Editor: Richard DoyleJet Propulsion Labdoyle@jpl.nasa.gov

A I i n S p a c e

episode might have passed unnoticed or have come tolight only days or weeks after the event. Now, thanks to the Earth-observing sensorweb developed by the JetPropulsion Laboratory and Goddard Space Flight Center,volcanologists around the world will have key sciencedata about eruptions within hours.

The needAlthough the South Sandwich Islands are uninhabited,

NASA’s Terra and Aqua satellites fly overhead four timesper day, skimming past at 7.5 kilometers per second andan altitude of 705 kilometers. Each spacecraft carries aModerate Resolution Imaging Spectroradiometer (MODIS)instrument, which acquires resolution data of 250 to 1,000

meters/pixel about the islands as part of a 2,700-kilome-ter-wide swath of imagery.

Streamed to Goddard Space Flight Center (GSFC), thesedata are processed at the Distributed Active Archive Center(DAAC) where MODVOLC (MODIS VOLCano Thermal AlertSystem) algorithms developed at the University of Hawaii(http://modis.higp.hawaii.edu) automatically detect the vol-canic activity’s hot-spot signature within hours of data acqui-sition. Software monitoring the MODVOLC Web site matchesthis new alert with a previously specified science team inter-est in volcanoes in this region, generating an observationrequest to the Earth Observing One (EO-1) ground system.

Based on the request’s priority, the ground systemuplinks the observation request to the EO-1 spacecraft.Onboard AI software evaluates the request, orients thespacecraft, and operates the science instruments to acquirehigh-resolution (up to 10 m/pixel) images with hyperspec-tral (220 or more bands) data for science analysis. Onboard,EO-1 processes this data to extract the volcanic eruption’ssignature, downlinking this vital information within hours.

A wide range of operational satellites and space platformsmake their data freely available, via either broadcast or theInternet, usually within from tens of minutes to severalhours from acquisition. For example, data from the MODIS

flying on the Terra and Aqua spacecraft are available viadirect broadcast in near-real-time for regional coverage andfrom 3 to 6 hours from acquisition from Goddard’s DAACfor global coverage. These data provide regional or globalcoverage with a wide range of sensing capabilities: MODIS

covers the globe roughly four times daily (two day and twonight overflights), while NASA’s Quick Scatterometer(QuickSCAT) covers the majority of the globe daily.

Unfortunately, these global-coverage instruments don’tprovide the high-resolution data many science applicationsrequire. Their resolution ranges from 250 m to 1 km for theMODIS instruments to 1 km and above for the other instru-ments. Ideally, high-resolution data would be availablecontinuously with global coverage. High-resolution assetstypically can image only limited swathes of the Earth,making them highly constrained, high-demand assets.

In our sensorweb application, sensors, science eventrecognizers, and trackers are networked with an automated

Near Antarctica, in a remote area of the South At-

lantic Ocean, a volcano rumbles. Following a few

minor tremors, fresh lava suddenly breaks to the surface,

flowing out of an existing vent. In years past, such an

An Autonomous Earth-Observing Sensorweb Steve Chien, Benjamin Cichy, Ashley Davies, Daniel Tran, Gregg Rabideau, Rebecca Castaño,and Rob Sherwood, Jet Propulsion Laboratory, California Institute of TechnologyDan Mandl, Stuart Frye, Seth Shulman, Jeremy Jones, and Sandy Grosvenor, Goddard Space Flight Center

16 1541-1672/05/$20.00 © 2005 IEEE IEEE INTELLIGENT SYSTEMSPublished by the IEEE Computer Society

The Autonomous Sciencecraft Experiment on NASA’s Earth Ob-serving One mission has taken a significant and prominent stepforward in validating onboard autonomous systems capability. ASEdemonstrates onboard autonomy for both science and engineeringfunctions of the mission by enabling autonomous science eventdetection and response. For the first time, the study of transientand dynamic scientific phenomena is made available, long assumedto be beyond the reach of spacecraft-based science investigations,particularly those at deep-space destinations. After operating forseveral months, ASE has passed that ultimate validation criterion:being promoted from a technology experiment to a baseline capa-bility for the ongoing EO-1 mission.

The work described here reports on the application of ASE capa-bility across a fleet of Earth-observing space platforms, further dem-onstrating the value and potential of this emerging autonomy-based science investigation paradigm.

—Richard Doyle

Editor’s Perspective

response system to form a sensorweb. Ourapproach uses low-resolution, high-cover-age sensors to trigger observations by high-resolution instruments (see figure 1). Seethe “Related Work in Sensorweb Research”sidebar for a discussion of other researchactivities in this realm.

