Indices

Satellite Data Interpretation – Indices

Interpreting satellite data to derive insights about Earth’s surface features and changes is a complex task. Index-based analysis has emerged as a powerful tool to extract information from satellite data. Examples of indices, derived from combinations of spectral bands, highlight specific features, patterns, and environmental conditions:

Vegetation: Vegetation indices are fundamental in monitoring plant health, biomass, and land cover changes. Indices like the Normalised Difference Vegetation Index (NDVI) use the contrast between the reflectance in the red and near-infrared bands to quantify vegetation density. High NDVI values typically indicate healthy and dense vegetation, while lower values suggest stressed or sparse vegetation. These indices are crucial for applications ranging from agriculture monitoring to ecosystem health assessments.

Soil: The Normalised Soil Moisture Index (NSMI) gives insight into the water balance of the soils, important information e.g. for agricultural activities.

Urbanisation: Urbanisation indices help analyse and monitor the extent and characteristics of urban areas within satellite imagery. The Urban Heat Island Index (UHII), for example, compares the temperature of urban and rural areas, highlighting the increased heat in urban environments. Other indices, like the Normalised Difference Built-Up Index (NDBI), focus on the built-up areas within the landscape, aiding in urban planning and infrastructure development studies.

Water: Satellite data are used to assess water quality through specific indices. The Normalised Difference Water Index (NDWI) is used to identify surface water bodies, while indices like the Water Quality Index (WQI) use multiple bands to assess parameters such as chlorophyll concentration and sediment loads, offering insights into aquatic ecosystems and water resource management.

Burned Area: Monitoring and assessing burned areas and wildfires are critical applications of satellite data. Indices like the Normalised Burn Ratio (NBR) highlight changes in vegetation cover after a fire. With them, analysts can quantify the severity and extent of the burned area, aiding in post-fire recovery planning and ecological restoration.

Exercises

• Satellite Map:
  • Use the layer selector to select the NDVI (normalised difference vegetation index) derived from the Sentinel-2 data. Compare with the true colour image and try to identify the land use and landcover classes in the region.
  • Find features with low/high NDVI and identify the respective land cover using the true colour image. Where is the NDVI low, where is it high? Do your findings fit with your expectations?
  • Select now the NSMI (normalised soil moisture index) and repeat what you did with the NDVI.
  • Take a special look at agricultural land, both with and without vegetation. Which parts appear to be driest (red colours in the NSMI)?
  • Look at built-up areas. The colours differ between yellow and red, i.e. between low and very low soil moisture. What does this tell us about the density of settlements?
  • Look at the NDWI (normalised water index) showing water bodies in blue and compare with the true colour image. Are the water bodies identified correctly?

• Copernicus Browser:
  • Open the case study area in the Copernicus Browser.
  • Find the most recent Sentinel-2 dataset covering the area displayed in the satellite map.
  • Select a natural colour representation.
  • Can you identify additional, recent changes in the area?
  • Select one by one the index visualisations offered by Copernicus Browser (NDVI, NDWI, NDSI, Moisture Index). Zoom into different sections of the area and check, where these indices can help to discriminate important land cover classes.
  • For advanced readers: Select the custom visualisation and there the tab “Index”. Try to define indices with band combinations of your own. What are your findings with respect to the representation of different landcover classes (water, agricultural land, built-up land, etc.)?

Links and Sources

Downloads:
	PDF document of the case study (includes exercises): English, German, French, Italian, Spanish
	This case study is covered on page 20 of the printed ESA Schoolatlas – download the PDF document of the page: English, German, French, Italian, Spanish