NDVI Imaging

NDVI, Normalized Differential Vegetation Index, can be used to show plant vigor: “how healthy a plant is”, in other words. An NDVI image is calculated using visible light and near-infrared light, reflected by plants and captured by a camera. NDVI harnesses the fact that healthy green plants reflect more near infrared light then unhealthy ones. The technology originally comes from work with satellite imagery (see Wikipedia entry.) There actually are more sophisticated methods for measuring plant vigor available today. But because NDVI can be done with relatively simple hardware, including modified, cheap point and shoot cameras, it has been seeing popularity as a “good enough”, simple way to look at plant vigor, using UAVs as cheap image acquisition platforms.

NDVI images are subject to interpretation. The actual recorded values depend on camera hardware, light conditions, exposure parameters and white balance. Without carefully controlled image acquisition procedures, NDVI data cannot be compared directly when taken on different dates or in different places. But still, there is meaning to be found in the data.

I have been wanting to try NDVI for a while, and I would like to see more people use this technology in land stewardship. I modified my own Canon camera with an Infragram filter. I am also developing my own NDVI software, NormalizerNDVI – more about that soon.

Actually, I modified five Canons, destroying the first three in the process, but then I got the hang of it. There are companies that will do the modification professionally. Might have been worth it.

The Images

First, here is a recent view of the corn field. This is a normal, visual image, composited from around a hundred photos taken with my unmodified UAV camera.
corn 6-20 jpg vis
Visual Image

Here is the same field, composited from photos taken with the modified camera, which records near infrared instead of red. This image is the starting point for running the NDVI calculation on it.
corn 6-20 jpg nir
Source image with NIR instead of Red

This is a direct NDVI image, without a color map.  Roads, buildings and ponds appear black, the empty wheat field on the left appears dark, and the corn field is lighter.
ElginNDVI raw unscaled
Pure NDVI Grayscale

To interpret an NDVI image, one takes the raw calculation output and maps it to a color table, using gradients or color segments. Choosing the right type of color table to get interpretable results is an important challenge. Here is a color-mapped NDVI image, showing a variety of vegetation types besides the corn field.

ElginNDVI 00-05 s2point2 small
Colormapped NDVI

To examine the corn field, we restrict the color mapping to a the subset of NDVI ranges found in the cornfield, providing visual differentiation in the corn field area, while neglecting the other areas of the image.
ElginNDVI 03-06 s01_small
Corn Field NDVI

Some structure becomes visible, such as the healthier plants along the terrace edges (the terraces collecting water are visible in an earlier blog post), and the lower vigor along the right edge. Some rectangular edges can also be seen. Those are probably artifacts of the image compositing process.

Here is a detail at higher resolution.
Corn Field NDVI Detail

The flights involved in this operation are performed as an experiment, not for payment, and not as part of a farming operation.

3 thoughts on “NDVI Imaging

  1. Well done Knut. Which filters are you using also, could you provide a little more info on the software yu are using to process the data.

    1. The filter is from the Instagram filter pack, which contains two filters. I think I used the one labelled #2007, since the camera is a CCD camera. The software is Fiji with the Photo Monitoring Plugin by Ned Horning. You can find a download link on the Flightriot site.

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