In this tutorial, you’ll explore post-fire vegetation recovery using drone and satellite imagery in GeoNadir. You’ll calculate greenness and burn indices, perform change detection, and interpret patterns of recovery and severity. By the end, you’ll understand how to combine drone spatial detail with satellite scale to monitor ecosystem resilience through time.
Getting started
1. Navigate to https://data.geonadir.com/myprojects
Note that your page will look different to this one, depending on the location of your projects.
2. Click "New project"
3. Click "Untitled"
4. Change the name of the project to "Fire example [[Enter]]"
5. Click "Add data", and then "Search existing drone data"
6. Click the "Search" field.
7. Type "Trinity park [[Enter]]"
8. Select the datasets captured on 22 Oct 2020, 7 Jan 2021, and 14 Nov 2020.
Click "Add to project"
Exploring the data
9. Click the eyeball icon to toggle the layers on and off.
Explore the difference that you can see between these three datasets.
Take a moment to visually compare features like burnt areas, unburnt vegetation, and built surfaces, this will help interpret results later.
You should notice in the top part of the scene that the October image has very dry vegetation.
This same area appears rather black in the November images, post fire.
And in January, there is considerable green regrowth.
Note that each flight covers a slightly different area, and the data were captured at different times of the day (the November image has eastern shadows). While this is not best practice, sometimes the ideal conditions are not practical.
Adding indices
Beyond observing the RGB imagery, we can also use indices to evaluate our data.
Indices are mathematical combinations of the different wavelengths of light and can help quantify certain features, and make them 'pop' for visualisation.
Now let's try calculating an index in GeoNadir!
Calculating indices is available to those with a Professional or Pro+ subscription. Contact us to arrange a free trial.
10. Click to select the November image
11. Click the 'Calculate indices' tool.
12. As this is an RGB dataset, there are only four options available for indices.
Click "GI" to choose the Greenness index.
13. Click "Add to project"
The greenness index in remote sensing is a measure used to assess the amount of green vegetation in a particular area. It is commonly derived from satellite, drone, or other aerial imagery and helps in monitoring and managing natural resources and agricultural practices.
The greenness index is calculated on GeoNadir using the following equation:
GI = green / (red + green + blue)
14. Use the same steps to add the Greenness index for the January data.
15. Select the November GI layer and drag it up the table of contents so that it is below the January GI layer.
16. Toggle the visibility of you GI image to easily compare the data between dates.
Areas with high GI values will appear green, while those with low values will be white.
Note that different lighting conditions or flight altitudes can affect index values. When comparing datasets, ensure flights are taken under similar conditions where possible.
Calculating the greenness index change
17. Select both the January and November datasets (hold CTRL / CMD to multiselect)
18. From the top tool bar, click to Detect change
Change detection is available to those with a Professional or Pro+ subscription. Contact us to arrange a free trial.
19. Click "Greenness index"
Tip: Note that note that subtraction order matters. For example:
January minus November (as per this example) highlights regrowth (positive values), while November minus January highlights loss (negative values).
20. Click "Run"
21. Toggle off both January and November GI layers to reveal the Change detection layer instead
For best results, use an RTK drone so that your drone datasets are properly aligned. Even small offsets between flights can create apparent “change” that isn’t real.
You should be able to see evidence of some mismatch with these example datasets, so take care when interpreting data close to the edges of features.
22. Let's look at what these green values mean.
Click to expand the Greenness change layer
23. You can see the legend for the colour ramp now with the numbers it represents.
Click the colour ramp.
24. Change the color ramp to 'Berry meadow'
25. Play with alternative colour ramps if you wish to find something that looks good to you.
When complete, close the Style bar.
Evaluating change values
26. From the top menu bar, click to open the Inspect tool
27. Click "All layers"
28. Click around your data to get the value representing your change.
A negative value means that the pixel became less green. A positive value means that it became more green.
Were you surprised at the extent of green up between November and January post fire?
This time of the year is the wet season for this location, which is in the Wet Tropics. So green-up can happen very quickly!
Try creating the GI for the October image and evaluating the patterns you see. October is at the end of the dry season in this region.
The bigger picture
29. The area covered by these drone data is rather small. How do we know how far the fire extended?
Adding satellite data to our project is a great way to understand broader patterns. So let’s zoom out to a broader perspective using Sentinel-2 data. This allows you see how the site fits within the wider pre- and post-fire landscape.
