Remote Sensing Methane Emissions in the Bowen Region

We have previously discussed technologies that can be used for the remote sensing of methane emissions. In this article, we are going to focus on the Bowen region of Queensland. We will review some of our ongoing R&D activities, and highlight promising areas for future development.

1. Detection and Quantification of Single Methane Plumes in TROPOMI

Large methane emissions from point sources, such as coal mines, may be clearly visible in daily TROPOMI products (if not occluded by cloud). In this case, they can be identified by inspection, and quantified using a method such as the cross-sectional flux (CSF), as reported by Sadavarte et al. [1].

In the CSF method, we draw transects across a methane plume, along the major axis of the wind vector. Flux can be calculated, based on the assumption of a 2D plume, using a modified formula for volumetric flow rate. i.e. flow velocity times cross-sectional area. An example CSF calculation is shown below for an emission from a Bowen coal on 6 October 2019.

Figure 1. Example cross-sectional flux calculation for a methane emission from the mine, in the Bowen region, on 6 October 2019. The pale blue box indicates the upwind area used to calculate background CH4. The pink line indicates the major axis of the wind vector. The small aqua box indicates the suggested point of origin. The TROPOMI CH4 data is shown in the bottom image, with a plume clearly visible.

The following figure shows some examples of quantified methane emissions, from super-emitting coal mines in the Bowen region. We have repeated, and independently verified, the flux values published by Sadavarte et al. for the methane emissions shown below, over the Bowen region.

Figure 2. Methane plumes visible in daily TROPOMI CH4 products. ERA5 wind vectors also shown. Bottom right: table of corresponding flux values by Sadavarte et al. (independently repeated / verified by GeoSynergy) [1].

In TROPOMI products, methane plumes often have characteristic shapes. Specifically, we are looking for a high methane level above the point source, with a plume that extends away from the source, along the direction of prevailing wind (at the time of sensing), and then declines in strength along its axis. One of the limiting factors, with regard to plume detection, is the need for visual inspection of TROPOMI products. However, the detection of TROPOMI plumes is a task well-suited to AI. This technology opens up the possibility of both persistent monitoring of single sites, as well as ongoing global monitoring for new emissions.

Since our last article on TROPOMI, Schuit et al. [2] have published their AI for this purpose. They used a two-stage approach: a Convolutional Neural Network (CNN) was used to detect plume-like structures in TROPOMI CH4 data; a Support Vector Classifier (SVC) was then used to screen for artefacts amongst the potential plumes. The SVC incorporated additional information, such as wind vectors. Quantification was also be automated, using the Integrated Mass Enhancement (IME) method, whereby the emission rate (Q) is calculated from the observed methane enhancement in the plume, and the local wind [3]. An example detection is shown below:

Figure 3: Left: TROPOMI CH4. Middle: Class Activation Map which highlights the areas identified by the CNN as “plume-like structure”. Right: resultant plume mask, where “x” indicates the suggested origin [2].

AI screening of TROPOMI CH4 has considerable scope for further innovation, and this is an active area of interest for us. Some areas of active development include the use of richer imaging, and hybrid, datasets; and the incorporation of supplementary data at the CNN stage. We suggest that this is preferred over the use of a two-stage model, because it enables the CNN to develop more nuanced feature maps. In addition to wind vectors, supplementary data might include a “prior” based on site-specific emission characteristics, something that was excluded from the cited study.

2. Detection of Methane Emissions using Wind-rotated TROPOMI

Some of the challenges of using TROPOMI CH4 data are its relatively low spatial resolution (7x5.5 km), and its reliance on cloud free observations. TROPOMI data is often sparse. It is not uncommon for known plumes to be extremely difficult to detect within a single TROPOMI product, due to data sparsity and/or cloud occlusions.

However, as we have previously discussed, the wind rotation processing technique can enhance TROPOMI data, and allow point sources to be localized with more confidence [4]. These wind-rotated methane plumes are mean emissions, compiled over some period (months to years). However, mean methane emission rates can be still be calculated, based on the IME method discussed above [3, 4].

We have been exploring how this technique can be developed, and applied to the detection of methane emissions from Australian coal mines. Although originally reported for periods of >=12 months [4], where TROPOMI data is sufficiently rich, the technique can be applied to monthly TROPOMI data.

The following examples show wind rotated methane plumes from coal mines in the Bowen region, over two different time windows.

Figure 5: coal mine in Bowen, Queensland. Left: Mean TROPOMI CH4, November 2020. Right: plume enhancement resulting from wind-rotation, November 2020.

With the requisite domain knowledge, both spatial (mine structure) and temporal (mine activity), we can attempt to interpret the cause of the observed emission.

References

[1] Sadavarte, P., Pandey, S., Maasakkers, J.D., Lorente, A., Borsdorff, T., Denier van der Gon, H., Houweling, S. and Aben, I., 2021. Methane emissions from superemitting coal mines in Australia quantified using TROPOMI satellite observations. Environmental Science & Technology, 55(24), pp.16573-16580.

[2] Schuit, B.J., Maasakkers, J.D., Bijl, P., Mahapatra, G., Van den Berg, A.W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D.J. and McKeever, J., 2023. Automated detection and monitoring of methane super-emitters using satellite data. Atmospheric Chemistry and Physics Discussions, pp.1-47.

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https://medium.com/sentinel-hub/how-to-co-register-temporal-stacks-of-satellite-images-5167713b3e0b

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