NicholasC Coops,CS BO Forestryand ForestProducts,Pf vateBag 10,ClaytonSouth3169 l"4elboLrrne
ALrstraa.
Ema n coops@ffp
csiroau
Comparisonof Topographicand PhysiographicPropertiesMeasured
on the GroundwithThoseDerivedfrom DigitalElevationModels
Abstract
With the widespreadavailability of digilal .le!ation nodels (DEIU) and fegi(nralsurleys of \oils. ropographica.d phlsiographic
featlrresof landscapesare now morc casily characlcri^d. Wilhir south$estem Oregon l9 l lield plots werc rcgislcird \lidin .r
gcographic infonnalion syslem(ClS) to digilized topographicand soil\ coveragesand propertjcscxlraclcd from the digital co!cragcscortparcd wilh lhoseestimatedin the field. The initi.rlcomparisonshowednrnjordiftircnccs in eslimatesofaspect. slope.
al1dmaxinum a!ailable soii \\'atercontent(e), althonghthe locationofplots showcdgcneralagreement\!ith elelation\ fecofded
on the maps. To extrapolrte climrtic data and interfrei h,vdrologicrcsponscsaccLrarely.an uu|on ted searchprccedufe xas
de!eloped $hereb)- the initi.rl location of each plot was. il necesrarv,shiiicd !rithin speciijed bouDdsto give closer rgfeement
$ith licld e\limates of aspect.slope. rnd €. Specifically,the searchroutine scqucntiall! idcn(i1icsrhe rearest 100 nl-resolution
ccll $ilhi. a scarch radius of 3 or 5 cells ir which diffefences afe within 1 22.5'of arDect.t 20ti; c'l sloDc-ard in close\l
agrccmcnl rith flcld .stimates of 0. The searchprocedufefesulted in imtroled agreement$ilh ficld csrimarcsirrs = 0.82 fbr
aspccl.0.56 lor slope.0.51 ibr 0. To obtain theseinrprovemenlsrequiredthat the initial plot localions be shilted, oD the .rvera8e.
l89n widir the 3 pixel searchrrdnrs. and ,135m wilhin the 5 pixel radius.wirh thc lcnain analysisproceduresde!eloped in $i\
paper. it is po\sible to (^ercome many problems associarcdwi!h rcgisrcring the prccise location of field pkxs upon digirizcd
topogfaphic and \oil maps.The pfocedureis parricularly appropria(cin siruddonswhere lhe environmentalfegimes associarcd
\iith r \pecified field location are to bc cxtrapolatcdacrosslandscapes.The approachalso pernits.r uealth of historical sur!c-plot dat.rio be incoryorrted inro a G1Sibrmat ard to bc spatially cxtnded.
Introduction
Basjc infbrmation on geographiclocation,elevation. slopeand aspectare collectedin most soil
and vegetationsurveys.Additionally.soil propertiesareoften characterizedto provide estimates
of maximumavailablcsoii watgrstoragein mm
(01 and drainageconditions.Indilectly, theseto
pographicand physiographicftaturesatTectthe
gro\\"thand distribution of vegetationas well as
thehydrologicresponse
oflandscapes
to precipi
tation(Runninget al. 191i9:Running1994,Waring and Running 1998).Although soil surveys
providebroadestimates
ofe. a number
-uenerally
of studies har,e demonstratedthat local spatial
variationoftopographict'eatures
helpsto explain
(50m
muchofthe variationin soil walercapacit)to 200 n) (Band1986:Bandet al. 1993:Nemani
ct al. 1993).Comparedto traditionalmethodsof
estimating0 frorr soil sericsdah. cstimatesderived fiorn digit lelevationnodels (DEM) plo
vide additional intbrmation bccausctopographic
dataare cdlecteclat higherresolutionthan soil
senesoata.
r f . p r t i r l l l c l a . \ i l 'i\r g ( \ t i n r i r t e \
T h ep o . . i h i l i t o
o l t e n u i nr r r i r b i l i n u i t hi n r t l i g i t cel n ri r o n n rn, t
hasbecilmepossiblewith thedevelopment
of ter
ll6
N o r t h w e sSt c i e n c eV. o l . 7 4 , N o . 2 . 2 0 0 0
a lorl0 br $eN.ihsrn
trlenrili! .\sorld
oi Alln-ehBrcir.d
rain analysisprogramsand DEMs. The general
trendin rcpresentations
ofter:rainfor environmental
modellinghasbeento move fion broad.conti
nental and regional scales.to finer scalcsmore
suitedto themodellingofsurfacehydrology,veg
clation and soil propefties.This trend can be ar
tributed to improvementsin methods for rcprcsentingfine scaLe
topographicshapeandsfucture,
suppofiedby the steadyincreascin the speedand
storagccapacityof computingplatfbrms.
Finc scalcDEMs. with spatialresolutionsfrom
5 to 100 m, are typicallyusedfof spatiallydistdbutedhydrologicalmodelling(Binley andBeven
1992.ZhangandMontgomcry199,1)
andfor the
analysisof soil propefiies(Cessleret al. 1996.1.
