MAPIR offers a complete environment monitoring solution to measure changes in how light reflects off of objects such as plants and soil. The spectrum of light that is reflected is known as the material's reflective spectral signature, and can tell you information about the material from afar (remote sensing).

To accurately measure the reflected light you need three types of hardware:

Light Sensor: measures the ambient light spectrum (340 - 1010 nm) illuminating the objects of interest. The light source can be the sun or artificial (LED, fluorescent, halogen, incandescent, etc). Light measurements are recorded against GNSS (GPS) time once a second, and as the light changes our post processing software will adjust the image reflectance calibration.

Reference Targets:  provide a lab-measured reference standard for reflectance and are used to correlate the camera's image pixel values to percent reflectance. Reflectance is a measurement of how intense the ambient light spectrum is bouncing off the object, and the differences in intensity is an object's spectral signature. Using a reference standard allows the reflectance data to be compared across time and different lighting conditions.

Multispectral Imaging Cameras: capture photos of the reference targets to establish the relationship between image pixel values and percent reflectance. After post processing each pixel in the processed image channel represents the percent of light reflected by the object in the spectrum(s) of light that the camera is sensitive to.

The light sensor measures the ambient light spectrum and the reference targets tell us how that light is reflected, so that the multispectral images can be calibrated and compared over time and location.

A single light sensor and reference target is required to process images from any number of multispectral cameras. The light sensor records the same ambient spectrum (340 - 1010 nm) which the multispectral cameras are capable of recording.

The light sensor can be used as a standalone device (left) or connected directly to the multispectral cameras (right, below):

The light sensor will measure the ambient light spectrum every second using a high resolution spectrometer.

Here is a typical spectral signature for sunlight measured by the light sensor:

While the light sensor is recording the ambient light spectrum, the multispectral cameras are used to capture images of the objects you want to measure the reflectance of. Images of the reference targets are also captured for post processing.

The reflectance reference target regions are measured by our software and the necessary reflectance corrections are calculated. The changes in the ambient light recorded by the light sensor are adjusted for during the reflectance calibration.

Our Survey3 multispectral cameras come in two lens options (W = wide, N = Narrow) and the below five filter model options. Depending on the filter model, up to three separate spectrums will be captured in each image.

Using the OCN (Orange, Cyan, NIR) filter model as an example,  you can see the filter transmission graph below. An example of a processed 3-channel image from the OCN camera, with channel 1 (615nm), channel 2 (490nm) and channel 3 (808nm) is below.

The image shows mostly healthy grass with some dead leaves. Splitting apart the calibrated image we can see the three image channels (left to right): orange, cyan, and near infrared (NIR):

The above three single spectrum images are each originally 12MP (4000 x 3000px), and are similar to the results you would obtain from a multi-sensor multispectral camera (MAPIR Kernel2, Micasense RedEdge, etc). Each channel is completely separate from one another, and there is no overlap or cross-talk after processing. Since the three images were produced from a single image sensor they can be captured at any distance, and are already perfectly aligned to one another.

You can then process the image channels using any multispectral index you prefer. A common vegetation index for plant health/vigor is NDVI, which compares the contrast differences between red/orange and near infrared (NIR) light. The green-yellow-red color gradient (lut) applied below shows healthy grass as green and unhealthy grass (or non-plants) as yellow to red.

Using the spectral data from the light sensor keeps the relationship (contrast) between the image channels consistent when the ambient light changes. The effect from shadows is mostly removed unless the shadow is very dark.