1. No category

Overview of the Oregon Transect Ecosystem Research Project
Author(s): David L. Peterson and Richard H. Waring
Source: Ecological Applications, Vol. 4, No. 2 (May, 1994), pp. 211-225
Published by: Ecological Society of America
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Ecological Applications,4(2), 1994, pp. 211-225
? 1994 by the Ecological Society of America
OVERVIEW OF THE OREGON TRANSECT
ECOSYSTEM RESEARCH PROJECT'
DAVID L. PETERSON
EcosystemScience and TechnologyBranch,NASA Ames Research Center,
MoffettField, California94035 USA
H. WARING
RIcHARD1
DepartmentofForest Science, Oregon State University,
Corvallis,Oregon 97331 USA
Abstract. The Oregon Transect EcosystemResearch (OTTER) project is a studyof
ecosystemfunctionsin coniferousforestsusing the methods of computermodeling,experimentaland theoreticalremotesensing,and ecological fieldand laboratorytechniques.
The studyis focused on predictingthe major fluxesof carbon, nitrogen,and water,and
the factorsthatdynamicallyregulatethem.The OTTER projectwas conceived to testtwo
major questions:(1) Can a generalizedecosystemsimulationmodel,designedto use mainly
parametersavailable from remote sensing,predictthe functioningof forestsacross an
environmentally
variable region?and (2) To whatextentcan the variablesrequiredby this
model be derived fromremotelysensed data? The scientificobjectives and scope of the
project demanded that a coordinatedeffortbe made to link groundmeasurementswith
remotesensingand modelingrequirements.OTTER was selectedas a focusfora National
Aeronauticsand Space Administration(NASA)-sponsoredMulti-sensorAircraft
Campaign
(MAC; combiningNASA aircraftand sensorswiththoseofothers)on thebasis ofexperience
gained in past ecosystemstudies and remote-sensingprojects,and the importanceof the
OTTER objectives to NASA's long-rangescience goals and plans. Having several independentapproaches available, both on the groundand fromvarious remote-sensing
platforms,proved valuable in estimatingand validatingmany of the criticalvariables. This
experienceand cross comparison should help simplifyfuturestudies of a similarnature.
Edited data sets fromthe OTTER project are now available to the scientificcommunity
on optical disks or via on-linedata banks at NASA (Washington,D.C., USA) and Oregon
State University(Corvallis, Oregon,USA).
Key words: coniferousforests;cooperativeresearch;data base; ecologicalfluxes; ecosystemsimulation; Oregon transect;OTTER project;remotesensing.
INTRODUCTION
AND BACKGROUND
In thelast decade much ofour understanding
ofhow
ecosystemsfunctionhas been formalizedinto mathematicalmodels. Ecosystemsimulationmodels now exist to estimatethe ratesof many importantfunctions,
butto runthesemodels over extensiveregionsrequires
measurements not easily obtainable. For example,
nearlyall ecosystemmodels requireknowledgeof the
incomingsolar radiationand of the abilityof vegetation to absorb this radiant energy.While these two
variables are fairlysimple to monitoron the ground,
few examples exist where theyhave been monitored
togetherover morethanone growingseason- and fewer yet have been monitored over large geographic
regionsusingremotesensing.Most ecosystemmodels
also require detailed knowledgeof soil nutrientconditions or availabilities and of soil water storagecapacity. These two variables, however, are extremely
' Manuscriptreceived 15 June 1992; revised20 May 1993;
accepted 14 June 1993; finalversionreceived 22 July1993.
difficult
to measure accurately,and only recentlyhas
a potentialmeans been found to estimatethe former
by remote-sensing
methods(Aber et al. 1989a). When
changesoccur,dynamicallywithclimateor phenology,
or throughsome type of disturbance,repeated measurementsbecome necessaryand are verydifficult
to
obtain over extensiveareas.
Much ofwhatwe knowabout thestructure
and function of the forestsof the Pacific Northwestregionof
the United Statesis fromstudiesinitiatedtwo decades
ago as partof the InternationalBiological Programme
(IBP) (Waringand Franklin 1979, Reichle 1981, Edmonds 1982). This programcollected intensive climatic and ecological data fromwhat is now the NationalScienceFoundation(NSF)-sponsoredLong-Term
Ecological Research site at the H. J. AndrewsExperimental Forest in Oregon. Similar sets of data were
obtained from youngerforestsin Washington state.
From data collected in these two areas, and research
in Colorado, mechanisticecosystemmodels were formulatedto matchobservationsmade on ratesofgrowth,
litterfall
production,litterdecomposition,evapotrans-
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212
DAVID L. PETERSON AND RICHARD H. WARING
piration,photosynthesis,and streamflow(Runninget
al. 1975,Waringand Running1976, Sollinset al. 1981).
The problem when applyingthese models over large
regions,especiallyif the parametersare derived from
remote-sensingdata, is how to make reliable predictions withoutlosing the site-specific,fine-scaleinformation required.
Remote-sensingdata are generallyunable to directly
measure ecosystemfluxesbut can be used to estimate
some of the key state and condition variables related
to these fluxesand simulated in ecosystem models.
Satelliteand airborneremote-sensingdata have been
used successfullyto map changes in land cover and
condition. Ecosystemcharacteristicssuch as leaf area
active radiation,
index, absorbed photosynthetically
standingbiomass, canopy water status,and the biochemical composition (nitrogenand lignin) of forest
data,
canopieshave beenestimatedfromremote-sensing
but withless certainty.These variables can be used in
ecosystem simulation models, such as the model of
Runningand Coughlan(1988), whichhas been applied
in a general way across large regions(Running et al.
1989). However, no tests or test data sets have been
available to studythese methods for the full combination ofcarbon,nitrogen,and waterinteractions.Accordingly,a primarygoal of the Oregon TransectEcosystemResearch (OTTER) projectwas to evaluate the
full spectrumof remote-sensingdata and algorithms
thatcan meettheparameterrequirementsofecosystem
models to assess ecosystemstructureand function.If
these methodscan be validated, theycan then be applied to thelargeregionalextentavailable fromremote
sensing.
The OTTER projectwas designedto develop a basis
foreventuallyextrapolatingpoint measurementsand
estimatesof ecosystemstructureand functionacross
largergeographicregions.Beforethis becomes possible, two major questions must be answered:(1) Can a
generalized ecosystem simulation model (FORESTBGC; Running and Coughlan 1988, Running and
Gower 1991) predictvariationsin functioningof forvariableregion?and (2)
estsacross an environmentally
To whatextentand withwhatlimitationscan the variables requiredby thismodel be derived fromremotesensing data? The large environmentalgradientsof
westernOregonprovidetheexperimentalvariationfor
investigatingboth of these questions. Any geographic
transectacross west-centralOregon covers a broad
rangeof climateregimesand associated foresttypes,a
rangethat representsmost climate typesfoundin the
zone. The OTTER project included
north-temperate
ground-basedmeasurementsofmanyvariablesneeded
to initializethe model, to drive the model, and to validate the predictionsof the model and of remote-sensing analyses. This paper describesthe overall project,
thedesignofexperiments,
made throughand theeffort
out theprojectto coordinatemodeling,remotesensing,
and ground-basedmeasurements.
Ecological Applications
Vol. 4, No. 2
REMOTESENSINGRESEARCHLINKEDTO
ECOSYSTEMRESEARCH
Runningand Coughlan (1988) developed a general
mechanisticecosystemmodel forforeststhatsimulates
the fluxesof carbon and water,and later expanded it
to includenitrogen(Runningand Gower 1991) and the
interactions
and controlsofall threewithinand through
the system.This ecosystemmodel, called FORESTBGC (BioGeochemical Cycling)servedas thekeystone
of the OTTER project.The model was originallyconceived to operate on parametersthatcould ultimately
be derived fromremote sensing.FOREST-BGC representsa series of interlinkedhypothesesthat specify
thewaythatwater,carbon,and nitrogenmove through
a forestecosystemand the mechanismsthat regulate
thesefluxes.On thebasis ofpreviousresearch,we could
identifythevariables requiredto initialize,to run,and
to confirmthe simulations.
