Regarding vegetation height calculation, using lidar yields exceptional accuracy but comes at a high cost. This technology generates a comprehensive 3D model of both the vegetation and surrounding objects within your assets.
With satellites, the challenge of determining height can be tackled in two approaches.
Firstly, by taking multiple stereo satellite images, we can generate digital elevation data. However, this method tends to be costly and generally provides accuracy comparable with the satellite imagery used, at best. For example, if you are utilizing 50-centimeter resolution satellite imagery, the best achievable accuracy for vegetation height measurement would likely be in the range of 0.5 to 1 meter.
Alternatively, what we offer is single-image height estimation. This involves algorithm-based techniques to assess the height of objects within and around a client’s asset. While it doesn’t provide precise figures like “this tree is 1,379 meters high,” it categorizes heights into ranges: from zero to two meters, two to four meters, four to six meters, and so forth.
This approach usually meets our clients’ vegetation height monitoring needs. Additionally, since this analysis stems from the same imagery used for vegetation encroachment analysis and third-party change detection, it essentially comes as a virtually cost-free add-on.
Indeed, they can.
Satellites provide the capability to assess various factors such as land cover, the height and condition of objects and vegetation, as well as the digital elevation of the site. Additionally, historical data can indicate whether the site is prone to flooding, high soil moisture, land motion, or landslides.
By considering these diverse aspects, one can determine the optimal route for the distribution lines. This can be used for activities like facilitating construction, selecting a more direct route, overcoming geographical challenges, or ensuring the long-term reliability of the upcoming infrastructure.
If your asset’s location accuracy is within approximately 10 meters, we can conduct our analysis with a margin of error of around two meters. However, providing an incorrect location can lead to either significant or minor consequences depending on the context. Your comprehension of the accuracy and reliability of your asset locations will guide the level of precision you seek.
In certain instances, during reviews of monitoring project outcomes, a client might assert, “Our gas pipeline is situated here,” only for us to discover that it’s located differently. Through satellite imagery, we can unmistakably observe disruptions, such as reduced crop yields on farmland, and determine that the pipeline is actually positioned 20 meters west of the client’s claim.
This highlights the crucial point that when engaging in geospatial analytics, in order to get accurate risk analysis, it’s imperative to communicate your asset locations accurately.
A monitoring service provider can assist in this regard too. For instance, to replace the daunting manual identification of each pylon, at Spottitt we’ve developed algorithms for automated pylon detection that aid our clients in the precise and swift identification of the pylons’ position.
One of the common uses of satellite imagery is to generate insights on the motion of land or assets, which can be achieved using either commercial or open-source data. The distinction is that commercial data demands fewer images for robust analysis and provides a higher density of data points due to the heightened image resolution.
However, achieving millimeter-per-year accuracy in analysis is feasible even with open-source imagery at a resolution of 5 by 15 meters. Open-source images are captured globally approximately every 12 days. By compiling around 20 images, you can establish a baseline, enabling the calculation of the movements in millimeters per year.
People often struggle to understand how a relatively coarse resolution image, such as 5 by 15 meters, can yield such precise measurements in millimeters per year for motion monitoring.
This precision can be explained by the active nature of Synthetic Aperture Radar (SAR) sensors. They send out radio waves and monitor the returning signals, assessing properties like backscatter, phase, and coherence. This data can be then employed to build up an accurate portrait of the movements of a specific land point, even quantifying it in millimeters per year, as these points typically don’t shift at high speed.
Once the baseline is established, historical data from previous years can be evaluated to determine average movement over periods like 2017, 2018, 2019. Furthermore, ongoing changes can be tracked by continuously adding new images to the stack every 12 days. Upon receiving the new image, you can calculate the relative motion of a data point in comparison to the previously captured image.
It’s crucial to note that this form of analysis is most effective in urban settings with man-made assets like roads and railways. In these contexts, SAR sensors yield substantial data point density.
An interesting development that we’ve been exploring with one of our clients is putting a SAR reflector, a metallic prism, on their extensive transmission network pylons. This enables significant refinement of the analysis by distinguishing the motion of the asset from that of the land or other nearby assets.
Advanced spaceborne technology enables the monitoring of methane and other greenhouse gases, which makes it a particularly valuable tool for overseeing gas networks, gas production, extraction and utilization facilities.
Open-source sensors in orbit can identify increases in atmospheric methane levels by 2% or more. A key commercial provider, GHGSat, using 3 to 4-meter resolution satellite imagery, is able to identify gas leaks ranging from around 60 to 100 kilograms per hour, depending on conditions. Upcoming satellite constellations promise even more heightened resolution and improved sensitivity.
Determining whether satellite technology is a fantasy or an appropriate monitoring tool largely rests with the European Commission. Their decisions on methane monitoring thresholds and regulations will determine whether gas network operators can adopt aerial or satellite-based solutions or are confined to low thresholds, necessitating direct inspection and literally walking and sniffing the lines. The most recent EU proposal suggested monitoring leaks as low as 17 grams per hour, which could constrain network operators to utilizing handheld devices near the pipelines.
The video version of the content can be viewed here.
The work done is part of the project co-financed by NCBR.
Spottitt CEO and FIRE EO Evangelist for Infrastructure
We’re looking for a Business Development Manager who’s ready to play a pivotal role in shaping our growth story.
You’ll be the driving force behind new client relationships, opening up industries that have yet to realise the potential of satellite data, and turning opportunity into long-term success.
Should you build a geospatial analytics team in-house or outsource to specialized providers while maintaining strong GIS systems and data management in-house?
As critical infrastructure evolves under the pressure of aging assets, regulatory demands, and environmental risks, geospatial analytics is no longer a “nice-to-have” – it’s a strategic necessity.
Throughout years, infrastructure monitoring has progressed from manual field inspections to advanced remote sensing using satellites, drones, LIDAR, and AI-powered analytics.
Each technology brings distinct advantages and limitations, making the choice complex.
This comparison sheet offers an overview of technologies to help identify the best-fit solutions for linear infrastructure owners and operators.
Welcome to your monthly roundup of the latest updates from the Spottitt Metrics Factory platform. As always, we’re working to make your asset risk analytics experience more insightful, flexible and faster.
Future Climate Data Models Now Integrated
Understanding how climate change affects infrastructure is no longer limited to the past or current. Spottitt MF now integrates future climate scenario models, based on global models developed by the Intergovernmental Panel on Climate Change (IPCC).
Welcome to Spottitt “Let’s Talk Satellite EO Data” eBook – your ultimate guide to understanding satellite-derived data types, their applications in asset monitoring, of the different types of data providers, and practical tips for navigating the challenges of using remote sensing data.
This resource is designed to help you make well-informed decisions about the use and integration of EO data, ultimately to ensure the safety and performance of your critical infrastructure.
At Spottitt, we’re constantly working to enhance your experience with our analytics platform, by making data visualization and reporting more intuitive, flexible, and insightful. This month, we’re introducing several updates which give you even more control and customization.
🌍 Your Free Climate Data Now Displayed as a Gradient Layer in a Map Format
Just like our other data layers, your free climate data can now be viewed on the map in a compact gradient color format. While generating a report on how each climate parameter impacts your assets is the best way to match this data to your assets and can be nice to view these data layers – helping you assess environmental exposure at a glance.
