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Open-source seagrass and blue carbon mapping in support of the nationally determined contributions

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Open-source seagrass and blue carbon mapping in support of the nationally determined contributions
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Open-source seagrass and blue carbon mapping in support of the nationally determined contributions Seagrasses are one of the world’s most productive ecosystems, playing an important role in climate change mitigation and adaptation. They are vast natural carbon sinks which have important yet underestimated implications into national climate agendas. Precise knowledge of seagrass distribution and site-specific in-situ carbon data is crucial for global seagrass carbon storage, but is limited to a few well-studied sites. Within the context of the Global Seagrass Watch project, funded by DLR and supported by the GEO-GEE program, we aim to develop open country-scale seagrass maps and related carbon stocks in support of the Nationally Determined Contributions of the Paris Agreement. We process open Sentinel-2 multi-temporal data within the open cloud computing platform of the Google Earth Engine to quantify seagrass and associated carbon stocks. Our generated data inventories will support interdisciplinary scientific research and management efforts within a regional and global climate action context. Track – Use cases & applications Topic – FOSS4G implementations in strategic application domains: land management, crisis/disaster response, smart cities, population mapping, climate change, ocean and marine monitoring, etc. Level – 1 - Principiants. No required specific knowledge is needed.
Open sourceMappingResultantTouchscreenHypothesisDeterminantFocus (optics)Pairwise comparisonSheaf (mathematics)Cloud computingAuditory maskingMedical imagingUltraviolet photoelectron spectroscopyReduction of orderAreaMaxima and minimaMultiplication signComputer programmingPixelService (economics)Forcing (mathematics)RainforestPerfect groupNoise (electronics)GoogolNumberRandomizationProjective planePresentation of a groupVector potentialPreprocessorBound stateLink (knot theory)Instance (computer science)Latent heatState observerSpeciesAdaptive behaviorOpen setSquare numberWave packetDatabaseMobile appPerspective (visual)EmailGreen's functionUniverse (mathematics)Water vaporComa BerenicesForm (programming)Data storage deviceTime zoneHeegaard splittingValidity (statistics)Mechanism designRootAsynchronous Transfer ModeDistribution (mathematics)AuthorizationMeeting/Interview
Smith chartEmulationMellin-TransformationOpen sourceHypothesisProjective planeCloud computingMeeting/InterviewComputer animation
Water vaporForm (programming)Green's functionRootDistribution (mathematics)Mechanism designSpeciesComputer animation
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MappingResultantMultiplication signAdaptive behaviorMaxima and minimaFocus (optics)Bit rateRainforest
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Medical imagingReduction of orderAreaPreprocessorNumberAuditory maskingTime zoneCloud computingWater vaporComputer animation
OpticsAreaWater vaporSource codeAreaComputer animation
Interior (topology)PixelWave packetAsynchronous Transfer ModeHeegaard splittingStochastic processValidity (statistics)Bound stateComputer animation
Data storage deviceBit rateMaxima and minimaResultantAreaAuthorizationNumberDatabaseData storage deviceMultiplication signVector potentialSquare numberComputer animation
AreaEmailLink (knot theory)MappingUltraviolet photoelectron spectroscopyMeeting/Interview
Multiplication signOpen setState observerMeeting/Interview
Transcript: English(auto-generated)
Okay, in our next section we're joined by Alina Bloom
who's going to be talking about the open source secrets and blue cupboard mapping in support of the Nationally Determined Contributions. She's recently graduated in applied geography from the R.W.T.H. ASEAN University.
And their main focus is on remote sensing and climatology. And she'll be presenting results of a master thesis where she focused on secret remote sensing and carbon stocks, and which completed in comparison with the German Aerospace Center. Over to you, Alina.
Hi, thank you for the introduction. Let me share my screen. Okay, perfect. Cool.
Okay, so hi everyone, thanks for the introduction. So as already mentioned, I'm going to present my master thesis and how I used open source data and the cloud computing platform Google Earth Engine to map Bahamian secrets and its carbon stocks. I will also talk about how this supports
the Nationally Determined Contributions of the Paris Agreement. I conducted this research in cooperation with the Global Secrets Watch Project of the German Aerospace Center, which is supported by the GE Orgo Growth Engine Program. So let's start with what seagrasses is. Seagrasses are marine flowering plants which grow in intertidal and sub-plated waters.
