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Forest canopy survey?

Would it be possible to use Pix4d to check the health of a forest?
Here in Corsica we have a lot of mixed forests combined with bushes at unreachable locations . It would be helpful to be able to control the damages caused by imported insects.
Thanks in advance for ideas.

Kind regards


( I would like to point out I just watched the webminar18 on agriculture.

Dear Dominique,

Our software can process images captured by any UAVs and any cameras (DSLR, large format or lightweight compact camera, perspective or fisheye lens). The only requirement is to have .JPEG or .TIFF images.

However not all sensors are suitable for the generation of a good reflectance map from which other relevant outputs are derived.

When working with common or modified cameras, they are probably not designed for radiometric fidelity. They can still be used with Pix4Dmapper and give you good results provided that you perform some pre-processing to the images before importing them into our software.
For example you should correct for vignetting, dark current, etc., which requires to have access to the .RAW images files.

There are some specific multi-spectral cameras that are designed for radiometric fidelity and the manufacturer usually provides the tools to produce accurate results.

About camera requirements for agriculture precision:
About NIR cameras:

The Index Calculator of Pix4Dmapper will allow you to generate Index Maps where the color of each pixel is computed using a formula that combines different bands of the reflectance maps. NDVI is one possible combination.
This index relies on the NIR and R bands and takes values between -1 and +1 depending on the health of the plant/tree. In general the closer to +1 the healthier the plant/tree.

The practical use behind Index Maps is to check the plant/tree health on the field for each range of values you defined and to classify your map according this information.

More on the Index Calculator:

In addition, we usually provided some flight recommendations to obtain reliable results including:

  • Good connection between images of at least 85% frontal overlap and 70% side overlap.
  • Low fight altitude to have a more detailed visual content.
  • Accurate image geolocation.