Thc deterrrination of appropriatespatial scales
for hydrological modelling is an active research
issuc (Bloschl and Sivaplan1995).Tenain attributescan be refe[ed to asDEM dcrivtrtivesas
their calculationis basedupon tjrst-orderand
sccond-order
derivatives
ofa continuous
3 D sur',
face.The surfacecan be basedon a combination
of discretetiles.grids.or triangularregularnctworks(TINs).or contourscgmcnts.
In any case.
thc algorithms are approximationsof those for
continuoussurfaces.Moore et al. ( 1991) reviewed
the applicationsof digitrJtemainmodelingand
listedthe majorprimaryterrainattributcsthatc$rld
be derived liom a DEM and their application.
Grid basedDEMs arc developeddirectly from
standarddigital topographicdata $,hich lray includespotheights.ele!ationcontours.streamlines
anddamsandlakes.The nethod producesDEMs
which rcspectsurtaceshapeald drainage.lt can
use sffeamlinc data,without llssociated
elevation data.to aidtherepresentation
ofsurfacedrainage.Thereadyavailabilityof DEMSdoesnot impl)
however,that they are*ithout error and otien are
not alwaysavailablcat the most applopriatescale
(Hutchinson1999).In addition.therearea number of potentialerrorsassociatedwith DEMs that
areindepcndcnt
of scale:
(a) coastaland 1li,itareasnay be representedas
tefflrcesdueto slopecalculationsmadewhere
tlal areasaltcmatewith narrowbaodsofsteeper
slopes.
(b) interpolation
enols mav resultovcr surfaces
causedby methodsfailing to interprel contour dataconcctly.
(c) localpeaksnot modeledat their corect height
and will appetirto be flattened duc to poor
placementof basc spot height data.
(d) oilsets related to slopc direction and steep
nesson onesideofa topographic
fcaturcwill
re:ull rn ,rI !'\ efe\lilll,1te
ol cl<\ ationon onL'
side and an undercstimateolr the other
Linkirg historical dala collected at specific
points within the DEM requiresadjustingfor crrors that may occurin assigningthe initirl location ol'plots to mapsthat dilIer in accuracvfron]
those now availablc and were located without
global positioningsatellile(GPS) technology.
Fortunately,
it is now possible,usingautomatcd
searchinglogic 1ocxanrinethe geographicposi
tion of eachplot location \\"ith respectto its local
terain ard pledict its most likclv geographicpo
sitionon currentDEN{S.
The objective of this paper is to prcscnta approachwhichcomparesplot-basedrneasurenents
of terrain variablesu'ith calculationsmadefrom
a 100 rn-resolution
DEM and enploys an algorithm that searches,within a definedradius.DEM
cellsto prcdictthe mostlikelypositionofthe plot
within the landscape.
Onceloctrtcd,estimates
of
slopcandaspectare extmctedliom the DEM and
0 is predictcdwith a generalized
model derived
ftom topographicand soil seriesdata.
StudyAreaand Data
The regionof southwestOregonlying bcLween
the southernCascadeMountainsandPaciflcCoast
is an areaof grcat diversity in climate, geology.
i l n J \ e f e l l i o n .T h e d c | e l o p i n gp a l r e r ni.n \ e g
etation diff'er.dependingon the topographiclocationanddilferencesin palentmaterial.At lower
elevationsnear thc coast, Port-Orford cedar
(Chanaec\pari.slawsonizr" (A. Murr) Parl.)and
Douglas fir (Pseudot.suga
ilet;lesil (Mirb.)
Franco)ar{]conrmonon noist sites.At higher
elevationwhere a wintcr snowpackaccumulates,
Shastared tir (ADi?rrnttgnrf ut't ar sha.sten
sis MtnT
Lemm.) and mountainhemlock (Isriga merteasirara(Bong.1Carr.)dominute.At mid elevations
on drier sites,Douglas-fir is ofien accompanied
b\ tanoak(Lithocarp
rr derisiflonrs(Hook.&Am.)
Rchd.). westernwhite pine (.PinLlsnu)tlticol.l
Dougl. ex D. Don) andsugarpine (Piirrn lamDerrlara Dougl.).On the mostcxlremesiteswith shal
low soilsor inhospitable
parentmatcrialsderived
fron serpenlincor peridotite,ponderosapine
(PinusporulerosaDougl.ex Loud), canyonlive
oak (Qrieirirs cllr\'.\oLepsisLiebm.) and Jeffiel'
pine(Pirnrsjeffre,r'i(Grev.and Balf../)arepresent
(Whittaker1960.Franklinand Dymess1973).
PlotData
From lglJl to l9E3 a seriesof temporaryplots
were establishedas a fbrest growth modelin-e
project $'ithin the solrthweslOrcgon ForestryIn
tensified ResearchPrograrn(Hann and Ritchic
1988.Hann and Wang 1990.Hann and Larsen
| 9 q l ) . A t o t aI o l ' . 1 8 -l i+' r e . r. t u n d .r a n g i n ei n si z c
tion 2 to 47 ha were sclcctedlirr lield sampling.
A total of 391 tenporarv researchpJotswere es
tablishedin thcsestandscoveringan areaof54.000
km'. The resealchplots wore placedin a cluster
designof,1to 10 sanrplingunitsover L5 to 4 ha.
