Who can use this feature
Editors of a project within a Professional or Pro + workspace.
Selection requirements for tool to work
A dataset - either drone mapping data or Sentinel-2 imagery
Indices are simple mathematical combinations of different image bands that highlight specific features of the landscape. By comparing how surfaces reflect light in different parts of the spectrum (e.g. blue, green, red, NIR), indices make it easier to see things like healthy vegetation, water, or burned areas that may not stand out in the raw imagery. Calculating indices from your drone or satellite data helps you turn pixels into insights - from measuring crop health to tracking environmental change - in just a few clicks.
Click on your dataset in the table of contents
From the top menu bar, click the
'leaf' buttonto calculate indices. You can also access this through the toolbar under enhancements > calculate indices.
Optionally preview the different indices
Select the index / indices you would like to calculate (maximum ten at once)
Click
Add to project
Each index will be added to your project as a new layer in the table of contents immediately above the dataset from which it was derived.
Index summary
Each index has common use cases, but those aren’t the only ways to use them. We encourage you to experiment with different indices to see which ones bring out the patterns most relevant to your project.
Index | Full name | Example use | Equation | Sentinel-2 bands |
NDVI | Normalized Difference Vegetation Index | General vegetation health | (NIR−Red)/(NIR+Red) | B8, B4 |
MSAVI | Modified Soil Adjusted Vegetation Index | Vegetation in sparse / soil-rich areas | (2NIR+1 − √((2NIR+1)²−8(NIR−Red)))/2 | B8, B4 |
SAVI | Soil Adjusted Vegetation Index | Vegetation in arid zones | ((NIR−Red)/(NIR+Red+L))*(1+L), L=0.5 | B8, B4 |
NDWI | Normalized Difference Water Index | Water body mapping | (Green−NIR)/(Green+NIR) | B3, B8 |
NDMI | Normalized Difference Moisture Index | Vegetation / canopy water content | (NIR−SWIR)/(NIR+SWIR) | B8, B11 |
NBR | Normalized Burn Ratio | Burn severity, fire recovery | (NIR−SWIR)/(NIR+SWIR) | B8, B12 |
GI | Greenness Index | Comparing vegetation cover with a normalised greenness score | G/(R+G+B) | B3, B4, B2 |
EVI2 | Enhanced Vegetation Index 2 | Dense canopy vegetation | 2.5*(NIR−Red)/(NIR+2.4·Red+1) | B8, B4 |
GNDVI | Green Normalized Difference Vegetation Index | Chlorophyll / stress detection | (NIR−Green)/(NIR+Green) | B8, B3 |
RENDVI | Red Edge Normalized Difference Vegetation Index | Stress detection via red-edge | (NIR−RE)/(NIR+RE) | B8, B5 |
NDBI | Normalized Difference Built-up Index | Built-up/urban mapping | (SWIR−NIR)/(SWIR+NIR) | B11, B8 |
VARI | Visible Atmospherically Resistant Index | Vegetation detection in variable lighting or atmospheric conditions | (G−R)/(G+R−B) | B3, B4, B2 |
ExG | Excess Green Index | Quick vegetation / non-vegetation separation | 2G−R−B | B4, B3, B2 |
NGRDI | Normalized Green-Red Difference Index | Simple vegetation health detection | (G−R)/(G+R) | B3, B4 |
Note that in cases where a denominator equals zero, the index value will return zero.
Index availability
The indices that can be calculated for any particular dataset depend on the spectral bands available within that dataset.
Index | RGB drone | Multispectral drone | Sentinel-2 (coming soon) |
NDVI |
| ✅ | ✅ |
MSAVI |
| ✅ | ✅ |
SAVI |
| ✅ | ✅ |
NDWI |
| ✅ | ✅ |
NDMI |
|
| ✅ |
NBR |
|
| ✅ |
GI | ✅ | ✅ | ✅ |
EVI2 |
| ✅ | ✅ |
GNDVI |
| ✅ | ✅ |
RENDVI |
|
| ✅ |
NDBI |
|
| ✅ |
VARI | ✅ | ✅ | ✅ |
ExG | ✅ | ✅ | ✅ |
NGRDI | ✅ | ✅ | ✅ |
Supported drones
The RGB indices work on all RGB datasets, irrespective of the drone / sensor manufacturer.
We have validated the multispectral indices on the following drones / sensors, but they may also work for others. Please reach out to us if they are not working as expected with your dataset.
Micasense RedEdge-M
Micasense RedEdge-MX (also called RedEdge P dual)
Parrot Sequoia
DJI Mavic 3 Multispectral
DJI Phantom 4 Multispectral