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#lulc โ€” Public Fediverse posts

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

  1. ๐Ÿ’ป I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    ๐Ÿ”ฅ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8โ€“10ยฐC throughout the summer. On certain days, this gap is likely even wider.

    ๐Ÿ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  2. #๐Ÿฏ๐Ÿฌ๐——๐—ฎ๐˜†๐— ๐—ฎ๐—ฝ๐—–๐—ต๐—ฎ๐—น๐—น๐—ฒ๐—ป๐—ด๐—ฒ - ๐——๐—ฎ๐˜† ๐Ÿฎ๐Ÿต: ๐—ฅ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ
    ๐˜–๐˜š๐˜”-๐˜ฃ๐˜ข๐˜ด๐˜ฆ๐˜ฅ ๐˜“๐˜œ๐˜“๐˜Š ๐˜ฎ๐˜ข๐˜ฑ of ๐˜’๐˜ข๐˜ณ๐˜ญ๐˜ด๐˜ณ๐˜ถ๐˜ฉ๐˜ฆ, ๐˜Ž๐˜ฆ๐˜ณ๐˜ฎ๐˜ข๐˜ฏ๐˜บ 2021๐Ÿ›ฐ๏ธ๐Ÿ—บ๏ธ

    Satellite imagery shows how our landscapes evolve. In the LaVerDi project, HeiGIT and @BKG combine OSM data with Copernicus Sentinel-2 imagery to make land-use and land-cover monitoring across Germany more precise and responsive.

    ๐Ÿ” More about LaVerDi: heigit.org/laverdi/

    #OpenStreetMap #OpenData #LULC #Karlsruhe

  3. Here is a quick land-cover breakdown for the Carpathian National Nature Park (Ivano-Frankivsk region, Ukraine), based on Copernicus Global Land Service remote-sensing data.

    The results show that closed evergreen needle-leaf forest dominates the territory (almost 60%), followed by mixed and deciduous forests. Urban areas, shrubs, and agricultural lands occupy only a tiny fraction of the park.

    This is part of my long-term project of analysing protected areas using open satellite datasets and reproducible geospatial workflows.

    #RemoteSensing #EarthObservation #Copernicus #LandCover #GIS
    #RStats #Rspatial #Conservation #Carpathians #Ukraine #Biodiversity
    #NationalParks #OpenData #EnvironmentalScience #LULC

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

    In 2019โ€“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 โ€” for general environmental understanding and for environmental impact assessment (EIA) related to the companyโ€™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

  5. My experiment with land-cover classification for Calgary using satellite imagery and with a machine-learning model trained on data from another continent.

    The results turned out surprisingly good โ€” most classes transferred almost perfectly.
    The only noticeable shift was the Forest class: tree and shrub vegetation in the source region differs from Calgaryโ€™s, so the model mapped it conservatively here.

    Still, the general structure of the landscape was captured very well, and community-level land-cover profiles look consistent.

    #Rstats #RemoteSensing #GIS #MachineLearning #LandCover #Calgary #EarthObservation #LULC #GreennessOfCalgary #QGIS #UrbanHealth #Alberta #Canada #Sentinel #Copernicus #CopernicusSentinel #Sentinel1 #Sentinel2 #ESA #DataScience #FOSS #UrbanEcology #UrbanNature

  6. ๐ŸŒณ Random Forests and Living Trees

    English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.

    What started as a local research project in Kryvyi Rih turned into something much larger โ€” the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.

    The methodology developed during that work is still being used today โ€” adapted for new environmental and urban projects.

    ๐Ÿ”— datastory.org.ua/random-forest

    #RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC

  7. ๐—Ÿ๐—จ๐—Ÿ๐—– ๐—–๐—ต๐—ฎ๐—ป๐—ด๐—ฒ: ๐—ต๐—ผ๐˜„ ๐˜๐—ผ ๐—ฐ๐—ฎ๐—น๐—ฐ๐˜‚๐—น๐—ฎ๐˜๐—ฒ ๐—ฐ๐—ฎ๐—ฟ๐—ฏ๐—ผ๐—ป ๐—ฒ๐—บ๐—ถ๐˜€๐˜€๐—ถ๐—ผ๐—ป๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐—น๐—ฎ๐—ป๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—น๐—ฎ๐—ป๐—ฑ ๐—ฐ๐—ผ๐˜ƒ๐—ฒ๐—ฟ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ

    With the ๐—–๐—น๐—ถ๐—บ๐—ฎ๐˜๐—ฒ ๐—”๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐—ก๐—ฎ๐˜ƒ๐—ถ๐—ด๐—ฎ๐˜๐—ผ๐—ฟ, you can calculate high-resolution estimates of emissions caused by #LULC changes.
    This makes it easier to plan locally targeted climate mitigation measures.

