launched

Last night, September 2 at 11:51 p.m. EDT at the Arianespace satellite launch base in Kourou, French Guiana (UTC-3) the VV16 mission launched 53 satellites, ranging in weight from 145 kg to 7 kg.  Among them was GHGSAT-C1 or "Iris", a 16 kg Canadian satellite that detects methane emissions from specific facilities at high resolution.

Background

Source Nature 14 July 2020

Annual emissions of methane (CH4) have risen by almost 10% in the past two decades primarily resulting from emissions from the oil and gas industry and livestock. A recent Environmental Defense Fund (EDF) study concluded that methane emissions from the U.S. oil and natural gas supply chain in 2015 amounted to about 2.3% of gross U.S. gas production and are about 60% higher than EPA estimates.

A 2016 study found that just a few natural gas wells account for more than half of the total volume of leaked methane gas in the United States. An extensive aerial infrared camera survey of 8000 production sites in seven U.S. oil and gas basins found that 4% of surveyed sites had one or more observable methane high-rate emission plumes. Furthermore the large variability in methane emissions for similar equipment and facilities in different geographies supports the hypothesis that equipment malfunction is a major source of emissions. To identify the sources of methane emissions oil and gas companies are increasingly attempting to detect and monitor emissions from their facilities including oil and gas wells, tank farms, and compressor stations.

An additional important business driver is the global carbon market which is currently estimated to be about $50B and growing. This provides a financial motivation for industrial emitters to measure and manage their emissions.

Until recently methane emissions have been measured on the ground in a a slow, laborious process using a hand-held scanner. Government-funded aircraft overflights or satellites have captured emissions over broad areas but with insufficient resolution to identify individual facilities as sources.

Satellite-based methane emissions measurement

In 2016 a privately-funded startup GHGSat launched a proof-of-concept cubesat named "Claire" weighing 15 kg to measure methane emissions from individual oil and gas wells, refineries, compressor stations, landfills, animal feedlots, rice paddies, and natural sources. The objective was to provide a satellite-based methane measuring device that would enable private industry to estimate emissions from its own facilities anywhere in the world. About the size of a microwave oven Claire orbits the Earth every 90 minutes. Its field of view is 12 km x 12 km and it has a spatial resolution of about 50 metres. Claire is able to measure emissions from over 1,000 sites per year.  The GHGSat-C1 or "Iris" satellite that was just launched has higher resolution (25 metres) and will be able to measure emissions much more rapidly with a 10x performance improvement compared to Claire.

Nanosatellites and drones represent a paradigm shift in measurement of methane emissions

Annual emissions of methane (CH4) have risen by almost 10% in the past two decades. The primary sources of methane are the oil and gas industry and agriculture, in large part from livestock.  Methane emissions are either measured on the ground, a slow, laborious process using a hand-held scanner or over a broad area using government-funded aircraft overflights or satellites.   But this is changing. In 2016 a privately-funded startup GHGSat launched a cubesat weighing 15 kg with 50 metre resolution to measure methane emissions from individual oil and gas wells, refineries, compressor stations, landfills, animal feedlots, rice paddies, and natural sources.

Background

Source Nature 14 July 2020

Annual emissions of methane have risen by almost 10% in the past two decades. Atmospheric concentrations of methane reached1,875 parts per billion in 2019 which is more than 2.5 times above pre-industrial levels.  The primary anthropogenic sources of methane are the oil and gas industry and agriculture, especially livestock production (enteric fermentation).

A study used spatial analysis to investigate the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Based on the results of this analysis the authors conclude that the US Environmental Protection Agency (EPA) underestimates methane emissions nationally by 50%.  The study found that methane emissions due to the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.

A study by Robert W. Howarth, Renee Santoro, and Anthony Ingraffea concluded that the emissions of methane from shale gas wells are between 30% and 100% more than methane emissions from conventional natural gas wells.  The study estimated that between 3.6% to 7.9% of the methane from shale-gas production escapes to the atmosphere in venting and leaks over the lifetime of a well.  The study suggested that the leakages are sufficiently large to erase the advantage of natural gas over coal in producing energy with lower emissions.

