Band-reconfigurable Multi-UAV-basedCooperative Remote Sensing for Real-time

Water Management and DistributedIrrigation Control

Haiyang Chao ∗ Marc Baumann ∗∗ Austin Jensen ∗

YangQuan Chen ∗ Yongcan Cao ∗ Wei Ren ∗ Mac McKee ∗∗∗

∗ CSOIS, Electrical & Computer Engineering Department, Utah StateUniversity, Logan, USA 84322-4120

∗∗ Ravensburg-Weingarten University, Weingarten, Germany 88241∗∗∗ Utah Water Research Laboratory, Civil & Environment Engineering

Department, Utah State University, Logan, USA 84322-4110

Abstract: This paper presents an overview of ongoing research on small unmanned autonomousvehicles (UAVs) for cooperative remote sensing for real-time water management and irrigationcontrol. Small UAVs can carry embedded cameras with different wavelength bands, whichare low-cost but have high spatial-resolution. These imagers mounted on UAVs can form acamera array to perform multispectral imaging with reconfigurable bands dependent on mission.Development of essential subsystems, such as the UAV platforms, embedded multispectralimagers, and image stitching and registration, is introduced together with real UAV flight testresults of one typical example mission.

Keywords: Unmanned aerial vehicle, low cost multispectral imaging, cooperative remotesensing, water management, irrigation control.

1. INTRODUCTION

Recently, there has been increasing interest in using smallunmanned air vehicles (UAVs) for civilian applications[Chao et al., 2007b] such as in traffic control, border patrol,forest fire monitoring [Casbeer et al., 2005], agriculturemapping, etc. UAVs can save human pilots from dan-gerous and tedious jobs, reduce maintenance and opera-tional costs and provide information more promptly andaccurately than manned aircrafts or satellites. Moreover,with the developments of wireless technology and microelectromechanical systems, groups or even swarms of smallUAVs can be employed for more challenging missions suchas real-time large-scale system mapping and surveillancein the near future.

Water management and irrigation control are becomingmore important due to the increasing scarcity of wateracross the world. However, it is difficult to sense andestimate the state of water systems because most watersystems are large-scale and need monitoring of manyfactors including the quality, quantity, and location ofwater, soil and vegetations. For the mission of accuratesensing of a water system, ground probe stations areexpensive to build and can only provide data with verylimited sensing range. Satellite photos can cover a largearea, but have a low resolution and a slow update rate.Small UAVs cost less money but can provide more accurateinformation from low altitudes with less interference fromclouds. Small UAVs combined with ground and orbitalsensors can even form a multi-scale remote sensing system.

UAVs equipped with imagers have been used in sev-eral agricultural remote sensing applications for collectingaerial images. High resolution red-green-blue (RGB) aerialphotos can be used to determine the best harvest time ofwine grapes [Johnson et al., 2003]. Multispectral images? Corresponding authors: Profs. YangQuan Chen and Wei Ren.Emails: yqchen@ieee.org and wren@engineering.usu.edu

are also shown to be potentially useful for monitoring theripeness of coffee [Johnson et al., 2004]. Water manage-ment is still a new area for UAVs, but it has more exactrequirements than other remote sensing applications: real-time management of water systems requires more andmore precise information on water, soil and plant con-ditions, for example, than most surveillance applications.Most current UAV remote sensing applications use largeand expensive UAVs with heavy cameras and collect onlyone band of aerial imagery. Images from reconfigurablebands taken simultaneously can increase the final informa-tion content of the imagery and significantly improve theflexibility of the remote sensing process. Furthermore, lesseffort might be required for the georeferencing problem(i.e., stitching the UAV images into a useful compositeimage).

Motivated by the above water resources problem, a band-configurable, small UAV-based remote sensing system hasbeen developed in steps. The objective of this paper is topresent an overview of the ongoing research on this topic.