A modrl soil pit was dug at the samplingunit
judgedto bc thc mostrepreseltative
of thc cluster basedon soil atlributes.
The aspectand slopc
were measuredard the dcplh and soil texture
describcdblr horizons do\\,r]to 1.,1m or to bedrock wererecorded.
Thc maxinrumavailablesoil
\ \ J l c r c ( , n l c nfr0 l r r i r se . t i t n a t e d
br li,,'unlin!
fbr variatior in soil texlureandrock contentdown
to themaximumdefineddepth(Hann1983.).
The
geographiclocationandclevationofeach plot was
estimatedtrom LI.S.G.S.15 minutetopoglaphic
maps. For the remainderof this papcr.the lilope.
Compalisonof GroundMeasuremeltswith Digital ElevationModels
lll
aspect.elcvationand geographicpositionof the
sarnplingunit which containedthe soil pit will
be usedto rcpresentthe plot as a wholc.
DEI\,4
Data
Digital elevation data fbr southwestemOregon
$'ereobtainedfrom the DcfenseMappingAgency
(DMA. PortlandOregon)with an initial pixel resoIution of 80 m. The data \\"erethen rectified to
1(Xlm pixelresolutionandrcgistered
on theUni
versal Transversc Mercator map plojection. A
gcncraldescriptionofthe DEM propeftiesis pre
sentedin Tablc 1.
TABLE L Propcrlics o1 the Digitized Elelation Nlodel lbr
Souihrcslcn1 Oregon.
Top Leli
Bol||nn Right
Easring
){orthing
105500
598000
,1805000
,16,10000
C e l l s i z e( m )
100
100
A numberoftenain attdbuteswere calculated
fron the DEM surflce. The first and simplest
variableto extractwas sitc elevation.The slope
of eachpixcl was calculatedby fitting a planeto
the ditlerencein elevationbctweena centralcell
andthe imlrediatclvadjacent3 X 3 aray of 100
m cells. The directionthat the plane faced defined the aspectfbr the centralcell tbllowing the
averagc maximum technique described b1'
Zeverbergen
andThorne( 1987)andMoore et al.
(1991). To deflne the direction of water flow
throughthe centralccll, the steepest
descentof
each cell in the 3 X 3 an'ay was estimatedfbllou ing the methodsof Jensonand Dominguc
(19138).
The 1lowaccumulation
wascalculated
on
a cell by cell basisto takeaccounlof thetotalupslopeareathat draincdinto andthrougha selected
ccntralcell.
The Conpound Topographiclndcx (CTI)
(unitless)wascomputedasa functionof thecon
tibuting areaup-slopeof a cenlralccll and the
slopeat thatcentralcell (Mooreet al. 1991).The
CTI is calculatedas:
t
- t r
1 . ,
r . r nr p I
r l r
Wherca is the up-slopecontributingareaand
B is tbe slope.
In areas*ith negligibleslope,a CTI valueof
0.001 was assigned.This \alue is smallerthan
that oblaincd tiom a 100 rn data set dift'cring in
I 18
Coops
ele!ationby I m. The CTI wasoriginallyusedin
hydrologicmodelsfbrsmallbasinswithhigh values
indicatinga greatcrlikelihoodof a saturatcd
contributing area(Kirkby andWeynan 197,1.Bevcn
and Kirkby 1979;Bevenand Wood 1983).The
higher valuesofCTI tendto be found at the lower
pafls of watershedsand in convcrgenthollow areas associatedwith soils of low hydraulic conductivity or areaswith more gcntle slope than
average(BevcnandWood l983). Soil depthand
silt and clay content tcnd to increasefrom idge
tops to the valley bottoms (Singer and Munns
1987).Soil erosionis alsorelatedto thedirection
of water flow, with the rates highly dependent
uponthedegreethat soilsrcmainsaturated(Zheng
et al. 1996).
Soils Data
Therc is a wide varietyinformationon soilsavail
ableal varyingspatialscales.Within the Unired
States.the U.S. Departmentof Agriculture's
(USDA) NaturalResourcesConscrvationService
(NRCS).formerlythe Soil Conservation
Service
(SCS),leadsthe NationalCooperativeSoil Sur\ e ) a \ C S S 'a n di . r e . p o n . i h lIeu re o l l e c r i n g . . r o r ing. maintaining,anddistributingsoil surveyin
fomation for privatelyownedlandsin the United
States.
Forregionalscalemappingand monitoringthc
Shte Soil Geographic(STATSGO) data base is
the most appropriatebecauseit has been com
piled at a consistentscale for all of the United
States(United StatesDepartmentof Agriculture
1991).STA|SGO soildataarecompiledfrom morc
detailed State Survey GeographicData Basc
(SSURGO) soil sulrey mapsand infomation on
geologv.topography.climate. and vegetation,
supplementedby imagesderivcdthroughremote
sensingfrorr satellites.Using the United States
GeologicalSun'ey's(USCS) l:250,000scalc,I by 2-degreequadlanglescriesas a map base,thc
soil dataaredigitizedasline segments
to comply
with nationalguidelincsandstandards.
STAISCO
soil data e often inadcquatefbr local or even
rcgion.rl
rnodeling.
e.pecidll in m,.runruinous
arca'.
