the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 May 2024
UAV Based In situ Measurements of CO2 and CH4 Fluxes over Complex Natural Ecosystems
Abstract. This study presents an unmanned aerial vehicle (UAV) platform used to resolve horizontal and vertical patterns of CO2 and CH4 mole fractions within the lower part of the atmospheric boundary layer. The obtained data contribute important information for upscaling fluxes from natural ecosystems over heterogeneous terrain, and for constraining hot spots of greenhouse gas (GHG) emissions. This observational tool, therefore, has the potential to complement existing stationary carbon monitoring networks for GHGs, such as eddy covariance towers and manual flux chambers. The UAV platform is equipped with two gas analyzers for CO2 and CH4 which are connected sequentially. In addition, a 2D anemometer is deployed above the rotor plane to measure environmental parameters including 2D wind speed, air temperature, humidity, and pressure. Laboratory and field tests demonstrate that the platform is capable of providing data with reliable accuracy, with good agreement between the UAV data and tower-based measurements of CO2 and CH4 , and wind speed. Using interpolated maps of GHG mole fractions, with this tool we assessed the signal variability over a target area, and identified potential hot spots. Our study shows that the UAV platform provides information about the spatial variability of the lowest part of the boundary layer, which up to this date remains poorly observed, especially in remote areas such as the Arctic. Furthermore, using the profile method, it is demonstrated that the GHG fluxes from a local source can be calculated. Although subject to large uncertainties over the area of interest, the comparison between the eddy covariance method and UAV-based calculations showed acceptable qualitative agreement.
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Status: closed
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RC1: 'Comment on amt-2024-74', Anonymous Referee #1, 03 May 2024
Summary:
This paper describes a study to conduct UAV surveys of GHG profiles (and emissions) using an interesting onboard-UAV GHG analyser in a test study in Jena and then in the Arctic to compare UAV-derived emissions with those from EC towers. The main outputs of the paper relate to the demonstration of the sensor-UAV platform and its uses, and the flux results themselves for e.g. Arctic ecosystems. It would be a valuable and interesting read to those following AMT and a growing community using UAVs for GHG emission work. It is a nice demonstration of a new system.
The paper is well written (thank you for no obvious typos) and well presented. There is careful attention to detail on instrument characterisation and calibration and a clear explanation of the study and its methods (except for flux uncertainties – see specific comments below). I recommend the paper for publication with some thoughts about the relatively minor and constructive comments below.
Specific comments:
A paper by O’Shea et al (below) looked at the spatial scalability of EC and chamber fluxes in the Arctic to 100s km scales using aircraft mass balance. May be useful to briefly discuss this in the intro when discussing Arctic scalability approaches.
O'Shea, S. J. et al.: Methane and carbon dioxide fluxes and their regional scalability for the European Arctic wetlands during the MAMM project in summer 2012, Atmos. Chem. Phys., 14, 13159-13174, doi:10.5194/acp-14-13159-2014, 2014.
Line 55: As written, it would indicate that this is an exhaustive list, but it is really only a few examples (so maybe add, “e.g.”). A recent paper that has calculated UAV emissions using GHG analysers onboard include the ref below.
Yong, H, et al, 2024: Lessons learned from a UAV survey and methane emissions calculation at a UK landfill, https://doi.org/10.1016/j.wasman.2024.03.025
Section 2.5 – The flux-gradient method is really interesting. Can you say anything about flux uncertainty here, i.e. can you quantify an uncertainty and what sources of error/bias may affect the fluxes calculated and why? You mention that only a small dataset is needed – this is true for the equations given in themselves, but doesn’t a small dataset mean you may not capture any uncertainty or variability? Can you offer more guidance here on the method and its limitations and thoughts on spatial and temporal sampling? I see later that there are +/- flux values in table 3, but it isn’t clear how these UAV flux uncertainties have been calculated – are they a statistical variability on many measured fluxes, or are they forward-modelled uncertainties on a single total flux? I see that the uncertainties are sometimes a factor 5 greater than the fluxes themselves (and always >100%) – can you comment on this? There is mention on line 357 that uncertainty is due to the small vertical gradients in GHG concs – but why? To know this, the reader needs info on how flux error is propagated and what it’s sensitive to. This needs quite a bit more explanation in the text, as uncertainty is equally (if not more) important than the flux itself (especially when it is higher than the flux itself as it is in this case).
Measuring winds on UAVs: I sympathise with the team and their woes with measuring winds using anemometers on UAVs. It is not easy. There is some recent work on this, where mounting the anemometer more than 2.5 rotor diameter has been shown to negate the flow field problem. It may be useful to briefly mention that winds remain a challenge but that there are ways to improve (this is also discussed in the Yong et al., 2024 paper referenced above).
Technical comments:
Line 84 – space between unit and quantity needed (e.g. “20m”) Check throughout.