This paper is organized as follows. The remote sensingrequirements for water management and irrigation controlare discussed in Sec. 2. Based on this, a band recon-figurable small UAV remote sensing system is proposedin Sec. 3. Then, coverage control of a single UAV andmultiple UAVs with certain imagers is explained in detail.Subsystem development of components such as UAV plat-form, embedded multispectral imager, image stitching andgeoreference capabilities are introduced in Sec. 4. Finallyreal field flight test results are shown in Sec. 5.

2. WATER MANAGEMENT AND IRRIGATIONCONTROL REQUIREMENTS

The goal of irrigation control is to minimize the waterconsumption while sustaining the agriculture productionand human needs [Fedro et al., 1993]. This optimiza-

Proceedings of the 17th World CongressThe International Federation of Automatic ControlSeoul, Korea, July 6-11, 2008

978-1-1234-7890-2/08/$20.00 © 2008 IFAC 11744 10.3182/20080706-5-KR-1001.2220

tion problem requires remote sensing to provide real-time(daily or weekly) feedback from the field including:

• Water: water quantity and quality with temporal andspatial information, for example water level of a canal.

• Soil: soil moisture and type with temporal and spatialinformation.

• Vegetation: vegetation index, quantity and qualitywith temporal and spatial information, for examplethe stage of growth of the crop.

2.1 Introduction to Multispectral Remote Sensing

The purpose of remote sensing is to acquire informationabout the Earth’s surface without coming into contactwith it. One objective of remote sensing is to characterizethe electromagnetic radiation emitted by objects [James,2006]. Typical divisions of the electromagnetic spectruminclude the visible light band (380−720nm), near infrared(NIR) band (0.72−1.30µm), and mid-infrared (MIR) band(1.30−3.00µm). Band-reconfigurable imagers can generateseveral images from different bands ranging from visiblespectra to infra-red or thermal based for various applica-tions. The advantage of the ability to examine differentbands is that different combinations of spectral bands canhave different purposes. For example, the combination ofred-infrared can be used to detect vegetation and camou-flage [Johnson et al., 2004].

2.2 Problem Statement of Remote Sensing

Let Ω ⊂ R2 be a polytope including the interior, which canbe either convex or nonconvex. A series of band densityfunctions ηrgb, ηnir, ηmir . . . are defined as ηi(q, t) ∈ [0,∞)∀q ∈ Ω. ηrgb can also be treated as three bands ηr, ηg, ηb,which represent RED, GREEN and BLUE band values ofa pixel. The goal of remote sensing is to make a mappingfrom Ω to η1, η2, η3 . . . with a preset spatial and temporalresolution for any q ∈ Ω and any t ∈ [t1, t2].

A ground-based sensing device can provide a highly accu-rate mapping but with a limited range (inch level spatialresolution and second level temporal resolution). Satellitephotos can provide global level resolution with a largerange (about 30-250 meter or lower spatial resolutionand week level temporal resolution). But these photos areexpensive and cannot be updated at the desirable spatialor temporal scales. UAVs can provide a high resolution(meter or centimeter spatial resolution and hour-level tem-poral resolution) even with an inexpensive camera sincesatellites can not fly so low as small UAVs.

3. REMOTE SENSING USING SMALL UAVS

Small UAVs are UAVs that can be operated by only oneor two people with a flight height less than 10,000 feetabove the ground surface. Many of them can be hand-carried and hand-launched. Small UAVs with cameras caneasily achieve a meter-level spatial resolution because oftheir low flight elevations. However, this also leads to asmaller footprint size which represents a limitation on thearea that each image can cover. In other words, moregeoreferencing work is needed to stitch or put images takenat different places together to cover a large-scale watersystem. Thus the UAV remote sensing mission can bedivided into two subproblems:

• Coverage control problem: path planning of the UAVto take aerial images of the whole Ω.

• Georeference problem: registry of each pixel fromthe aerial images with both temporal and spatial

information, for example the GPS coordinates andtime at which the picture was taken.

