geospatial-raster

A hands-on practice repository for Module 6, focused on core geospatial concepts using a small synthetic raster dataset. The project demonstrates GeoTIFF-style multi-band rasters, pixel-level spectrum extraction, spectral signatures, and simple indices.

Project Structure

geospatial-raster/
  README.md
  src/
    synthetic_raster_spectral_signature.py
  data/
    synthetic_pixel_spectrum.csv

M6: Geospatial Data Practice (Synthetic Raster + Spectral Signature)

This project is a simple practice setup for Module 6 concepts:

• What geospatial data represents (location, attributes, time)
• Raster vs vector thinking
• Multi-band raster structure (bands, rows, columns)
• Extracting a pixel spectrum using (row, col)
• Plotting spectral signatures
• Basic georeferencing with an affine-style transform (row/col to lon/lat)
• Optional NDVI calculation using Red and NIR bands

What's inside

• src/synthetic_raster_spectral_signature.py

  • Creates a small 3×3 synthetic multi-band raster with six bands: Blue, Green, Red, NIR, SWIR1, SWIR2
  • Extracts the spectral values for a selected pixel
  • Converts pixel indices to approximate longitude and latitude
  • Plots the spectral signature
  • Computes NDVI

• data/synthetic_pixel_spectrum.csv

  • A simple band-level table representing reflectance values for a single pixel

Requirements

Python 3.9 or newer is recommended.

Install dependencies:

pip install numpy pandas matplotlib

Usage

Run the main script:

python src/synthetic_raster_spectral_signature.py

This will:

1. Generate a synthetic 3×3 raster with 6 spectral bands
2. Extract the spectral signature at pixel (1, 2)
3. Display the pixel's geographic coordinates (lat/lon)
4. Plot the spectral signature curve
5. Calculate NDVI (Normalized Difference Vegetation Index)
6. Save results to data/synthetic_pixel_spectrum.csv and spectral_signature.png

Band Information

Band	Central Wavelength (nm)	Spectral Range (nm)
Blue	490	450-510
Green	560	520-600
Red	665	630-690
Near Infrared (NIR)	865	760-900
Shortwave Infrared 1 (SWIR1)	1610	1550-1750
Shortwave Infrared 2 (SWIR2)	2200	2100-2300

Learning Outcomes

After completing this practice, you should understand:

• How raster data is structured as arrays with bands, rows, and columns
• How to extract and work with spectral signatures from raster data
• The relationship between pixel indices and geographic coordinates
• How to calculate simple spectral indices like NDVI