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description: Lab-measured panels used to calibrate captured data in post processing
---

# Calibration Targets

MAPIR offers various calibration targets to cover a range of applications. The compact T3-R50 seen below contains 4 panels that have been measured for light reflectance from 250 - 2,500 nm.&#x20;

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FPG0TO0OeFEJxWGmDO3iK%2F2023-04-05%2011.01.36_e_c.jpg?alt=media&#x26;token=a966acfc-7273-4cc3-91d8-ae1c278ebc13" alt=""><figcaption><p>MAPIR T3-R50</p></figcaption></figure>

The T3 diffuse reference targets have the following reflectance curves, [data download here](https://cdn.shopify.com/s/files/1/0878/4732/files/MAPIR_Absolute_Reflectance_Radiometric_Calibration_Target_Data_T3.xlsx?v=1680675392):

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FdKGTodXQcsKfxdyzZzDQ%2FMAPIR%20Diffuse%20Reflectance%20Standard%20Calibration%20Target%20Data%20T3%20(250-2500nm).png?alt=media&#x26;token=c6fff60f-9456-4bc5-a46a-ba783d110234" alt=""><figcaption><p>MAPIR T3 Reflectance</p></figcaption></figure>

Looking at the reflectance graph you can see that the values are wavelength (x-axis) versus reflectance percent (y-axis). When we capture an image of the calibration target we then create a relationship between pixel value and reflectance percent, within the spectrum that each of the camera's sensor bands are sensitive to.&#x20;

This means that with every image you capture with our cameras, you can use a photo of our reflectance targets, such as the [T3-R50](https://www.mapir.camera/collections/calibration-targets/products/diffuse-reflectance-standard-calibration-target-package-t3-r50) to calibrate the images for reflectance. Once calibrated each pixel in the image is equal to reflectance.

If you output the calibrated images in MCC as the typical JPG or TIFF then the reflectance percent is calculated by dividing the pixel value by the bit depth of the image format. So for JPG divide by 255, and for TIFF divide by 65,535. You can also choose the PERCENT format output in MCC, and then each pixel will range from a percent value of 0.0 to 1.0 (0% to 100% reflectance). Just keep in mind that some image applications cannot accept the percent (floating point) images, and they are large in size storage wise.

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Below you will find a non-calibrated 16-bit TIFF image of our V2 target captured by our [MAPIR Survey3W RGN camera](https://www.mapir.camera/collections/survey3/products/survey3w-camera-red-green-nir-rgn-ndvi), with the addition of the 4 colored regions over the targets added by the processing in MCC during [reflectance calibration](../interface-tabs/process-tab#reflectance-calibration). We have added green call-out overlays highlighting the target region average pixel values and some low/high vegetation points. We have also brightened the image to make it easier to view here.

Here is a download link to the working files below. You can try them in MCC yourself.

## [DOWNLOAD HERE](https://drive.google.com/file/d/1agfMEEZboUgD2VKsvUJsok7mFWAZO_xH/view?usp=sharing)

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2F5eRjuzckkLK1cX4itb57%2Frgn_orig_lbls.jpg?alt=media&#x26;token=cfee1030-4a00-4659-9568-8f7bf51c3787" alt=""><figcaption><p>Image brightness was increased for easier viewing, pixel data was not changed</p></figcaption></figure>

Processing the image in MCC for [reflectance calibration](../interface-tabs/process-tab#reflectance-calibration) we obtain the following calibrated image, with updated pixel values:

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FCrz42wu9wjxfdzcruoen%2Frgn_calib-px_lbls.jpg?alt=media&#x26;token=9526e406-a131-495c-b10d-17729290da6c" alt=""><figcaption></figcaption></figure>

Converting to percent reflectance by dividing by the pixel bit-depth (65,535) we obtain:

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FYQoai3DF36Zn4iEhgZFw%2Frgn_calib-per_lbls.jpg?alt=media&#x26;token=0beba2ea-0757-4546-81a0-4f8090f57f58" alt=""><figcaption></figcaption></figure>

Once the image is calibrated we can perform some raster math with the pixels in each of the image bands/channels. For the vegetation we can process various indices like NDVI, GNDVI, OSAVI, etc. For the NDVI index a value of 0.86 means the vegetation is likely very healthy. A NDVI value of 0.28 typically means low to no health (dead) vegetation. You can see some example indices below.

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FAq1hx55YZEIz8yvHxLsf%2Frgn_ndvi_lbls2.jpg?alt=media&#x26;token=85cf13e8-d9df-4074-ac10-41e58f997157" alt=""><figcaption></figcaption></figure>

Below are some further examples from our [MAPIR Survey3W RGN camera](https://www.mapir.camera/collections/survey3/products/survey3w-camera-red-green-nir-rgn-ndvi):

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FhD1Krs5iIAPaLYzzLjvY%2Frgn_ndvi.jpg?alt=media&#x26;token=d90f47a7-5082-4176-802f-9aaa0210656a" alt=""><figcaption><p>MAPIR Survey3W RGN - NDVI</p></figcaption></figure>

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2FCWm48l3QSx8iLru7fHa6%2Frgn_gndvi.jpg?alt=media&#x26;token=18d056fa-38d5-4c7f-9049-1dfcfdb46f90" alt=""><figcaption><p>MAPIR Survey3W RGN - GNDVI</p></figcaption></figure>

<figure><img src="https://2599555469-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fo044KN3Ws0uIDvOmSkcR%2Fuploads%2F10KI0LMZRWAn1ZG6wEXg%2Frgn_osavi.jpg?alt=media&#x26;token=5176c7dd-f33f-44ef-9800-246d077cb5ca" alt=""><figcaption><p>MAPIR Survey3W RGN - OSAVI</p></figcaption></figure>