For a numberof years precedingOTTER, remotesensingresearchhad been carriedout to develop methods to derive many of the variables requiredby FOREST-BGC. A varietyof remote-sensingdata has been
examined and the continueddevelopmentof new sensors has opened the opportunityto enhance these
methodsor to provide additional variables. The basic
questionsthatmustbe addressed in linkingecosystem
modeling to remote sensing are: (1) What variables
must be derived?(2) What variables can be obtained
fromremote sensing?and (3) What limitationsmust
be recognizedand addressed in usingremotelysensed
data?
Standing biomass (and maintenancerespiration)
In earlierversionsof FOREST-BGC the calculation
of maintenancerespirationwas scaled as a functionof
leaf area index. However, standingbiomass is physiologicallyrelatedto thisvariablebuthas been a difficult
variable to derive fromremotesensingon a point-bypoint basis. Regional estimatesof total forestbiomass
and volume wereestimatedforforestedregionsof Oregon as earlyas 1976 usingdata fromtheLandsat MSS
system (Douglas County; Peterson and Card 1977).
These estimates were based on multistagesampling
estiusing a Landsat classificationas the first-stage
matorand aerial photographyand groundplots as the
second and thirdstageestimators,respectively.While
these estimatesare accurate fora region,theydo not
predictwell forsinglepoints or sites.Approaches that
are suitablefora continuousmappingof standingbiomass use eitherthe optical propertiesor the backscatteringpropertiesto a radar source. In the former,Li
and Strahler(1986) explain the brightnessvariationin
remote-sensing
data bytreatingthetreesas geometrical
objects,such as cones, withilluminatedand shadowed
parts, over shaded or illuminated backgrounds.The
spacingand sizes of the treescan be used to calibrate
the image brightnessand then the model can be in-
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May 1994
OVERVIEW OF OTTER PROJECT
vertedto calculate biomass (see Wu and Strahler1994
[this issue]). Standingwet biomass with fixedforms,
such as boles of trees,interactswith a radar beam to
alterthe backscatteredsignal(Durden et al. 1989, Kasischkeand Christensen1990). The backscattersignal
can be modeled and theninvertedto estimatethecomponentsresponsible.Each ofthesemethodshave proven usefulin low-densityforests.The challengeto each
of these approaches is whethertheyare able to quantitatively
discernvariationsin standingbiomassofdense
forestsabove 100-200 Mg/ha in a regionsuch as Oregon wheremaximum standingbiomass of forestcan
exceed 1000 Mg/ha.
Leaf area index (and
ecosystem-atmosphere
exchange)
The surfacearea offoliageavailable to interceptlight,
transpirewater,and exchange carbon dioxide is anotherstructuralindex of keyimportancein ecosystem
function.FOREST-BGC uses leaf area index (LAI) in
manywaysto calculateecosystemexchangerates.The
well-knownoptical propertiesof leaves-strong chlorophyllabsorptionin the visible spectrumand strong
scatteringor diffusereflectancein the near infraredprovide a basis forestimatingLAI usingremotesensing. Many studies have shown canopy reflectanceof
solar radiationas measured by remotesensorscan be
relatedto variationsin LAI. For example, red reflectance is inverselyand asymptoticallyrelated to LAI
while near infrared(NIR) reflectanceis directlyproportionalto LAI. The ratio of thesetwo observations,
NIR/red(theso-called"Simple Ratio"), usingdata from
the Landsat Thematic Mapper (TM) has been shown
to be directlyand slowlyasymptoticallyrelatedto seasonal maxima of LAI in the coniferforestsof Oregon
(Petersonet al. 1987, Spanner et al. 1990a). Comparable resultshave been foundforthe coarser(1 km vs.
30 m) data of the Advanced Very High Resolution
Radiometer(AVHRR), a National Oceanographicand
AtmosphericAdministration(NOAA) satellite with
daily coverageof the worldforwesternconiferousforests (Spanner et al. 1990b). Use of only the seasonal
maxima in FOREST-BGC simulations would overestimate fluxes.In OTTER we chose to estimatethe
seasonal variationin LAI usingbothTM and AVHRR
data. This introducesadditional problems having to
do with large variations in illuminationand atmosphericconditions,directionalviews of AVHRR, all
convolved with the seasonal variabilityof LAI (see
Spanneret al. 1994, Pierce et al. 1994).
activeradiation
Interceptedphotosynthetically
(IPAR) (and photosynthesis)
The actual amountofIPAR setsthemaximumavailable energyforconversionof carbon dioxide to photosynthate.LAI is relatedto thismaximumamountin
an exponentialway. Thus, the relationshipof IPAR to
the Simple Ratio is approximatelyexponentialas well.
213
A transformof the Simple Ratio, called the "Normalized DifferenceVegetationIndex" (NDVI), tends
to linearizethisrelationship(Sellers 1987). While global maps of NDVI have been generated from the
AVHRR data (Tuckeret al. 1985) and used in models
ofcarbondioxide exchange(Fung et al. 1987), theyare
difficultto validate. And, in regions of considerable
disturbance,the NDVI values are complicatedby the
highspatial variationin cover conditions.In OTTER
we have attemptedto estimatethe dynamicvariations
in IPAR using a varietyof fieldspectralinstruments
and remote-sensingdata, fromvery fine (1-2 m) to
coarser (30-1000 m) spatial scales (Goward et al.
1994b).
Solar radiation(and photosynthesis)
Cloud cover affectsthe fractionof solar radiation
that reaches ecosystemsas well as the ratio of direct
to diffuseradiation.Studies of cloud cover over fairly
flatterrain,such as the plains of the centralUnited
States,have shownthatcloud coveras a mean monthly
estimatecan be obtained fromdata obtained fromthe
GOES satellite(Eck and Dye 1991). The Total Ozone
Mapping Spectrometer(TOMS) is sensitiveto ultraviolet lightreflectedfromclouds and integratesthis
measure over its 2.5 x 2.50 field of view at nearly
hourlyintervals.The accuracyand suitabilityof these
measurements over mountainous terrain remains
largelyunexplored(Goward et al. 1994b).
Ambientair temperature(and ecosystemfluxes)
In addition to the estimatesof foliaramount interceptingPAR, one needs to know the temperatureconstraintson photosynthesis,respiration,transpiration,
and otherprocesses.When theair is less thansaturated
withwatervapor, a deficitor atmosphericdemand is
createdthatcan influencestomatalbehavior in many
plants of westernOregon even withadequate supplies
ofsoil water(Runninget al. 1975, Marshalland Waring
1984, Runyonet al. 1994 [thisissue]). Remotelysensed
canopytemperaturescan be used to estimatenighttime
A freezingtemperature
minimumair temperatures.
can
lead to closed stomatesthefollowingday.And previous
workhas shownthatthe minimumnighttimetemperature approaches dewpoint temperature,allowing a
calculation of the absolute humidity(Running et al.
1987). The AVHRR satelliteacquires both daytime
and nighttimetemperaturefields.The daytime data
can be used to scale the absolute humidityforrelative
humiditiesduringthe day. Thermal infraredsensors
have the capacityto estimatethe integratedor brightness temperatureof ecosystemsunder clear sky conditions(Luvall and Holbo 1989). In an open forestor
a disturbedone, the sensor integratesobjects at a varietyof temperatures,giving a composite brightness
temperature.However,theaerodynamicallyroughand
tall coniferousforestunder closed-canopyconditions
is closelycoupled to the surroundingambientair tem-
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214
DAVID L. PETERSON AND RICHARD H. WARING
perature,makingtheseareas ideal forsensingambient
air temperature.In OTTER we chose to make direct
measurementsof ambient air temperatureat meteorologicalstationsnear each site studiedwithwhich to
validate the remotelysensed temperatures.
