DroneMapper

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/

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

Once classification is completed, DroneMapper generates a shapefile with management zones. The shapefile output is UTM WGS84 format and compatiable with major agriculture software providers such as SST, SMS, Apex, etc. 

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'

This allows sorting or grouping of the polygons based on .dbf column values.

"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!

Here is a simplified Excel spreadsheet that can be used to assist UAS aerial photo mission planning for those that don’t have access to mission planning software either provided with the UAS or custom written. The spreadsheet uses the following inputs (cells are highlighted in yellow) provided by you for your mission:

• Camera and lens parameters including the CCD focal plane size in pixels and millimeters and the actual lens’ focal length. This is the true focal length, not 35 mm equivalent, and should be the value written into the photo’s EXIF metadata during imagery collection. A great source of camera and lens information can be found here: www.dpreview.com. Go to the camera tab at the top of the page and then select camera manufacturer and search for your model. You’ll find focal plane info in the specifications tab.
   
• Mission and imagery collection parameters including UAS ground speed, flight elevation above ground, imagery overlap, both forward and side, and the area of interest (AOI) width and length. The AOI assumes a rectangle with the shorter side the width and longer side the length.

After you have input the various parameters for your mission the spreadsheet provides the following outputs (these cells have no color fill and should be locked to the user):

• Focal plane pixel size,
• Camera shutter speed to minimize image blur,
• Imagery ground sample distance or size of the pixel on the ground,
• Photo or frame size on the ground, width and length,
• Flight line spacing to achieve side lap input and the number of flight lines for the AOI,
• Distance between successive photos in the flight line to achieve forward overlap input and the number of images for each flight line,
• And, the total number of images for the AOI to achieve the coverage desired.

  Suggestions:
   

• Platform ground speed input should consider the fastest speed the UAS could achieve during the mission in case of a tailwind, for example,
• Dronemapper recommends no less than 60% forward overlap and no less than 40% side lap. For difficult homogenous scenes and significant terrain elevation changes one should increase overlaps to 75% forward and 60-75% side.
• Always extend the width and length of the AOI to insure full coverage of the complete scene of interest. This minimizes digital elevation model (DEM) noise at the AOI boundaries.
• Discussion of the image collection geometry and formulas used can be found here: https://dronemapper.com/uas_photogrammetry_processing.

We utilized Globalmapper (GM), version 16.1, to determine stockpile volumes for the BLM gravel pit example. A Canon SX 260 HS was used to collect 240 images at a ground sample distance of 3.2 cm. Dronemapper processed the imagery generating the geo-referenced orthomosaic and DEM for the scene. For this analysis we imported the DEM into GM and performed volumetric estimation of three stockpiles. The three piles are identified in the DEM image below.

After importing the DEM the digitizer tool was used to generate an area at the base of each of the stockpiles. Once the pile areas were defined we applied elevations from the terrain to each of the three pile areas. We then computed pile volumes. GM generates a flattened terrain surface (reference) beneath the pile and a pile surface. The difference between these two 3-D surfaces computes the volume of the pile. The table outlines the measurements made on each pile.

The three measurements are illustrated in the next three screenshots. The last screenshot shows a 3-D view of the identified piles that were measured to give the viewer perception of their relative sizes. Besides cut volumes, GM also performs fill volumes, valuable for the mining and construction industries.

This same technique can be used for estimating above ground biomass for crops. A section of a 3-D model of a research corn field is illustrated below. Imagery was collected at 5 cm GSD in the NIR. One of the plots within the field is identified with a plot shapefile (in yellow). The average plot height was estimated at 216 cm using the volume and area generated by GM. In this case we could not utilize two 3-D surfaces to compute the volume since adjacent plots obscured the terrain below the plot of interest. Instead, we used a fixed mean elevation of the terrain as the reference to compute the volume of the plot’s 3-D surface.

We will be working with precision agricultural specialists this growing season to track a set of crops/fields on a weekly basis. The DEM will be generated for each imagery collection of the crop through the growing season. Biomass volume estimation will be computed for each collection for all of the plots in the field. Plot biomass accumulation will be tracked weekly, statistically analyzed and correlated with plant genetics, field treatments and other phenomenology collections. We hope to report on this by the end of this year – so stay tuned!