There can be two types of solutions. The open-loop solu-tion is to solve the two subproblems separately with thecoverage control problem before the UAV flight, and thegeoreference problem after it. This method is simple andeasy to understand but requires significant experience toset up all the parameters. The close-loop solution is to dothe path planning and georeference in real-time so thatthe information from the georeference part can be used asthe observer for the path planning controller. This paperfocuses on an open-loop solution.

3.1 Coverage Control

Coverage Control Subproblem

Given a random area Ω, UAVs with functions of altitudeand speed maintenance and waypoint navigation: speedv ∈ [v1, v2], possible flight height h ∈ [h1, h2], camerawith specification: focal length F , image sensor pixelsize: PSh × PSv, image sensor pixel pitch PPh × PPv,the interval between images acquired by the camera (the“camera shooting interval”) tshoot, the minimal shootingtime tshootmin

, the desired aerial image resolution res, thecontrol objective is:

min tflight = g(Ω, h, v, q1, . . . , qi, tshoot, res), (1)

s.t.v ∈ [v1, v2], h ∈ [h1, h2], tshoot = k × tshootmin . wheretflight is the flight time of the UAV for effective coverage,g(Ω, h, v, tshoot) is the function to determine the flight pathand flight time for effective coverage, k is a positive integer.

The control inputs of the coverage controller includebounded velocity v, bounded flight height H, a set ofpreset UAV waypoints q1, q2, . . . , qi and the camerashooting interval tshoot. The system states are the realUAV trajectory qt1 , . . . , qt2 and the system output is aseries of aerial images or a video stream taken between t1and t2.

Assume the imager is mounted with its lens verticallypointing down towards the earth, we use an approximationhere; its footprint (shown in Fig. 1) can be calculated as:

FPh =h× PPh × PSh

F, FPv =

h× PPv × PSvF

.

Fig. 1. Footprint Calculation.

Most UAVs can maintain altitude while taking pictures sothe UAV flight height h can be determined first based oncamera and resolution requirements. Assuming differentflight altitudes have no effect on the flight speed, we get

h =√res× F

max(PPh, PPv). (2)

Given the flight height h and the area of interest Ω, theflight path, cruise speed and camera shooting interval

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11, 2008

11745

must also be determined. Without loss of generality, Ωis assumed to be a rectangular since most other polygonscan be approximated by several smaller rectangles. Themost intuitive flight path for UAV flight obtained bydividing the area into strips based on the group spatialresolution, shown in Fig. 2(a). The images taken duringUAV turning are not usable because they may have badresolutions. Due to the limitation from the UAV autopilot,GPS accuracy and wind, the UAV cannot fly perfectlystraight along the preset waypoints. To compensate theoverlapping percentage between two adjacent sweeps omust also be determined before flight; this compensationis based on experience from the later image stitching asshown in Fig. 2(b).

(a) Ideal Trajectory (b) Real Trajectory

Fig. 2. UAV Flight Path.

Given the overlapping percentage o% between sweeps, theground overlapping og can be determined by:

og = (1− o%)× FPh. (3)

The minimal camera shooting interval is computed as:

tshootmin=

(1− o%)× FPvv

. (4)

This open-loop solution is intuitive, robust to all thepolygons and requires little computation. However, thismethod requires that many parameters, especially theoverlapping percentage o% to be set up based on expe-rience; it cannot provide an optimal solution. More workon a close-loop real-time solution is needed for an optimalsolution.

3.2 Georeference Problem

After the aerial images are taken and sent back to theground, post flight image processing is needed since theUAV cannot maintain perfectly level flight all the timeespecially during turning [Randy and Sirisha, 2005, Xiangand Tian, 2007, Sridhar et al., 2007].

