Plant health or vegetation management is an essential part of photogrammetry. WebODM also provides plant health management as a core feature. Using 16 different vegetation indices (VI), WebODM allows users to better assess land properties. Let us look at how to utilize this feature:
Vegetation Indices
Before using WebODM, it is recommended to have a basic understanding of some of the important vegetation indices. Algorithms like NDVI, ENDVI, VARI, GLI, and SAVI are widely used and are easier to read. For the below-mentioned demonstration, we will be using ENDVI, VARI, and GLI as our primary indices for measurement.
The datasets used below were captured by two separate cameras. The first one being a Full Spectrum camera with Near-Infrared (NIR), Green (G), and Blue (B) as its color bands. While the second camera was a standard RGB camera with the visible Red (R), Green (G), and Blue (B) color bands. NDVI and SAVI indices make use of both visible Red (R) and Near-Infrared (NIR). Therefore, if you wish to measure these two indices or any other index that makes use of NIR and R bands, make sure to capture your images using a multispectral camera or a modified RGB camera with a NIR filter. However, let’s first understand all the indices:
NDVI
Normalized Difference Vegetation Index (NDVI) is the oldest and most widely used Vegetation Index in photogrammetry. To define NDVI, it is the difference between near-infrared light (NIR) and visible red light (R). Vegetation tends to absorb the visible red light while reflecting NIR and green light. Thus, NDVI helps to identify unhealthy vegetation from healthy ones using this measurement. NDVI values range from -1 to +1. Higher the value, the healthier the vegetation is, and vice versa. Therefore, NDVI is a great VI to measure chlorophyll levels. To capture NDVI imagery, you will require either a modified RGB camera with a NIR filter or a multispectral camera.
ENDVI
Enhanced Normalized Difference Vegetation Index (ENDVI) is similar to NDVI. Traditional NDVI only uses red and near-infrared spectral data. However, ENDVI uses visible blue and green light instead of red in the algorithm. The ENDVI algorithm better isolates plant health indicators, as the plant’s absorption of blue light and high reflectance of green and near-infrared waves is a reliable marker of plant health. To capture ENDVI imagery, you will either require a NIR camera or a multispectral camera.
VARI
Visual Atmospheric Resistance Index (VARI) is another commonly used vegetation index. It shows the presence of vegetation over an area of land. This VI was initially designed for satellite imagery. What makes VARI unique is its sensitivity to atmospheric effects. It is minimally sensitive to atmospheric effects, allowing for vegetation to be estimated in a wide variety of environments. Another great thing about VARI is that it does not require a NIR/multispectral camera. It can process images taken from a standard RGB camera which is far cheaper than multispectral cameras.
GLI
Green Leaf Index (GLI) was originally created to determine the grazing impact of wheat. It is used to represent green leaves and stems. If the value is negative it represents soil or something nonliving, and if the value is positive it is either green leaves or stems. Therefore, GLI is another efficient algorithm for detecting green leaves on agricultural land. GLI uses data from standard RGB cameras in its algorithm.
SAVI
Soil-Adjusted Vegetation Index (SAVI) is a modified version of NDVI. In areas where vegetative cover is low and the soil is exposed, soil can then reflect different amounts of visible red and NIR light. This can influence vegetation index values altogether. Thereby, SAVI corrects for the influence of soil brightness when the vegetative cover is low.
Which Vegetation Index Should You Use?
This solely depends on the type of data that you collect. If you capture multispectral data, you can use NDVI, ENDVI, NDRE, or SAVI as they are industry-standard algorithms. However, multispectral or NIR cameras can be expensive and as a beginner, you may want to start off with RGB imagery. If you capture your data using RGB sensors, VARI and GLI indices are sufficient. VARI alone can identify crop stress points in your field and provides good indicative results.
Vegetation Management
Multispectral (ENDVI) Output
To begin, log in to the WebODM dashboard. In the dashboard, navigate to your project and select the task you want to analyze. We will start with the Multispectral dataset which was captured by a Full Spectrum camera (NGB color bands). Select the option to ‘View Map’.
Plant Health Model: An Orthomosaic or Orthophoto map (learn about creating Orthomosaics here) will then load on the screen. Before navigating to the plant health feature, you can make several basic adjustments in your orthophoto. Like, changing the base layer (which is set to Google Maps Hybrid by default), modifying the exposure and contrast in the layers panel, etc.). On the top right, you will see a ‘Plant Health’ tab, select that to load the plant health model.
Layers Panel: Once the plant health model loads, you can click on the Layers panel and begin making adjustments. Here, you can toggle through different vegetation indices in the Algorithm drop-down box, change filters, colors, and adjust the histogram.
ENDVI: The image shown above has been zoomed into the agricultural field. Change the vegetation index to ENDVI. The ‘Filter’ is set to NGB (Near-Infrared, Green, Blue) since ENDVI uses these three bands in its algorithm. The color for the histogram is set to RdYlGn (Red, Yellow, Green). Adjust the values in the histogram to highlight the color difference between healthy (green) and unhealthy (red) vegetation. As shown in the image, there are several sections of the land highlighted in red. This can indicate low chlorophyll levels, unhealthy vegetation, or barren land.
RGB (VARI and GLI) Outputs
Now, let’s look at the RGB dataset. The process is the same. Log in to your WebODM dashboard, select the task, and view the map. Once the Orthophoto loads, select the Plant Health tab on the top-right.
VARI: Next, select the VARI index from the algorithm drop-down box. The filter is set to RGB and the color to the default RdYlGn. Adjust the minimum and maximum histogram values until the color difference is properly visible. VARI displays the vegetation density and as shown above, there are rows of crops that are less dense than others. This is another reason VARI is often used for forest management. The red markers indicate crop stress points.
GLI: The Green Leaf Index can be useful in many different ways. It is especially useful in measuring chlorophyll levels in certain types of plants like rice, wheat, and corn. Additionally, as GLI calculates regions of green leaves or stems, it can be useful for detecting pests and diseases. Change the Algorithm to GLI and set the filter to RGB, color is set to RdYlGn, and the histogram is adjusted to highlight the red-yellow areas.
Finally, you can export the plant health models as GeoTIFF files. Additionally, you can even make measurements atop the plant health model. In case, you want to measure the distance, area, or volume of an unhealthy or problematic portion of land, use the measurement tools to directly make measurements on the layer. Thus, when you export your project, the measurements will also be exported. Learn more about WebODM Cloud here.
