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Zhang UAV at USCID
- 1. Unmanned Aircraft System Applicationon Precision Irrigation and Feasibility Study Dr. José L. Chávez, Joseph Yu Zhang October, 2016
- 2. Background • The demandof irrigation water is increasing because the world’s population continues to grow sharply together with the food consumption. • The precision agriculture irrigation management systems and technologies are required to save water nowadays and are developing, including the remote sensing technologies. • CSU’s unmanned aircraft system (UAS) equipped with RGB, thermal and multi-spectral cameras can provide high spatial and temporal resolution data in the thermal-infrared, near-infrared and electromagnetic spectrum.
- 3. Background • The UASis one of the technologies which can deliver high resolution data. • The data will be used to estimate actual crop evapotranspiration (ETa) and soil water deficit (SWD), then determine water demand on certain data and location.
- 4. Background • The Field1070 at ARDEC (Agricultural Research, Development and Education Center, Fort Collins, CO) provides the opportunity of flying the UAS above different species of corns under 3 irrigation treatment. • The Conservation Gardens at Northern Water (Berthoud, CO) contains more than 700 varieties of plants and many irrigating treatments, which was used for UAS application too.
- 5. CSU Tempest UAS •Made in 2015 • Weight: 18lbs (8.2kg) with cameras and battery • Wingspan: 127” (3.2m) • Length: 61” (1.6mm) • Max Speed: 100mph • Flight Time: 1.5hr with the same battery • Flight height: 400ft (122m) AGL • Radio Range: 10miles • AutoPilot system is installed in the aircraft
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- 7. Payload Sensors Sony A6000FLIR Tau 2 Tetracam SNAP ADC Application RGB Thermal Multi-spectral Wavelength Visible Thermal Green, Red, NIR 130m AGL Resolution 9.5cm 11.76cm 6.5cm
- 8. Flight information atARDEC • 2 minutes to fly and cover the whole Field 1070. • Image overlap rate: 50-75%. • 60-110 images in each camera taken to cover the whole facility. • 400 feet (120 meters) AGL.
- 9. Flight information atConservation Garden • 3 minutes to fly and cover the whole Northern Water facility. • Image overlap rate: 50-75%. • 80-180 images in each camera taken to cover the whole facility. • 400 feet (120 meters) AGL. • Launch and land at the west alfalfa field.
- 10. Mosaic Map Image •ENVI OneButton was used to generate the orthomosaic images. • Each individual photo was attached with a GPS coordinate. • ERDAS Imagine was used to create the models between the images and ET data. RGB image mosaic Multi-spectral image mosaic
- 11. Formulas NVDI = (NIR–RED)/(NIR+RED) NVDI: normalized difference vegetation index NIR: spectral reflectance measurements in the near infra-red regions RED: spectral reflectance measurements in the red regions Neale et al. (1989)
- 12. Formulas CWSI=(dT-dTmin)/(dTmax-dTmin) CSWI:crop water stressindex dT: difference between the canopy temperature and the air temperature dTmin and dTmax are measured Idso et al. (1982) Green Band Red Band Sample Image of Tetracam
- 13. Formulas ETa=(1-CWSI)xETr ETa: actual cropevapotranspiration CWSI: crop water stress index ETr: reference evapotranspiration Idso et al. (1982) Sample of Thermal Image
- 14. Formulas Di=Di-1+ETa-(P-SRO)-In+DP-GW Di:the soil waterdepletion at the end of day i Di-1: the soil water depletion at the end of day i-1 ETa: the actual crop evapotranspiration P: the gross precipitation SRO: the surface runoff In: is the net irrigation on day I DP: the deep percolation on day I GW: the ground water capillary contribution from the water table on day I Hoffmann et al., 2007
- 15. Turfgrass ET (mm/day)8/12 PM False color VIS/NIR image Grass evapotranspiration (ET), mm/d
- 16. Problems to overcome •The fixed wing airplane was hard to operate due to the lack of fly experience, unstable weather, launching and landing procedure. • The high flying speed of the airplane required ground pilot’s high attention because of the safety concerns. • The Multi-spectral sensor was operated slower than others, so it couldn’t collect the same amount of images or geo-tag them.
- 17. Problems to overcome •With the commercial mosaic software, the thermal imagery couldn't be orthomosaicked, as a result, it required manually mosaic process. • Due to the belly landing procedure, the frame of the airplane might be damaged. A replacement would be made dozens of hours of operation. • The UAV application required high attention to operate. At least 3 people were needed during flights (ground safety pilot, autopilot operator and observer).
- 18. Problems to overcome •Airplane maintenance and image processing demanded different sets of skills. Diverse operation crews would provide efficient results and safety. • An effective auto-mosaic software could avoid time-consuming manually process of the image mosaic. • A good radio connection between the airplane and the auto-pilot system would secure the UAV operation.
- 19. Contacts • José L.Chávez, Ph.D. Associate Professor, Irrigation Engineering Extension Specialist - Irrigation Water Management (EXT) Department of Civil and Environmental Engineering (CEE) Colorado State University Office Ph: (970) 491-6095; Fax: (970) 491-7727 Email: jose.chavez@colostate.edu or jlchavez@rams.colostate.edu CEE: http://www.engr.colostate.edu/ce/ EXT: http://www.ext.colostate.edu/pubs/pubs.html Publications: Link to articles publications • Joseph Yu Zhang, EIT, CWP Water Engineer Department of Civil and Environmental Engineering (CEE) Colorado State University Phone: (425)343-2904 Email: josephyuzhang@gmail.com