#spatialanalysis

πŸ’§πŸŒ Greg CocksGregCocks@techhub.social
2026-01-29

See The Mississippi River’s Hidden History, Uncovered By Lasers
Using hyperprecise LiDAR data,. a cartographer [well, hydrographer!] maps the river’s bend and channels over time with mesmerizing results…
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nationalgeographic.com/science <-- shared technical / media article
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dancoecarto.com/ <-- shared @Daniel Coe portfolio and more
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usace.contentdm.oclc.org/digit <-- shared Harold Fisk's 1944 USACE report, β€œThe Alluvial Valley of the Lower Mississippi River”
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#GIS #spatial #mapping #water #hydrography #hydrology #LiDAR #remotesensing #spatialanalysis #spatiotemporal #hydrogeomorphology #Mississippi #River #Fiske #cartography #visualisation #meandering #channels #landforms #floodplains #opendata #3DEP #topography #geomorphology #levees #dikes #oxbows #channel #paleohydrology
#DanielCoe | @nationalgeographic | #USGS | #USACE

2026-01-28

New updates to Intro to GIS and Spatial Analysis by Manuel Gimond πŸš€

The spatial analysis section now includes expanded coverage of spatial patterns.

Explore the update: mgimond.github.io/Spatial/chp1

#GISchat #SpatialAnalysis #OpenEducation

πŸ’§πŸŒ Greg CocksGregCocks@techhub.social
2026-01-27

Learn GRASS GIS [etc]
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grass-tutorials.osgeo.org/ <-- shared GRASS tutorials, etc
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grass.osgeo.org/download/ <-- @GRASS GIS download page
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H/T Doug Newcombe
β€œA new tutorial on creating hydroflattened DEMs directly from airborne point cloud data has been posted in the GRASS tutorials site... There is a link in the tutorial to a more detailed version on Zenodo.
Be sure to check out the other tutorials on the recently renewed tutorial site…”
#opensource #GRASS #GRASSGIS #spatial #mapping #tutorial #free #onlinelearning #learning #selflearning #tutorials #spatialanalysis #tools #download #Windows #LINUX #MacOS #Docker #sampledata
@grassgis

πŸ’§πŸŒ Greg CocksGregCocks@techhub.social
2026-01-18

Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
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doi.org/10.1016/j.rsase.2025.1 <-- share paper
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β€œHIGHLIGHTS:
β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
β€’ District-level RF/XGBoost model training with expert-weighted validation.
β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
#Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

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