Getting Started

New to remote sensing? Read these pages in order. Each step builds on the one before it, and none of them needs a particular software platform.

1. Understand what an image is

1. Digital Imagery: start here; every later page assumes this picture of an image.
2. Spectral Bands: what each layer of the image records.
3. Visualization: how to look at those layers without fooling yourself.
4. Resolution: what a sensor can and cannot resolve.

2. Put images on the map

5. Raster vs Vector: how imagery meets points, lines and polygons.
6. Coordinate Systems: needed before combining any two datasets.

3. Turn pixels into answers

7. Time Series: one place, many dates.
8. Cloud Masking: remove what hides the surface before you measure it.
9. Classification Basics: labelling what the pixels show.
10. Change Detection: comparing dates with care.

4. Choose real data

11. Sentinel-2 and Landsat: the two open archives most examples use.
12. STAC Catalogs: how to find scenes from either one.

5. Run a complete workflow

13. NDVI Monitoring: the steps above, applied to one question from start to finish.

6. Try one yourself

14. Your First NDVI Map: choose QGIS or Python to make and check a vegetation-index map from one fixed NASA HLS scene. Both routes use local, free tools; direct imagery downloads require a free NASA Earthdata Login.

Where to go next

• Applied Workflows for other methods, and Applications for the fields that use them.
• Data Sources for open imagery and ways to find it, and Engineering for open formats and repeatable pipelines.
• Implementation Tracks for optional platform-specific examples. The current Earth Engine track is not required for the concepts, workflows, or first exercise.

For how the site is organized, see What is Remote Sensing Labs?.