Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

33 Pages·2016·8.66 MB·English

remote sensing
Article
Capability Assessment and Performance Metrics
for the Titan Multispectral Mapping Lidar
JuanCarlosFernandez-Diaz1,2,*,WilliamE.Carter1,2,CraigGlennie1,2,RameshL.Shrestha1,2,
ZhigangPan1,2,NimaEkhtari1,2,AbhinavSinghania1,2,DarrenHauser1,2
andMichaelSartori1,2
1 NationalCenterforAirborneLaserMapping(NCALM),Houston,TX77204,USA;
[email protected](W.E.C.);[email protected](C.G.);[email protected](R.L.S.);
[email protected](Z.P.);[email protected](N.E.);[email protected](A.S.);
[email protected](D.H.);[email protected](M.S.)
2 DepartmentofCivilandEnvironmentalEngineering,UniversityofHouston,Houston,TX77204,USA
* Correspondence:[email protected];Tel.:[phone]
AcademicEditors:JieShan,JuhaHyyppä,GuoqingZhou,WolfgangWagnerandPrasadS.Thenkabail
Received:17August2016;Accepted:3November2016;Published:10November2016
Abstract: In this paper we present a description of a new multispectral airborne mapping light
detection and ranging (lidar) along with performance results obtained from two years of data
collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech
Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths
simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF)
thatrangefrom50to300kHzperwavelength(maxcombinedPRFof900kHz). TheTitansystem
can perform simultaneous mapping in terrestrial and very shallow water environments and its
multispectralcapabilityenablesnewapplications,suchastheproductionoffalsecoloractiveimagery
derivedfromthelidarreturnintensitiesandtheautomatedclassificationoftargetandlandcovers.
Field tests and mapping projects performed over the past two years demonstrate capabilities to
classifyfivelandcoversinurbanenvironmentswithanaccuracyof90%,mapbathymetryunder
more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions.
Inadditiontoitsmultispectralandperformancecharacteristics,theTitansystemisdesignedwith
severalredundanciesanddiversityschemesthathaveproventobebeneficialforbothoperationsand
theimprovementofdataquality.
Keywords: airborne laser scanning; mapping lidar; multispectral lidar; lidar bathymetry; lidar
accuracy;lidarrangeresolution;activeimagery
1. Introduction
Overthepasttwodecadesairbornemappinglightdetectionandranging(lidar), alsoknown
as airborne laser scanning (ALS), has become one of the prime remote sensing technologies for
samplingtheEarth’ssurfaceandlandcoverinthreedimensions(3D),especiallyinareascoveredby
vegetationcanopies[1]. Inadditiontotherange(spatial)informationderivedfromtime-of-flight(ToF)
measurements, pulsed airborne lidar sensors deliver an arbitrarily scaled measure of the strength
of the optical backscattered signal that is proportional to the radiance incident on the detector,
typically referred to as intensity. Intensity is correlated with a target’s reflectance at the given
laser wavelength, making it useful in the interpretation of the lidar spatial information [2] or as
a standalone data source for identifying the characteristics of the likely backscattering surface for
eachreturn[3]. Theintensityalsodependsonothertargetcharacteristicssuchasroughnessandthe
lidarcross-sectionandonsensor-targetgeometryparametersincludingrangeandincidenceangle.
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Currentlythegeneralpracticeistodigitallyscalethe“intensity”signaltonormalizeittoagivenrange.
However,thequalityofintensityinformationcanbeimprovedthroughgeometricandradiometric
correctionsforotherfactorssuchasincidenceangle,andatmosphericattenuation[4–6]. Takenastep
further,radiometriccalibrationmethodstransformintensitiestophysicalquantitiessuchastarget
reflectance[7],allowinginterpretationwithrespecttoknownmaterialspectra.
Inthepast,intensityinformationhasbeenusedforvariousapplications,includinglandcover
classification[3,5];enhancementoflidargroundreturnclassification[8,9];fusionwithmultispectral
and hyperspectral data to enable a better characterization of terrain or land cover [10]; derivation
of forest parameters and tree species mapping [11–13]; and production of greyscale stereo-pair
imagerytogeneratebreaklinestraditionallyusedinphotogrammetrythroughatechniquenamed
lidargrammetry[14]. However,theusefulnessoflidarintensityinformationhasbeenfundamentally
limited because it provides a measure of backscatter at a single, narrow laser wavelength band.
This is usually a near-infrared (NIR) wavelength (1064 or 1550 nm) for topographic lidar systems
and a green-blue wavelength [15] for bathymetric lidar systems. Currently, the second harmonic
ofneodymium-dopedyttriumaluminumgarnet(Nd:YAG)lasers,532nm,isacommonchoicefor
bathymetriclidars[16]. Thissinglewavelengthspectrallimitationhasbeenpreviouslyrecognizedand
severalexperimentshaveattemptedtomitigateitbycombiningdataobtainedfromindividualsensors
thatoperateatdifferentlaserwavelengths[17,18]orbyobservationsfromprototypemultispectral
lidar systems [19–21]. These multispectral lidar experiments are testament to the potential of this
newly developing remote sensing technique, and as with any other technology, the potential is
coupledwithchallenges. Someofthesechallengesarerelatedtophysicalprinciples(atmospheric
transparency,backgroundsolarradiation,etc.) andhardwarelimitations(availablelaserwavelengths,
eye safety) [22], while other limitations are related to software and algorithms (e.g., radiometric
calibrationoftherawlidarintensities)[17].