Problem Statement for Post Flight Image Stitching

Given the aerial images stream I1, I2, . . . , Im and theUAV flight data logger stream t1, t2, . . . , tn, q1, q2, . . . , qnψ1, ψ2, . . . , ψn, θ1, θ2, . . . , θn, φ1, φ2, . . . , φn, mapη(R3, ψ, θ, φ, t) back to η(R2, 0, 0, φ, t), where ψ, θ and φrepresent the roll, pitch and yaw angles respectively.

This problem can be solved by feature based mapping, orgeology information based mapping, or both.

Feature Based Stitching With feature based stitching,the aerial images are stitched together based on somecommon features such as points, lines or corners. Thesefeatures can either be specified manually by humans orrecognized automatically by some algorithms. For exam-ple, PTGUI software needs reference points to performimage stitching. This method works well for photos sharingseveral common features. A simple example is shown in

Fig. 3. The aerial photos were taken at about 150 metersabove the ground by a GF-DC (introduced in Sec. 4)mounted on a model airplane controlled by a human op-erator. The problem with this method is that the photosare not georeferenced, and with no absolute coordinatesto tie to, the error can keep increasing with the imagenumbers. Because of the small image footprint of smallUAVs and the lack of permanent distinguishing featuresof most agricultural fields, it is almost impossible to findenough common features in every picture to correctly tiethe photos together. There are also feature-based stitchingmethods that georeference the photos. These take featureson each photo and compare them to photos which alreadyhave GIS information. This method performs the stitchingtask better because each photo is georeferenced and hassome absolute coordinates to tie to. However, the photoswith the GIS information are often taken once a year. Forsome photos taken from the UAV, they are not currentenough to find similar features.

(a) Photos before Stitch (b) Photos after Stich

Fig. 3. Feature Based Stiching.

UAV Position and Attitude Based Stitching This methoduses the data from the UAV including R3, θ, φ, ψ to mapthe image back to the related ground frame and registryeach pixel with its ground coordinates and electromagneticdensity values. The advantage is that this method uses theinformation from the UAV and can guarantee a boundedglobal error even for the final big image. However, itrequires perfect synchronization between the aerial imagesand the UAV attitude logging data, which means the fullauthority in UAV autopilot and sensor package. It is oneof the disadvantages of using off-the-shelf UAVs for remotesensing missions.

The related reference frames for UAV remote sensing aredefined as follows, shown in Fig. 4(a):

(1) Camera Frame: Fcam, the reference frame with theorigin at the focal point and the axes as shown inFig. 4(b).

(2) UAV Body Frame: Fbody, the reference frame withthe origin at the UAV center and the axes pointingforward, right and down.

(3) Navigation Frame: Fnav, the reference frame witha specific ground origin and the axes pointing theNorth, East and down to the Earth center.

(4) GPS Frame: FGPS , the reference frame used by GPS.It has a format of Latitude, Longitude and Altitude.

A three-dimensional (3D) mesh Mhw needs first be gener-ated in Fcam, which describes the spatial distribution ofits corresponding image. This mesh has n × n elements,illustrated in Fig. 4(b). Each element of (Mhw) can becalculated as:

q(w, h)cam =

[x(w, h)camy(w, h)cam

f

],∀q(w, h)cam ∈Mwh, (5)

x(w, h)cam =Sw(2w − n)

2n, y(w, h)cam =

Sh(2h− n)2n

,

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11, 2008

11746

(a) Coordinates (b) Camera Frame & Mesh

Fig. 4. Definition of Coordinates & Meshes.

where w, h ∈ [1, n], f is the focal length of the camera, wis the column index of the mesh, h is the row index of themesh, Sw and Sh are the width and height of the imagesensor respectively.

The mesh needs to be rotated first with respect to Fbody,Fnav and then translated to the ground frame Fgps. Themanner in which the camera is mounted on the UAV bodydecides the rotation matrix Rcambody, which can be a constantor a dynamic matrix. For example, a gimballed camerathat can rotate. The angles (yaw, pitch and roll ψc, θc, φc)describe the orientation of the camera with respect to thebody.