Soil moisture,temperature,
and drought
(and stomatal behavior)
Soil moisturevaries a greatdeal in forestsof Oregon
due to summerdroughtand oftenlow soil waterholding capacities. As soils dryout, thisexertsan increase
in resistanceto water flow into tree roots, which is
reflectedin an increase in stomatalresistance.Again,
sensor brightnesstemperaturesmay help to discern
thesesituations.The integratedtemperatures
described
above include heated soils as well as the canopy temperatures-thetemperaturefielddeterminedin partby
the moisturestatus of the various cover types being
sensed.Nemani and Running(1989) hypothesizedthat
areas of low LAI or low values of NDVI should have
elevated temperaturesrelativeto the temperaturesof
the nearby forestcanopy, and that the slope of the
relationshipbetweenNDVI and brightnesstemperatureover a small regioncould be an index to drought
stressand stomatalresistance.At the same time,areas
of high NDVI would measure ambient air temperatures.These hypotheseswerefurther
evaluated in OTTER using sensors fromlow-flying
aircraftand from
satellites(Waringet al. 1993, Goward et al. 1994b).
Canopy biochemicalcontents(and nutrient
cyclingand photosynthesis)
The biochemicalcompositionoffoliageis a potential
index to the rates of nutrientcyclingand to the maximum ratesof photosynthesis
(e.g., Field and Mooney
1986, Aber et al. 1989b). The chlorophyllcontentis
relatedto photosynthetic
capacity(Yoder 1992), while
nitrogenconcentrationhas been relatedto the rate of
nitrogenturnoverand to productivity
(Vitousek 1984).
Meentemeyerand Berg(1986) and Melillo et al. (1982)
have shown thatthe lignin-to-nitrogen
ratiosare a determinantof the rates of decomposition,while lignin
concentrationshave been inverselyrelatedto the annual rate of nitrogenmineralizationin deciduous forests (Aber et al. 1989b). Myrold et al. (1989) studied
the annual rates of N-mineralizationacross a transect
of Oregon and correlatedN-mineralization,microbial
biomass, and soil respirationto LAI and climate.The
balance oftheserelationshipscan be changedbychronic additions of nitrogenvia acid deposition,wherein
the excess nitrogenavailabilities can lead to changes
in thelignin-to-nitrogen
ratiosand concentrations
(Aber
et al. 1989a). Much less is known about the seasonal
variationsin nutrient-cycling
processesand the effects
of fertilization.
In OTTER, studieswereconductedon
these effectsthroughuse of control and fertilization
experiments(see Matson et al. 1994 [ this issue]).
Ecological Applications
Vol. 4. No. 2
The remote sensingof plant biochemical composition has been based on the recentaccess to veryhigh
spectralresolutioninstruments
and on theunderstanding of how organicbonds produce differential
absorptionin thevisibleand shortwaveinfraredregions.Well
knownare the absorptionsdue to chlorophylland the
accessory pigmentsand the effecttheycreate on the
position of the red edge, the long wavelengthend of
the absorption spectrumnear the infrared.Estimates
of chlorophyllcontentsby detectionof the positionof
the red edge were conducted in OTTER usingseveral
instrumentsand various radiative transfermodels
(Johnsonand Peterson1991, Johnsonet al. 1994). The
harmonics, overtones, and combination bands produced by fundamentalvibrationsof theorganicbonds
of the otherbiochemicals tend to occur in the shortwave infrared
region.Whileevidencesuggeststhatsome
ofthesepropertiesmaybe predictedfromhighspectral
resolutionimagingspectrometerdata (e.g., ligninconcentrationsby Wessman et al. [1988]; nitrogenby Petersonand Running[1989]), thegeneralapplicationof
these methods is stillin question. For example, there
are concernsabout the covariance withwatercontent,
itself dominating the shortwave infraredspectrum.
Other structuralvariationsas well as species compositional changes also cloud the interpretation.Oregon-with itsextensiveconiferousforestsofa fewspecies and with the fertilizationexperiments-offers
conditions for testingwhetherthe biochemical compositionof forestcanopies can be sensed remotely(see
Johnsonet al. 1994, Matson et al. 1994 [thisissue]).
DESIGN OF THE EXPERIMENT
Six studysites were selectedfromforestecosystems
across the temperature-moisture
gradientsof Oregon.
These six sitesare located along a roughlywest-to-east
transect between
440
and 45? north latitude beginning
at the coast and extendinginland -250 km. The location of the sitesis shownin referenceto an AVHRR
(Advanced Very High Resolution Radiometer) false
color image of westernOregon (see Interceptedphoactiveradiation... , above) (Fig. 1). The
tosynthetically
transectbeginsnear Cascade Head ExperimentalForest, northof Lincoln City along the Pacific coast. It
extends250 km to the east to a juniper woodland near
Redmond in the high desert interiorof Oregon. The
names and numericalcodes associated witheach site
are providedin Table 1 withdetaileddescriptionsprovided in Runyonet al. (1994 [thisissue]). Climate variation across the transectis extreme,withprecipitation
rangingfrom -250 cm to 25 cm/yr,west to east. The
coastal and valley sites (10, 1G, and 2) rarelyexperience snow. The subalpine forest(site 4) experiences
moderate to deep snowpack every year,and freezing
temperaturespersistformuch of the winter,both here
and at sites 5 and 6. Drought is extremein the WillametteValley (site 2), the Ponderosa pine site(5), and
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May1994
OVERVIEW OF OTTER PROJECT
~
.*
~
215
_i
-itg
composFia. 1. AVHRR false-color
Oregon overite image of northwestern
_
->
_
ta
_
v
_
J
*s
t
w
4
4,
thejuniper woodland (site 6). Leaf area indicesand net
varyby morethan 10-foldacross
primaryproductivity
the transect.
Understandingtheroleof nitrogenin controllingthe
ratesof various ecosystemprocesses led to additional
sites and measurementsacross the transect.At the
coastal Cascade Head site (1), we selected a stand of
alder (Alnus rubra,site I A);
deciduous nitrogen-fixing
locationsof
laid withtheapproximate
the six studysites.Red tonesindicate
of all types.Darkerredsare
vegetation
redsare
forests,
brighter
usuallyconifer
landsand broadleafforests;
agricultural
bluishtonesare drydesertareas with
baresoils;and thesmallwhiteareasare
a fewcloudsor snow.This imageis a
compositeof individualdailyAVHRR
and only
images;cloudsare minimized
valuesrelatedto
thehighestbrightness
are retainedpointby point
vegetation
theimage.
throughout
TER project(site 31F). Finally,a stand of Ponderosa
pine (Pinus ponderosa) receivingsurfaceapplicationof
sewage sludge forthe 5 yrpreviousto this projectwas
selected near site 5 (Metolius: site 5F). Each of these
foursites were compared to the controlor untreated
sites.
Seasonal coverage
menat site3, Scio,a Tsugaheterophylla-Pseudotsuga
Four principal campaigns were planned to match
changes in ecosystem functionthroughoutthe year.
3F) and part of that stand continued to be fertilized The timingof these campaigns was based on the foltwice a year throughoutthe durationof the 3-yrOT- lowingseasonal criteria:
ziesii stand was selected that had been fertilized(site
TABLE 1.
codesforthe six primaryand fiveancillarystudysites ofthe OTTER projectin Oregon.