Thispaperpresentsageneraloverviewofthedesignandperformanceofthefirstoperational
multispectralairbornelaserscannerthatcollectsrange,intensityandoptionallyfullwaveformreturn
dataatthreedifferentlaserwavelengths(1550,1064,532nm)throughasinglescanningmechanism.
This airborne multispectral lidar scanner is the Teledyne Optech Titan MW (multi-wavelength)
whichwasdevelopedbasedonthespecificationsandtechnicalrequirementsoftheNationalScience
Foundation(NSF)NationalCenterforAirborneLaserMapping(NCALM).Thesystemwasdelivered
totheUniversityofHouston(UH)inOctoberof2014. Sincethen,thesensorhasundergonesignificant
testing,improvementandfinetuninginawiderangeofenvironments[23].
This paper is intended to highlight the flexibility of the Titan to perform 3D and active
multispectral mapping for different applications (bathymetry, urban mapping, ground cover
classification, forestry, archeology, etc.) without delving too deeply into one specific application.
Individual papers that provide more details for specific applications are currently in preparation.
ThebasicresearchquestionaddressedinthispaperrelatestotheperformanceoftheTitansystem,
which,givenitsoperationalflexibility,hastobeassessedusingavarietyofmetricsthatvaryaccording
to the application. The performance metrics discussed within this work are: (a) the accuracy of
experimentalgroundcoverclassificationbasedonun-calibratedmultispectrallidarreturnintensity
andstructuralmetricsinanurbanenvironment(Houston,TX,USA);(b)maximumwaterpenetration,
and(c)accuracyofthemeasuredwaterdepthsunderidealbathymetricmappingconditions(Destin,FL,
USA, and San Salvador Island, Bahamas); (d) canopy penetration; (e) range resolution in tropical
rain forests (Guatemala, Belize and Mexico); and (f) precision and accuracy of topographically
derivedelevations.
It is important to clarify that since the delivery of NCALM’s original Titan system,
themanufacturerhasproducedotherTitansensors;however,notallunitshavethesameengineering
specificationsorsystemdesign. Thedesignofthesensorisflexible,allowingtheexactconfiguration
andperformanceoftheunittobeadaptedtothespecificneedsofacustomer.Thediscussionpresented
RemoteSens.2016,8,936 3of34
belowisspecifictothedesignandperformanceoftheNCALMunitandmaynotbeapplicableor
Remote Sens. 2016, 8, 936 3 of 34
reproducibleforotherTitanunits,eventhoughtheycarrythesamemakeandmodeldesignation.
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design schemes; and (e) vertical positional precision and accuracy; finally, conclusions are presented
and(e)verticalpositionalprecisionandaccuracy;finally,conclusionsarepresentedinSection4.
in Section 4.
2. TitanInstrumentDescription
2. Titan Instrument Description
The first Titan MW lidar sensor (Serial number 14SEN/CON340) was developed to meet
The first Titan MW lidar sensor (Serial number 14SEN/CON340) was developed to meet
operational specifications and requirements established by NCALM. The specifications called for
operational specifications and requirements established by NCALM. The specifications called for a
amultipurposeintegratedmultichannel/multispectralairbornemappinglidarunit,withanintegrated
multipurpose integrated multichannel/multispectral airborne mapping lidar unit, with an integrated
high resolution digital camera that could seamlessly map terrain and shallow water bathymetry
high resolution digital camera that could seamlessly map terrain and shallow water bathymetry
environments from flying heights between 300 and 2000 m above ground level (AGL). NCALM’s
environments from flying heights between 300 and 2000 m above ground level (AGL). NCALM’s
operationalexperiencewithairbornelidarunitsoperatingat1064and532nmwavelengths,andthe
operational experience with airborne lidar units operating at 1064 and 532 nm wavelengths, and the
requirementtoperformsimultaneousterrestrialandbathymetrymapping,determinedtwoofthethree
requirement to perform simultaneous terrestrial and bathymetry mapping, determined two of the
laserwavelengths. Therewereseverallaserwavelengthsconsideredforthethirdchannel,including
three laser wavelengths. There were several laser wavelengths considered for the third channel,
950and1550nm;however,the1550nmoptionwasselectedbecauseoftheeaseincomplyingwith
including 950 and 1550 nm; however, the 1550 nm option was selected because of the ease in
eye safety regulations, the proven reliability of the laser sources, and because it results in nearly
complying with eye safety regulations, the proven reliability of the laser sources, and because it
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NCALM’s Titan has two fiber laser sources. The first source “Laser A” has a primary output at
NCALM’sTitanhastwofiberlasersources. Thefirstsource“LaserA”hasaprimaryoutputat
1064 nm. Part of the 1064 nm output is directly used as channel two for the lidar and another part of
1064nm.Partofthe1064nmoutputisdirectlyusedaschanneltwoforthelidarandanotherpartofthe
the output is passed through a frequency-doubling crystal to obtain 532 nm wavelength pulses for
channel three of the lidar unit. The second laser source, “Laser B”, has its output at the 1550 nm
RemoteSens.2016,8,936 4of34
outputispassedthroughafrequency-doublingcrystaltoobtain532nmwavelengthpulsesforchannel
threeofthelidarunit.Thesecondlasersource,“LaserB”,hasitsoutputatthe1550nmwavelengthand
isusedasthesourceforlidarchannelone. Bothlasersaresynchronizedandcanproducepulserates
between50and300kHz,programmableat25kHzintervals. ThelaseroutputoftheTitancorresponds
to Class IV as per United States Food and Drug Administration, 21 Code of Federal Regulations
1040.10and1040.11; InternationalElectrotechnicalCommission60825-1. Thecharacteristicsofthe
individuallasersourcesaswellasothercharacteristicsofeachlidarchannelrequiredforassessing
systembehaviorandperformancearepresentedinTable1.
