#wetland — Public Fediverse posts

PAWI - The First High-Resolution, Multi-Class, Pan-Arctic Wetland Inventory
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https://doi.org/10.1016/j.jenvman.2026.130889 <-- shared paper
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https://app.geo.ca/en-ca/map-browser/record/8b83fd58-5953-4b80-8009-94d44ff89844 <-- shared open dataset, API details, etc
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H/T @Michael Wulder | Senior Research Scientist at Natural Resources Canada
“New circumpolar wetland inventory distinguishing bog, fen, swamp, marsh, and water at 10 m spatial resolution. Developed using Sentinel-1, Sentinel-2, ALOS PALSAR-2, ArcticDEM, and machine learning.
➡️ Overall accuracy of 89%
➡️ Estimates that ~20% of the defined Arctic landmass is wetland
➡️ Provides a consistent baseline for methane modeling, climate vulnerability assessment, and conservation planning…”
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“HIGHLIGHTS:
• First 10 m resolution wetland inventory covering the entire Pan-Arctic.
• Arctic wetlands occupy approximately 20% of the regional landmass.
• Fen peatlands dominate vegetated wetlands across much of the Arctic.
• PAWI provides a baseline for climate, methane, and conservation studies.
ABSTRACT: Arctic wetlands are key regulators of global methane (CH4) emissions, yet uncertainty in wetland spatial extent and class composition limits the accuracy of CH4 budget models. Current mapping products lack circumpolar coverage and consistent thematic classification standards, constraining the ability to model ecosystem responses to Arctic warming. Here, [the authors] present the first high-resolution Pan-Arctic Wetlands Inventory (PAWI), produced at 10 m resolution using multi-sensor satellite imagery (e.g., Sentinel-1, Sentinel-2, and ALOS PALSAR-2), ArcticDEM topography, and environmental and hydrological datasets, using a machine learning Random Forest classifier. Wetlands are classified into bog, fen, swamp, marsh, and water. The final product achieves an overall accuracy of 89% (Kappa = 0.86) and estimates that 20% of the Arctic landmass is wetland. The PAWI provides a consistent, ecologically relevant baseline for improving CH4 flux modeling, assessing climate vulnerability, and supporting conservation planning. By integrating advances in remote sensing, machine learning, and multi-national data harmonization, this work addresses a critical gap in Arctic wetland mapping and establishes a transferable framework for large-scale ecosystem classification in remote regions…”
#GIS #spatial #mapping #Arctic # circumpolar #wetland #inventory #bog #fen #swamp #marsh #machinelearning #AI #cloudcomputing #Methane #emission #remotesensing #satellite #sentinel #ArcticDEM #ALOSPALSAR #spatialanalysis #spatiotemporal #climatechange #climatevulnerability #conservation #planning #PanArctic #peatlands #vegetation #methane #CH4 #spatialextent #water #hydrography #hydrology #environmental #landcover #assessment #ecosystems #remoteregions #opendata #API
@Geo.CA

This review synthesized existing evidence on how plant #functionaltraits and #functionaldiversity influence key #wetland #ecosystemfunctioning, such as productivity, carbon cycling and nutrient cycling.

#restoration

https://doi.org/10.1093/jpe/rtag125

Our latest wetland / flooding a forest video! https://www.youtube.com/watch?v=qcVMX32c0io #Rewilding #MossyEarth #Nature #Wildlife #Wetland #Forest #Ecology