#geospatialanalysis — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #geospatialanalysis, aggregated by home.social.
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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
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https://doi.org/10.3390/geosciences15030110 <-- shared paper
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H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
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“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
In this interesting #SOTMUS presentation, Julio May shares a process for multi-criteria geospatial analysis to understand more sustainable urban #mobility: https://youtu.be/pqzFV2XAfoc
🦩
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Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
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https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
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https://doi.org/10.1007/s11600-022-00943-z <-- shared paper
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H/T @Kuldeep Dutta | Geology-Earth Science
“… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
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“Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
#EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow -
Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
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https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
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https://doi.org/10.5194/esurf-13-1281-2025 <-- shared paper
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https://doi.org/10.1007/s11069-025-07766-3 <-- shared paper
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[I recognise that the photo is instead for the floods, etc in Lubra, Nepal - but felt it better showed the hydrogeomorphical setting (sic) for the 'casual' post viewer...]
H/T @Kuldeep Dutta
“In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains…”
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“Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km2 of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm/day/) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R² ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R² ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades...”
#EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #Flooding #alluvial #fluvial #water #hydrology #hydrography #flood #flooding #spatialanalysis #spatiotemporal #mountain #plain #hydrometeorological #hydrogeomorphology #ArunachalPradesh #Assam #India #hillslope #floodplain #rainfall #precipitation #extremeweather #engineeringgeology #massmovement #landslide #debrisflow #risk #hazard #monitoring #GIS #spatial #mapping #remotesensing #satellite #Sentinel #sedimentation #humanimpacts #infrastructure #damage #cost #economics #public #safety #model #modeling #downstream -
Mapping The Likely Routes Of Narco-Trafficking In Costa Rica
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https://doi.org/10.1007/s11067-026-09745-y <-- shared paper
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#GIS #spatial #mapping #spatialeconomics #NarcoTrafficking #GeospatialAnalysis #IllicitSupplyChains #CostaRica #CentralAmerica #NetworkModeling #illegaldrugs #enforcement #monitoring #spatialanalysis #crime #crimeanalysis #routing #interception #illegalsupplychain #supplychain #logistics #cocaine #consumers #road #maritime #transithub #geospatialriskmodeling #trafficking #maximumflowframework #alldataisspatial #datagathering #geoint -
The provided link appears to be a source for geographical information. Further context is required to assess its relevance or content. #OSINT #GeospatialAnalysis
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The provided link directs to a geographical resource. Further context is required for detailed analysis. #GeospatialAnalysis #DataInterpretation
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Please provide specific geographic data or context for analysis. #OSINT #GeospatialAnalysis
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Coordinates indicate the presence of a Law Enforcement Force facility, an IRGC base, and a military base in the specified region. These locations are critical for assessing security dynamics. #OSINT #GeospatialAnalysis
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Nose Hill Park, Calgary
Median composite of Sentinel-2 imagery (mid-May to mid-September 2025).
Band combination: 12-8-3, emphasizing substrate contrasts, vegetation structure, and moisture patterns.#RemoteSensing #EarthObservation #Sentinel2 #OpenData #GIS #QGIS #GeoDataArt #GeoSpectralArt #Calgary #Alberta #UrbanEcology #GeospatialAnalysis #Canada #GreennessOfCalgary #Copernicus #CopernicusSentinel #NoseHillPark
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🌿 Greenness of Calgary Communities (Summer 2024)
📎 https://www.datastory.org.ua/greenness-of-calgary-communities-summer-2024/Last year I published my first attempt to analyze the actual vegetation condition across Calgary and to build a data-driven ranking of its communities based on median summer NDVI. It was my very first experiment in assessing urban greenness at the neighbourhood scale — but the results turned out surprisingly insightful.
Some patterns were expected, while others revealed unexpectedly low vegetation density in places that looked green from the ground.This exploration later grew into a much larger line of research on Calgary’s greenness, climate resilience, and spatial variability in vegetation health. You can find some results here with thematic hashtag #GreennessOfCalgary
If you're working on urban ecology, remote sensing, or land-cover analysis of Canadian cities — I’d be happy to exchange ideas.
#NDVI #RemoteSensing #Calgary #UrbanEcology #Sentinel2 #QGIS #RStats #Alberta #Canada #EnvironmentalMonitoring #GeospatialAnalysis
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🗺️ Making atlases better, one version at a time
Not everything works perfectly on the first try — sometimes the most obvious design ideas come late and suddenly! 🤣
Here’s one example: showing community boundaries so that people unfamiliar with cartography can at least find their own street.
In the second version (right), I added a subtle shading effect that keeps the map’s analytical context but visually highlights the selected community.
Each atlas is generated automatically in QGIS Report Designer — one atlas per city sector, with multiple communities.
