Data Processing

When you fly over a crop collecting UAS imagery, you can collect hundreds of photos that cover the entire field. These images are not useful on their own, so part of data processing is to stitch all these images together. There is software readily available and user-friendly that can do this. One common software is Pix4D which can be accessed by purchasing a subscription. To create a mosaic from your images, you just need to upload them to the software and change the settings to your preference. This is where the GCP coordinates are used to correct any distortions. The challenge with stitching images is the processing time and computing power needed to run the software. In general, as the field size you are imaging increases, your project size will increase and require more RAM, CPU, storage space and processing time.

Every one of these images you create is actually a matrix containing information. If you zoom in on any point within the matrix, the value of that pixel is an element in an array. The final processed image from an RGB sensor is three, two-dimensional arrays laid on each other to create a matrix (Fig. 17). Similarly, an image from the example multispectral sensor is five, two-dimensional arrays laid on each other to create a matrix. Each array containing the values for the NIR and red-edge reflectance in addition to the RGB reflectance values. This is how the images can be used to extract objective information rather than subjective information.

The RGB file that is created from stitching all the continuous images together can be used to create a shapefile using a polygon grid. A polygon grid is just a grid that divides a file into cells based on the coordinate system. This polygon grid will allow us to systematically ‘clip’ the NDVI raster file to extract NDVI for each individual plot. There are many accessible software programs for users interested in creating a shapefile. Two commonly used programs to do this are ArcGIS and QGIS. The main difference between the two is that ArcGIS is proprietary software whereas QGIS is an open-source software. Every software has benefits and detriments, so it is up to the user to determine which is the best fit for their analysis.

One of the biggest areas of interest when using a multispectral sensor is the ability to calculate vegetation indices. This step is the most technical and requires a basic understanding of programming languages to extract data from the images. We can calculate the vegetation indices for the entire field using software called ArcGIS. There is a raster calculator that can be used to process the initial image and calculate vegetation indices. However, to extract more advanced vegetation indices will require the use of a programming language like Python.

The processed image will be saved as a raster file which allows us to extract information. The way a raster file works is that it is a matrix of pixels. If you zoom in enough on a small section of a selected plot, you will end up with a bunch of pixels. Every pixel is going to have its own value. To extract the vegetation index for a specific plot, requires the use of a programming language like Python. We can write a code that uses the shapefile with a polygon grid created earlier to clip the vegetation index raster file into individual plots. The average of the vegetation index is then calculated using every pixel within the area of a specific plot.

Some common indices calculated from multispectral imagery include the Normalized Difference Vegetative Index (NDVI), Normalized Difference Red Edge (NDRE), and the Green Normalized Difference Vegetative Index (GNDVI). Some common indices calculated from RGB imagery include the Green Leaf Index (GLI) and the Visual Atmospheric resistance index (VARI). 

All of these indices will fall between a range of -1 to +1. Think back to when we discussed which wavelengths reflect off plants. In the visible spectrum, green wavelengths are going to reflect off the plant, causing the vegetation to look green. Red and blue wavelengths of light are used to drive photosynthesis. This provides plants with food to grow, and aids in chlorophyll production. NIR bands are also reflected by the chlorophyll in the plant. When calculating the vegetation indices for a healthy plant, there is going to be high chlorophyll production resulting in a higher amount of light reflectance. This provides vegetative index close to +1. If a plant is not healthy or experiencing stress, there is going to be less chlorophyll production which in turn lowers the amount of light reflectance. This would give a vegetative index close to 0. Extracting the vegetation index for individual plots provides the plant breeder with the information they need to make decisions.