T h e . ei n c d c q u r c i er .e l l e c t h er r r i l t i o n p r e r e n r
within cells, insufficient sampling of each mapping unit. and difficulty of drawingboundalies
betu'eentwo different mapping units (Burrough
19ii6.Mark and Csillag 1990).Never the less.
theSTATSCOdatabaseprovidestheonly availablc
ctrrriedout
state-wide mtippingof soil attributes
tn a conslstentmanner.
For eachsoil series,the thicknessof eachsoil
horizon and its nean availablesoil u'ater capac
ity wascomputedandsunrmedfbrtheentireprolile
to provide an estimatcof 0 for cachpolygon.This
vector coveragewas then convefiedto fasterfor
mat with a spatialresolution (size of the cell) of
250 m, approximatclyequivalcntto a 1:250,000
scale.If an individual cell wascomposedofpo)ygons representingmore than one soil sedes,the
spatially dominant serieswas selected.
Soil Water l\,4odel
Although it was possibleto developregressions
bet*eenfleld estimates
ofe andthoseinterpreted
from the STAISCO dataset.thereare advantages
in attemptingto predict0 direcLlyfrom hydrological models.First. regressionrelationships
canlot be usedin regions where STATSCOdata
arenot availableandsccond,staticmodclsmakc
it difficult to assessthe dynamic impact of large
stormsandthe implicationsofroad buildingand
other activities oD water movement.Zheng et al.
(1996)proposeda simplemodelk) estimate0using
the nean valuesof 0 fiom the STAISCO dataset
to transfbrmthe tlne-scrle variationdeflned with
theCTl. Equation2 showsthata gcneralpositive
rclationshipexitsbetweenCTI and0 (in mn) such
that:
d= 1\{,N'l.lln(
,a,rlo)
12)
where M, and M. are case specific coefficients.
one of which is chosentbr each pixeI. Zheng et
.tl. (1996')showedthat the distributionsof the
calculatedCTI when comparedto 0 as obtained
fiom STATSCOdatasetswerealwayssmaller$ith
longer tails in the high end.Thus, two scalarsare
neededto avoid transtbrmingright-skewed
distributions of CTI into dght skeweddistributions
of 0. When a pixel's CTI value is Iess than or
equrl to its mean,the coefficientM,, is usedin
Equation2. M, is determinedir equation3 by:
6 ,,,,,,
{15 1CZ +C2.,. l
STATSCO0 or other relevantdata.The lunction
of Mr is to re-scalethe CTI valuesthat are less
than or cqual to its mcan at a rate fbr which the
median value of CTI is equal to 0.rn,..,,.
When a pixel's CTI value is greaterthan its
mean,th(] coefficicntM, is usedin Equation2
and is estinated by equation,+:
n
a
where 0,r., ...u,.u. is the maximum 0 occurring
in theSTATSCOdatabase
fbr theregionandCT\,.,
is the nraximumCTI value with an occuryence
rateat least1%.Again, 0-,. canbe cstimatedfor
a given spatialextentliom maximum STATSCO
layer or from other relevantdata.The function of
M. is to rc-scalcCTI valuesthat are largerthan
the CTI mean at the rute that makesthe defined
maximumCTI vriue equalto e,"",.
In this study,Mr fbr SouthwestemOregon(100
m pixel resolution)was 17.4/ (0.5 * (3.8 + 1.3))
= 6.8.The maximurncalculatedCTI value$'ith
an occurence rate ofovel l7o was determinedto
be l6.3. A valueof 40 cm wasusedfbr the maximum 0 ftom STAISCO to detemine M, for SouthwesternOregon.Thesedifferent maximum val
uesarecloseto the maximumvalueof0 estimated
from field survcys(Table2).
TABLE 2. Summary ofinput data used in ihe searchroutine
1l)rc posilion licld plots.
STATSCO PlorDara
(6) (mm)
(61(mn)
Data set
Pixel Resolution 0n)
Number Pl0ts
\umbcr Pollgons
Mean
\{arimum
\{i.imunr
N{ode
100
250
391
l:l l,l
330139,r
3.8
21
0
1.3
168
,100
5t
111
269
39
l5l
G l S S e a r c hA l g o r t h m
(3)
wherc CTI-,, and CTI -.," arc modc and mcan
valuesof the calculatedCTI, respectivelyand
0..,,,r,,,is the meanvalue 0 firr l given spatial
extent.estimatedfrom either the mean of the
An iDitialtestof the accuracyof thc initial plot
positionsandthe compatjbilityof the elevahon.
slope,and aspectdatawith the cell-bascdprcdictions was completedfor all 391 plots. Basedon
this initial comparison,a searchalgorithm was
developedto intcrrogate
individualpixelsu ithin
Compadsonof Ground Measruements$'ith Digital Elevation Models
1l9
a speciliedrectangular
subsetof cclls(knownas
a boxcar)thatexhibitsimilarslope,aspect.ande
attributesas thoserecordedin thc fie1d.
tl cach plot was located accurately.and the
tefialn attributeswere all calculatedwithout crrot then the central pixel il the boxcar would
sharc all the attdbutcs recordedat the field plot,
a srtuationthat rarely happens.