Citation: https://doi.org/10.5194/amt-2024-74-RC1 - AC1: 'Reply on RC1', Abdullah B., 10 Jul 2024
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RC2: 'Comment on amt-2024-74', Anonymous Referee #2, 11 Jun 2024
General comments:
This manuscript presents a novel UAV-GHG platform and its applications on characterizing and quantifying GHG emissions and fluxes for natural ecosystems over heterogeneous terrains. UAV-GHG flux measurement is an innovative topic, and the applied methodology is sound. This paper is well written, and the methodology is clearly presented. It consists of GHG sensors' lab tests including Allan deviation tests. However, how will the sensors perform against temperature changes and water vapor. These parameters would impact the analyzers’ performance especially for the field applications. Please refer to Comment 5 and 6 on the laboratory tests. This study conducts demonstration flights in Jena comparing to EC tower measurements and comprehensive grid flights in Stordalen Mire (Arctic ecosystem).
This paper is highly suitable for AMT. I would recommend publication after consideration of the following comments and minor corrections.
Specific comments:
- Section 3.1 Laboratory tests of gas analyzers would fit better to Section 2.1. Logically, the analyzers should be introduced first before describing the integrated UAV platform. Field site descriptions would be more suitable before the section flight strategies.
- Section 2.2, how long is the inlet and what are the flow rates for both sensors? Is time synchronization considered for the system (GPS, CO2 and CH4 readings, etc.)?
- Line 119, what data were pre-processed (from anemometer or GHG sensors)? And how the low-quality data were defined?
- Line 148 with known CO2 and CH4 mole fractions here, could you track the criterion of these cylinders and provide information here? Please refer to Liu et al., (2022) Laboratory tests part as an example.
- The long-term test conducted in the laboratory lasted for four hours with a linear drift for CO2. The CO2 sensor may be still warming-up for four hours. Are there any long-term tests over 24 hours performed? Calibration on the field was applied every 24 hours. How large are the sensor’s drifts over 24h?
- Laboratory tests, how was the sensors’ performance against water vapor and temperature changes? The field campaign lasts for days, how large is the temperature difference and the humidity during the day? Will these changes during the day impact the sensors’ performance?
- Line 154-155, could you explain the numbers (380 ppm,460 ppm, etc.) chosen to filter the dataset?
- Table 3 shows the estimated fluxes corresponding to large uncertainties. It would be nice to add a paragraph here to discuss how the large uncertainties were obtained. What are the sources attributed to the uncertainty? Any thoughts to improve the methodology to reduce the uncertainty? The instruments’ noise can also impact on the flux error.
Technical corrections:
- Line 167: Fig.5 shows before Fig.2 in the text. Please correct the order.
- Line 182: Eq.10 should be replaced by Eq. 9.
- Line 185: Eq.10 should be replaced by Eq. 9.
References:
Liu, Y., Paris, J.-D., Vrekoussis, M., Antoniou, P., Constantinides, C., Desservettaz, M., Keleshis, C., Laurent, O., Leonidou, A., Philippon, C., Vouterakos, P., Quéhé, P.-Y., Bousquet, P., and Sciare, J.: Improvements of a low-cost CO2 commercial nondispersive near-infrared (NDIR) sensor for unmanned aerial vehicle (UAV) atmospheric mapping applications, Atmos. Meas. Tech., 15, 4431–4442, https://doi.org/10.5194/amt-15-4431-2022, 2022.
- AC2: 'Reply on RC2', Abdullah B., 10 Jul 2024
Status: closed
-
RC1: 'Comment on amt-2024-74', Anonymous Referee #1, 03 May 2024
Summary:
This paper describes a study to conduct UAV surveys of GHG profiles (and emissions) using an interesting onboard-UAV GHG analyser in a test study in Jena and then in the Arctic to compare UAV-derived emissions with those from EC towers. The main outputs of the paper relate to the demonstration of the sensor-UAV platform and its uses, and the flux results themselves for e.g. Arctic ecosystems. It would be a valuable and interesting read to those following AMT and a growing community using UAVs for GHG emission work. It is a nice demonstration of a new system.
The paper is well written (thank you for no obvious typos) and well presented. There is careful attention to detail on instrument characterisation and calibration and a clear explanation of the study and its methods (except for flux uncertainties – see specific comments below). I recommend the paper for publication with some thoughts about the relatively minor and constructive comments below.
Specific comments:
A paper by O’Shea et al (below) looked at the spatial scalability of EC and chamber fluxes in the Arctic to 100s km scales using aircraft mass balance. May be useful to briefly discuss this in the intro when discussing Arctic scalability approaches.
O'Shea, S. J. et al.: Methane and carbon dioxide fluxes and their regional scalability for the European Arctic wetlands during the MAMM project in summer 2012, Atmos. Chem. Phys., 14, 13159-13174, doi:10.5194/acp-14-13159-2014, 2014.
Line 55: As written, it would indicate that this is an exhaustive list, but it is really only a few examples (so maybe add, “e.g.”). A recent paper that has calculated UAV emissions using GHG analysers onboard include the ref below.
