Methane is one of the most potent greenhouse gases affecting climate change. It will also likely become one of the biggest targets for regulation as countries aim to meet their climate change commitments.

During the OGC (Open Geospatial Consortium) Member Meeting, I joined a panel in a climate change special session to share my thoughts about how the open geospatial data standards for IoT (Internet of Things) data can help with one of the biggest environmental problems of our time.

Methane emissions are caused by agriculture, oil and gas, waste management, mining and more. Some of these emissions, especially fugitive methane emissions, can be detected, repaired and prevented. In fact, they will have to be because there’s no excuse not to fix the leaks.

How do you fix what you can’t see?

Methane is invisible. Even though we know methane leaks are bad, how can we fix them, if we can’t see them? We need methane sensors to find the locations and flow rates of the leaks.

However there’s not one sensor that is the best. Multiple types of sensors have to work together, to complement each other. They all have different temporal and spatial-temporal scales, at different levels of accuracy.

I’ll explain what I mean by that by giving some examples of different sensor types and how they can be used to detect methane leaks. Figure below shows a summary of the different types of methane sensors and their spatial-temporal scale. I found this National Academies report very informative if you are interested in this topic.

Handheld instruments

Right now the most popular sensor is a handheld infrared camera. They are very accurate but also very time-consuming. They are useful at the scene of a known leak to determine the exact source, but since they’re handheld, they can’t be used to detect leaks early.

Fixed, in-situ sensors

Fixed sensors (in-situ, or “in situation”) are becoming more popular. They cost between $2k and $10k each, however, they can’t tell you if there are actually leaks. They can tell you there are possible leaks in a certain direction, depending on the wind direction and wind speed.

Terrestrial Mobile Methane Mapping Systems

These are mounted to vehicles and the good thing about them is that trucks can move very fast and cover a big area. However, it doesn’t tell you exactly which component it is that leaks. This type of sensor is also highly dependent on the weather and wind direction.

Airborne systems

Now we are starting to see airborne systems that use technology like LiDAR (light detection and ranging) and various other sniffers. These are also great for covering a large area. However, these have a temporal shortcoming in that you cannot fly a plane or drone 24/7. At least not yet.


These cover a very large area and are also quite a good temporal solution, with possible daily visits. However, a satellite doesn’t tell you where the leaks are. It can tell you which facility or site has emissions but can’t tell you which compressor or battery specifically is leaking.

We’ve done it before

When we use standards for these types of data, we can connect the data we collect from all of these sensors and serve it to specific users effortlessly. Building an integrated sensor web is not new for us. For example, part of my PhD thesis was to build an integrated sensor web by combining satellites, in-situ soil moisture sensors and prediction models for crop yielding prediction (paper 1, paper 2). I am very excited to learn that methane emissions reduction is a perfect use case for an integrated sensor web. In the SensorThings working group, we’re working on an integrated methane sensor web, based on existing standards but defined specifically for the types of sensors I mentioned above.

An integrated methane sensor web gives you the ultimate flexibility and extensibility

One of the advantages of such an interoperable sensor web approach is its flexibility. Users do not need to worry about being “locked-in” by a certain sensor type, vendor, or cloud provider. They will be able to assemble their own methane sensor web by choosing from a list of cloud-enabled methane sensors based on open standards.

Would you like to know more about how to combine your methane sensors and data into a coherent integrated sensor web, and then using AI (artificial intelligence) to help detect specific events even before they happen?

Contact us for a demo of SensorUp and we will show you how it works.