home.social

#landcover — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #landcover, aggregated by home.social.

  1. Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    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…”
    --
    “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

  2. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “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

  3. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “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

  4. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “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

  5. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “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

  6. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “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…”

  7. Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”
    #GIS #spatial #mapping #gridded #population #urban #dataanalysis #shrinkingcities #census #censusvalidation #WorldPop #GHSPOP #GPWv4 #builtupareas #sustainability #SDG #casestudy #Croatia #spatialanalysis #spatiotemporal #SustainableDevelopmentGoals #populationdecline #demographics #urbanplanning #planning #city #cities #PopulationData #RemoteSensing #UrbanAnalytics #OpenData #SDG #model #modeling #urbanisation #density #QGIS #landcover
    @MDPI

  8. Scientists See More Vegetation In The Himalayas - But It Is Not Good News, Because That Extra “Green” Can Disrupt Water, Snow, And High-Mountain Biodiversity | Plants Growing Higher Across Himalaya As Climate Warms
    (Vegetation On The Move: Elevational Shifts And Greening Dynamics Across The Himalayan Alpine Zone)
    --
    ecoticias.com/en/scientists-se <-- shared technical article
    --
    news.exeter.ac.uk/faculty-of-e <-- shared technical newsitem
    --
    doi.org/10.1002/ecog.08259 <-- shared (2026) paper
    --
    doi.org/10.1111/gcb.14919 <-- shared (2020) paper
    --
    “For years, the biggest climate warning from the Himalaya was easy to picture because glaciers were shrinking on the roof of Asia. Now, researchers are pointing to a quieter signal, one that can look almost harmless from a distance. The mountains are getting greener.
    New research [link above] shows alpine vegetation moving higher across six Himalayan regions from 1999 to 2022, pushed in part by warming and reduced snow depth. That might sound like nature recovering, but in this fragile landscape, more plant cover at extreme heights may change how snow is stored, how water runs downhill, and how rivers behave for communities far below…”
    #GIS #spatial #mapping #remotesensing #earthobservation #satellite #landsat #landcover #NDVI #Himalaya #Nepal #India #Bhutan #climatechange #glacier #vegetation #alpine #level #greening #spatialanalysis #spatiotemporal #snow #water #ice #hydrography #hydrology #ecosystems #humaninpacts #phenology #model #modeling #HighMountainAsia #greenness #ERA5 #vegetationline #altitude #climatictrends #warming #precipitation #rainfall

  9. A few years ago, I carried out a personal initiative project while working at UkrGazVydobuvannya (Oil&Gas).

    In 2019–2020, I performed a full land-cover analysis for all company license areas using openly available Copernicus Global Land Cover data.

    I built two variants of the analysis based on FAO UN land-cover classifications and calculated Shannon diversity indices for each license area.
    Later, I expanded the work and produced detailed plots and spatial summaries for every site.

    These analytics were used by both field personnel and upper management — for general environmental understanding and for environmental impact assessment (EIA) related to the company’s production activities.

    Everything was done using open data and the R language.

    #LandCover #Copernicus #RStats #OpenData #EnvironmentalScience #GIS #ShannonIndex #RemoteSensing #Ukraine #FOSS #DataScience #LULC #LandCover #CopernicusLandCover #Energy #UGV

  10. Improving our Coasts with High-Resolution Land Cover Data [#NOAA]
    --
    coast.noaa.gov/states/stories/ <-- shared technical article
    --
    coast.noaa.gov/ccapatlas/ <-- on-demand, online NOAA CCAP Landcover Atlas
    --
    coast.noaa.gov/digitalcoast/da <-- #opendata C-CAP High-Resolution Land Cover
    --
    Use Case Examples:
    • Flood Inundation Modeling and Risk Assessment
    • Stormwater Management and Water Quality Protection
    • Heat Risk and Urban Forestry
    • Wetland Monitoring, Conservation, or Restoration Planning
    • Other – e.g., Discovering Gaps in Broadband Access
    --
    #GIS #spatial #mapping #NOAA #DigitalCoast #LandCover #CoastalManagement #GeospatialData #EnvironmentalData #ResilientCommunities #landcover #usecase #economics #remotesensing #earthobservation #opendata #floodinnundation #waterquality #water #hydrology #risk #hazard #spatialanalysis #spatiotemporal #stormwater #management #heatrisk #urbanforestry #wetland #monitoring #conservation #planning #resortation
    @noaa #NOAAOfficeForCoastalManagement

  11. Small Waterbodies Of Large Conservation Concern - Towards An Integrated Approach To More Accurately Measuring Surface Water Dynamics
    --
    doi.org/10.1016/j.ecolind.2025 <-- shared paper
    --
    “HIGHLIGHTS
    • Hydrologic models were used to assess remotely sensed surface water estimates.
    • The accuracy of satellite surface water estimates diminished for waterbodies < 2 ha.
    • Vegetation reduces accuracy of surface water detection, especially during wet years..."
    #GIS #spatial #mapping #waterbodies #Conservation #planning #surfacewater #Hydrologicmodeling #remotesensing #earthobservation #water #hydrology #hydrographic #model #modeling #spatialanalysis #spatiotemporal #waterbody #small #conservation #inventory #monitoring #accuracy #aquatic #vegetation #climate #NorthAmerica #kettle #landforms #landcover #glaciation #prairie #pothole

  12. Historical Geospatial Dataset Of Cyprus From British Administration Maps Of The 19th Century
    --
    doi.org/10.1016/j.dib.2024.111 <-- shared paper
    --
    kitchener.hua.gr/en <-- storybook / historic maps
    --
    [as a ‘colonial’ myself (New Zealand), I have a fascination with this sort of history – and throw in some early-ish survey and national cartography not long after Cyprus was ceded by the Ottomans…]
    #GIS #spatial #mapping #Cyprus #hydrology #hydrography #landcover #population #demographics #survey #cartography #French #tradition #triangulation #national #colonial #British #administration #Kitchener #roads #infrastructure #cultural #history #historic #census #spatiotemporal #HistoricalGIS #takingstock

  13. Whew! Looking forward to not mapping charts anymore and admiring the other #Day28 maps for the #30DayMapChallenge. I used open data from the United Nations to calculate the percentage of landcover in square km for Madagascar. Modified Paul Satchell's tutorial on visualizing charts in #ArcGISPro. resource.esriuk.com/blog/visua
    Using subdivide polygon into 100 sections is the key to layering the data.

    #Arcpro #Madagascar #FAO #FoodandAgricultureOrganizationFAO #landcover #Africa

  14. Today our article "#Landuse and #landcover as a conditioning factor in landslide #susceptibility: a #literaturereview" was accepted to be published in #Landslides, the leading journal on this subject.

    This article is a part of my PhD thesis focused on landslide susceptibility and the influence of spatial heterogeneity and #LUCC.

    I am very happy to end 2022 with this news!

    Thank you to all the collaborators!!