That means this improvement wasn’t just “drawing a border manually,” but rather a data-driven programming and sorting task integrated into the atlas workflow.🟩 Left: earlier version
🟩 Right: current improved version#QGIS #Cartography #DataVisualization #UrbanMapping #GIScience #OpenData #GeospatialAnalysis #Calgary #Automation #AtlasDesign #Rstats #RemoteSensing #Copernicus #Sentinel2 #GreennessOfCalgary #UrbanEcology #OpenStreetMap #Alberta #Canada
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🌊 Modeling a potential dam breach — and how panic spread beyond the real watershed
This project assessed the potential consequences of a dam failure at a mine-water impoundment in the Svystunova Gully.
🔹 Reconstructed the original pre-impoundment terrain from archival topographic maps.
🔹 Used Sentinel-based land-cover classification to assign variable Manning’s roughness coefficients.
🔹 Built a modern DEM including the dam structure and current flooded zone.
🔹 Simulated dam-break flood dynamics in GRASS GIS — frame by frame.
🔹 Produced a video visualization to help local communities understand real risks (and ignore fake ones).
🔹 The results were later featured in regional media and used in discussions about mine-water safety.🎥 Watch the short video visualization:
🔗 https://youtu.be/JTcCLXqvWlE?si=NS7ly7UAZu1XttWy#Hydrology #DamSafety #GIS #GRASSGIS #MineWater #HydrodynamicModeling #GeospatialAnalysis #EnvironmentalRisk #OpenScience #IndependentResearch #SvystunovaGully
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🌳 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 — 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 — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC
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🌿 NDVI change in Calgary’s Weaselhead Flats (2024 → 2025)
This map shows how vegetation in one of Calgary’s most diverse natural areas responded to the city’s unusually wet summer of 2025.
Greener shades mark zones where NDVI increased most strongly compared to 2024 — the same floodplain and forest patches that locals know for dense canopy recovery.Even modest year-to-year shifts in temperature and rainfall leave clear spatial traces in NDVI — a reminder of how sensitive urban ecosystems are to climate variability, and how well open-data satellite products can capture it.
🛰 Data and processing: Sentinel-2 + R + QGIS
#NDVI #RemoteSensing #Calgary #UrbanEcology #ClimateImpact #EnvironmentalMonitoring #GIS #QGIS #RStats #DataVisualization #GeospatialAnalysis #Sentinel2 #CopernicusSentinel2 #CopernicusProgram #Copernicus #GreennessOfCalgary #Alberta #Canada
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🌿 Calgary’s vegetation — satellite comparison (2024 → 2025)
Median NDVI maps from mid-May to mid-September show a clear difference between the two seasons.
In 2025, NDVI values are noticeably higher — vegetation stayed greener and denser for longer.
The wetter summer had a strong effect on canopy productivity across most Calgary communities, especially in parkland and tree-covered zones.🛰️ Based on Sentinel-2 imagery and R + QGIS processing.
#RemoteSensing #NDVI #UrbanEcology #Calgary #GeospatialAnalysis #GIS #Sentinel2 #EnvironmentalData #DataVisualization #OpenScience #EarthObservation #ClimateImpact #RStats #QGIS
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🧠 Full-Stack Science — from raw data to the final PDF
While preparing the new version of my monograph, I realized something funny:
if I listed all the software I used, the “Used software” section would look more like a Linux manual than a scientific appendix.Because, honestly — everything mattered.
From grep, awk, and apt, to PHREEQC, R, QGIS, and finally LaTeX.Every single stage — data cleaning, modeling, visualization, mapping, typesetting — I did entirely on my own.
No outsourcing. No “sending for refinement.”
Just a full-stack, open-source workflow — from the first script to the final monograph PDF.📘 Draft available on Zenodo:
🔗 https://zenodo.org/records/16741148#OpenScience #IndependentResearch #Geochemistry #Hydrogeology #DataScience #PHREEQC #RStats #QGIS #Linux #LaTeX #EnvironmentalData #GeospatialAnalysis #FullStackResearch #ScientificWorkflow #Zenodo #SvystunovaGully
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🌎 Mapping dominant chemical speciation in a polluted carbonate aquifer
In my recent modeling work, I modeled the dominant forms of element speciation in groundwater within a carbonate system.
Beyond pure geochemical curiosity, this approach provides a practical lens:
– it reveals where mineral precipitation is most probable,
– and helps identify zones where remediation can be most effective.💻 Combining thermodynamic modeling (PHREEQC) with spatial analysis in R and QGIS turns subsurface processes into actionable insights for water-quality management and contamination mitigation.
Two figures below show the modeled distribution of cadmium species across the aquifer and their evolution with dilution
📘 Full details in the draft monograph:
🔗 https://zenodo.org/records/16741148#Geochemistry #Hydrogeology #PHREEQC #GroundwaterContamination #MineWater #EnvironmentalGeochemistry #Metasomatism #Aquifer #RStats #QGIS #GeospatialAnalysis #OpenScience #IndependentResearch #WaterQuality #Remediation #SvystunovaGully
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“If the map does not match the terrain — trust the terrain!”
