the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jul 2024
ESD Ideas: Near real-time preliminary detection of carbon dioxide source and sink areas using a Laplacian filter
Abstract. The constant rise in atmospheric CO2 concentrations is warming the planet and causing climate change. Here, we detect ecosystem areas with weighty changes in the CO2 concentration using digital filtration, similar to image processing techniques, to identify terrestrial CO2 sources and sinks. This approach may improve CO2 monitoring capabilities and enable near real-time detection of CO2 sources and sinks.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1981', Anonymous Referee #1, 23 Aug 2024
Summary:
In this work, the authors proposed a new method of using Laplacian filter on near real time CO_2 concentration data to qualitatively detect potential CO_2 sources and sinks in small areas.
This idea essentially boils down to using Laplacian filter to perform edge detection on digital images, and specifically in this work, the CO_2 concentration datasets are used as input digital images and the objects of interests are the CO_2 sources and sinks. The Laplacian filter are widely used in digital image processing/computer vision for edge detection purposes and generally performs well since the filter calculates the second derivatives of the given image and detects edges regardless of direction, but using the filter on CO_2 concentration data can impose some challenges including the shape of the object of interests can be often irregular and diffusive (as opposed to detecting made-made structures that often have crisp edges), and the spatial resolution of CO_2 concentration datasets.
Overall, it’s good to introduce image-based methods of CO_2 sources and sinks detection to the general community of earth sciences, but revisions and clarifications are needed to resolve some confusions in the manuscript.
Main Comments:
- For the paragraph starting around line 70:
- Detailed assumptions are needed for equation (2): Why would you assume the inequality? As described in previous paragraphs, for the ‘small area’ and a short time period, if the emitting rate of CO_2 is stable and the removal rate of CO_2 is also stable (external and internal), why would CDC(t_1) be greater than CDC(t_0) at any given location (X, Y, or Z)?
- Equation (1) and equation (2) seems identical, any reason why equation (2) needs to appear?
- For the figures in appendix:
- I am confused about the figures in appendix: how Figure A1and FigureA2 are related? It seems Figure A2(a) is served as validation for results in FigureA1 (line 138) and FigureA2(b) and FigureA2(c) are for a completely different case study regarding CO_2 sinks (line 158). If that’s the case, why Figure A2(a) is together with and FigureA2(b) and FigureA2(c)?
- Could you also clarify what are the isolines on both figures and the way to interpret? If the pixels in Figure A1(a) are already CO_2 concentrations, then how are the isolines calculated and what do those lines mean?
Specific notes:
- What is CDC? If it’s CO_2 concentration dataset why it’s not CCD?
- Line 138: For the CO_2 flux dataset (Lesley, 2020), that is spatial resolution of the datasets and how is it when compared with the CDC containing the fire event? Does the flux dataset contains the fire event in 2016? And how are the isolines calculated in the validation plot (Figure A2(a))?
Citation: https://doi.org/10.5194/egusphere-2024-1981-RC1 - For the paragraph starting around line 70:
- RC2: 'Comment on egusphere-2024-1981', Anonymous Referee #2, 28 Oct 2024
Data sets
Test Datasets (CDC, Fire Fluxes, Land Cover and shapefiles) and code Yana Savytska and Viktor Smolii https://doi.org/10.5281/zenodo.12532657
Model code and software
Test Datasets (CDC, Fire Fluxes, Land Cover and shapefiles) and code Yana Savytska and Viktor Smolii https://doi.org/10.5281/zenodo.12532657
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