zhang uav at uscid
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Unmanned Aircraft System Application on Precision Irrigation and Feasibility
StudyDr. José L. Chávez, Joseph Yu Zhang
October, 2016
Background• The demand of 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.
Background• The UAS is 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.
Background• The Field 1070 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.
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
CSU Tempest UAS
PayloadSensors Sony A6000 FLIR 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
Flight information at ARDEC• 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.
Flight information at Conservation 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.
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 mosaicMulti-spectral image mosaic
Formulas NVDI = (NIR –RED)/(NIR+RED)
NVDI: normalized difference vegetation indexNIR: spectral reflectance measurements in the near infra-red regionsRED: spectral reflectance measurements in the red regions
Neale et al. (1989)
FormulasCWSI=(dT-dTmin)/(dTmax-dTmin)
CSWI:crop water stress indexdT: difference between the canopy temperature and the air temperaturedTmin and dTmax are measured
Idso et al. (1982)
Green Band
Red Band
Sample Image of Tetracam
FormulasETa=(1-CWSI)xETr
ETa: actual crop evapotranspiration CWSI: crop water stress indexETr: reference evapotranspiration
Idso et al. (1982)
Sample of Thermal Image
FormulasDi=Di-1+ETa-(P-SRO)-In+DP-GW
Di:the soil water depletion at the end of day iDi-1: the soil water depletion at the end of day i-1 ETa: the actual crop evapotranspirationP: the gross precipitationSRO: the surface runoffIn: is the net irrigation on day IDP: the deep percolation on day IGW: the ground water capillary contribution from the water table on day I
Hoffmann et al., 2007
Turfgrass ET (mm/day) 8/12 PM
False color VIS/NIR image Grass evapotranspiration (ET), mm/d
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.
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).
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.
Contacts• José L. Chávez, Ph.D.
Associate Professor, Irrigation EngineeringExtension Specialist - Irrigation Water Management (EXT)Department of Civil and Environmental Engineering (CEE)Colorado State UniversityOffice Ph: (970) 491-6095; Fax: (970) 491-7727Email: [email protected] or [email protected]: http://www.engr.colostate.edu/ce/EXT: http://www.ext.colostate.edu/pubs/pubs.htmlPublications: Link to articles publications
• Joseph Yu Zhang, EIT, CWP Water EngineerDepartment of Civil and Environmental Engineering (CEE)Colorado State UniversityPhone: (425)343-2904Email: [email protected]