    ๐Ÿ“‘ Read more: heigit.org/unveiling-the-heigi
    ๐Ÿ“Š Try it out: climate-action.heigit.org/

    #EarthOvershootDay #MoveTheDate

  8. Artificial area and croplands have increased by 133% and 6% between 1992 and 2020, respectively. If global land use continues to change at historical rates, global GHG emissions would increase to 76โ€‰ยฑโ€‰8 Gt CO2eq in 2050.

    However, #ecosystem conservation and restoration can be effective strategies to partially offset GHG emissions from fossil fuel combustion.๐Ÿ›ข๏ธ๐Ÿšซ

    #LandUse #ClimateChange #LULC

    Reference: onlinelibrary.wiley.com/doi/10

  9. #30DayMapChallenge Day 6 (Raster):

    ๐ŸกLand Use & Carbon Emissions๐Ÿก

    With our plugin for Land Use Land Cover (LULC) Change Emissions Estimation, we can quantify carbon emissions resulting from changes in the land use or land cover within a selected area and time period.

    ๐Ÿ”Ž This map shows how #LULC changes impacted #CarbonEmissions in Heidelberg between 2017 and 2024.

    ๐Ÿ—บ๏ธ Data by #OpenStreetMap/#Esri. Map by Satvik Parashar, modified according to Ulrich et al. (2024, in submission)

  10. ๐Ÿ›ฐ๏ธ A new paper "scikit-eo: A Python package for Remote Sensing Data Analysis" on a tool for analysis with various machine learning and neural networks algorithms.๐Ÿ›ฐ๏ธ

    Article: doi.org/10.21105/joss.06692
    Software: yotarazona.github.io/scikit-eo/

  11. review of landslide susceptibility studies focused on LULC.

    The development of this study had the collaboration of researchers from ESPOL/CIPAT-ESPOL, Ecuador, Universidad de Almerรญa, Spain,
    and University of Potsdam, Germany.

    Thank you very much Andrรฉs Velรกstegui Montoya, Nรฉstor Montalvรกn, Fernando Morante-Carballo, Oliver Korup, and Camilo Daleles Rennรณ.

    #LULC #LUCC #landslide #landslidesusceptibility #susceptibility #Disaster #review #bibliometrics #academicresearch #phd #doctorate

  12. Diana E. Frimpong et al. (2022) assessed the relationship between land use land cover #LULC change & air quality trends over the past 30 years in East Baton Rouge, Louisiana using #Landsat5 & #Landsat8 imagery, #Sentinel5p & air quality index from the EPA. #LoLManuscriptMonday bit.ly/Frimpong_2022

    Cheers to Dianaโ€™s co-authors and affiliated organizations for this manuscript, and thank you especially to our lead Matilda Anokye for leading this Manuscript Monday feature! #EOChat #GISChat

  13. Here's our #introduction! We are the Global Environmental Analysis and Remote Sensing (GEARS) Laboratory, which is led by (me), Dr. Jonathan A. Greenberg. We focus on addressing questions of the impacts of #climatechange and land use/land cover (#LULC) change on vegetated #ecosystems using #remotesensing data. Our lab website is at gearslab.org

  14. Hello!

    I'm a geospatial researcher currently focusing on future scenario modelling of & in the elementslab @ University of British Columbia (Vancouver, Canada).

    Research interests include & - including & implications - also , , (), , + other open-source software.

    I also love , , , , , , , , !

    ๐ŸŒณ ๐ŸŒฟ ๐ŸŒป ๐Ÿ™๏ธ ๐Ÿ„ ๐ŸŒฑ ๐ŸŒฒ