Vegetation Analysis with NDVI
Calculate NDVI from satellite bands and inspect vegetation patterns within a broader raster-analysis demonstration.
How can vector and raster analysis work together in one reviewable GIS process?
This walkthrough brings building footprints, roads, census tracts, elevation and Sentinel-2 imagery into a single GIS demonstration. It shows how attribute inspection, spatial relationships and raster processing can be combined without treating each operation as a separate application.
The same video covers NDVI and false-color imagery, building distance to a coastline, and mean elevation within building footprints. These are different analytical tasks within the original demonstration, presented together here.
Topics in this video
Building Proximity to the Coastline
Use near-distance analysis to add coastal-proximity evidence to building records.
Building-Level Elevation Analysis
Summarize a digital elevation model by building using zonal statistics.
Source video
Watch the example
Full demo
Watch on YouTube (opens in a new tab)Advanced Geospatial Analysis Without Coding | GriidAI Demo (Vector & Raster Workflows)
From question to analysis
Data and inputs
- Building footprints, roads, census tracts and a coastline layer
- A digital elevation model and Sentinel-2 imagery
- Compatible coordinate systems, attribute definitions and a study boundary
Analysis described in the video
- Explore the source attributes and prepare the vector and raster inputs.
- Apply vector operations such as buffers, clipping, selection and spatial joins.
- Inspect raster tasks including masking, NDVI, false-color composites and reclassification.
- Review proximity and zonal-statistics outputs alongside the source layers.
Understand the output
Outputs described
- Mapped vector and raster results from the combined demonstration
- NDVI and supporting imagery visualizations
- Coastal-distance and building-level elevation attributes
What to review
- Check coordinate systems, raster alignment, distance and elevation units.
- Review imagery dates, spectral bands, clouds and missing pixels.
- Interpret proximity, terrain summaries and vegetation indices according to their individual methods.