UAS Colorado and Black Swift Technologies team up to Showcase the San Luis Valley, a Drone Paradise

UAS Colorado and Black Swift Technologies team up to Showcase the San Luis Valley, a Drone Paradise

After pushing through the daunting 10,709 foot Hayden pass, the San Luis Valley (SLV) stretched out for miles below us. The Cessna 172’s windshield failed to contain the vast landscape, guarded on all sides by snow-capped mountains, and both pilot and passenger found themselves craning their necks to take in the view. The area is truly unique, featuring a diverse collection of farmlands, ranching, canyons, vast national forest and wildland areas, and mountains over 14,000 feet high. It’s also unique due to the abundance of local support for both unmanned and manned aviation, including specific improvements to airports to support UAS operations, as well as securing permission for partnering companies to operate up to 15,000 feet MSL.


The incredible diversity of the SLV, including 14,000 foot peaks and vast agricultural areas.
The incredible diversity of the SLV, including 14,000 foot peaks and vast agricultural areas.

Black Swift Technologies and UAS Colorado recently partnered to showcase the outstanding flight and regulatory conditions of the SLV in Saguache County, Colorado. On April 22, 2016 Jack Elston, CEO of Boulder based Black Swift Technologies, and Constantin Diehl, President of UAS Colorado, flew the 172 from the Denver area to meet with County Commissioners, Airport Managers and Economic Development representatives to showcase the area’s excellent conditions and the ease with which they can be utilized.


Jack Elston, CEO of Black Swift Technologies (left) with SwiftTrainer UAS and Constantin Diehl of UAS Colorado (right).
Jack Elston, CEO of Black Swift Technologies (left) with SwiftTrainer UAS and Constantin Diehl of UAS Colorado (right).

In a regulatory environment that is not always conducive to commercial UAS operations, there has been a special effort to create a local environment for UAS testing and evaluation in Colorado. In fact, the operational limitations in the SLV are less restrictive than in most of the designated FAA UAS test sites across the country because of the coordinated efforts between 6 Counties. Additionally, airport managers and county commissioners have partnered to improve local facilities in support of extended UAS operations. Michael Spearman, project coordinator for Leach airport and Director for “High Altitude UAV Testing” in Saguache County was excited to talk about the improvements that they’ve recently completed:

Michael Spearman, Project Coordinator for Leach Airport and Director for “High Altitude UAV Testing” in Saguache County was excited to talk about the improvements that they have recently completed and those that they are currently working on.

“We have added specific areas for conducting UAS operations as well as added improvements to make multi-day operations feasible without having to leave the Airport.
UAV operators, will be able to park their equipment, set up their ground infrastructure and support teams for as long as necessary to accomplish their objectives. 100 LL fuel is available for aircraft. Secure overnight clean storage areas are available for UAV’s as well as adequate electrical outlets for charging batteries. Outdoor shaded areas with tables and propane grill are available, so is a Lounge/Conference room with conference table, refrigerator, microwave, water, telephone, Wi Fi and bathrooms.


(left to right): Jack Elston, Randy Wright, Michael Spearman, and Ken Anderson observing the mapping flight.
(left to right): Jack Elston, Randy Wright, Michael Spearman, and Ken Anderson observing the mapping flight.

Spearman, along with County Commissioner Ken Anderson and Executive Director of the Alamosa County Economic Development Corporation Randy Wright, met Constantin and Jack when they landed, eager to see a particular piece of cargo: The SwiftTrainer UAS from Black Swift Technologies. Although compact and incredibly simple to operate, the vehicle is quite capable. Among a host of other features, it contains a proprietary algorithm to determine location extremely accurately. This allows an operator to make use of images automatically obtained from the on-board 24 MP camera to create maps with centimeter-level accuracy, even without RTK GPS.

The group set off for nearby Penitente Canyon, a rugged grouping of igneous rocks left as a reminder of the area’s violent geological past. With exciting variation in elevation and limited open space to operate from, the area was perfect to test the SwiftTrainer UAS’ capabilities. After minimal setup, Jack threw the aircraft into the air and it began its mapping task, hardly struggling at an altitude of nearly 8000 ft. 15 minutes later the mapping was complete and the aircraft set itself down right next to the group. The data was sent for analysis to local Colorado company DroneMapper, who has been a key partner in realizing the incredible accuracy of data from the SwiftTrainer platform. Pierre Stoermer, CEO of DroneMapper, reported of the data set, “Once again, very nice flight lines and camera triggering. We are seeing a real photogrammetry collection platform in action.”


The Penitente Canyon mapping area seen from the Cessna (top left), The resulting 3D map (top right) and a close-up showing the incredible terrain detail (bottom).
The Penitente Canyon mapping area seen from the Cessna (top left), The resulting 3D map (top right) and a close-up showing the incredible terrain detail (bottom).

Following an additional mapping mission at a nearby ranch, the group was joined by Francis Song, OEM Director for Alamosa County Emergency Management. UAS represent a real opportunity to improve their ability to save lives. The SLV’s incredible collection of hiking, mountain biking and climbing opportunities bring throngs of visitors to the area every year, and with them the need for a well prepared search and rescue operation.

Francis, Ken, Randy and Michael are all representative of the incredibly supportive group in the SLV, excited to play an important role in the emerging UAS market. “Although it was only a short trip today, we were able to witness firsthand the incredible opportunities in this area. Black Swift Technologies already has plans to return in the near future as part of a NASA SBIR to sample plumes above high altitude volcanoes, and we’re incredibly lucky to be so close to one of the few places in the US where we can test the system at its intended altitudes,” commented Jack Elston. Given the incredible opportunities in the Valley, Black Swift Technologies won’t be the only company looking to travel here in the near future.

For further information on UAS Colorado, contact Constantin via email at cdiehl@uascolorado.com. For more on Black Swift Technologies, contact Jack Elston at elstonj@blackswifttech.com.

DroneMapper Labs: geoBit Ground Control Point Target Systems

DroneMapper Labs: geoBit Ground Control Point Target Systems
Jon-Pierre Stoermer, CTO - DroneMapper.com
 
geoBits.io



DroneMapper Labs: Grand Mesa 3D Cesium/GIS Demo

DroneMapper Labs: Grand Mesa 3D Cesium/GIS Demo
Jon-Pierre Stoermer, CTO - DroneMapper.com
 

An online browser based 3D GIS demo using CesiumJS and multiple data sources. The demo is also 6DoF/spatially aware if you are using a compatible mobile device.

Grand Mesa 3D Map



DroneMapper Labs: Automated Management Zone, Crop Health Alerts and Area of Interest Extraction From NDVI

Automated Management Zone, Crop Health Alerts and Area of Interest Extraction From NDVI
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/



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!




Excel Spreadsheet for Drone Mapping Mission Planning

Excel Spreadsheet for Drone Mapping Mission Planning
Pierre Stoermer, CEO - DroneMapper.com
 

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.


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