Names and numerical
Nu-
location
Geographical
merical
Lat. (IN)
Long. (?W)
CascadeHead (Gholz'site)
4503'
123057'30
Tsuga heterophylla/control
fixation
Alnus rubra/nitrogen
growth
Tsuga heterophylla/old
2
Waring'sWoods (the Corvallis site)
44036'
123016'
Pseudotsugamenziesii
3C
3F
Scio
Scio (fertilized)
44040'30"
122036'40"
Pseudotsugamenziesii/control
fertilization
Pseudotsugamenziesii/single
Pseudoisuga menziesii/doublefertilization
Santiam Pass
44025'20"
121?50'20
Tsuga mertensiana
Metolius
44025'
121?40'
Pinus ponderosa/control
Pinus ponderosa/fertilized
Juniper
44017'30
121020'
Juniperusoccidentalis
Name
code
IG
IA
10
31F
4
5C
5F
6
Cascade Head (alder)
Cascade Head (old growth)
fertilized)
Scio (intensively
Metolius
Primaryspecies/treatments
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216
DAVID L. PETERSON AND RICHARD H. WARING
EcologicalApplications
Vol. 4, No. 2
TABLE2. Ecosystemand environmentalvariables and processes measured,remotelysensed, and simulated by ecosystem
and radiativetransfer(RT) models.
Variable:
Processes
How measured
Meteorological:
Incidentshortwavesolar
radiation
Cloudiness
Precipitation
Air temperature
Humidity
Soil temperature
Optical depth (atmospheric)
Meteorological
station
Meteorological
station
Meteorological
station
Meteorological
station
Direct at site
Ecophysiologicaland nutrientcycling:
Pressurebomb
Pre-dawnwater
potentials
Gas exchange
Photosynthesis
Conductance
Gas exchange
Litterfall
Field meas.
N-mineralization
Field tech.
Soil waterstorage
Field methods
Soil properties
Field methods
Soil respiration
Field chambers
Maintenance respir.
Stand growth
Field methods
Stem cores
Remotely
sensed*
TOMS
MT-CLIM
MT-CLIM
Wrigleyet al.
1992
Sunphotometer
Reference
panels
FORESTBGC
FORESTBGC
AVHRR; Ul- FORESTBGC
tralight
FORESTBGC
FORESTBGC
Evapotranspiration
Nemani and
Running
Leaf area index (LAI)
Ceptometers
LI-COR LAI
meter
Biomass, standing
Field cruise
AVHRR; Ultralight
TMS,
NSOO1;
AVIRIS
CASI; FLI
AVHRR;
FORESTBGC
TMS;
AVIRIS;
CASI; FLI;
NSOOI
ASAS; TMS
AIRSAR
Hemisphericalalbedo
Canopy temperature
AirTherm*
ASAS; Ultralight
TIMS
B. Yoder; R. Waringand
S. Goward; S. Running
D. Myroldand B. Claycomb; S. Running
D. Myroldand P. Claycomb; S. Running
D. Myroldand P. Claycomb; J. Runyon
D. Myroldand P. Claycomb
K. Mattson; S. Running
M. Ryan
R. Waringand J. Runyon; S. Running
S. Running
D. Myroldand P. Claycomb; S. Running
S. Runningand J. Glassy
Canopy and treebiophysicalcharacteristics:
Field and labo- Truckboom
Spectralreflectanceof
and ultracomponents
ratoryspectrometers
lightspectrometers
Interceptedphotosynthet-Ceptometers
ically active radiation
R. McCreightand J.
Runyon
S. Goward and D. Dye
R. McCreightand J.
Runyon
R. McCreightand J.
Runyon;J. Glassy
R. McCreightand J.
Runyon;J. Glassy
D. Myroldand R. Waring
M. Spanner
R. McCreight
B. Yoder; S. Running
FORESTBGC
FORESTBGC
FORESTBGC
FORESTBGC
Field methods
Principal
scientists
R. Waring;S. Running
FORESTBGC
Growthrespiration
Decomposition rate
Simulated(models)t
RT
Ecosystem
S. Goward and F. Huemmrich; L. Johnson;A.
Strahler;S. Ustin; R.
McCreight;B. Law; J.
Miller
R. McCreightand R.
Waring;S. Goward; M.
Spanner; L. Johnson;J.
Miller,P. Gong and J.
Freemantle
Miller et al.
M. Spanner; L. Johnson;
S. Running;J. Welles;
J. Miller and P. Gong
Geometricoptics A. Strahlerand W. Yucheng
Microwave model S. Durden and M. Moghaddam
Geometricoptics L. Johnson;A. Strahler;
R. McCreight
R. McCreight
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May 1994
TABLE
2.
217
OVERVIEW OF OTTER PROJECT
Continued.
Variable:
Processes
How measured
Remotely
sensed*
Leaf and canopy biochemical characteristics:
Chlorophyll
Wet chemistry CASI; FLI;
AVIRIS
Nitrogen,lignin
Wet chemistry AVIRIS
Cellulose, starch
Wet chemistry AVIRIS
Simulated(models)t
Ecosystem
RT
Miller; PROSPECT
Principal
scientists
C. Billow and P. Matson;
J. Miller; L. Johnson
C. Billow and P. Matson;
L. Johnson
C. Billow and P. Matson;
L. Johnson
C. Billow and P. Matson
Amino acids
Wet chemistry
* Sensors: AIRSAR = Airbome SyntheticApertureRadar; AirTherm= Teletemp model 43 infraredradiometer;ASAS =
Advanced Solid-stateArraySpectrometer;AVHRR = Advanced Very High Resolution Radiometer; AVIRIS = Airbome
Visible-InfraredImagingSpectrometer;CASI = Compact Airbome Spectrographic
Imager; FLI = FluorescenceLine Imager;
TIMS = ThermalImagingMulti-channelScanner;TMS = Thematic Mapper Simulator(also: NSOO1); TOMS = Total Ozone
Mapping System.
t Models: MT-CLIM = mountain climatologymodel, Running et al. (1987); FOREST-BGC = mechanisticecosystem
code
simulationmodel, see Runningand Coughlan (1988), Runningand Gower (1991); Geometricoptics = radiativetransfer
ofLi and Strahler(1986); Nemani and Running(1988) = methoddevelopedto estimatestomatalresistancebased on brightness
temperatureand vegetationindex; Miller et al. (1990) = model fitting
spectralcurves at the red edge to predictchlorophyll
and LAI; PROSPECT = model to simulate the visible-infrared
spectrumof leaves; Jacquemond,S. and F. Baret (1990);
Microwave = unnamed model used by Durden et al. (1989) to simulatebackscatterreturnof vegetatedscenes.
(1) February-March:pre-budbreakconditionsat all
sites, when leaf area is at a minimum and low temperaturesare likelyto be constrainingmanyecosystem
processes;
(2) May-June:budburstperiod when soils are near
saturation,and maximum rates of many ecosystem
processesshould be observed;
(3) August:fullexpansion of foliageon all sites and
the probabilityof droughtis high;and,
(4) September-October:foliage of deciduous trees
and shrubs is in full autumn color and rainfallhas
normallybegunto rechargesoils; freezingtemperatures
are unlikelyto extendthroughthe day.
Ecological and meteorologicalmeasurements
Table 2 indicatesthe kinds of data collected in the
field for ecological analysis. Meteorological data includingair temperature,relativehumidity,precipitation, and shortwave(400-1200 nm) incomingradiation were recorded by minute and stored as hourly
averages (see Runyon et al. 1994 [thisissue]). While
we chose to locate a meteorologicalstationin clearings
near each site (except site 6), the distance fromthe
actual siteto thestationlocationstillnecessitatedsome
minor correctionsusing the model MT-CLIM (Runninget al. 1987).
In contrast to the meteorological measurements,
much of the physiologicaldata were gatheredat seasonal intervalsor in conjunctionwiththe campaigns.