Click the icon to add new data.
30. Click "Stream online data"
31. Click "Most recent"
32. Click "Custom"
33. Click the "MM/DD/YYYY" field for the start of the search period
34. Type "10202020" to start the search at the 20th October 2020
35. Click the "MM/DD/YYYY" field for the end of the search period
36. Type "11052020" to finish the search on the 5th November 2020
37. Click "Apply"
38. Select the images captures on 30 Oct and 2 Nov. Click "Add to project"
39. Repeat the above process to find data from 31 Jan 2021.
Click "Add to project"
Take the time to explore the three satellite images and the patterns that you can see in each. How do they differ from the drone data?
40. Toggle off your layers until you can see the image from November
41. Can you see the smoke? What about the area that was burnt?
42. The true colour imagery is quite dark and its difficult to clearly see the area that was burnt. Let's turn to some false colour composites that contain wavelengths beyond human vision to help out.
Understanding colour composites
43. Expand the November dataset in the table of contents
44. Toggle off the true colour option
45. Toggle on the standard false colour.
46. Notice that the smoke is harder to see, but the burnt area is much clearer.
The standard false colour composite uses green, red, and NIR light instead of the standard RGB.
Healthy vegetation strongly reflects NIR light, and is displayed by the computer as red. So you can see a lot of vegetation in a red colour in this image.
The dark area is the burnt area.
Also, as we are not using blue light in this composite, the smoke is harder to see. Longer wavelengths such as NIR can pass through some smoke, while the short wavelengths like blue get reflected and scattered.
47. Now turn on the false natural colour
48. And turn off the standard false colour
49. The smoke is even harder to see here. But the burnt area really stands out!
The false natural colour composite uses green, NIR, and SWIR light instead of the standard RGB.
Healthy vegetation strongly reflects NIR light, and this time is displayed by the computer as green. So you can see a lot of vegetation in a green colour in this image.
The dark area is the burnt area.
The smoke is now nearly invisible as both NIR and SWIR are long wavelengths that can pass right through it.
Satellite and drone data differ in resolution and calibration. Compare your data qualitatively only to see spatial patterns rather than to extract exact numeric matches.
Satellite indices
50. Just like with our drone data, we are able to calculate indices with satellite imagery to further evaluate our features of interest.
Select the November image.
51. Open the Calculate indices tool
52. Because the Sentinel-2 satellite data has many more wavelengths than our drone data, we have lots more index options.
Click "NBR"
The NBR or Normalised Burn Ratio is great for looking at burn severity and fire recovery. It's calculated by:
(NIR−SWIR)/(NIR+SWIR)
NIR = Near infrared; SWIR = Shortwave infrared
Notice that we use this instead of the Greenness index that we used with the drone data. The NBR is better for looking at burnt areas, but cannot be used with RGB drone data because it needs NIR and SWIR wavelengths.
53. Click "Add to project"
54. Repeat these steps to calculate the NBR for the January data.
Click "Add to project"
55. Expand the legend items for both NBR images to reveal the values of the colour ramp
56. As the ranges are slightly different between datasets, we need to standardise them so that we can confidently compare them.
Click the colour ramp of the NBR from January
57. Expand the contrast options
Alert: Contrast enhancement is available to those with a Professional or Pro+ subscription. Contact us to arrange a free trial.
You can continue this tutorial on an Essentials subscription, however keep in mind that there are slight differences in colour display between your datasets.
58. Click this number field.
59. Change from -15 to -32
60. Click this number field.
61. Change from 0.43 to 0.45.
You should be able to see the numbers against the colour ramp in your table of contents automatically update.
62. Toggle the visibility of the January NBR image to reveal the difference between it and the November NBR image
63. Notice how clearly the burnt area stands out with this index
Spend some time exploring the differences between the November and January images, and their NBR layers.
Try this: Create a third NBR image for October. How does that contribute to the time series? You may like to toggle between the indices as well as the original images to explore the local environment.
Think about it: Why might some bright urban surfaces resemble burnt areas in NBR imagery? How might seasonal variation influence your interpretation of recovery? What trade offs can you see between using drones vs. satellites as a data source?
Analysing post-fire regrowth across multiple dates helps us understand how ecosystems respond to disturbance. Combining drone and satellite imagery provides both the fine detail and the broader landscape context needed to assess recovery and resilience.