Automated Management Zone, Crop Health Alerts and Area of Interest Extraction From NDVI
Jon-Pierre Stoermer, CTO - DroneMapper.com
Jon-Pierre Stoermer, CTO - DroneMapper.com
We often get requests from our customers to develop additional value-added features and algorithms to extract more meaningful information from aerial or terrestrial data collections. Recently, the area of Precision Agriculture has seen enormous growth triggered by reduced technology costs and overall interest in new/emerging technologies. The integration of UAS systems, GPS, RTK and other geo-spatial technologies into the farming sector allows a whole new world of opportunities. Although technology can't solve all of our tasks, it can certainly help in new and exciting ways!
A common task for an Agronomist is the creation of management zones or "area's of interest" based on the available data. The data could be an aerial imagery collection in NIR, yield data from a terrestrial collection, EC soil samples, or a combination of all. Historically, a large amount of research has been devoted to automating the process of extracting appropriate zones when high resolution spatial data is available. This can also be a complicated and time consuming task.
"Normalized difference vegetation index (NDVI) are closely related to many vegetation parameters such as leaf area index, vegetation cover, vegetation biomass and crop growth, so is often used to monitor crop growth and predict crop yield." [1]
We've developed a set of automated algorithms to quickly exploit the types of data mentioned above to generate zones using imagery processing and computer vision technologies. An example is shown below using the "Precision Agriculture R-G-NIR, Switzerland" example on the following page: https://dronemapper.com/sample_data/
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NDVI GeoTIFF
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Classified NDVI GeoTIFF
The image above shows a classified NDVI GeoTIFF where each pixel falls into one of the following categories:
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Classified NDVI GeoTIFF w/ Shapefile Overlay - "A" and "L" Management Zones Highlighted
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Shapefile w/ All Management Zones Shown (UTM 32 Northern)
Each polygon in the generated shapefile includes records in the .dbf file with the following information:
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"EH", "A" and "VH" Management Zones w/ NIR Orthomosaic
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A 3D Geo-Referenced Crop Health Map (viewed in GlobalMapper)
NDVI GeoTIFF
Classified NDVI GeoTIFF
The image above shows a classified NDVI GeoTIFF where each pixel falls into one of the following categories:
- VL - Very Low Health
- L - Low Health
- A - Average Health
- H - Healthy
- VH - Very Healthy
- EH - Extreme Health
Classified NDVI GeoTIFF w/ Shapefile Overlay - "A" and "L" Management Zones Highlighted
Shapefile w/ All Management Zones Shown (UTM 32 Northern)
Each polygon in the generated shapefile includes records in the .dbf file with the following information:
- Classification - 'class'
- NDVI Indice Value - 'z'
- Fill Color - 'color'
- Polygon Id - 'id'
- Area Acre - 'area'
"EH", "A" and "VH" Management Zones w/ NIR Orthomosaic
A 3D Geo-Referenced Crop Health Map (viewed in GlobalMapper)
KML/KMZ Representation of Alerts and Area's of Interest
The algorithm completes processing after a few minutes and could easily be adapted to provide point data for Crop Scouting locations, ground truthing, etc.
Download the data generated in this post here or on our samples page. For more information on agriculture management zones we recommend the following link. Please let us know if you have any questions!