The highestprioritywas placed on measurementsof
pre-dawnwater potentials.These values best characterize the intensityof droughton vegetationand set
the upper limitson potentialgas exchange.
Structuraldata on each stand were collected with
techniquesmodifiedfromthose thatforestersapplyto
assess timbervolumes.Allometricrelationsequate tree
diameteror sapwood cross-sectionalarea to the biomass of foliage,branches,and stems.Additional data
on treeheightsand thelengthand widthsoftreecrowns
were also measured (see Wu and Strahler 1994 [this
issue]).Growthwas estimatedbymeasuringringwidths
on cores extractedfromsampled treesat each site.The
foliarbiomass of understoryplants in most sites was
not a large fractionof total foliarbiomass except the
eastemmostsites.At site5, a Ponderosa pine sitewhere
most of the overstoryhad been removedthroughharcomponentis present.
understory
vesting,a significant
biomass and LAI (leafarea index) was
This understory
carefullymeasuredand analyzed (see Law and Waring
1994). A varietyof indirectmethodswas used to estimate LAI and checked against the allometric approach. In general, our estimates of annual abovegroundnet primaryproductionand of foliarbiomass
were based on the overstorytreesonly.
Leaf litterfall,an importantvariable for checking
model predictionsof carbon and nitrogencycling,was
measured only at sites 1 and 3. We used previously
collecteddata fromtheIBP (ForestScience Data Bank)
and publishedinformationon leafturnoverto provide
at the othersites. D. Myrold
estimatesof leaf litterfall
and his colleagues in the Soil Science Departmentof
OregonStateUniversitymeasurednitrogencyclingparameterssuch as nitrogenmineralization(net and tomicrobialbiomass,
tal), ammonification,nitrification,
soil CO2 respiration(measured by K. Mattson of the
through
UniversityofIdaho), and varioussoil properties
theyearin situ.Soil and littermoistureconditionswere
measuredat the time of the campaigns.Summaryvalues ofthesevariableshave been added to thedata base
(see Data storageand retrieval,below).
During each of the fourcampaigns,foliagewas collected by Oregon State University personnel and
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218
DAVID L. PETERSON AND RICHARD H. WARING
EcologicalApplications
Vol. 4, No. 2
TABLE3. Airborneand satelliteplatformsand sensor systems,includingground-basedinstruments,used in the OTTER
project.
Altitude
Sensor*
Platform
Time of day
Satellite
NOAA 9,10
GOES
AVHRR
TOMS
1400
20000 m
ER-2 aircraft
AVIRIS
TMS
TIMS
CIR (color infraredaerial
photography)
Noon
10 600 m
DC-8 aircraft
SAR
Noon
4500 m
C- 130 aircraft
ASAS
NS001
TIMS
CIR
Sunphotometer(sun-tracking)
Noon, mom/afternoon
Noon
600 m
Apache lightplane
FLI
450 m
Cessna lightplane
Spectron(modified)
Noon
150-450 m
Ultralightaircraft
Spectron,AirTherm
Noon, mom/afternoon
6m
Truckboom
Ground
Portables
SpectronSE590
Barnes MMR
SpectronSE590 and SE
393, ALEXA, SIRIS,
LI-COR, Barnes MMR,
Ceptometers,fisheye
lens, digitalcamera,
Sunphotometer
* CIR = color infraredaerial photography;SAR
sensor acronymsare explained in Table 2.
=
syntheticapertureradar; MMR
=
MultiModular Radiometer. Other
shipped on dry ice to NASA Ames Research Center
forwet chemical analysis (see Matson et al. 1994 [this
issue]). These samples were analyzed fora numberof
biochemical constituentsincludingchlorophylla and
b, amino acids, nitrogenand proteincontents,lignin
starch.Some
and cellulose,and thelabile carbohydrate,
of these samples were also measured spectrallyin labforlatercorrelationanaloratoryspectrophotometers
ysis withthe biochemical composition.
The ecosystemmodel, FOREST-BGC, requires-accurate estimatesof waterstoragecapacity in the root
zone. Previous informationgatheredby Gholz (1982)
was used in the modelinganalysis,but thesedata have
proved to be in errordue to inaccurateestimatesof
rootingdepthand due to spatialvariabilityin soil properties (see Running 1994 [this issue]). No furtherattemptto independentlyestimatesoil waterholdingcapacityhas been made.
to theretrievalof thesevariables,potentiallyresolving
limitationsobservedin earlierstudies,or potentialaccess to new variables, such as hemisphericalalbedo
and standingbiomass, that were unavailable earlier.
Moreover, thereis the potentialfor synergyby combiningsensorsofa wide varietyincludingradarsensors
when all of the sensorsacquire data at the same time
and under similar conditions. This allowed us to examine questionsabout the robustnessof algorithmsto
remove atmosphericeffects,forexample, or to compare the consistencyand informationcontentof differentsensors operatingwith a range of spatial and
spectral scales. We sought to determineunder what
conditions and with what techniques each remotesensing instrumentcould provide valid estimates of
the various biophysical and biochemical parameters
across the transectand at each site. And-for the first
time for these forests-we sought to examine these
parametersforseasonal variation.
Remote-sensing measurements
Table 3 presentsthe spectralmeasurementsin three
Past remote-sensingresearchcoupled to the devel- components:groundpoint observations,airborneand
opment of FOREST-BGC had identifiedmany of the satelliteimaging and point observations,and observariableswe feltwerepossible to retrievefromremote- vations of the atmosphere.Table 2 provides a sumsensing data. However, much of this work had con- maryof thesemeasurements,the sensorand radiative
centratedon forestsat peak mid-season conditionor transfermodel used (if any), and the investigatorinon forestsdistributedthroughoutthe north-temperate volved. J. W. Skiles and G. L. Angelici (contractor
zone. In addition,thedevelopmentof new sensorsop- report4557 [1993] to National Aeronauticsand Space
eratingfromaircrafthad opened up new approaches Administration)
providea completedescriptionofeach
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May 1994
OVERVIEW OF OTTER PROJECT
sensor and the record of data acquired and available
throughthe data bank.
Ground: Spectral measurementsof forest components.-The interpretation
ofthecompositereflectance
signals of imagingsensors requires knowledgeof the
spectralreflectanceof the componentsof the object,
the forest,includingfoliage,branch, and stem bark,
litter,and soil. At most sites thesedata wereacquired
both undernaturalsolar illuminationin the fieldusing
and undercontrolledillumination
spectroradiometers
using laboratoryspectrophotometers(Goward et al.
1993). Some of the samples collectedforbiochemical
assays were also measured as whole leaves in laboratoryspectrophotometers.
A numberof investigatorsbroughtsimilarspectroradiometersto the fieldfor these measurements.Althoughmanyoftheseinstruments
werepurchasedfrom
the same manufactureror had similar spectralband
widthsand sensitivities,the intercalibrationof these
instrumentswas compared in the fieldforwavelength
accuracy.Variationsas greatas 20 nm were observed
betweeninstrumentsof the same manufacture.
Variation in radiometricsensitivitywas frequently
checked against white referencepanels under a range
ofdifferent
illuminationconditions,derivedfromsolar
or artificialilluminationsources.Spectrally"flatfields"
consistingof asphalt parkinglots, bare soil, or gravel
pits were located near each site to serve as references
forboth fieldspectrometersand for the sensor data.
These flatfieldswere used to monitorspectralperformance betweensensorsand throughoutthe seasons of
thecampaigns.Radar sensingrequiresa different
source
forcalibration,consistingof large triangularsheetsof
Threesuch
aluminumassembledintoa cornerreflector.
werepositionednearsitesacrossthetransect.
reflectors
Satellite and airbornesensors.-Virtually everyremote-sensinginstrumentcurrentlyavailable and used
for biospheric studies was included in OTTER, and
theirpropertiesare shown in Table 3. Each sensorhas
a unique instantaneousfieldof view or groundresolution,different
spectralbandwidthsand view angles,
and differences
in radiometricperformance.Although
one can use some sensorsto derive more than one of
the requiredvariables,each sensoroftenhas optimum
characteristicsforspecificparameters.A briefreview
of the sensorsused in OTTER follows.
