#landcover — Public Fediverse posts

Topographic Regime Controls The Response Of Erosion To Large Earthquakes [New Zealand]
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https://doi.org/10.1130/G54785.1 <-- shared paper
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[as an engineering geologist in a past professional life – and being born, raised and going to university for geology here - this was a super interesting study especially]
H/T @Geological Society of America
🏔️ How can the same earthquake reshape neighboring mountain landscapes in very different ways? The answer may lie in the topography.
New research [link above]… examines how mountain landscapes responded to the great A.D. 1717 Alpine Fault earthquake in New Zealand, revealing that factors including slope steepness, hillslope-to-channel connectivity, and sediment transport capacity can strongly influence the depth and style of post-earthquake erosion.
Researchers compared sediment records from Lake Mapourika and Lake Paringa, two nearby catchments with similar climate, vegetation, geology, and tectonic setting. Using carbon and nitrogen isotopes, molecular biomarkers, and geomorphic analyses, they reconstructed where eroded material came from and how those sources changed following the earthquake.
Despite their similarities, the landscapes responded very differently. In the steeper Mapourika catchment, deep erosion and bedrock landsliding already dominated before the earthquake, so the event dramatically increased sediment delivery without fundamentally changing the style of erosion. At Paringa, however, erosion shifted from predominantly shallow surface soils toward deeper material following the earthquake.
The findings show that pre-existing topography helps determine whether a major earthquake primarily strips away shallow soils or mobilizes deeper soil and bedrock - with implications for sediment transport, landscape evolution, and the movement of organic carbon through tectonically active mountain systems…”
#earthquakes #geomorphology #NewZealand #alpinefault #erosion #massmovement #geology #fault #faulting #tectonics #uplift #engineeringgeology #mountain #elevation #slope #steepness #hillslope #water #hydrology #sediment #riverine #fluvial #vegetation #climate #soils #factors #parameters #hydrogeomorphology #landscape #landcover #topography #geomorphometry #dating #research #carboncycle #timescale #spatialanalysis #spatiotemporal #lacustrine #landslide #debrisflow #ecology

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

🔥 Did Calgary Get Hotter in the Summer of 2026? A Satellite View of Our City
🛰️ Following pre-processing of satellite time series, I evaluated the Land Surface Temperature (LST) across a random spatial sample of 15,000 valid pixels within city boundaries for the summers of 2025 and 2026.
What the Distribution Statistics Reveal:
🔹 Median Shift: During clear-sky midday hours, Calgary's median land surface temperature rose from 33.5 °C in 2025 to 36.5 °C in 2026. A 3.0°C shift in baseline indicates a substantial intensification of the city's clear-sky thermal regime.
🔹 Shift in the Thermal Core (Mode): In 2025, the most prevalent surface temperature was approximately 35°C. In 2026, the modal peak expanded into the 37–39°C range.
🔹 Percentile Comparison: The 2025 median matched the lower quartile of 2026. This means that 75% of the city’s area in 2026 experienced higher surface temperatures than half of Calgary did a year earlier.
#GIS #RemoteSensing #EarthObservation #Calgary #LandCover #GreennessOfCalgary