Table1.PerformancespecificationsofeachoftheTitan’schannels.
Channel1 Channel2 Channel3
LaserWavelength(nm) 1550 1064 532
Lookangle(degrees) 3.5forward nadir 7.0forward
PulseRepetionFrequency(kHz) 50–300 50–300 50–300
BeamDivergence(mRad) ~0.36 ~0.3 ~1.0
PulseEnergy(µJ) 50–20 ~15 ~30
PulseWidth(ns) ~2.7 3–4 ~3.7
Otherequipmentprovidersoffersingle-passmultispectrallidarsystemsbymounting,eitherin
tandemorsidebyside,individualsensorheadsoperatingatdifferentwavelengths[22]. However,
for the Titan system, we required the operation of the different wavelengths’ lasers through a
single scanning mechanism to provide near-identical swaths from the three wavelength channels.
Thechannelsarearrangedsuchthatthe1064nmchannelpointsatthenadir,andthe1550and532nm
channels are pointed 3.5◦ and 7◦ forward of the nadir, respectively. The primary reason for this
configurationwastominimizereturnsfromthewatersurfaceandmaximizetheprobabilityofwater
penetrationforthe532nmpulses. Asecondaryreasonwastomaximizecorrelationwithlegacylidar
datasetscollectedwitha1064channelpointingtothenadir. TheTitanscannerhasa±30◦ fieldofscan
andamaximumscannerproduct(halfscanangle×scanfrequency)of800degrees-Hertz. Thebeam
divergencevaluesarecloseto0.3milliradiansforthe1064and1550nmchannelsandonemilliradian
forthe532nmchannel(seeTable1). Boresightparametersforeachchannelarecurrentlydetermined
independently,andcombinedwiththesensormodelinthemanufacturer’sproprietarysoftwareto
obtainageometricallycorrectandconsistentpointcloud. Individualpointcloudfilesaregenerated
foreachflightlineandchannel.
Thelaserreturnsignalisanalyzedinrealtimethroughananalogueconstantfractiondiscriminator
(CFD) [24,25] which detects and records discrete laser returns (range and intensities) at all pulse
repetitionfrequencies. Whilethesystemcandetectalargenumberofreturnsperpulse,itonlyrecords
up to four returns for each outgoing laser pulse (first, second, third and last). In addition to the
analogueCFD,theoutgoingandreturnwaveformsofallthechannelscanoptionallybedigitizedata
12bitamplitudequantizationresolutionandatarateof1gigasample/s.Currently,thisdigitizationcan
onlybedoneforeachoutgoingpulseandreturnwaveformsatamaximumPRFof100kHz;forhigher
PRFs,fullwaveformdigitizationisonlyperformedforadecimatedsubsetoftheemittedpulses.
TheTitaniscapableofrangingbeyondthesinglepulse-in-the-airlimit(rangeambiguity),meaning
thatitisabletoobtainaccuraterangesathighPRFswhenthereareseverallaserpulsesfromeach
channel in the air simultaneously before a return from the first emitted pulse is obtained [26,27].
TheTitaniscapableofmeasuringinafixedmulti-pulsemode,whichmeansthatthesensorneedsto
beawareofhowmanypulsesareplannedtosimultaneouslybeintheairinordertocomputeaccurate
ranges. Thisisdoneintheplanningphaseofthedatacollection. If,forsomereason,theactualnumber
ofpulses-in-the-air(PIA)isdifferentthantheplannedvalue,thesystemwillproduceerroneousrange
values. This fixed multi-pulse capability has certain combinations of ranges and PRFs where the
sensorisnotabletoresolvetherangeambiguitiesandwhichthemanufacturercalls“blindzones”.