1. Advanced Very High Resolution Radiometer
(A VHRR). -The frequent(fourtimes daily) coverage
of the Earth by AVHRR, operatingfrom a NOAA
satellitein polar orbit,the spectralband placement,
and the broad geographicswath widths of the I-km
spatialresolutiondata make AVHRR data particularly
usefulformonitoringprocessesthatvaryslowlyacross
the landscape at this scale, such as IPAR (intercepted
active radiation) and canopy temphotosynthetically
perature,butwhichvarymorerapidlyfromday to day.
AVHRR data were acquired for clear or cloud-free
conditions across the transectfromthe EROS Data
219
Center of the U.S. Geological Survey in Sioux Falls,
South Dakota. The near-infraredand red bands of
AVHRR were transformedinto the NDVI and used
to calculate seasonal changes in IPAR and LAI. The
thermalchannelwas used in conjunctionwiththeNDVI
values to estimatestomatalresistance.The broad swath
widthof AVHRR (severalthousandkilometres)introduces largeand significant
variationsin groundviewing
angles,solar illuminationconditions,and atmospheric
path length.These directionaleffectsmust be understood to make the best use of these data.
2. Landsat Thematic Mapper (TM) and airborne
Thematic Mapper Simulator (TMS). -Unlike the
AVHRR, theLandsat TM observesanypointon Earth
only every 16 d, even thoughit, too, is in polar orbit.
This is because of the narrowswathwidth(_ 100 km)
and higherspatialresolution.Such infrequent
coverage
is, however,a disadvantagein cloudy regionssuch as
the PacificNorthwest.Since we had to coordinatethe
data collectioneffortsof many sensors in shorttimewindow campaigns,we chose to use the airbornesimulator,TMS, operatingat 20 km fromNASA's ER-2
highaltitudeaircraft.The TM and theTMS have narrower,thoughsimilar,bandwidthsthanAVHRR, supplementedby channels in the blue, green,and shortwave and thermalinfrared.Moreover, TM and TMS
have a spatial resolutionof 30 m, usefulforresolving
smaller targetssuch as foreststands or for mapping
high-frequency
changes in cover conditions at scales
common to most ecological work.
For the OTTER project, TMS data were used to
calculateseasonal and across-transect
variationsin LAI,
standingbiomass, and IPAR. A similarsensor,called
theNS00 1 ThematicMapper simulator,operatingfrom
NASA's C- 130 aircraftat 5-8 km was also used to
collectdata and had theadditionaladvantageofhaving
on board a sun-trackingsunphotometer.This sensor
observes the solar disk at the position of the sensor
and its data are used to calculate additive atmospheric
effectson the NSOO1 data fromthe groundup to the
aircraftaltitude.
3. Thermal Infrared Multi-channel Scanner
(TIMS). -This instrumentincludes four thermalinfraredchannels between 8 and 12 Am that measure
emitted thermal radiation from the Earth's surface.
Though designed initiallyforgeologic studies,TIMS
data have been used in studies of ecosystemtemperatures and used in energybalance models of evapotranspiration.The TIMS operatesfromthe C- 130 aircraftand its data were collected simultaneouslywith
the NS0O1 data. The TIMS data have not been used
in any OTTER analysis as yet,but forcompleteness,
are available to futurescientistsforanalyses (see Data
storageand retrieval,below).
4. AirborneSynthetic-Aperture
Radar (AIRSAR). AIRSAR is a side-lookingimagingradar systemmaking measurementsof backscatteredand polarized radiation emittedby the sensorsystemin threefrequen-
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220
DAVID L. PETERSON AND RICHARD H. WARING
cies called L, C, and P bands. AIRSAR producesimages
witha spatialresolutionof 30 m whileoperatingfrom
theNASA DC-8 aircraftat - 15 km. We obtaineddata
with AIRSAR in March, June,and August to assess
whethervariationsin standingbiomass acrossthetransect could be detectedquantitatively(Moghaddam et
al. 1993) and to test whetherthe droughtconditions
of Augustrelativeto the wet springconditionswould
reduce tree water status enough to alter theirtissues'
dielectricpropertiesand therebychange the backscatteringintensityor the polarization.
Imaging Spectrometer
5. AirborneVisible-Infrared
(A VIRIS). -AVIRIS is NASA's only instrumentcadata. It
pable of producing high-spectral-resolution
generatesimages with228 continuousspectralbands,
each ; 10 nm bandwidth,coveringthe spectralrange
from400 to 2400 nm. AVIRIS operatesfromNASA's
ER-2 aircraft,producingimages with a spatial resolutionof 25 m. For OTTER, AVIRIS data wereused
to assess whetherdifferencesin biochemical composition between sites or over time could be estimated
reliably,usingmainlydata fromtheshortwaveinfrared
portionof the data. The relativelynarrowbandwidths
in the visible also give hope that variations in chlorophyllcontentor shiftsin therededge associated with
chlorophyllcould be measured(see Johnsonet al. 1994,
Matson et al. 1994 [thisissue]).
6. Advanced Solid-state Array Spectrometer
(ASAS). -This unique new instrumentprovidesfairly
data in the rangefrom400 to
high spectral-resolution
viewingangles.
900 nm and does so forseven different
The sensoracquires images at 150 intervalsrelativeto
the aircraft'sdirectionof flightstartingat 450 forward
viewingand going to 45? backward viewing.Thus, it
producesat least one nadirviewingimage corresponding to all of the otheroptical sensors.ASAS also operatesfromNASA's C- 130 aircraft,producingimages
with a spatial resolutionof 20 m. ASAS data are
being studied using a geometricoptics model to estimate standing biomass and hemispherical albedo
(Abuelgasimand Strahler1994). The data can also be
used to estimatebiophysicalparameterssuch as LAI
view angles,whichmayreduceconusingtheoff-nadir
foundingeffectsdue to open canopies and contrasting
backgrounds.The hemisphericalalbedo calculations
will be used to adjust estimatesof IPAR and to adjust
directionalobservationsforbi-directionalcanopy reflectancevariations.
7. CompactAirborneSpectrographic
Imager (CASI)
and FluorescenceLine Imager (FLI). -These two instrumentswere provided by Canadian investigators
John Miller of York University(Toronto, Ontario,
Canada) and his associates. These sensorsproduce the
highestspectralresolutionof any sensor used in OTTER, ~2 nm throughoutthe visible to near infrared.
They operate fromvarious lightaircraftand can produce imageswithspatialresolutionsof - 2 In. The high
spectraland spatial resolutionscould not be obtained
EcologicalApplications
Vol. 4. No. 2
simultaneously,so we chose to acquire thefullspectral
resolutiondata fora subsample of lines across the image, with one spectralchannel devoted to full spatial
resolutionimaging. This spectral resolutionresolves
many fine spectral featuresdue to atmosphericand
biospheric phenomena that are not resolved on the
othersensors.The data are beinganalyzed usingmodels to extractchlorophyll,LAI, and treedensityinformation (see Gong et al. 1992, Matson et al. 1994 [this
issue], Spanner et al. 1994 [thisissue]).