They can form vast meadows and like terrestrial plants, they use photosynthesis to grow and have a pollination mechanism to reproduce. They also have rhizomes and roots to kind of anchor in the soil. Here you can see the global distribution of seagrass in green and the species richness in red. We can find the seagrass ecosystem in at least 157 countries
where it covers over 300,000 square kilometers. Just to put that in perspective, that's about the size of Germany. Seagrass provides a number of benefits for humans and the global climate. For instance, it reduces wave energy and minimizes erosion. It serves as a food and nursing ground for many marine animals
and therefore supports the fishing industry. But most importantly, it sequesters carbon through photosynthesis. Seagrass accounts for about 10 to 18% of the oceanic helium carbon burial with sequestration rates 35 times faster than tropical rainforests. This carbon is stored in the biomass and the soil.
The importance of the seagrass ecosystem has been recognized by several countries which have included this ecosystem within their NDCs to support the Paris Agreement's goal of climate change mitigation adaptation. Today, I will focus on my mapping results for the Bahamas.
As coastal habitats have been mapped before, but seagrass wasn't the main focus and these approaches used imagery with either a lower resolution or with a low signal-to-noise ratio. The first step of our mapping approach was to create a multi-temporal composite
on which I could base the classification. For this, I utilized four years worth of Sentinel-2 data which is freely available within Google Earth Engine. The Bahamian exclusive economic zone covers over 600,000 square kilometers, resulting in a total of about 19,000 images for those four years. I applied a number of pre-processing methods
like cloud masking and sun-clean reductions to create the composite you can see here. This composite combines about 10,000 images. To reduce misclassification, I mask areas in which seagrass can't grow, so land in optically deep waters. The sea source with a shallow water area
of about 114,000 square kilometers. And here you can see some examples of Bahamian seagrass meadows. For the binary classification, so seagrass versus non-seagrass, I used two different approaches which were based on data taken from the Ellen Carl Atlas.
This data includes ground-truth data and the data classified by the Atlas. I harmonized the data by discarding all classified pixels which didn't fall within the spectral bounds of the ground-truth data. And to avoid mixed pixels, I also discarded seagrass pixels, which didn't fall within the 80th percentile of the ground-truth data.
So pixels which are brighter and therefore probably include sandy areas. The first approach used the ground-truth data and a geographical split to create training and validation data. The second approach used the ground-truth data as validation data and the classified data as training data. For both approaches, I used a random process classifier in probability mode.
Here you can see the Sentinel-2 composite, the minimum and maximum seagrass extending green and the marine protected areas in orange. The ground-truth data-based approach showed a smaller seagrass stand than the approach based on the classified data.
I mapped an area of 12 to 28,000 square kilometers of seagrass, which equals 25 to 60% of the area mapped by the Avocallen coral atlas. I combined these numbers with country-specific in-situ carbon data taken from freely available literature. And this results in a carbon storage potential
of 182 to 456 million mega grams. And a yearly sequestration of 17 to 40 times the amount of CO2 emitted by the country in 2019, making the Bahamas carbon neutral. This number shows the importance of the seagrass ecosystem
for the global climate, but especially for the Bahamas. However, only six to 11% of this area in the Bahamas lies within marine protected areas. To preserve the seagrass ecosystem services, Bahamian authorities need to conserve and restore this habitat. Thank you all for listening.
And if you would like to take a closer look at my mapping results, you can check out my Earth Engine app, which I will post in the chat, and feel free to contact me via email or LinkedIn. Thank you, Alina, for that presentation.
Yeah, if anyone wants to reach out to Alina, please, you may capture those details and reach out to her. If you have any questions or link ups or research areas you may want to discuss with her.
I think that keeps off our sessions for the day. And thank you, Alina, again, for joining us. I will say goodbye for now. Bye. Excellent. Yes, thank you, Alina, very interesting.
And thank you, Isaacson. I think that was our last presenter. So I think that concludes our session right now. Okay. I appreciate everyone that tuned in to listen to the very first open Earth observations track
at FOS4G this 2021. And I'm just glancing in the comments here. And I think we can conclude, Isaacson. And maybe a year from now, we can all meet somewhere in the world in person for FOS4G 22, but we will see. Yeah, true. We will see.
I look forward to that. Yes, indeed, me too. Okay. Thank you, everybody, for your time and your attention. Please take care and have a wonderful day. Bye-bye. Bye-bye.