The lormal computation presentedin thc algorithnrcan be describedin equation5.
frfr
ln rlP
lvr
t s1.,rf.0\ Pt\,p,,!st.l., rt
.:l
nhere W,, is the re-conlputedgeographicposition ofplot P.P(Aspect.Slope.0),./arethetenain
attributesfor the cell coincidentwith the original
plot location on thc DEM, and P(Aspect,Slopc.
0) is the aspect,slope and 0, as recorded in the
field at the plot location.The searchis undertaken
fbr all cells from n = I to n = m u'here rn is the
numberof cells.
T h eo p e r r t i o n rsl t e p r. r c . h o \ n J i i r g r c m m . l l i cally in Figure I and are applied for eiich plot as
tbllows:
. Initiall)'.theplot aspectis comparedto theaspect
of all cellsin a specifiedboxcarlllrcr. If any
cellshaveaspectsthat lie within I 22.5'. rhe
cel1saretaggedas acceptable
candidates.
Figurc L Proceduresfor sclcclirg rells \|idr attributesir closen o\'crall alreement to lbosc descfibedfnnn field Dreasurcnrcnts.
Plol centre is indicaled b) \laf with thc searchfoutine mo!il1g ir a clockwisc direcrion as indicatcd arrows. 1hc sc
quencetiom uppcr ]eli lo lo$ef righ! is lierachical: ( lst) aspccr.(2nd) slope. (3 i) e. und finallt. rcpositiming of rhc
ploL.AWSC: A\.'ailablc\Varef Soil Capaci!t: DEN{: Digiral elc\adon modet.
120 Coops
.
.
.
This processis repeatedon the slopelayer with
cclls havingvaluesuithin t 20% ol the plot
slopebeingtaggedas acccptable
candidates.
All cellsin the searchregionare thcn ranked
basedon thc aboveclassiiications.
with cells
having t\\,o successfulpasscs(i.e.,both aspect
and slopebeing within the specificdthresholds)rankedthe highest(code3), followedby
plots with aspeclswithin specificthresholds
but not slopes(code2). cellswith slopescorr-ectbut not aspect(code1). and finally cells
whcrc ncither the DEM slope nor aspectlays
within the specificdthrcsholds.
Only cells with thc maximurncode;Lrekept;
all othercellsale discarded.
For all remaining
possibtecandidates.
the predicted0 of each
cell is extractedand comparedto the value of
0 rccordcd at the plot. The cell with the highestcodebasedon aspectandslopesuitability
and the smallestabsolutedifferencebetween
e prcdictcd by the DEM and measuredin the
tield was then selectedas thc new plot position.
lmplicit in this approachis a hierarchyin the
. r p p l i ct i o no f t h e t h | c s h o i du. r l h p l , \ li l \ l r c l i n
this projecl sclcctedrs the nrost impolant ter
rain variableto match.This is primarv due to the
stfong ellect of aspecto1lmdiation. especiall] at
silcswith high iatitudes.For example,the incor
rectpositioningol the plot locationbasedon aspcctby loci,iting
it on an exposedratherthanprotecl(daspccl.(Jn hJre l mljor cfl, , t , 'n its. urecl
environmcntal
description.
ln this implementrtion.aspectand slopeare
1lrstnatched wilh thc plot estinrates.
The comparisonofe is only undertaken
in thc final phase.
As a result,if cells surroundingthe odginalplot
positionhavepredictede valucsidenticaltothose
mcasurcdin the field and the slope and aspects
do not lie \\,ithin the selectedthrcsholds,the cells
arc not candidates
lbr plot selection.
The CIS-searchalgorithm*as inplemenlcd
al 2-boxcarfilter sizes,the first with a maxlmum
boxcarfilter of5 pixelsfrcm theplot ccnter(equating to a maximumradiusof 848rn from theoriginal
p l o t p o . i l i o n )r n d a . e c , - r n dn .t , r | rt., r n s e r r l r i r e
searchlimit with I maxinrumof 3 pixels from
the plot center(equivalentto a maximumradius
of 565 m frornthe originalplot position).Statistical analysiswas undetrken in the STATISTICA
solirvarepackage(Statsoft1995).
Results
Extrcmes in elevation exffactedt'[om thc DEM
(with a cell resolutionof 100 m) over the study
arearangefrom sealevel (0 m) to 2840 m with a
mean elevation of 850 m and a standarddeviation of 507 m. This can be comparedto the high
estelevationin the study legion (Mt. Mclouglin
at 289:lmASL). Figure 2(a) showsthe tiequency
distributionsof the field measuredslopesof the
391 plots and the slopesextractedfor the cells
con'espondingto the odginal plot positions.Fig
ure 2(b) shows the frequencydistribution of aspectslbr the 391 plots and thc aspectsextracted
tbr all correspondingcells.