— Principle of field geoscienceThis is how the effective catchment area of the Inhulets River looks within the study region.
The upstream part — above the Karachunivske Reservoir's dam, the outlet of the Saksahan derivative tunnel, and the confluence of the Stara Saksahan River — was excluded from the calculation.
Within the analyzed area, surface runoff is possible only from the highlighted zone.
The rest of the “catchment basin” is hydrologically inactive: runoff is intercepted by ponds, settling tanks, and other anthropogenic landforms.🌍 The analysis was based on the Copernicus GLO-30 DEM, integrated with hydrological modeling and terrain processing in open-source GIS.
#Hydrology #Geochemistry #InhuletsRiver #GIS #SAGAGIS #QGIS #HydrologicalModeling #RemoteSensing #GeospatialAnalysis #EnvironmentalData #RStats #LandscapeGeochemistry #Copernicus
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🌿 Calgary Greenness Dynamics (2024–2025)
Mapping ΔNDVI between the summers of 2024 and 2025 shows how Calgary’s communities changed in their vegetation cover.
🟢 Some areas became noticeably greener this year — likely due to the wetter and milder summer.
🔴 Others show slight declines, possibly linked to construction, soil dryness, or limited tree canopy recovery.These patterns reveal how different parts of the city respond to seasonal variability — and where future urban greening might have the most impact.
🛰️ Based on Sentinel-2 data and NDVI analysis in R.
#Calgary #NDVI #RemoteSensing #UrbanEcology #DataVisualization #GreennessOfCalgary #EnvironmentalData #GIS #RStats #GeospatialAnalysis #yyc #Alberta #Canada
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🌿 Where Calgary Got Greener — and Where It Didn’t?
A quick look at how Calgary’s residential communities changed in greenness (NDVI) between 2024 and 2025.
🟢 Some neighbourhoods show a clear recovery of vegetation — probably thanks to a wetter, milder summer, better soil moisture, or local greening efforts.
🔴 Others stayed stagnant or even lost NDVI — maybe new construction, dry soils, or sparse vegetation played a role.The bar chart shows Top-5 and Bottom-5 communities by NDVI change. It’s fascinating how uneven the “greening pulse” can be within one city.
📊 Based on Sentinel-2 data, mid-May – mid-September, processed in R.
#Calgary #NDVI #RemoteSensing #UrbanEcology #EnvironmentalData #GIS #Sentinel2 #ClimateImpact #GeospatialAnalysis #DataScience #RStats #OpenData #GreennessOfCalgary #Alberta #Canada
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Explore the blog series on comparing spatial patterns in raster data using R. 🌍📊
- Techniques for analyzing continuous and categorical data
- Handling overlapping and arbitrary regions
- Advanced methods for comparing spatial patternsFind the full series at https://buff.ly/s35030O
#GIS #Rstats #SpatialAnalysis #RasterData #GeospatialAnalysis #rspatial
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Geospatial analysis (Cartography 🗺️)
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or t...
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Anothhhhaaa Medium Article looking at Raster Data Preparation - specifically a quick recipe to slice GeoTiffs into bite sized chunks that are optimized for use in Geospatial Machine Learning & AI Solutions with GDAL. 🗺️
#GIS #gischat #datascience #machinelearning #ai #geospatial #GDAL #geospatialanalysis
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This week on STEAM Powered, our conversation is with Helen McKenzie, Geospatial Advocate.
Geographic information systems. There, I've lost a few of you already, but you're missing out because GIS is more than about where things are, but why they're there, and quite a few of those reasons are around why people do people things. Helen McKenzie is a cartographer, data visualiser, and geospatial advocate who is passionate about finding meaning in complex data and making information beautiful.
We speak about Helen's love of maps and how we can use geospatial analysis to elegantly give us more meaning to the way we live in the world around us and keep our societies ticking away.
Watch or listen at: https://link.chtbl.com/steampowered
Show Notes at https://steampoweredshow.com/shows/helen-mckenzie
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Control technologies are critical to combat #IUU fishing.
A good example of this ? the use of #EarthObservation Services and shared capacities of other #EUAgencies 🇪🇺@CMEMS_EU @EU_MARE
RT @eusatcen: 🌍Today, on the Day Against #IUUFishing, SatCen is proud to contribute to the combatting of illegal fishing activities by supporting @EFCA_EU !#Geospatialanalysis is an important tool to ensure responsible fishing practices that safeguard our oceans for future gene…
🐦🔗: https://n.respublicae.eu/EFCA_EU/status/1665743469032226817
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I want to draw the smallest polygon around 80% of n number of points on a map. What tools should I look at to investigate how to do this? #lazyWeb #GIS #geospatialAnalysis #python #R