Rcambody = (Rbodycam )T

= (Rzyx(ψc + 90, θc, φc))T, (6)

Rcambody, Rbodycam ∈ SO(3).

The rotation matrix from Fbody to Fnav can be determinedfrom the orientation of the aircraft (ψ, θ, φ) with respect tothe navigation coordinate system, which can be measuredby the UAV onboard sensors.

Rbodynav =

[cθcψ −cφsψ + sφsθcψ sφsψ + cφsθcψcθsψ cφcψ + cφsθsψ −sφcψ + cφsθsψ−sθ sφcθ cφcθ

], (7)

Rbodynav ∈ SO(3),

where cθ, sθ stand for cos(θ) and sin(θ) respectively andsimilarly for the other terms.

The rotation transformations can be then calculated withall the above rotation matrices:

qnav = Rbodynav Rcambody qcam, (8)

The mesh now represents the location of the image sensorin the Navigation Frame Fnav. To represent the locationof the picture on the earth, the vectors in the mesh arescaled down to the ground. It is assumed that the earth isflat because each picture has such a small footprint. qnavis the element of the new projected mesh, qnav(z) is the zcomponent of the element in the unprojected mesh, and his the height of the UAV when the picture was taken.

qPnav =h

qnav(z)qnav. (9)

The elements of the mesh are now rotated into ECEFcoordinates using the latitude (λ) and the longitude (ϕ)of the UAV when the picture was taken. Then the mesh istranslated by the position vector (−→P ) of the UAV in ECEFcoordinates.

Rnavgps = Ryyz(−λ, 90, ϕ), Rnavgps ∈ SO(3), (10)

qgps = Rnavbody qPnav +−→P , (11)

After the above calculations, the meshes can be furtherprocessed by some 3-D imaging software like World Wind[NASA, 2007]. World Wind can place the meshes correctlyon the earth and provide an interactive 3-D displaying.More details are shown in Sec. 4.

3.3 Multi-UAV Approach

Some irrigation applications may require remote sensingof a large land area (more than 30 square miles) within ashort time (less than one hour). Acquisition of imageryon this geographic scale is difficult for a single UAV.However, groups of UAVs (which we call ”covens”) cansolve this problem because they can provide images frommore spectral bands in a shorter time than a single UAV.

The following missions will need multiple UAVs (covens)operating cooperatively for remote sensing:

• Measure η1, η2, η3 . . . simultaneously.• Measure ηi(q, t) within a short time.

To fulfill the above requirements, UAVs equipped withimagers having different wavelength bands must fly insome formation to acquire the largest number of imagessimultaneously. The reason for this requirement is thatelectromagnetic radiation may change significantly, evenover a period of minutes, which in turn may affect the finalproduct of remote sensing. The “V” or “—” formationkeeping algorithm, similar to the axial alignment [Renet al., 2008], can be used here since the only differenceis that the axis is moving:

qdm = −∑

n∈Jm(t)

[(qm − qn)− (δm − δn)], (12)

where qdm is the preset desired waypoints, Jm(t) representsthe UAV group, δm = [δmx, δmy]T can be chosen toguarantee that the UAVs align on a horizontal line with acertain distance in between.

4. SUBSYSTEMS DEVELOPMENT

To achieve the final goal of measuring the density functionsof different bands simultaneously, subsystems have beenpurchased, upgraded or developed separately including theUAV platform subsystem, embedded imager subsystemand image processing subsystem.

4.1 UAV Platform

Three types of different UAV platforms are undergoingtesting in the Center for Self Organizing and IntelligentSystems (CSOIS) including off-the-shelf Procerus UAV,Xbow UAV with open source software and Parparrazi UAVwith both open source software and open source hardware.For details, see [Chao et al., 2007a, Baumann, 2007].