The two aspectdistributionsshowsimilarffends
with I or 26lcdifferencesin thc number of plots
in eachof thc 16 categories
of aspect.Both distributions indicate that thefe are fewcr p]ots locatedin an easterlythan westerly direction.Thc
m o i t c o m m o nf i e l d m e a . u r e dJ \ p e e l\ r a \ i n r
southerlydirection.while the maxirrum number
of cells from the DEM are located in northerll'
directions.The slopedistributionsare distinctly
different with large variationsin the number of
plots in each of the slope percentageclasses.A
major effect of thc 100 m spatial resolution of
the DEM is to impose a smoothinglunction on
its representation
ofterain. which rcsultsin many
high licquencv spatialt'eaturessuchasridgcsand
gullies being smoothedby the DEM algodthm at
thc 11X)
m cell scale.Consequently,
slopcscomputed from the DEM are less steep than those
measurcdin the field. Thus in Figure 2a DEM
cells have slopesgrealcrthan60 % yet 3 q. ofthe
391tield plotshaveslopesexcecding60 %. There
fore, there is a nuch greaterpercentagcof cells
fuom the DEM plots in the lower slopeclasses
thanestinatedat eachplot.
A Kdmogorov-Smimovstatisticprovidesa
methodto vcrity the simjlarityof the two disrributions by tcsting if the samplesare representative of the samedistribution.The KolnogorovSminov asscsses
the hypothesisthattwo samplcs
(the field dataand thc dataextractedliom the DEM)
were drawn fron diflercnt populations.The test
is sensitiveto differencesin the gcncral shapes
of the distributionsin the two samplesandif rhc
testis statisticallysignilicant resultsin the rejection ofthe hypothcsisthat the DEM datamatches
(Kolmogorovl94l). Thc
the field observations
testindicates
thatthcdistributionsandthevadances
Comparisonof GroundMcasurements
with Digital ElevationModels
121
(a)
14
12
1t
6 1 0
6 o
a 3 8
.o
o
7
t s o
g 5
o - ' '
1
o
0
10
20
30
40
50
60
70
80
90
>100
Slope(Percent)
Figure 2a. Frequencydistributions of slopesas measuredin the field at their original locationsand thosederived fron DEM
(b)
c
'
zo "
x
c
a
9
(L
o
2
1
0
30
60
90
120 150 180 210 240 270 300 330 360
Aspect(degrees)
Figure 2b. Frequenct distributionsof a\pect as nreasufedin the field at therf original locationsand as derived ffoln DEM.
122 Coops
of the DEM and plot data tbr both slopes and
aspectsaresignificantlydifferentat the 0.05level,
with the differences in slope distributions and
variances
beinghighly significant(P < 0.01).
Figurc 3a shows the field estimatesof elevation (in meters)plotted againstthe elevation of
the 391 cells correspondclosely to valuesmea
suredattheoriginalplotlocations.Figure3b shows
the tield estimatesof aspcct(in degrees)plotted
againstthe aspectofthe 391 cells recordedat the
original plot locationsandFigure 3c for the slope
estimates(in pcrccnt.).Theseresultsindicatethat
while the elevationsare in good agreement,the
correspondenceof DEM estimatesof the slopes
and aspectswith datarecordedat the 391 plots is
poor, paticularly tbr thc latter attribule.
1800
1600
1400
.E aoo
tr.l
400
200
0
200
400
800 1000 1200 1400 1600 1800
(m)
PlotElevation
600
Figufe 3a. Relationshipbetwecn elcvation recorded ar the original locations in the field and that
predicred$ith the DE\4 (r: = 0.91, DEM Elevation = 1.0 * Pkx Ele\ation - 0.02, n=391,
P< 0.0001)
360
330
300
9':da
f; reo
o " "
60
30
0
90 120 150 180 210 240 270 300 330 360
Fial.l A.^A.r
/na^raae\
Figure 3b. Relationshipbetlveenaspecrrecordedar lhc original locationsin the field and thar predicted$ith
the DEN{ (rr = 0.23. DEM Aspccl = 0.,t9 * Plot Aspect + 9.1.6.n=l9l P < 0.0001)
ComparisonofGround Measurementswith Digital Elevation Models
123
90 I
80 I
9? ^^
!o 40
@
>30
uJ
10
0
30
4A
50
60
FieidSlope(percent)
Figure 3c. Relrtion\hip bcn!een slope rccordedat the original locarionsjn the fickl and thar prc
d i c r c d $ i t h t h e t ) E M ( f = 0 . 3 0 . D E M s t o p c= 0 , 1 1 : p t o l S t o p e+ 9 . t . n = 3 9 1 .p <
0.0001)
The good agreemcntbetweenthe elevations
recordedat the plots and thoseexfacted from the
DEM indicate that, while the gcographic location of the plots andthe terraincellsmay not directlycorrespond,
thclocationsarcgenerallywithin
close proximity, and dif'terencesare mainly associated$'ith local vadationsin slopcand aspect.
Figures,laandb showthc relationshipbetween
the field aspectsand DEM aspcctsin the new
positionsdeterntinedwith the GIS sear.ch
algorithm.Two setsofresultsareprescnted
usingthe
3-andthe 5 pixel-radius-scarch
boxcarfilters.The
ligures show the hierarchy of the searchproce
dure with the code 3. 2. and I resultsreprescnted
as ditlerentsyrrbols.ln a numberof cases.thc
plot mcrsured aspectand slopescould not be
matchedto any cells in the DEM searchfilters.
