#geospectralart — Public Fediverse posts

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

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

Exploring large-scale landscape structures with a simple Principal Component Analysis (PCA) applied to a MODIS composite.

The PCA reveals striking contrasts in surface materials, vegetation patterns, and terrain transitions across western Canada — from the Rocky Mountains into the prairies and the Canadian Shield.
Even with moderate-resolution MODIS data, spectral variability produces a surprisingly rich view of regional geography and geomorphology.

#RemoteSensing #EarthObservation #Geology #PCA #MODIS #OpenData #GIS #QGIS #Geomorphology #Geoscience #RStats #Lithology #GeoDataArt #GeoSpectralArt #BorealForest #Taiga #Canada #Alberta

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

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

🛰️ Today I’m sharing one of my favourite large-scale remote sensing experiments:
a Principal Component Analysis (PCA) of MODIS composite data for the three central provinces of Canada (Alberta, Saskatchewan, Manitoba).

On this map:
- PC1 emphasizes broad ecological zones and vegetation productivity
- PC2 highlights soil and surface moisture differences
- PC3 captures subtle spectral variations — often linked to geology, wetlands, disturbance patterns, or local microclimates

Even though it looks abstract, PCA is a kind of “spectral fingerprint” of the land. It summarises thousands of square kilometres into a single visual structure that shows how the Canadian Prairies and Boreal regions differ and transition into one another.

#RemoteSensing #MODIS #Geospatial #EarthObservation #Rstats #DataVisualization #PCA #SatelliteData #Canada #Alberta #Saskatchewan #Manitoba #EnvironmentalData #GeoDataArt #GeoSpectralArt