8. Ultralightaircraftcarryinga varietyof instruments.-This projectis the firstof NASA's to employ
an ultralightaircraftas a sensorplatform.The aircraft
does not possess enoughpayload capacityto carrythe
heavy sensorsdescribedabove, but was equipped with
several pointinginstruments,
two spectroradiometers
(thesame as used on theground),a stereovideo camera
for continuous imaging,and a thermalsensor. This
unique platformoffersmanyadvantagesforecological
studiesdue to its readiness,relativelyinexpensiveoperation,reasonable stability,and slow speed-and the
techniquesdeveloped by R. McCreightto optimizeits
use forsensing.Using on-board referencepanels, the
aircraftcan cruiseat variousaltitudesand therebymeasurevariationswithaltitudein theatmosphericoptical
properties.Being located on site,thisaircraftwas able
to operate virtuallyanytimependingsuitableweather
conditions.The data collected fromthe ultralightaircraftare used to calculate NDVI and surfacetemperaturesfortheestimationofstomatalresistance,having
correctedfor atmosphericeffectsusing the reference
panel measurementsduring the flight.The data can
also be used to cross-checkothersensors.
Atmosphericcorrections
Under all conditions the atmospherescattersradiation and absorbs radiation, causing additive and
transmissiveeffectson airborne sensor data. In OTTER we had sensorsoperatingat many different
altitudes (from300 to 20 000 m) and throughout2 yrand
fourseasons. In addition,forestfiresin Augustadded
an extra atmosphericburden to the data. Over such
large climatic extremes,even for "clear" days, these
effects
can sometimesdominatethesensorsignals,particularlyin the visible region. Clearly,they must be
removed or minimized (see Spanner et al. 1994 [this
issue]).
We used threemethodsto correctforthese effects.
of the campaigns,and as
First,duringthe overflights
near as possible to the actual time when the aircraft
were above each site, two portable sunphotometers
were moved across the transectto each site to make
direct measurementsof the spectralsolar irradiance.
The sunphotometermakes irradiance measurements
in 10 narrow spectralbands in the visible and near
infraredregions.These measurementsare used with
instrumentcalibrations to calculate aerosol optical
depths. Using the model developed by Wrigleyet al.
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May 1994
221
OVERVIEW OF OTTER PROJECT
are used
(1992), Rayleighand aerosol scatteringeffects
to correcttheimagedata foratmosphericturbidity(see
Johnsonand Peterson 199 1, Spanner et al. 1994 [this
issue]). The second means ofcorrectingforatmospheric optical contributionswas with the referencepanel
measurementsmade on board the ultralightaircraft
described above. By making observationsat various
altitudesand thenregressingback to groundlevel, the
data could be correctedfortheseeffects.The
ultralight
thirdmethodemployedthespectrally"flat"fields.Each
field was carefullymeasured using field spectroradiometersand thensensordata over each fieldcould be
utilizedto normalize foratmosphericvariations.
DATA STORAGE AND RETRIEVAL
science necessitatesharEffortsin interdisciplinary
ing of the large amounts of data generatedamong investigators-particularlywith the use of remotesensing. Data base managementhas become an advanced
technologythatcan minimize many of the difficulties
associated with maintainingsuch complex data sets,
withdisseminatingthe data among collaborators,and
witharchivingand preservationof data forfutureuse.
Active researchprojectssuch as OTTER need an interactivedata system.We accomplished this by couplingtwo existingdata systems,one fromNASA and
one fromOregonStateUniversity,and expandingtheir
servicesfromarchivingto includeinteractiverolessuch
as rapid retrievalof data (Skiles and Angelici 1993).
NASA had establishedthe Pilot Land Data System
data, for
(PLDS) formanagingNASA's remote-sensing
maintainingdocumentationabout data filesin thedata
bank, forprovidinga user interfaceto this documentation,and forthe orderingof data sets. Priorto OTTER, PLDS had been mainly used forarchival functionsand had notbeen used to supportan activescience
project. Active project support requires more interactive communicationand customized extensionsof
servicesto accomplish data base tasks.
PLDS does not manage the kinds of ecological and
meteorologicaldata generatedin OTTER. At Oregon
State University the National Science Foundation,
throughits Long-Term Ecological Research (LTER)
projects,is supportingthe developmentof a more interactivedata managementsystemunderthedirection
of S. Stafford
of the Departmentof ForestScience with
her associate, G. Spycher.This data base was used to
storeand provide access to most of the ecological and
meteorologicaldata sets. An electroniclink between
the LTER data systemat Oregon State Universityand
the PLDS node at Ames Research Center was establishedso thatscientistscould reachbothofthemthrough
one interface,the PLDS one. Small data files were
maintained on line for immediate access via communicationnetworks,whilethelargedigitalimagedata
sets had to be orderedand filledusingothermedia.
During the finalyearof the project(1993), the carefullydocumentedand validated data sets (a subset of
thefulldata taken)have been masteredonto fourCDROM disks. The disks include both the ecological and
meteorologicaldata as well as spectralimage data sets.
These diskswillbe themain mediumfordisseminating
the OTTER data sets to a wide community.
ORGANIZATION
The OTTER projectwas initiatedat a NASA-sponsored workshop.A core of investigatorswithprevious
experienceworkingin OregonwerepresentfromNASA
Ames Research Center,Oregon State University,the
Universityof Montana, and the Universityof Maryland. We saw forthe firsttime an opportunityto bring
and previouslydeveloped scitogethermanydifferent
ence techniques into a coordinated project. And we
recognizeda need to understanddynamic ecosystem
processes that we could address with remote-sensing
technologyand computermodeling.Ratherthan continue to modifyand perfectthe modelingand remote
sensing,we chose to conducta testto see how well the
existingmodels and algorithmscould predictthe processestheyweredesignedto predict.A combinedground
and modelingproposal
measurement,remote-sensing,
was generatedto serve as the nucleus for attracting
cooperatingscientists.We knew fromexperiencethe
need forground validation and correlationdata, and
chose to installmeteorologicalstationsnear each site
for continuous monitoringof these data. Given the
emphasis on thedynamicalaspects of ecosystemfunction, we recognizedthe need foryear-roundcoverage
by aircraftand satellitesensing.The data base management activitywas added to the core project as a
prototypeexperimentin theecological sciencesto evaluate the needs of thiscommunity.In addition,a number of independentlyfunded scientistsand their instrumentswere attractedto the projecton the basis of
the value of collectingnew or makingunique analyses
of shared data. These added investigationswere complementaryto the core effortsratherthan redundant,
and significantly
expanded the scope of the effort.In
addition,we succeeded in a proposal forspecial NASA
Campaign(MAC)
designationas a Multi-sensorAircraft
to secure betterprioritiesin schedulingand access to
these resources.
In any complex project such as OTTER, problems
must be expected to arise that compromise some of
the original objectives. We tried to minimize these
throughsettingpriorities,maintainingopen and frank
communication,throughsemi-annual team meetings,
and throughthe use of electronicmail.