Yong, H, et al, 2024: Lessons learned from a UAV survey and methane emissions calculation at a UK landfill, https://doi.org/10.1016/j.wasman.2024.03.025
Section 2.5 – The flux-gradient method is really interesting. Can you say anything about flux uncertainty here, i.e. can you quantify an uncertainty and what sources of error/bias may affect the fluxes calculated and why? You mention that only a small dataset is needed – this is true for the equations given in themselves, but doesn’t a small dataset mean you may not capture any uncertainty or variability? Can you offer more guidance here on the method and its limitations and thoughts on spatial and temporal sampling? I see later that there are +/- flux values in table 3, but it isn’t clear how these UAV flux uncertainties have been calculated – are they a statistical variability on many measured fluxes, or are they forward-modelled uncertainties on a single total flux? I see that the uncertainties are sometimes a factor 5 greater than the fluxes themselves (and always >100%) – can you comment on this? There is mention on line 357 that uncertainty is due to the small vertical gradients in GHG concs – but why? To know this, the reader needs info on how flux error is propagated and what it’s sensitive to. This needs quite a bit more explanation in the text, as uncertainty is equally (if not more) important than the flux itself (especially when it is higher than the flux itself as it is in this case).
Measuring winds on UAVs: I sympathise with the team and their woes with measuring winds using anemometers on UAVs. It is not easy. There is some recent work on this, where mounting the anemometer more than 2.5 rotor diameter has been shown to negate the flow field problem. It may be useful to briefly mention that winds remain a challenge but that there are ways to improve (this is also discussed in the Yong et al., 2024 paper referenced above).
Technical comments:
Line 84 – space between unit and quantity needed (e.g. “20m”) Check throughout.
Citation: https://doi.org/10.5194/amt-2024-74-RC1 - AC1: 'Reply on RC1', Abdullah B., 10 Jul 2024
-
RC2: 'Comment on amt-2024-74', Anonymous Referee #2, 11 Jun 2024
General comments:
This manuscript presents a novel UAV-GHG platform and its applications on characterizing and quantifying GHG emissions and fluxes for natural ecosystems over heterogeneous terrains. UAV-GHG flux measurement is an innovative topic, and the applied methodology is sound. This paper is well written, and the methodology is clearly presented. It consists of GHG sensors' lab tests including Allan deviation tests. However, how will the sensors perform against temperature changes and water vapor. These parameters would impact the analyzers’ performance especially for the field applications. Please refer to Comment 5 and 6 on the laboratory tests. This study conducts demonstration flights in Jena comparing to EC tower measurements and comprehensive grid flights in Stordalen Mire (Arctic ecosystem).
This paper is highly suitable for AMT. I would recommend publication after consideration of the following comments and minor corrections.
Specific comments:
- Section 3.1 Laboratory tests of gas analyzers would fit better to Section 2.1. Logically, the analyzers should be introduced first before describing the integrated UAV platform. Field site descriptions would be more suitable before the section flight strategies.
- Section 2.2, how long is the inlet and what are the flow rates for both sensors? Is time synchronization considered for the system (GPS, CO2 and CH4 readings, etc.)?
- Line 119, what data were pre-processed (from anemometer or GHG sensors)? And how the low-quality data were defined?
- Line 148 with known CO2 and CH4 mole fractions here, could you track the criterion of these cylinders and provide information here? Please refer to Liu et al., (2022) Laboratory tests part as an example.
- The long-term test conducted in the laboratory lasted for four hours with a linear drift for CO2. The CO2 sensor may be still warming-up for four hours. Are there any long-term tests over 24 hours performed? Calibration on the field was applied every 24 hours. How large are the sensor’s drifts over 24h?
- Laboratory tests, how was the sensors’ performance against water vapor and temperature changes? The field campaign lasts for days, how large is the temperature difference and the humidity during the day? Will these changes during the day impact the sensors’ performance?
- Line 154-155, could you explain the numbers (380 ppm,460 ppm, etc.) chosen to filter the dataset?
- Table 3 shows the estimated fluxes corresponding to large uncertainties. It would be nice to add a paragraph here to discuss how the large uncertainties were obtained. What are the sources attributed to the uncertainty? Any thoughts to improve the methodology to reduce the uncertainty? The instruments’ noise can also impact on the flux error.
Technical corrections:
- Line 167: Fig.5 shows before Fig.2 in the text. Please correct the order.
- Line 182: Eq.10 should be replaced by Eq. 9.
- Line 185: Eq.10 should be replaced by Eq. 9.
References:
Liu, Y., Paris, J.-D., Vrekoussis, M., Antoniou, P., Constantinides, C., Desservettaz, M., Keleshis, C., Laurent, O., Leonidou, A., Philippon, C., Vouterakos, P., Quéhé, P.-Y., Bousquet, P., and Sciare, J.: Improvements of a low-cost CO2 commercial nondispersive near-infrared (NDIR) sensor for unmanned aerial vehicle (UAV) atmospheric mapping applications, Atmos. Meas. Tech., 15, 4431–4442, https://doi.org/10.5194/amt-15-4431-2022, 2022.
- AC2: 'Reply on RC2', Abdullah B., 10 Jul 2024
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