RemoteSens.2016,8,936 5of34
ThePRFsandrangeregionswheretheTitancanworkwithoutsufferingrangeambiguityforagiven
PRFaredisplayedinFigure2asthewhiteregionsbetweenthecoloredbandsandlabeledaccording
thenumberofpulses-in-the-air(PIA)atagiventime.
Remote Sens. 2016, 8, 936 5 of 34

Figure 2. Pulse repetition frequency (PRF) versus range operation regions for NCALM’s Titan
Figure2.Pulserepetitionfrequency(PRF)versusrangeoperationregionsforNCALM’sTitansensor.
sensor. The graph shows both regions of operation (white) as well as range ambiguity regions
Thegraphshowsbothregionsofoperation(white)aswellasrangeambiguityregionsdepictedasthe
depicted as the solid colored bands.
solidcoloredbands.
Technically, the range ambiguity only occurs at a specific range or multiples of this range value.
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be tolerated by the sensor without entering into the range ambiguity regions depicted in the figure.
terrainvariationorelevationofmanmadestructures. ThehigherthePRF,thelessterrainreliefcanbe
The laser shot densities obtainable with the Titan are mainly a function of instrument
toleratedbythesensorwithoutenteringintotherangeambiguityregionsdepictedinthefigure.
parameters (laser PRF, scan angle, scan frequency), and flying parameters (ground speed and flying
ThelasershotdensitiesobtainablewiththeTitanaremainlyafunctionofinstrumentparameters
height above terrain). However, eye safety regulations and range ambiguity also limit the maximum
(laser PRF, scan angle, scan frequency), and flying parameters (ground speed and flying height
measurement density obtainable for a specific flying height. Figure 3 illustrates the laser shot density
above terrain). However, eye safety regulations and range ambiguity also limit the maximum
operational envelope of the Titan as a function of the flying height for a single channel and single
measurementdensityobtainableforaspecificflyingheight. Figure3illustratesthelasershotdensity
pass (i.e., no swath overlap). The figure is for reference only and is based on the specific assumptions
operationalenvelopeoftheTitanasafunctionoftheflyingheightforasinglechannelandsingle
described below; density values outside the envelope can be obtained under specific circumstances
pass(i.e.,noswathoverlap). Thefigureisforreferenceonlyandisbasedonthespecificassumptions
but are not considered to be normal mapping operations.
describedbelow;densityvaluesoutsidetheenvelopecanbeobtainedunderspecificcircumstances
butarenotconsideredtobenormalmappingoperations.
RemoteSens.2016,8,936 6of34
Remote Sens. 2016, 8, 936 6 of 34

Figure3.LasershotdensityenvelopeforasinglepassandsinglechanneloftheTitansensor.
Figure 3. Laser shot density envelope for a single pass and single channel of the Titan sensor.
InFigure3,thelowerlimitoftheenvelopeisobtainedbyassumingthelowestPRFof50kHz,
In Figure 3, the lower limit of the envelope is obtained by assuming the lowest PRF of 50 kHz, a
agroundspeedof150knotsandascanneroperatingat25Hzatthemaximumfieldofview(±30◦).
ground speed of 150 knots and a scanner operating at 25 Hz at the maximum field of view (±30°). At
Atlowerflightheightsthemaximumdensityislimitedbyeyesafetyconsiderations. EachlaserPRF
lower flight heights the maximum density is limited by eye safety considerations. Each laser PRF has
has a nominal ocular hazardous distance (NOHD) that increases as the PRF increases. While it is
a nominal ocular hazardous distance (NOHD) that increases as the PRF increases. While it is
technicallypossibletooperateathigherPRFsatlowaltitudes,thisdoesnotcomplywithapplicableeye
technically possible to operate at higher PRFs at low altitudes, this does not comply with applicable
safetyregulationsandthusitisnecessarytousethemaximumPRFthatensuresexceedingtheNOHD
eye safety regulations and thus it is necessary to use the maximum PRF that ensures exceeding the
atgroundlevel(seeFigure3). Theupperlimitoftheenvelopeisobtainedbyassumingthehighest
NOHD at ground level (see Figure 3). The upper limit of the envelope is obtained by assuming the
possiblePRFforagivenflyingheight(takingintoaccounttherangeambiguityregions),aground
highest possible PRF for a given flying height (taking into account the range ambiguity regions), a
speedof150knotsandascanneroperatingat70Hzandaverynarrowfieldofview(±10◦). Thepeaks
ground speed of 150 knots and a scanner operating at 70 Hz and a very narrow field of view (±10°).
andvalleysintheupperenvelopelimitarecausedbytherangeambiguityregions(Figure2)given
The peaks and valleys in the upper envelope limit are caused by the range ambiguity regions (Figure
thatforspecificflyingheightstheTitan’smaximumoperationPRFof300kHzperchannelfallswithin
2) given that for specific flying heights the Titan’s maximum operation PRF of 300 kHz per channel
arangeambiguityregionandthusthePRFhastobereducedtomeasureunambiguousranges.
falls within a range ambiguity region and thus the PRF has to be reduced to measure unambiguous
ThedigitalcameraintegratedintotheTitansensorisaDIMACUltralight(alsoknownasD-8900).
ranges.