I n t h e5 p i x e l r a d i u s . 3 . 3 T
o fcp l o t s( n = 1 3 )c o u l d
not be matched.with 3.6 7r unmatched(n=l:f)
usingthe 3 pixcl radius.As a result.theseplots
uere rcmured lrom lhe dalr\cl rnd are n.'t in
cludedin thc presentation
of the rcsults.
Figures.lcud d showtherelationshipbetwecn
the field slopesand the slopesof rhe DEM cells
in the new positionsdeterminedusing the GIS
searchalgorithm.Again both setsof searchesarc
presertedandthe figures shorvthc hierarchvwith
t h c L o d e3 . 2 r n L l I i d e n r r l i e dF.i g u r c . 4 er n d I
showsthepredictedversusmeasurede asextracted
124
Coops
at new plol positionsfor both rhc 5-and3-pixel
boxcar-search
windows.
Figurc.la and b show rhal thereis a significant increasein the colrespondencebetweenll.Spect measuredat the plot and that al the repositioned plot locationsextractedfrom thc DEM. ln
thecaseof5-pixelradius,themajorityofthc plors
(84%) have beencoded3. which indicatesthar
both the slopeandthe aspectol the repositioned
plots were within the slope and aspectthreshold
classes.
Additionally,87. of plots were coded2
which indicatesaspectof the repositioned
plots
were within the threshold establishedalthough
no cellswith slopeswithin theprescribed
threshold could be located.Bccauseof the 360. rcpresentationofaspect.values< 22.5oor > than347.5.
transf'erto the NE or NW quadrants;This results
in their locationappcaringto be moreerroneous
than is actuallythe cascon the scatterplot.
The distributionofplots with code I (plorswith
slopc within thc slope thresholdsbut no agrcement with aspect)appearsto be randomover the
rangeofaspectindicatingthat,in spiteofmatching slopes.therewasa continualmisnatch at some
plots.The resultsofthe largersearch$,indow(5
pixels)is a tighterclumpingof themeasured
versuspredictedaspectsalongthe I :l line with more
cellsclassifiedascode3 and lessas code2 than
resultedu'ith the more conservittivewindow.Thc
,
330
'
300
*.'l^
,.!r.i..r;u&'
"
-n.-if,..ait:.
..rJ
-
^ 270
E zao
,lr' t a
..aielt '
€ 210
E 180
o
o '150
l{ir
?.d"I'"'
> 120
ul
o 9 0
30
0
0 3 0 6 0 9 0 120 150 '180 210 240 270
PlotAspect(degrees)
Figure la. Relationshipbetlr'eer aspccl rccorded at the original locationsir fie iield and drat adjustcd $ith
theserrchroulincfora5pixelradiuswindou(i=0.82.DEMAspect=0.91*PlotAspect+ll,l,
.=19l. P< 0.0001)
360
330
300
270
o
q)
q)
240
210
'180
2-:
{,
G
ll,
o
150
120
90
.t';
.a.'j'li
30
0
90 120 150 180 210 240 270
PlotAspect(degrees)
Figure,lb. Relationshipberwecnaspcclrecordedat the origiDrl Locationsin $c llcldand lhat predictedr\ilh a
3 p i x e l n d i u s $ i n d o \ \ '( . : = 0 . 7 3 ,D E N IA \ p e c t = 0 . 8 5 + P l o l A s p c c l+ 2 ; 1 . l .n = 1 9 1 ./ r < 0 . 0 0 0 1 )
with Digital ElevationModels
Comparisonof GloundMeasurements
125
60
'
.
.t..
..1t'
Q)
o 6n
:
()
o 4 n
d
. . : .
-
!
6
=30
:
LI'I
o
-r':'r
.l
I
"
. , ' l
't"
..
..':i;.:.'i
20
.:r.
.'.1;tt"
It t'
:
al r.
...
..
.,lii;"'
30
40
50
PlotSlope(percent)
Figurc,lc. Relationshipbet$een \lopes rccc'rdedafthe original tocationsin rhc field and that prcdicGd $irh a 5
p i x e l r a d i u s$ j n d o $ ( r r = 0 . 5 6 ,D E N I S l o p e= 0 . 6 7 * p l o t S t o p e+ 6 . 5 3 .n = j 9 1 . p < 0 . 0 0 0 1) .
8o.r
70
. Code3
" Code2
, Code 1
. ; .
q)
t
.
o
qDAN
6
>30
uJ
t . : '
' . . ' ! r , . . ..'t
'
....i":".;:,
l
:-" o'
t.
it
.'
..:ri;!
20
40
60
PlotSlope(percent)
Figurc,ld. Relarionship
bcrweenslopesrecordcd
arfie originattocalions
in thefietdandrhatpredicrcd
$ilh a 3
pixelradiusr\indo$(rr=0.'17.DEN|stope=058*PtolSlope+785.n=-t9l.p<0.0001)
126
Coops
E zoo
g
(J
I rso
. ,i
llJ 1nn
o'--
...
. .-.;
'rt
^'.
*." .,J...t".
j!?..