Sensorweb scenarioAs figure 2 shows, components in the EO-1

sensorweb architecture operate as follows:

1. A first asset Asset1 (such as Modis) ac-quires data (usually global coverageat low resolution).

2. Data from Asset1 is downlinked andsent to a processing center where it’sautomatically processed to detect sci-ence events.

3. Science event detections go to a retask-ing system (labeled “retasking” in thefigure), which generates an observationrequest that’s forwarded to an automatedplanning system. This automated plan-ning system then generates a command

sequence to acquire the new observation.4. This new command sequence is uplinked

to Asset2 (for example, EO-1), whichthen acquires the high-resolution data.

5. Asset2 then downlinks the new sci-ence data. On the ground this data isprocessed and forwarded to the inter-ested science team.

To date, Asset2 has been EO-1, the firstsatellite in NASA’S New Millennium Pro-gram Earth Observing series. EO-1’S primaryfocus is to develop and test a set of advanced-technology land-imaging instruments.

EO-1 launched from Vandenberg AirForce Base on 21 November 2000. Its orbitallows for 16-day repeat tracks, with atleast five overflights per cycle, with achange in viewing angle less than 10°.Because EO-1 is in a near-polar orbit, itcan view polar targets more frequently.

EO-1 has two principal science instru-ments, the Advanced Land Imager and theHyperion hyperspectral instrument. ALI is amultispectral imager with 10-m/pixel pan-

MAY/JUNE 2005 www.computer.org/intelligent 17

Considerable effort has been devoted to closed-loop science forrovers at NASA’s Ames Research Center, JPL, and Carnegie Mellon.1–3

These efforts have some similarity in that they have science, execu-tion, and, in some cases, mission-planning elements. However,because surface operations such as rovers are very different fromorbital operations, they focus on integration with rover path plan-ning and localization and reliable traverse, whereas our efforts focuson reliable registration of remotely sensed data, interaction withorbital mechanics, and multiple platforms. The Multi-Rover Inte-grated Science Understanding System also describes a closed-loopmultirover autonomous science architecture.4

One closely related effort led by Keith Golden at NASA Amesseeks to enable real-time processing of Earth science data suchas weather data.5 However, this work focuses on the problem’sinformation-gathering and data-processing aspect and thus iscomplementary to our sensorweb work, which focuses on op-erations. Indeed, we’ve discussed with Golden the possibilityof a joint sensorweb information-gathering demonstration.

The Autonomous Sciencecraft Experiment on EO-1 demon-strates an integrated autonomous mission using onboard scienceanalysis, replanning, and robust execution.6 The ASE selects andautonomously retargets intelligent science data. ASE representsa single-spacecraft, onboard, autonomous capability. In contrast,the sensorweb uses multiple assets in concert and uses the ASEonboard capability to leverage ground-coordinated requests.

The Remote Agent eXperiment was the first flight of AI softwareto control a spacecraft.7 RAX represented a major advance for space-craft autonomy and operated the Deep Space One mission for sev-

eral days in 1999. RAX operated DS1 during cruise and thereforeperformed primarily engineering operations. ASE has flown over aperiod of over 18 months and has been the primary science andengineering operations system for EO-1 since November 2003 and isexpected to continue as such until the end of the EO-1 mission.

References

1. V.C. Gulick et al., “Autonomous Image Analysis during the 1999Marsrokhod Rover Field Test,” J. Geophysical Research, vol. 106,no. E4, 2001, pp. 7745–7764.

2. R. Castano et al., “Increased Mission Science Return through In-SituData Prioritization,” Proc. IEEE Aerospace Conf., IEEE Press, 2003.

3. T. Smith, “Science Autonomy in the Atacama,” Proc. Int’l Conf. Ma-chine Learning Workshop on Machine Learning for AutonomousSpace Applications, 2003; www.lunabots.com/icml2003/docs/Smith_icml_wkshp_2003.pdf.

4. T. Estlin et al., “Coordinating Multiple Rovers with InterdependentScience Objectives,” Proc. 4th Int’l Joint Conf. Autonomous Agentsand Multiagent Systems, 2005, pp. 495–500.

5. K. Golden et al., “Automating the Processing of Earth ObservationData,” Proc. Int’l Symp. AI, Robotics, and Automation in Space, 2003.

6. S. Chien et al., ‘The EO-1 Autonomous Science Agent,” Proc.Autonomous Agents and Multiagent Systems Conf. (AAMAS2004), 2004, pp. 420–427.

7. N. Muscettola et al., “Remote Agent: To Boldly Go Where No AISystem Has Gone Before,” Artificial Intelligence, vol. 103, nos. 1–2,Aug. 1998, pp. 5–48.