4.2 Embedded Imager Development [Baumann, 2007]

The weight, shape of the imager, and manner in whichthe imager is mounted can significantly affect UAV flightdynamics. Due to the physical limitations of a small UAVplatform, the embedded imager must be small, light andconsume low power. For example, the Procerus 48” UAVcan only carry extra payload of less than one pound. Fourtypes of embedded imagers have been developed for smallUAV applications: ghost finger RGB/NIR digital camera(GF-RGB/NIR-DC), ghost finger RGB/NIR digital videocamera (GF-RGB/NIR-DV).

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11, 2008

11747

Ghost Finger Digital Camera (GF-DC) The “ghost fin-ger digital camera” (GF-DC) is developed from a Pen-tax OPTIO E30 digital camera. Three photo capturingmodes are included: timer triggered, remote control (RC)triggered, and digital switch triggered. An ATMEG8Lmicrocontroller is embedded with the camera to serve as acamera trigger controller. The timer trigger shot mode isfor aerial photo capturing with a preset shooting intervaltshoot. The RC triggered mode is to use spare channelfrom RC transmitter and receiver to control the picturecapture. The switch triggered shot mode is for triggeringfrom the UAV autopilot to achieve a better geospatialsynchronization. The camera GF-DC is shown in Fig. 5.The images are saved on the SD card within the camera.The GF-DC has been tested to work for more than onehour in timer triggered mode with one 1GB SD card anda 3.7 V Li-poly battery (950mA-hour).

(a) GF-DC (b) GF-DV

Fig. 5. Ghost Finger Embedded Imager.

Ghost Finger Digital Video Camera (GF-DV) GF-DV isdeveloped for real-time image transmission. It is comprisedof a single board analog 1/4” CCD camera, one 2.4GHzblack widow wireless video transmitter, and peripheralcircuits. The CCD camera only weighs about 7.09g. TheGF-DV is shown in Fig. 5(b). The communication rangefor the wireless transmitter and receiver can be up to onemile with the 3 dB antenna. The GF-DV requires a wirelessvideo receiver to get the video back in real time and thevideo stream can be saved in an MPEG file.

RGB+NIR Imager Array Most CCD chips used oncameras are only sensitive to the electromagnetic lightwith a spectral wavelength ranging from 400 to 1100 nm,which includes both the visible and NIR bands. Digitalcameras use an IR blocking filter to block wavelengthsabove 750nm. A CCD-based camera can be changed into aNIR sensor by removing the IR blocking filter and addingone visual band blocking filter. A Lee 87 NIR filter isused in our GF-DC and GF-DV to block the light withthe wavelength smaller than 730nm. So, the Ghost FingerNIR imager has a band of about 730− 1100nm. The NIRfilter is added on both GF-DC and GF-DV to form anarray of two imagers, which can measure the RGB andNIR simultaneously. Sample RGB and NIR photos takenby GF-DC2× 1 are shown in Fig. 6.

(a) RGB Photo (b) NIR Photo

Fig. 6. Sample Photos by GF-DC 2× 1.

5. PRELIMINARY EXPERIMENTAL RESULTS

Preliminary experimental results are shown in this sectionto demonstrate the effectiveness of the whole UAV remotesensing system on both the hardware and software levels.One typical sample application is introduced in detail onacquisition of photographic data over Desert Lake, Utah.More experiment results can be found in CSOIS report[Chao et al., 2007a].

Desert Lake in west-central Utah (latitude: 3922′5

′′N ,

longitude: 11046′52

′′W ) is formed from return flows from

irrigated farms in that area. It is also a waterfowl man-agement area. This proposes a potential problem becausethe irrigation return flows can cause the lake to havehigh concentrations of mineral salts, which can affect thewaterfowls that utilize the lake. Managers of the DesertLake resource are interested in the affect of salinity controlmeasures that have been recently constructed by irrigatorsin the area. This requires estimation of evaporation ratesfrom the Desert Lake area, including differential rates fromopen water, wetland areas, and dry areas. Estimation ofthese rates requires data on areas of open water, wetland,and dry lands, which, due to the relatively small size andcomplicated geometry of the ponds and wetlands of DesertLake, are not available from satellite images. A UAV canprovide a better solution for the problem of acquiringperiodic information about areas of open water, etc., sinceit can be flown more frequently and at less cost.