TheD-8900isbasedonachargedcoupleddevice(CCD)with60megapixels,eachwithadimensionof
The digital camera integrated into the Titan sensor is a DIMAC Ultralight (also known as
6µm×6µm. Thepixelsarearrangedinanarrayof8984pixelsorientedperpendiculartotheflight
D-8900). The D-8900 is based on a charged coupled device (CCD) with 60 megapixels, each with a
directionand6732pixelsalongtheflightdirection,whichtranslatestoaCCDphysicalframesizeof
dimension of 6 μm × 6 μm. The pixels are arranged in an array of 8984 pixels oriented perpendicular
5.39cm×4.04cm. Theimageisformedonthefocalplanethroughacompoundlenswithanominal
to the flight direction and 6732 pixels along the flight direction, which translates to a CCD physical
focallengthof70mm. ThecombinationofthelensandCCDarrayyieldsatotalfield-of-view(FOV)of
frame size of 5.39 cm × 4.04 cm. The image is formed on the focal plane through a compound lens
42.1×32.2andagroundsampledistance(GSD)of0.0000825×flyingheight. ThepositionoftheCCD
with a nominal focal length of 70 mm. The combination of the lens and CCD array yields a total
isadjustedduringflightbyapiezoactuatortocompensateforthemotionoftheaircraftduringan
field-of-view (FOV) of 42.1 × 32.2 and a ground sample distance (GSD) of 0.0000825 × flying height.
exposure,reducingthepixelsmearatverysmallGSDs. Thedigitalcameracanbetriggeredinavariety
The position of the CCD is adjusted during flight by a piezo actuator to compensate for the motion of
ofmodes(timeinterval,position)fromtheTitancontrolsoftware,orindependentlythroughitsown
the aircraft during an exposure, reducing the pixel smear at very small GSDs. The digital camera can
controlsoftware.Inadditiontoitsintegratedcamera,theTitansystemcaninterfacewithotherimaging
be triggered in a variety of modes (time interval, position) from the Titan control software, or
sensors;forexample,NCALMhasintegratedtheTitanwithanITRESCASI-1500hyperspectralcamera.
independently through its own control software. In addition to its integrated camera, the Titan
Physically, the Titan consists of a sensor head and sensor control rack. The sensor rack in
system can interface with other imaging sensors; for example, NCALM has integrated the Titan with
its base configuration consists of an electrical power unit and a control computer. Depending on
an ITRES CASI-1500 hyperspectral camera.
whatcomponentsarebeingoperatedinconjunctionwiththelidarsensor,thecontrolrackcanalso
Physically, the Titan consists of a sensor head and sensor control rack. The sensor rack in its
incorporateuptothreeadditionalcontrolcomputersforthewaveformdigitizers(oneperchannel)
base configuration consists of an electrical power unit and a control computer. Depending on what
andthecontrolcomputerforthedigitalcamera. Thesensorheadisbasicallyacylinderwithabox
components are being operated in conjunction with the lidar sensor, the control rack can also
incorporate up to three additional control computers for the waveform digitizers (one per channel)
and the control computer for the digital camera. The sensor head is basically a cylinder with a box on
RemoteSens.2016,8,936 7of34
Remote Sens. 2016, 8, 936 7 of 34
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3. Field Testing of Capabilities
3. FieldTestingofCapabilities
Since the delivery of the Titan sensor to NCALM in October 2014, the sensor has been used to
Since the delivery of the Titan sensor to NCALM in October 2014, the sensor has been used
collect data for more than 30 projects across the United States and in remote locations such as
to collect data for more than 30 projects across the United States and in remote locations such as
Antarctica and the Petén jungle in Guatemala. With over 140 hours of laser-on operation, the Titan
AntarcticaandthePeténjungleinGuatemala. Withover140hoflaser-onoperation,theTitansensor
sensor has been tested in a wide variety of environments, for diverse applications and throughout its
hasbeentestedinawidevarietyofenvironments,fordiverseapplicationsandthroughoutitsdesign
design operational envelope. Table 2 presents details of the mapping and test projects acquired with
operational envelope. Table 2 presents details of the mapping and test projects acquired with the
the Titan sensor. In addition to project data collection, NCALM has also performed tests to assess the
Titansensor. Inadditiontoprojectdatacollection, NCALMhasalsoperformedteststoassessthe
performance of the sensor under different operational conditions. This paper presents results from
performanceofthesensorunderdifferentoperationalconditions. Thispaperpresentsresultsfrom
some of these performance tests focused on ground cover classification based on multispectral lidar
someoftheseperformancetestsfocusedongroundcoverclassificationbasedonmultispectrallidar
intensity and structural metrics in urban environments, bathymetry (maximum detectable depth
intensityandstructuralmetricsinurbanenvironments,bathymetry(maximumdetectabledepthand
and accuracy of bathymetric elevations), the ability to penetrate thick tropical forest canopies, the
accuracyofbathymetricelevations),theabilitytopenetratethicktropicalforestcanopies,theability
ability to finely resolve the vertical structure of vegetated environments (range resolution), and its
tofinelyresolvetheverticalstructureofvegetatedenvironments(rangeresolution),anditsvertical
vertical positional accuracy.
positionalaccuracy.