Another source of methane in the atmosphere are leaks from natural gas distribution systems.  A partnership between the Environmental Defense Fund (EDF) and Google Earth released interactive maps showing methane leaks from gas distribution systems under the streets of Boston, Indianapolis and part of New York City.

In 2016 the Obama administration issues new rules regulating methane emissions from the oil and gas industry and requiring the monitoring and reporting of emissions.

The U.S. Environmental Protection Agency (EPA) has estimated U.S. emissions taking a bottom-up approach in which emissions from individual facilities are aggregated.  Other sources have used top-down measurements from aircraft overflights and satellites funded by government agencies.  A recent Environmental Defense Fund (EDF) study concluded that methane emissions from the U.S. oil and natural gas supply chain in 2015 amounted to about 2.3% of gross U.S. gas production and are about 60% higher than EPA estimates.

Measuring methane emissions

Measurements of oil and gas CH4 emissions are either captured by top-down (TD) or bottom-up (BU) collection methods. Top-down measurement of methane use aircraft and satellites and estimate emissions from many sources across large geographies. These are generally government funded.  Bottom-up studies measure emissions from individual pieces of equipment, operations, or facilities on site or directly downwind and can be industry funded, government funded or funded by a not-for-profit such as the EDF. Currently the way to scan an oil or gas field for methane emissions is visiting each facility and pointing a handheld device at the facility and looking for a telltale wisp indicating a methane leak. For an oil and gas field this can take weeks.  New technologies including nanosats and drones are radically changing how methane emissions are measured.

GHGSat

In 2016 a venture capital funded startup GHGSat based in Montreal launched a demonstration cubesat weighing 15 kg and named Clair.  The objective was to provide satellite-based methane measuring device that would enable private industry to estimate emissions from its own facilities anywhere in the world. An important business driver is the global carbon market which is currently estimated to be about $50B and growing. This provides a financial motivation for industrial emitters to measure and manage their emissions.

Claire orbits the Earth every 90 minutes. Its field of view is roughly 12 km x 12 km and it has spatial resolution of less than 50 metres. Claire is able to measure emissions from over 1,000 sites per year. 

The next two satellites (GHGSat-C1, or "Iris" and GHGSat-C2, or "Hugo") are scheduled for launch in 2020.  Iris, scheduled to launch in the middle of August, will have higher resolution (25 metres) than Claire and will be able to measure emissions much more rapidly.

Drones

In a recent Microdrones webinar, Bret Burghdurf described how drones are being used to measure methane emissions from 30 to 80 feet up in the air.  Capturing methane emissions using a methane detector carried by a drone is generally reliable, the only areas where it may not be are over standing water and deep snow.  The drone carries a camera in addition to the methane sensor allowing the facilities being monitored to be captured visually.  The device is capable of capturing 10 readings per second.  Typically over a facility such as a chemical plant, tank farm, compressor station or landfill a grid pattern is flown. Over a linear asset such as a pipeline the flight plan follows the pipeline.  The market for this type of measuring is typically the oil and gas industry itself.

Sentinel 5p TROPOMI

The European Space Agency (ESA) launched the Copernicus Sentinel 5 Precursor (5p) with the TROPOMI sensor onboard in 2017.  It can capture regional emissions of methane, nitrogen dioxide, sulfur dioxide and other gases and aerosols for the entire earth every day. Sentinel 5p weighs about 900 kg and captures a swath width of 2,600 km with a pixel size of 7 km x 7 km.

MethaneSAT

Intended to launch in 2023 MethaneSAT will cover a 200-kilometer view path with a revisit time of every few days. The satellite weighs 350 kg and has a resolution of 100 m x 400 m across a 260 km swath. MethaneSAT is being developed by a subsidiary of the non-profit Environmental Defense Fund (EDF).

Conclusion

Methane is a greenhouse gas whose concentration in the atmosphere is rapidly increasing.  The source of most of this increase is anthropogenic and being able to efficiently measure and monitor methane emissions is going to be critical in controlling and reducing emissions.  New technologies from privately funded companies and others including nanosat- and drone-based methane sensors are providing more efficient and higher resolution devices for measuring methane emissions from many different sources in the fossil fuel industry and agriculture.