The close proximityof the studylocation,mid-state
Oregon,to the NASA Ames Research Center(Moffett
Field, California)was an advantage that is not always
generallyavailable. Ames is thehome of NASA's main
platformaircraft(ER-2, DC-8, and C- 130). The coordinatorfor the MAC, M. Spanner, being at Ames
to explain the scientificreasons for
helped significantly
our requirementsto theaircraftprogrammissionman-
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EcologicalApplications
Vol. 4, No. 2
DAVID L. PETERSON AND RICHARD H. WARING
222
Studies
Remote-Sensing
and Studies
FieldMeasurements
NASA-Ames Research Center
D. L. Peterson (co-PI)
Oregon State University
R. H. Waring (coPI)
D. Myrold
J. Runyon
Satellit
B.Yoder
S. Goward
NASA-Ames Research
NASA-Ames Research Center
P. Matson
C. Billow
UCD
Airbome
NASA-Ames Research
Universityof Maryland
R. McCreight
M. Spanner
S. Ustin
Center
M. Spanner (MAC coord.)
L. Johnson
Center
Boston University
A. Strahler
York University
J. Miller
EPA (University of Idaho)
University of California, Davis
Data Management
NASA-Ames Research Center: PLDS
G. Angelici
J. Skiles
L. Popovici
S. Ustin
JPL
S. Durden
Oregon State University
R. McCreight
Oregon State University
S. Stafford
EcosystemModeling
University of Montana
R. Running
J. Glassy
FIG. 2. Organizationalstructureand areas of responsibilityforOTTER. PI = principalinvestigator;NASA = National
Aeronauticsand Space Administration;UCD = Universityof Californiaat Davis; EPA = U.S. EnvironmentalProtection
Agency;JPL = JetPropulsion Laboratory(Pasadena, California);PLDS = pilot land data system.
agers,to plan and thencoordinatethe oftenconflicting
and compromisingproblems and operational issues
duringthe campaigns,and to facilitatethe deliveryof
the data acquired. For example, sensor malfunctions,
poor weatherconditionsforclear skyviewing,absence
of key groundand aircraftpersonnel,and so forthalteredschedules.In addition,when an occasional flight
opportunitywas missed due to one or more of these
problems,we were able to reschedulesuch flightsfor
futureand comparable time periods. In this way, we
managedto acquire virtuallyall ofthedata we planned
to acquire. Ultralightaircraftwereunavailable earlyin
the project,but became available duringthe firstyear.
This aircraftprovided importantdata in its own right
as well as redundantor backup data to fillin important
holes in data acquisition.
Fig. 2 provides a simplifieddiagram of the organizational structureand responsibilitiesin OTTER. The
four main areas of responsibilitywere: (1) Ground
measurements,(2) Remote-sensingmeasurements,(3)
Ecosystem modeling,and (4) Data management.All
decisions regardingground-based measurements in
Oregon were ultimatelythe responsibilityof the coR. H. Waringand his colleague,
principalinvestigator,
D. Myrold. The otherco-principalinvestigator,D. L.
Peterson, coordinated all exchanges between NASA
Centers and other groups interestedin providingre-
mote-sensingcapabilitiesto theproject.M. Spannerat
Ames Research Center coordinated the operation of
all aircraftinvolved in the project.S. W. Runningand
his colleagues at the Universityof Montana directed
the ecosystemmodelingeffortand interactedwith all
membersof the researchteam. G. L. Angelici of the
Ames PLDS staffheld responsibilityforcoordinating
data managementand forthelinksto theOregonState
Universitysystems,while J. W. Skiles provided the
day-to-dayinteractionsand responses with all members of the science team. A number of the OTTER
remote-sensingscientistsbroughta theoreticalunderstandingto the project,led by S. Goward at the Universityof Maryland,L. Johnsonof Ames, J. Miller of
York University,and A. StrahlerofBostonUniversity.
aircraftwas largelymade
The inclusionoftheultralight
possible by the effortsof R. McCreight. Many other
and planes
broughttheirown instruments
investigators
to participatein the OTTER project. With these contributions,the projectgained nearlythe fullmeasure
capabilitiesthatcould be applied to
of remote-sensing
researchat this time.
terrestrial
CONCLUSION
The OTTER project took advantage of three opportunities.The OTTER project was a natural next
stepand extensionof previousresearchin Oregonand
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May 1994
OVERVIEW OF OTTER PROJECT
elsewhere.The earlierdevelopmentofthegeneralecosystemmodel, FOREST-BGC, was an essential prerequisiteforthe project. The model included the key
drivingvariables that we believed could be retrieved
fromremote-sensingdata and would allow comparisons to be made across a wide range of coniferous
ecosystemsfromdifferent
climate regimesand to be
treatedin various ways. Thus, theexistenceof and the
need forvalidation ofpredictiveecosystemmodels for
eventual extrapolation to larger geographic regions
providedthe firstopportunityand motivatedthe project.
A second opportunityhad to do withthe historyof
researchin Oregon. Much of the understandingof the
ofclimateon physiologicalresponsesofthevegeffects
etationhad been developed in thepast in Oregon.And
much of the destructivesampling needed to develop
theallometricequationspredicting
foreststructure
from
moreeasilymeasuredpropertieswerealreadyavailable
in Oregon fora wide varietyof species, avoiding the
necessityof doing such work beforehand.The LTER
data systemat Oregon State Universitymaintained
most of thispast researchin theirdata bases forready
access.
In additionto theearlierecologicalresearch,a number of keyremote-sensing
studieshad been conducted
in Oregon using similartransects.Thus, a base of understandingand relationshipshad alreadybeen established on which to build the project and that would
permita realisticappraisal of expected results.This
was part of NASA's decision to supportOTTER and
to designatetheprojectas a Multi-sensorAircraft
Campaign (MAC). The completenessof the measurement
strategyattractedother scientists,particularlysome
fromtheremote-sensing
sciencecommunity,a keyfactor in awardingMACs. This thirdopportunity,combined withthe close geographicproximityof the transectto Ames' aircraftbase, was criticalforsuccessfully
conductinga projectthatdemanded such a largecommitmentof aircrafttime and duration.
Finally,we notethatno attemptwas made to achieve
permanentgrowthin staffat any institutionduringthe
course of the project. OTTER is a good example of
collaborativescience by geographicallyseparatedparties. Clear and sharedobjectives,and clear policies for
bringingin new participantsto avoid duplication,to
maintainintellectualpropriety,and to assure cooperation were importantto the project'ssuccess. A spirit
of tolerance and cooperativeness characterized the
project.No singleinstitutionhas or probablywill have
all of the scientificexpertiserequiredforsuch research
so an open and shared leadershipstyleis necefforts,
essaryto encourageparticipationby othergroups. In
additionto theroles of the universityparticipants,scientistsfromthreeotherNASA Centers(JetPropulsion
Laboratory,Goddard Space FlightCenter,and Marshall Space Flight Center)joined OTTER. And staff
fromthe U.S. EnvironmentalProtectionAgencyand
223
the USDA Forest Science Laboratoryin Oregon also
were active in theproject.Perhapsthe temporaryconfederationmodel used by the OTTER projectoffersa
flexiblealternativeto assembling a large temporary
stafffora singleprojector forassigningresidentstaff
to criticaltasksthattheyhave notpreviouslymastered.
While this puts a heavier burden on communication,
it improvesinteractionamong scientistsand allows for
buildinggood team strengthand cooperation.
The articles published in this special featureare
among the firstreportsoftheOTTER project.Another
set appears in Remote Sensing of Environment47(2)
along with papers froma separate NASA-sponsored
MAC called the Forest EcosystemDynamics Project.
We also anticipatemanyothersinglepapersin thenear
future,particularlythose dealingwiththe nutrient-cyclingaspectsof OTTER. We hope thattheseand other
studies mergingthe talentsof ecologistswiththose of
the remote-sensingcommunitywill prepare the way
for a betterunderstandingof how the biosphere operates at regionaland global scales.
AcKNowLEDGxMENTS
Whilethisandotherpapersinthisissuegivecredittomany
oftheprincipal
in theOTTER project,
participants
a hostof
otherpeoplemade significant
contributions
to theproject.
Thislistis longindeedandshouldincludetheefforts
ofmany
peoplemakingfieldmeasurements
in supportofspecific
researchtasks,theefforts
oftheNASA Aircraft
Programand
otheraircraft
whoassuredthatthedata missions
personnel
werea success,and thepersonnel
whoassuredthatthedata
weremanagedwithcertainty.
Helpfulcommentswerereceivedon theearlierdraftsof thismanuscript
by TonyJanetos,BeverlyLaw, Rich McCreight,
BarbaraYoder,John
Runyon,and JeannePanek.This workwas supportedby
NASA grantsNAGW-1717to OregonStateUniversity
and
manyrelatedgrantsto otherparticipants.
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