RemoteSens.2016,8,936 8of34
Table2.SummaryofprojectsandtestcollectionstodatewiththeTitansystem.
CollectionYearand LaseronTime
Project/TestLocation PrimaryApplication
DayofYear (H)
2014:289,300
Baytown,TX,USA 2015:47,222 Systemtest
2016:127–129
Houston,TX,USA 2014:289 Systemtest 0.4
Jordan,MT,USA 2014:291 Geomorphology 0.7
HebgenLake,MT,USA 2014:292 Tectonics 1.1
BigCreekRiver,ID,USA 2014:293 Riverbathymetry 1.0
GreysRiver,WY,USA 2014:295 Riverbathymetry 1.3
Bishop,CA,USA 2014:296 Geomorphology 0.7
WheelerRidge,CA,USA 2014:296 Geomorphology 0.8
Yucaipa,CA,USA 2014:297 Forestry 0.9
Beaver,UT,USA 2014:299 Rivermorphology 0.7
McMurdoDryValleys,Antractica 2014:338to2015:19 Geomorphology 47.5
ElCeibal,Peten,Guatemala 2015:77–82 Archaeology 5.5
Zacapu,Michoacan,Mexico 2015:88 Archaeology 0.7
Angamuco,Michoacan,Mexico 2015:88 Archaeology 0.8
Teotihuacan,Mexico 2015:91–92 Archaeology 1.69
LaserServicing
TrinityRiver,TX,USA 2015:219–222 Rivermorphology 4.8
NASAJSCClearLake,TX,USA 2015:223–227 Climatechangeresiliency 5.1
BaratariaBay,LA,USA 2015:228–230 Marshresponsetooilspill 6.8
DestinInlet,FL,USA 2015:231 Bathymetrytest 1.5
Apalachicola,FL 2015:232–233 Aquaticecosystem 1.6
RedfishBay,TX 2015:235 Bathymetrytest 0.8
TexasGulfCoast,USA 2015:235 Coastalmorphology 0.5
ReynoldsCreek,ID,USA 2015:289,294 Ecology 2.6
SantaClaraRiver,CA,USA 2015:291–293 Rivermorphology 3.4
CalhounCreek,SC,USA 2016:057 Ecology 2.2
LaserServicing
Campeche,Mexico 2016:138–141 Archaeology 7.6
LakePeakFault,CA,USA 2016:155 Tectonics 1
InyoDomes,CA,USA 2016:158 Geomorphology 0.5
Monterey,CA,USA 2016:157,159 Urbanspectralclassification 0.7
Bastrop,TX,USA 2016:161 Orthophotos 0.3
NorthWesternBelize 2016:184–186 Archaeology,Geomorphology 5.0
SanSalvador,Bahamas 2016:189–191 Islandhydrology 7.0
MayanBiosphereReserve,Peten,Guatemala 2016:197–207 Archeaology,Ecology 23.7
3.1. MultispectralCapabilities
The Titan senor was designed as a flexible multi-purpose and multi-application system;
therefore, it may not outperform systems that were designed exclusively for bathymetry or for
topographic mapping from high altitudes. However, one application for which it can excel is
providing high resolution active multispectral data derived from lidar intensity. In theory and in
practice, multispectral lidar intensity can be used for many applications, including return/target
classification[28,29];individualtreeidentification,parameterizationandclassification[30];water/land
interfaceidentification;andarchaeologicalfeaturedetection[31],amongmanyothers. Severalyears
willgobybeforescientistsineachoftheabove-mentionedfieldswillhaveanopportunitytoassessthe
utilityoftheTitanmultispectrallidardatasetsascomparedtowhathasbeentheorizedorexperimented
sofar. Forbrevity,thispaperwillonlybrieflydescribetheuseofTitanmultispectraldatatogenerate
activelidarintensityimages,describetheadvantagesandlimitationsoftheseintensityproductsand
provideanexampleofgroundcoverclassificationusingTitandata.
It does not take much imagination to realize that the simplest way to use multispectral lidar
intensity is to enhance the visualization of lidar data, just as single-wavelength intensity values
have been used in the past. As such, the most basic way of utilizing the multispectral intensity is
for rendering false colored point clouds, where the intensity of each return is used to assign hue
saturationandbrightnessvalues. Inthisapplication,thehueandsaturationareassignedbasedonthe
laserwavelengthandthelidarreturnintensityvaluedeterminesormodulatesthebrightnessvalue.
RemoteSens.2016,8,936 9of34
Theadvantageofthisapproachisthattherenderingscanretainthepreciseandcomplex3Dspatial
natureofthelidardatadespitethereturnlocationsbeingirregularlyspaced.