5.1 Mission Description & Path Planning

The whole Desert Lake area is about 2 × 2 miles. It iscomprised of four ponds and some wetland areas. Theflight path must cover both the open water and wetlandareas.

GFDV 2×1 is mounted in the middle of the Procerus UAV,and the lens is pointed vertically downwards to providereal-time, simultaneous RGB and NIR videos. Maxtream900MHz modems are used for the UAV and ground stationcommunication, and a 20 dB ground antenna is added toguarantee a communication range of 3 miles. The UAVflight parameters are computed before flight based on priorflight experience.

The flight height is computed as H < 400m because sub-meter pixel resolution is enough for this application. Dueto the safety issue and FMA regulations, the flight heightis set to 200 meters. The flight path is planned based onthe calculated parameters: flight height 200m, cruise speed14 m/s, sweep overlap 270 m, camera sampling interval 5seconds. The flight trajectory is shown in Fig 7.

Fig. 7. UAV Preplanned Flight Path for Desert LakeMission.

5.2 Experimental Result

Both the RGB and NIR videos are transmitted back tothe ground station in real time and saved on the base

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11, 2008

11748

laptop as a MPEG file for further processing. A timestamp with each photo is compared with the data logof the UAV to georeference each photo. Then the photosare viewed on an open source 3D interactive world viewercalled World Wind [NASA, 2007]. A customer-built UAVplug-in combined with World Wind allows us to place eachpicture on the globe to see how well the georeferencing isaccomplished. Currently the plug-in takes the photos withthe information from the UAV and displays them all on theglobe to check how well the aerial photos match. Futureplans are to improve stitching by blending the overlappingphotos together. Fig. 8 shows about 120 pictures whichhave gone through this process and been displayed onWorld Wind.

(a) RGB Band Aerial Photo (b) NIR Band Aerial Photo

Fig. 8. Photos after Stitch with GF-DV.

However, this application also shows some problems leftunsolved including:

• Synchronization: The Procerus UAV sends the UAVdata down to the base station and it is recordedon the computer at 1 to 3Hz depending on thecommunication. At the same time, our frame grabbergrabs the pictures at 15 to 30 fps. The problem isthat the picture may not match up perfectly withthe UAV data on the data log. The picture mayhave been taken in between the data samples andmay have an error up to 333ms. Methods for bettersynchronization are currently being developed andmay show better results.

• Calibration: it is difficult to make a direct calibrationfor the aerial image and further stitching since theUAV usually needs to investigate a large area.

• Band configurable ability: the current platform onlyincludes the RGB band and NIR bands, further morebands need to be added.

• Further image processing: the exact boundary of thelake needs to be decided combined with satelliteimages after applying advanced filtering techniques.Also, the current software requires manual post pro-cessing. For the application to be attractive to man-agers of real irrigation systems, manual post process-ing is unacceptable.

• Multiple UAVs: preliminary results have shown thatthe sensing range of the Procerus UAV with GFDV2is about 2.5 × 2.5 miles, given the current batteryenergy density. However, light reflection varies a lotin one day and accurate NIR images require at mosta two-hour acquisition time for capture of the entirecomposite image. This motivates the use of covens,or multiple UAVs, for this type of application, witheach UAV carrying one imager with a certain bands.

6. CONCLUSION

This paper characterizes the problem of using UAVs forremote sensing in water management and irrigation con-trol applications, and provides an outline of a band re-configurable and multiple UAV solution to the problem.

The big structure of the whole system, including boththe hardware and software, are explained in detail. Thepreliminary results show the effectiveness of the proposedsolution. More work on multi-spectral imagers and multi-UAV applications will be done.

