#DniproHesFlood
🛰️ Dam-break modelling with GRASS GIS

I used the r.damflood module in GRASS GIS on a historical scenario:
the destruction of the DniproHES dam by Soviet forces in August 1941 during their retreat from Ukraine.

I reconstructed a pre-war DEM of the Dnipro River from old topographic maps, modeled the dam structure, and created a breach raster.

Using r.damflood, I simulated the propagation of the flood wave over the first 24 hours after the dam explosion.
Based on this model, I generated a visualization showing the wave dynamics.

All tools used are fully open-source.
The results have not yet been published anywhere.

#Geospatial #GIS #HistoricalGIS #DigitalHistory #Cartography #OpenData #DataAnalysis #DEM #TerrainModeling #Hydrology #DamBreak #Dnipro #DniproHES #Ukraine #Zaporizhzhia #QGIS #SAGAGIS #GRASS #FOSS4G

Result of a Principal Component Analysis (PCA) created from combined median composites of Sentinel-1 and Sentinel-2 imagery — somewhere in the Sudanese desert.

This PCA composite brings out contrasts between lithologies, structural domains, drainage patterns, and surface materials, highlighting features that are nearly invisible in standard RGB representations.

And yes — it also happens to look stunning.

#RemoteSensing #EarthObservation #Geology #PCA #Sentinel1 #Sentinel2 #SAR #Radar #OpenData #GIS #QGIS #Geomorphology #Geoscience #SAGAGIS #RStats #Desert #Lithology #GeoDataArt #GeoSpectralArt #Sudan #Copernicus #CopernicusSentinel

Geospatial analysis and modeling is not only about:
1. downloading Copernicus data,
2. generating pretty maps,
3. calling it “analysis”.

In reality it often means years of collecting and reconciling heterogeneous data from different eras: digitizing historical maps, fixing coordinate systems, validating geometry through indirect evidence. This stage can easily take more time than all later modeling.

The images show how I digitized and georeferenced 1920s–1940s topographic maps of the lower Dnipro region. After transforming them into a unified coordinate system, I used them to reconstruct the pre-flood terrain (before the Kakhovka Reservoir).
That DEM later became the basis for modeling the 1941 DniproHES dam-break wave.

…This data-preparation phase alone took me almost four years.

#Geospatial #GIS #HistoricalGIS #DigitalHistory
#RemoteSensing #Cartography #OpenData #DataAnalysis #DEM #TerrainModeling #Hydrology #DamBreak #Dnipro #DniproHES #Ukraine #Zaporizhzhia #QGIS #SAGAGIS #GRASS #FOSS #DniproHesFlood

Result of a Principal Component Analysis (PCA) based on combined median composites of Sentinel-1 and Sentinel-2 satellite imagery — somewhere in the desert of Sudan.

The PCA highlights contrasts between different lithologies, structural domains, and surface materials, revealing subtle geological features that are hard to distinguish in standard RGB composites.

And yes — it also looks simply beautiful.

#RemoteSensing #EarthObservation #Geology #PCA #Sentinel1 #Sentinel2 #Radar #OpenData #GIS #QGIS #Geomorphology #Geoscience #SAR #SAGAGIS #RStats #Desert #Lithology #GeoDataArt #GeoSpectralArt

After several weeks of thinking and DEM preprocessing, I finally generated a complete geomorphons map for the part of Inhulets River basin (Ukraine).
It was far from easy — the original Copernicus GLO-30 DEM required careful cleaning, correction, and multi-step preparation before meaningful terrain forms could emerge.

What makes geomorphons truly valuable for me is how well they correspond to geochemical landscape types — eluvial, transeluvial, superaquatic, and subaquatic zones.
This overlap allows interpreting geomorphons as functional terrains with distinct element migration patterns, bridging geomorphology and environmental geochemistry.

All computations were done using R + SAGA GIS + QGIS, with the excellent Rsagacmd package for seamless tool integration.

#Geomorphons #SAGAGIS #Rsagacmd #QGIS #Geochemistry #RStats #Geospatial #Hydrology #RemoteSensing #Copernicus #OpenData #GIScience #InhuletsRiver #EnvironmentalGeochemistry #FOSS

📚 A short note for those who just joined my profile

Over the past few days, I’ve been sharing many posts — not for promotion, but to open parts of my long-term research archive.

During more than a decade of independent work, I’ve accumulated a huge amount of data, methods, and materials: from geochemical modeling and groundwater studies to urban greenness mapping and environmental visualization.

Many of these works were completed years ago but remained unpublished outside of narrow expert circles — so I’m gradually bringing them here, one by one, to make them visible and interconnected.

The pace will slow down soon 🙂 — what you’re seeing now is a process of unfolding a whole scientific story.

#OpenScience #Geochemistry #Hydrology #GIScience #EnvironmentalData #UrbanEcology #IndependentResearch #ScienceCommunication #DataVisualization #Rstats #QGIS #SAGAGIS #FOSS #Linux #DataScience

🌊 Hydrological patterns in the Inhulets River Basin

I’m working on a large-scale study of the technogenic impact on the Inhulets River system (Ukraine).
The workflow combines R + SAGA GIS + QGIS for geomorphometric and hydrological analysis.

Over the past week, I’ve been refining digital elevation models and tracing surface runoff connectivity — not an easy task in a region reshaped by century of mining.

The map below shows one of the calmest and most “well-behaved” areas, far from the mining zone.
As for the drainage network over the mining areas… let’s just say it’s geological chaos down there 😄

🛰️ Data: Copernicus DEM (GLO-30)
🧭 Tools: SAGA GIS (Terrain Analysis), R, QGIS

#Hydrology #Geomorphometry #SAGAGIS #QGIS #RStats #DEM #GIScience #EnvironmentalModeling #MiningImpact #Geodata #InhuletsRiver #Ukraine #KryvyiRih #Copernicus #CopernicusDem

🌳 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

“If the map does not match the terrain — trust the terrain!”
— Principle of field geoscience

This 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

One of the biggest challenges was handling non-drainage depressions — they can be both real hydrological features and digital artefacts.

Instead of “filling sinks”, I used a mixed approach:
– identifying real depressions manually (tailing ponds, collapse funnels, drainage portals),
– and letting the model naturally route flow into them.

It turned out to be an effective way to simulate realistic runoff in a heavily disturbed terrain.

#sagagis #RStats #RStats #environmentaldatascience