#landcover — Public Fediverse posts on home.social

Ireland @[email protected] · 2026-09-15 · 10:52 UTC

https://www.europesays.com/ie/689549/ An Early Look at Fall Color in Canada #EarthObservatory #Éire #IE #Ireland #LandCover #Landsat9 #Science #Space #Vegetation

#vegetation #space #science #landsat9 #landcover #ireland

OptimusPrimeBot @[email protected] · 2026-09-14 · 05:21 UTC

An Early Look at Fall Color in Canada 🌎🛰️

#LandCover #Vegetation

▶️ 1 new picture from NASA (Website) https://commons.wikimedia.org/wiki/File:An_Early_Look_at_Fall_Color_in_Canada_%281320015_-_727%29.jpg

#landcover #vegetation

OptimusPrimeBot @[email protected] · 2026-09-14 · 05:21 UTC

An Early Look at Fall Color in Canada 🌎🛰️

#LandCover #Vegetation

▶️ 1 new picture from NASA (Website) https://commons.wikimedia.org/wiki/File:An_Early_Look_at_Fall_Color_in_Canada_%281320015_-_727%29.jpg

#landcover #vegetation

OptimusPrimeBot @[email protected] · 2026-09-14 · 05:21 UTC

An Early Look at Fall Color in Canada 🌎🛰️

#LandCover #Vegetation

▶️ 1 new picture from NASA (Website) https://commons.wikimedia.org/wiki/File:An_Early_Look_at_Fall_Color_in_Canada_%281320015_-_727%29.jpg

#vegetation #landcover

OptimusPrimeBot @[email protected] · 2026-09-14 · 05:21 UTC

An Early Look at Fall Color in Canada 🌎🛰️

#LandCover #Vegetation

▶️ 1 new picture from NASA (Website) https://commons.wikimedia.org/wiki/File:An_Early_Look_at_Fall_Color_in_Canada_%281320015_-_727%29.jpg

#landcover #vegetation

Canada @[email protected] · 2026-09-14 · 04:30 UTC

An Early Look at Fall Color in Canada

As North America rode out a summer of remarkable heat, fall foliage and cool, crisp weather still seemed…
#Canada #EarthObservatory #LandCover #Landsat9 #Vegetation
https://www.europesays.com/canada/208380/

#vegetation #landsat9 #landcover #earthobservatory #canada

OptimusPrimeBot @[email protected] · 2026-08-09 · 06:16 UTC

Glacial ridges line Björkö in Finland's Kvarken Archipelago

#CoastalEnvironment #LandMonitoring #Landcover #MarineEnvironment

▶️ 1 new picture from EU Space https://commons.wikimedia.org/wiki/File:Glacial_ridges_line_Bj%C3%B6rk%C3%B6_in_Finland%27s_Kvarken_Archipelago_%28Copernicus_2026-08-09%29.webp

#coastalenvironment #landmonitoring #landcover #marineenvironment

💧🌏 Greg Cocks @[email protected] · 2026-07-14 · 03:14 UTC

Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
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https://doi.org/10.1038/s43247-026-03515-x <-- shared paper
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H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
“This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
Two findings stand out.
1. Old-growth forests had the highest multifunctionality index of any land cover type.
2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
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“Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
#Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

#patagonia #chile #aysen #coyhaique #landcover #mapping

💧🌏 Greg Cocks @[email protected] · 2026-07-02 · 19:07 UTC

Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
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https://doi.org/10.1016/j.rse.2026.115553 <-- shared paper
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“HIGHLIGHTS:
• [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
• HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
• Time-series minima and maxima are used to predict sowing, harvest, and tillage.
• Validation is performed across several years, crop types, and European locations.
• Phenology detection is improved compared to HRL-Cropland.
ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
#Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling