Getting Started with Vegetation Indices

Now that you’ve learned how to access satellite data from our guide to free satellite data sources, you might be wondering: what can you actually do with this data? One of the most powerful applications is vegetation analysis using spectral indices. Most high-resolution satellite imagery comes with four fundamental spectral bands: Red, Green, Blue (RGB), and Near-Infrared (NIR). While the RGB bands show us what we naturally see, the NIR band reveals crucial information about vegetation health that’s invisible to the human eye. By combining these bands mathematically, we create vegetation indices - powerful tools that help us understand plant health, biomass, and growth patterns. Here are the five most commonly used vegetation indices in remote sensing, each offering unique insights into vegetation characteristics.

1. Normalized Difference Vegetation Index (NDVI)

Formula: NDVI = (NIR - Red) / (NIR + Red)

NDVI is mainly used for crop health monitoring, biomass estimation, drought assessment, and long-term vegetation studies. It provides a value ranging from -1 to +1, where healthy vegetation typically ranges from 0.2 to 0.8, and bare soil sits around 0.1. The higher the NDVI value, the healthier and denser the vegetation. This makes NDVI particularly effective for tracking vegetation changes over time and comparing plant health across different areas.

Key Values:

• Dense, healthy vegetation: 0.6 to 0.9
• Moderate vegetation: 0.2 to 0.5
• Sparse vegetation: 0.1 to 0.2
• Bare soil: 0 to 0.1
• Water bodies: -0.25 to 0

Key Applications:

• Agricultural monitoring
• Crop yield prediction
• Environmental management
• Carbon sequestration estimation

2. Enhanced Vegetation Index (EVI)

Formula: EVI = G × ((NIR - Red) / (NIR + C1 × Red - C2 × Blue + L))

The coefficients adopted in the MODIS-EVI algorithm are: L=1, C1 = 6, C2 = 7.5, and G = 2.5. EVI produces values that better represent vegetation health in areas where NDVI might overly saturate. The values typically range from -1 to +1, with healthy vegetation showing values between 0.2 to 0.8. Unlike NDVI, EVI remains sensitive to changes in dense canopy areas, making it particularly valuable for monitoring rainforests and other areas of high biomass. The addition of the blue band also helps correct for atmospheric interference and soil background noise.

Key Values:

• Dense, healthy vegetation: 0.4 to 0.9
• Moderate vegetation: 0.2 to 0.4
• Sparse vegetation: 0.1 to 0.2
• Bare soil: < 0.1
• Water: < 0

Key Applications:

• Rainforest monitoring
• Areas with dense vegetation
• Canopy structure studies

3. Soil Adjusted Vegetation Index (SAVI)

Formula: SAVI = ((NIR - Red) / (NIR + Red + L)) × (1 + L)

where L is the soil brightness correction factor (typically 0.5) SAVI generates values similar to NDVI but with better accuracy in areas where soil is visible through the vegetation. It produces values from -1 to +1, with healthy vegetation typically ranging from 0.2 to 0.8. The key difference is its ability to minimize soil brightness influences, making it especially useful in arid regions or areas with sparse vegetation where soil background effects can significantly impact other vegetation indices.

Key Values:

• Dense vegetation: > 0.7
• Moderate vegetation: 0.4 to 0.7
• Sparse vegetation: 0.2 to 0.4
• Exposed soil: < 0.2

Key Applications:

• Arid region monitoring
• Early crop growth stage monitoring

4. Normalized Difference Water Index (NDWI)

Formula: NDWI = (Green - NIR) / (Green + NIR)

NDWI produces values that indicate vegetation water content and water stress. Values range from -1 to +1, where positive values generally indicate healthy, well-watered vegetation, and negative values suggest water stress. Water bodies typically show high positive values (>0.3), while dry vegetation and soil show negative values. This makes NDWI particularly effective for monitoring drought conditions and irrigation needs.

Key Values:

• Water bodies: > 0.3
• Wet vegetation: 0.1 to 0.3
• Dry vegetation: -0.1 to 0.1
• Soil: < -0.1

Key Applications:

• Water stress monitoring
• Irrigation planning
• Fire risk assessment
• Wetland mapping

5. Normalized Difference Red Edge (NDRE)

Formula: NDRE = (NIR - RedEdge) / (NIR + RedEdge)

NDRE produces values that indicate chlorophyll content and nitrogen status in vegetation. Values typically range from -1 to +1, with healthy vegetation showing values between 0.2 to 0.5. This index is particularly sensitive to subtle changes in plant health and can detect stress before it becomes visible to the naked eye or shows up in NDVI analysis. It’s especially valuable for precision agriculture where early detection of plant stress is crucial.

Key Values:

• Healthy vegetation: 0.2 to 0.5
• Stressed vegetation: 0.1 to 0.2
• Very stressed/senescent: < 0.1
• Non-vegetation: < 0

Key Applications:

• Precision agriculture
• Crop yield optimization
• Crop management
  • Early stress detection
  • Disease monitoring
  • Growth stage assessment

Choosing the Right Index

The choice of vegetation index depends on several factors:

• Your specific application and goals
• Environmental conditions of your study area
• Vegetation density and types
• Available spectral bands in your imagery
• Required accuracy and sensitivity Often, using multiple indices in combination provides the most comprehensive understanding of vegetation conditions.

Need Help With Your Vegetation Monitoring Project?

Whether you’re monitoring crop health, tracking deforestation, or quantifying carbon sequestration, choosing the right satellite imagery and vegetation indices is crucial for your project’s success. While free satellite data sources are great for many applications, some projects require higher resolution or more frequent monitoring. We can help you find the right satellite data for your specific needs. Explore available imagery through our Pera Portal or get in touch to discuss your project specific requirements.