C-SCALE ML-Pipeline for Wetland Monitoring
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Methane is around 25 times as powerful in trapping heat in the atmosphere, but because it does not stay in the atmosphere as long, it more has a short-term influence on the rate of climate change.
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C-SCALE ML-Pipeline for Wetland Monitoring
about: Applied sciences
about: Earth observation
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date created: 2023-07-12 07:20:55.783495+00:00
date modified: 2025-03-05 00:50:04.115964+00:00
date published: 2023-07-12 07:20:55.783495+00:00
description: With this pipeline we aim to provide users with the ability to train spatiotemporally robust machine learning models to detect and monitor wetlands and thus assessing their state over time. Wetlands play a vital role in the ecosystem, but also have critical influence on methane emissions. Methane is around 25 times as powerful in trapping heat in the atmosphere, but because it does not stay in the atmosphere as long, it more has a short-term influence on the rate of climate change. See also this news release by NOAA for more details. Wetlands have been one of the major drivers of methane in the atmosphere, acting as source instead of a sink while not being stable, including water stress as well as renaturation.
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keywords: C-SCALE community
keywords: ML
keywords: Sentinel-1
keywords: U-Net
keywords: Wetlands
license: no-permission
main entity: Software
publisher: mrs.geo.tuwien.ac.at
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field of research: environmental sciences
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sentence: Wetlands play a vital role in the ecosystem, but also have critical influence on methane emissions.
sentence: With this pipeline we aim to provide users with the ability to train spatiotemporally robust machine learning models to detect and monitor wetlands and thus assessing their state over time.
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cite-as: Raml, Bernhard, and Matthias Schramm. "C-SCALE ML-Pipeline for Wetland Monitoring." ROHub. Jul 12 ,2023. https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce.

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Sentinel-1 Global Harmonic Parameters: A Seasonal Model for Flood Mapping and More
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Wetlands play a vital role in the ecosystem, but also have critical influence on methane emissions.
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With this pipeline we aim to provide users with the ability to train spatiotemporally robust machine learning models to detect and monitor wetlands and thus assessing their state over time.
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