100
150
200
Plot AWSC (mm)
Figurc ,lc. Rclalionshipb.r\!.cn 6 rccordedar lhe origiml locationsin the field rnd that pfedicted$'ith a 5 pixel
r a d i u s! \ i n d o \ \ '( . : = 0 . 5 1 .D E N j 0 = 0 . 6 3 I P l o t 0 + 6 9 . 5 . n = 1 9 1 . P < 0 . 0 0 0 1 )
. C o d e3
a Code 2
, Code 1
E 200
E
o 150
ts
=
uJ
'100
o \
oit
f:':.
r
" . . : '
0
100
150
200
PlotAWSC(mm)
Figu.e .1f. Relarionshipbei\\'een0 recordedat the original locationsin th. fi.ld and thal predictedwitb I 3 pi\el
radnrs\i indo" (r = 0 27. DEN{ 0 = 0..11Plot 0 + 98.,1.n=39 l. P < 0.000 | )
Comparisor of Ground Measurementswilh Digital Elevation Models
t2'7
percentageof plots that produced a code I (aspect incorect, slopecorrcct) ( 157' for thc 5 pixel
fllter and 8dl,:for the 3 pixel filter) indicatesthat
the most of the plots more easily fell within the
slopethresholdthanwith the aspectthrcsholds.
Figure 4c and d indicate that, like the aspect
comparison,thereis a significantimprovement
in the agrcementbetweenmeasuredslopcandthat
extractedat the new locationsfrom the DEM. The
wider searchwindow providcs a slightly closcr
matchingofthe tield observations
with thosederived with theDEM. With wider window,thccodcs
2 and code I assignmentsare also lessfi'equent.
The eilect of topographic srnoothing at l00m
spatialresolution
canbe seenwith anoverallbias;
a field measuredslope of 80% correspondsto
approximately
707oon the 100mDEM.
Figurc4d andc shorvthc rclationship
bctween
the measurede and that predictedby the DEM at
the new celi positionsusingthe searchalgorithm
lor both the 5 and 3 pixcl boxcarwindows.The
results.as expected,are similar to the slope and
aspectanalysiswith signitlcxntlyimprovedrela-
tionships.The searchwith a 5-pixel radius prt>
duceda signiticantlytightercorrespondence
with
the plots than that with a 3-pirel radius. This is
expcctcdasthe algorithmin its final stepattempts
to minimizethedifferencebetweenfield eslimatcs
and modeled0 over all the cells with the maxi
num codc value. Becausethe 5-pixel radius
hasa largernumberof cel1s.a tightcr relationship betweenthe obsen'edand predictedvaluesresults.There appearsto be no significant
patternin thc location of the code 2 or code I
valuesin thesefigures.Overall, modcl predictions tend to underestimate0 valucs rccorded
in the field.
Figule 5 shorvsstatisticson the distanccsrequired to move the odginal plot positions to krcationswheretellain attributesarein closeragree
mcnt. Thc distributiursrre sho*n tbr-both the
3-and-5-window
searchcs.
Dctailsofthe distances
can be seenin Table 3. Ovcrall. thc mean nrovementof the plots1()thcir ncw locationusingthe
5-pixel radiussearchwindow was,135m and rvith
the 3-pixelradius,289m (Tablc3).
O 5 pixelradius
40
l3 pixelradius
a 9 a
E
a
^o ..
10
80
140 200 260 320 380 440 500 560 620 680 740 800
Distancefrom OriginalLocation(m)
Figure 5. Histogram of distancesmoled from original locationsof plots using 5 and 3 pixcl radius $il1dos s rcspectivelv.
128 Coops
TABLE 3. Sunl mat] of distancestatislicsrclalcd lo repositioning lield plots u \ing hierarchicallcarch rcudne \!itb two windou
SiTeof Search
R r d i u s( p i x e l s l
\{aimuln
Distancconl
D i s t a n c e( m )
7.19
.16l
t.1.8
Discussion
Thc hicrarchical-searchapproachpresentedhere
providesan approachofrcpositioningplots,u'hose
position was originally detemrincd on dif'ferent
basenrapsanduithout accessto globalpositioning tcchnologli.The approachrelies on utilizing
thel ge numberoftopographicandphysiographic
dalasclsthat usuallyaccompanyfield surveys.
As desclibedin thc introduction,DEMs are
rarely uithout enor and are often unavailableat
thc most appropriatescale(Hutchinson1999).An
inherentassumption
of this techniqueis thatthe
tenain lrtffibutesextractedfrom thc DEM are realisticrcpresentations
of the local tenain.Finer
scaleresolutionDEM are currentlybecoming
availablcovcr the continentalU.S. r'ith spatial
resolutionsof 30 m. Obviously,the techniques
dcveloped in this paper can be applied to these
fine scalc DEMs. An added beneflt of utilizing
thesefiner scaleDEMs is thc nroreaccuraterepresentationofUne scaletopographicfeaturessuch
as slopesand gu1lies.
Civen potentialerors in both llcld location of
plots and in the interpolationtiom DEM surfaces,
the approachdescribedin this paper provides a
link betweenthe two typesof datasetsby positioningthe plotson the DEM, not by theirabsolute geographiclocations but by attempting to
matchtheirrecordedten'ainelrvtonmentascloscly
as possible.In nany cascswheregxtrapolation
of climaticdataandmodelingofgrowth anddistributionarc involved.the actualgeographic
po
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