New satellite constellation will capture 30 cm imagery with up to 15 revisits per day

I remember back in August 2014, when the first images from WorldView-3 were being downloaded. WorldView-3 was the first one-foot-resolution commercial satellite. It operated at an altitude of 617 km, flew in a sun synchronous orbit and could collect up to 680,000 km² per day.  It had 31 cm panchromatic resolution, could capture 680,000 km² per day, and had an average revisit time of less than a day.  In 2021 MAXAR is planning to launch the WorldView Legion constellation, six satellites with 30 cm resolution, better than 5 meter absolute accuracy, able to capture 5 million km² every day, and with up to 15 revisits per day.

At GeoIgnite Online 2020 Kumar Navulur, Chris Formeller, and Kathryn Seitz presented an overview of the specifications and capabilities of the Legion constellation. The total land mass of the Earth is 148.9 million km2.  The new constellation will be able to capture 30 cm imagery of over 3 % of that every day.  Over much of the lower 48 states in the U.S. it will be able to revisit the same location 15 times per day. Over Canada it will be closer to 8 revisits per day.  Some of the satellites will fly sun synchronous orbits and others mid-inclination orbits.  What this means practically is that it will be possible to capture imagery at different local times of the day, unlike with sun synchronous orbits where imagery is captured at the same local time every day. For many customers imagery will be available within 24 hours of capture.

Using satellite imagery to prioritize vegetation management for utilities

A recent startup LiveEO is focusing on the digitalization of energy and transportation network assets using satellite imagery. LiveEO uses current multi-band imagery including radar and hyperspectral data at various resolutions from earth observation satellites to provide services to railway, electric power and pipeline operators.  The services include vegetation, third party encroachment, and ground distortion monitoring. I recently had the opportunity to chat with Sven Przywarra, co-founder of LiveEO.

Transmission line inspections are essential in ensuring grid reliability and resilience.  They are generally performed by manned helicopters often together with a ground crew.  There are serious safety issues when inspections are conducted by helicopter. Data may be collected with cameras and analyzed to detect a variety of conditions including corrosion, evidence of flash over, cracks in cross arms, and right-of-way issues such as vegetation encroachment. in North America annual inspections are mandated by NERC  and are not optional. With over 200,000 miles of high-voltage transmission lines and 5.5 million miles of distribution lines in the United States, improving the efficiency and reducing the risk of inspections would have a major impact on the reliability of the power grid. 

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation. Vegetation encroachment initiated the massive North American northeastern outage of 2003. It is so critical for utility operations that some utilities have a vice president responsible for vegetation management. It can be costly for utilities to identify areas of high risk vegetation encroachment. Previously I have blogged about an electric power utility in Brazil that used Airbus Pléiades satellite imagery combined with GIS data to identify high risk areas for vegetation encroachment in a power grid.

A recent startup LiveEO is focusing on the digitalization of energy and transportation network assets using satellite imagery. LiveEO analyzes current multi-band imagery including radar and hyperspectral data for vegetation, third party encroachment, and ground distortion monitoring. LiveEO uses multi-band imagery at various resolutions to distinguish vegetation and network assets, stereo imagery to estimate tree heights, difference detection to identify changes, and interferometry to detect subsidence and other ground distortions at the cm level.  LiveEO is developing its own inhouse training data for machine learning algorithms. 

For electric power utilities LiveEO applies machine learning multi-band satellite imagery from multiple sources to automatically identify areas of high risk for vegetation encroachment and fallover anywhere on the planet. The results of these analyses are available to clients over the web on handheld and other devices.  This enables follow-up ground inspections of areas of high criticality to develop pruning and other response programs. LiveEO has applied this approach to transmission as well as distribution circuits.  To demonstrate the wide ranging capability of their approach LiveEO is offering a demonstration of the analysis of 50 km of line anywhere in the world.

A number of companies, realizing that earth observation imagery in itself is of limited value, are not in the business of selling imagery, but the results of analytics applied to the imagery.  I have blogged about a new company GHGSat who use their own nanosatellite to estimate emissions from industrial plants, leaks from flow and gathering infrastructure in natural gas fields, and even livestock.  LiveEO is another example of a company who apply analytics to the huge amount of earth observation data to provide a valuable service that is safer and less expensive than the traditional approach to transmission and distribution line vegetation monitoring.