Multispectrallidarintensitycanalsobeusedtogeneratetwo-dimensional(2D)activeintensity
images by combining the spatial and spectral information of each laser return. There are several
approachestoproduceintensityimageryfromirregularlyspacedlidarpointclouddata. Thefirstand
simplermethodconsistsofassigningagreyscalevaluecorrespondingtothelidarintensitytoeach
returnandthenrenderingthepointcloudintwodimensionstocreateatop-viewintensityimage.
Asecondapproach,whichismoreelaborateandprovidesmorecontroloverthespatialintegrityof
theimage, consistsofinterpolatingtheintensityvaluesintoaregulartwo-dimensionalhorizontal
array(grid)usingmethodssuchastriangulationwithlinearinterpolation,kriging,orinversedistance
weighting,etc. Thismethodisparticularlyusefulwhenthehorizontaldistributionofthelidarreturns
issparseascomparedtothedesiredgrid(raster)resolution.
Whenthereturndensityishighenoughtoprovideseverallaserreturnsperrastercell,itispossible
touseathirdapproach. Thisapproachconsistsofderivingasingleintensityvalueforeachrastercell
fromtheintensityvaluesofallthereturnswithinthegivenrastercell. Theresultantintensityraster
canbeproducedbyaveragingintensityvaluesusingsimpleorweightedmethodsordetermining
minimum,maximum,standarddeviationoranyotherstatisticalmetrictocharacterizethereturns’
intensitywithintheraster[32]. Herein, werefertothisapproachas“binning”. Oneadvantageof
binning,especiallywhenitusessomesortofaveragingmechanism,isthatitreducesthevariability
ofintensityvaluesduetofactorsthataredifficulttoaccountfor,suchasirregularincidenceangles
andvaryingsurfaceroughness. However,thegreatestadvantageofthismethodisthatitenablesthe
generationoffalsecolorimageryfrommulti-wavelengthlidarintensitiesevenwhenthecentersof
thefootprintsatthedifferentwavelengthsmaynotbeexactlycollocated,whichisthecaseforthe
Titansensor.
Figure5a–cshowintensityimagesgeneratedusingthebinningmethodfordatacollectedwiththe
TitansensoroverthecampusoftheUniversityofHouston. Whenintensityinformationisavailable
forthreeormoreindependentwavelengths,itispossibletogeneratefalsecolorRGBimagery. Inthe
caseoftheTitan,withthreedifferentwavelengths,itispossibletocombinetheintensityimagesin
sixdifferentarrangements. Figure5dshowsafalsecolorRGBcombinationusingtheintensityfrom
the1550nmwavelengthastheredchannelandthe1064and532nmwavelengths’intensitiesforthe
greenandblueimagechannels,respectively. Bycombiningindependentintensityimagesintoafalse
colorRGBimage,itispossibletoaccessthemultidimensionalityofthecolorspacewhichcanhighlight
featuresthatperhapsarenoteasilyidentifiedinasingle-wavelengthgrayscaleintensityimage.
In addition to using binning to generate intensity rasters, the same technique can be applied
totheelevationvaluesofthereturnswithinabintogeneratewhatinthispaperarereferredtoas
“structural”images(Figure5e–g),astheyprovideinformationontheverticalstructureofthetargets
withinarastercell. Similartothewaytheintensityimagesareproduced,thestructuralimagesare
producedbyassigningasinglevaluetotherasterbasedonsomestatisticalmeasurederivedfromthe
elevationsofthereturnswithinacell. Thesestructuralrasterscanbegeneratedfromeitherabsolute
geodeticelevations(ellipsoidalororthometric)orrelativeelevationssuchasheightabovelocalground.
For land cover classification purposes, statistical dispersion metrics such as elevation spread and
standard deviation can be computed from either absolute or relative elevation values. However,
for averaged elevation metrics to be of any use for classification, they need to be computed from
relativeelevationabovelocalgroundvalues. Thesestructuralimageshavegreatpotentialforassisting
landcoverclassificationtasksbecausedifferentkindsoftargets(buildings,roads,trees,powerlines,
etc.) haveverydistinctelevationsignatures. Otherstructuralmetricscanbederivednotonlyfrom
elevationvaluesbutfromreturnstatisticssuchasthenumberofreturnsdetectedfromagivenraster
cell(returndensity)andtheratioofreturnstolasershots(Figure5g). Thesereturnmetricsimagescan
beusedtosegregateimpervious(roads,ground,buildingroofs)anddiffusetargets(vegetationand
water). Structuralmetricsfromdifferentlidarwavelengthscanalsoaidinlandcoverclassification.