ACKNOWLEDGEMENTS

This work is supported in part by the Utah Water Re-search Laboratory (UWRL) MLF Seed Grant (2006-2007)on “Development of Inexpensive UAV Capability for High-Resolution Remote Sensing of Land Surface HydrologicProcesses: Evapotranspiration and Soil Moisture.” Theauthors would also like to thank Professor Raymond L.Cartee for providing the USU farm at Cache Junctionas the flight test field, Dr. Roger Hansen and his staffsof the Bureau of Reclamation’s Provo Area office, USDepartment of Interior for many stimulating meetings anddiscussions.

Haiyang Chao and Yongcan Cao were partly supportedby Utah State University Vice President of ResearchFellowship.

REFERENCES

Marc Baumann. Imager development and image processing forsmall UAV-based real-time multispectral remote sensing. Master’sthesis, University of Applied Sciences Ravensburg-Weingarten andUtah State University, Weingarten, Germany and Logan, Utah,October 2007.

D. W. Casbeer, S. M. Li, R. W. Beard, T. W. McLain, andR. K. Mehra. Forest fire monitoring with multiple small UAVs.Proceedings of the American Control Conference, 5:3530–3535,June 2005.

H. Y. Chao, M. Baumann, A. M. Jensen, Y. Q. Chen, Y. C. Cao,W. Ren, and M. McKee. Band-reconfigurable multi-UAV-basedcooperative remote sensing for real-time water management anddistributed irrigation control. CSOIS Internal Technical ReportUSU-CSOIS-TR-07-02, Utah State University, 2007a.

H. Y. Chao, Y. C. Cao, and Y. Q. Chen. Autopilots for small fixedwing unmanned air vehicles: a survey. Proceedings of IEEE In-ternational Conference on Mechatronics and Automation, pages3144–3149, August 2007b.

S. Zazueta Fedro, G. Smajstrla Allen, and A. Clark Gary. Irrigationsystem controllers. Series of the Agricultural and BiologicalEngineering Department, Florida Cooperative Extension Service,Institute of Food and Agricultural Sciences, University of Florida,1993. URL http://edis.ifas.ufl.edu/AE077.

B. C. James. In Introduction to Remote Sensing, pages 28–52.Guilford Press, 4th edition, 2006.

L. F. Johnson, S. R. Herwitz, S. E. Dunagan, B. M. Lobitz, D. V.Sullivan, and R. E. Slye. Collection of ultra high spatial andspectral resolution image data over california vineyards with asmall UAV. Proceedings of the 30th International Symposium onRemote Sensing of Environment, 2003.

L. F. Johnson, S. R. Herwitz, B. M. Lobitz, and S. E. Dunagan.Feasibility of monitoring coffee field ripeness with airborne multi-spectral imagery. Applied Engineering in Agriculture, 20:845–849,2004.

NASA. World Wind software. 2007. URLhttp://worldwind.arc.nasa.gov/index.html.

R. P. Randy and P. Sirisha. Development of software to rapidlyanalyze aerial images. American Society of Agricultural andBiological Engineers Annual International Meeting, 2005.

W. Ren, H. Y. Chao, W. Bourgeous, N. Sorensen, and Y. Q. Chen.Experimental validation of consensus algorithms for multi-vehiclecooperative control. IEEE Transactions on Control SystemsTechnology, to appear 2008.

S. M. Sridhar, C. C. Collins, G. D. Chandler, M. T. Smith, andJ. E. Lumpp. CATOPS: A composite aerial terrain orthorecti-fication photography system for unmanned aircraft. AIAA In-fotech@Aerospace 2007 Conference and Exhibit, May 2007.

H. Xiang and L. Tian. Autonomous aerial image georeferencing fora UAV based data collection platform using integrated navigationsystem. American Society of Agricultural and Biological Engi-neers Annual International Meeting, 2007.

17th IFAC World Congress (IFAC'08)Seoul, Korea, July 6-11, 2008

11749