RemoteSens.2016,8,936 10of34
Remote Sens. 2016, 8, 936 10 of 34
and diffuse targets (vegetation and water). Structural metrics from different lidar wavelengths can
Forexample,adifferenceinmeanelevationbetweenstructuralimagesbasedonthe532nmandeither
also aid in land cover classification. For example, a difference in mean elevation between structural
oftheNIRwavelengths,foragivensetofpixels,willindicateapotentialwaterbody,becausethe
images based on the 532 nm and either of the NIR wavelengths, for a given set of pixels, will indicate
532 nm wavelength may produce multiple returns from the water surface, the water column and
a potential water body, because the 532 nm wavelength may produce multiple returns from the
thewbaetenrt hsiucrlfaaycee,r ,thweh wileattehre cNolIuRmwn aavnedle nthget hbsewntihllicd elatyeecrt, rewtuhirlne stohne lyNIfRro mwatvheelewngatthers swuirlfla dceetoecrtn o
retruertnusrnast oanlll.y from the water surface or no returns at all.

Figure 5. Intensity and structural images generated from the Titan multispectral data. (a) Intensity
Figure5. IntensityandstructuralimagesgeneratedfromtheTitanmultispectraldata. (a)Intensity
image generated from the 1550 nm channel; (b) intensity image for the 1064 nm channel; (c) intensity
imagegeneratedfromthe1550nmchannel;(b)intensityimageforthe1064nmchannel;(c)intensity
image for the 532 nm channel; (d) false color multispectral intensity image generated by using the
imageforthe532nmchannel;(d)falsecolormultispectralintensityimagegeneratedbyusingthe
1550 nm intensity for the red channel and the 1064 and 532 nm intensities for the green and blue
1550nmintensityfortheredchannelandthe1064and532nmintensitiesforthegreenandblue
channels; (e) structural image based on the spread of the returns height; (f) structural image based on
channels;(e)structuralimagebasedonthespreadofthereturnsheight;(f)structuralimagebasedon
the height above ground; (g) structural image based on the number of returns per pulse; (h) ground
theheightaboveground;(g)structuralimagebasedonthenumberofreturnsperpulse;(h)ground
cloud classification results map.
cloudclassificationresultsmap.
Active intensity and structural images have some advantages over traditional passive images;
speAcciftiicvaellyin ttheenys:i t(ya)a neldimsitnruatcet utrhael dimepaegnedsehnacyv eosno smoelaar dilvluamntiangaetisoonv (eimrtargaedriyt iocanna lbpe acsoslilveectiemd aagt es;
spencigifihct aolrly betlhoewy :th(ea )cloeluimd cineialitnegt)h; e(b)d gerpeeantldye rnecdyucoen thseo elaffreciltlsu omf sinhaatdioowni(nigm aangde roycccluansiobnes ccaoullseecdte d
atbnyig bhutilodrinbgesl oanwd tthoepocgloraupdhyc;e (icli)n pgr)o;v(ibd)e ggroeoadtl yknroewdluecdegeth oef tehffee icltlusmofinsahtiaodno swouinrcge a(wndavoeclecnlugsthio, ns
cauasmepdlitbuydeb, upilhdaisneg, spaonladriztaotpioong, raeptch.)y w; h(ci)chp, rionv ipdreincgiopoled, skhnoouwldle adlgloewo ffotrh eeaislileurm cianliabtriaotnionso tuor ce
(wraevmeloetnelgyt ho,bataminp ltiaturgdeet, pphhyassieca,l pporloapriezratiteios;n ,anedtc .()dw) hpircohv,idien ipmriangceisp lteh,ats haorue ldalmalolostw pfeorrfecetalysi er
orthorectified, only limited by the positional accuracy of the lidar returns. However, there are also
calibration to remotely obtain target physical properties; and (d) provide images that are almost
limitations or disadvantages of active intensity imagery, including: (a) the limited number of
perfectlyorthorectified,onlylimitedbythepositionalaccuracyofthelidarreturns. However,thereare
available laser wavelengths defined by the energy level transitions of the lasing materials and
also limitations or disadvantages of active intensity imagery, including: (a) the limited number
processes; (b) atmospheric attenuation of laser energy in the visible and near-infrared wavelengths
of available laser wavelengths defined by the energy level transitions of the lasing materials and
by low altitude (below sensor flying level) atmospheric phenomena such as clouds, haze and fog;
processes;(b)atmosphericattenuationoflaserenergyinthevisibleandnear-infraredwavelengths
and (c) the target surface is only partially illuminated (spatial sampling) by the lidar beams rather
by low altitude (below sensor flying level) atmospheric phenomena such as clouds, haze and fog;
than fully illuminated as in the case of passive imagery where almost the entire area (with the
and(c)thetargetsurfaceisonlypartiallyilluminated(spatialsampling)bythelidarbeamsratherthan
exception of areas with shadows) is illuminated by the sun. The active lidar intensity and structural
fullyilluminatedasinthecaseofpassiveimagerywherealmosttheentirearea(withtheexceptionof
images can be analyzed with the same techniques that are used to analyze images derived from
areaswithshadows)isilluminatedbythesun. Theactivelidarintensityandstructuralimagescan
passive optical sensors, for example to generate land cover classification maps (Figure 5h).
beanalyzedwiththesametechniquesthatareusedtoanalyzeimagesderivedfrompassiveoptical
sensors,forexampletogeneratelandcoverclassificationmaps(Figure5h).