Related Work in Sensorweb Research

Figure 1. Sensorweb applications involvea networked set of instruments in whichinformation from one or more sensorsautomatically serves to reconfigure theremainder of the sensors.

18 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

band resolution and nine spectral bandsfrom 0.433 to 2.35 µm with 30-m/pixel res-olution. ALI images a 37-km-wide swath.Hyperion is a high-resolution imager thatcan resolve 220 spectral bands (from 0.4 to2.5 µm) with a 30-m/pixel spatial resolu-tion. The instrument images a 7.5 by 42 kmland area per image and provides detailedspectral mapping across all 220 channelswith high radiometric accuracy.

EO-1 sensorweb architectureAs figure 3 illustrates, components in the

sensorweb’S automated retasking elementwork together as follows.

Science-tracking systems for each sci-ence discipline automatically acquire andprocess satellite and ground network data totrack science phenomena of interest. Thesescience tracking systems publish their dataautomatically to the Internet, each in their

own format—in some cases through theHTTP or FTP protocol, in others via emailsubscription and alert protocols.

Science agents either poll these sites(HTTP or FTP) to pull science data or sim-ply receive emails to be notified of ongoingscience events. These science agents thenproduce science event notifications in astandard XML format, which sensorwebslog into a science event database.

The science event manager (SEM)processes these science event notificationsand matches them with science campaigns,generating an observation request when amatch occurs. The ASPEN automated mission-planning system processes these requests,integrating them with already scheduledobservations according to priorities and mis-sion constraints. If a new request can fitwithin the existing schedule without remov-ing a higher priority observation, an obser-vation request is uplinked to the spacecraft.For an observation to fit within the schedule,the spacecraft must be able to acquire theobservation without violating spacecraftconstraints such as having adequate powerfor the observation, having a time of theobservation that does not conflict with ahigher-priority observation, or that there isadequate storage for the data onboard.

Onboard EO-1, the Autonomous Science-craft software will accommodate the obser-vation request if feasible.1 In some cases,onboard software might have additionalknowledge of spacecraft resources or mighthave triggered additional observations, mak-ing some uplinked requests infeasible. Later,the spacecraft downlinks the science data toground stations where it is processed anddelivered to the requesting scientist.

Event tracking and observation request generation

The science agents encapsulate sensor-and science-tracking-specific information by producing a generic XML alert for eachscience event tracked. The flexibility enabledby these modules lets users easily integratewith numerous science tracking systemseven though each one has its own uniquedata and reporting format. These formatsrange from near-raw instrument data toalerts in text format, to periodic updates to a wide range of text formats. The postingmethods have included HTTP, HTTPS, FTP,and email. Table 1 lists the science-trackingsystems integrated into our system.

The science event manager lets scientists

TERRA/aquaMODIS low-data

(250 m to 1 km/pixel)

In-situassets

Eventdetection

No humanin the loop

Retasking

EO-1 Hyperion:Obtain high-resolution

data of event (10 m/pixel)

Rapid downlink ofrelevant data

Figure 2. Sensorweb event detection and response architecture.

Observation requests

Updates to Onboard plan

ASPENSchedule observations on EO-1

Sciencecampaigns

Science alerts

Science event managerProcess alerts and

prioritize response observations

AGENTmonitor sensors

Scene acquired

EO-1 Flight dynamicsTracks orbit, overflights,momentum management

Figure 3. Sensorweb response, showing how automated retasking elements worktogether.

specify mappings from science events to ob-servation requests, track recency and eventcounts, and perform logical processing—forexample, triggering an observation if twoMODVOLC alerts and a GOESVOLC alert occur in a 24-hour period. The SEM also permitstracking based on target names or locationsand other event-specific parameters.

As an example, because the Kilaueavolcano is often quite active, a volcanolo-gist there might specify that several track-ing systems would need to report activitywith high confidence before an observa-tion is requested. On the other hand, evena single low-confidence activity notifica-tion might trigger observation of Piton dela Fournaise or other less active sites.

Event response: Automated observation planning

To automate mission planning, we use the ASPEN/CASPER planning and schedulingsystem (ASPEN is the ground-based batchplanner and CASPER is the embedded, flight-based planner; both share the same coreplanning engine).2 ASPEN represents mis-sion constraints in a declarative format andsearches possible mission plans for a planthat satisfies many observation requests(respecting priorities) and also obeys missionoperations constraints. ASPEN has served in a wide range of space mission applications,including spacecraft operations scheduling,rover planning, and ground communicationsstation automation.

ASPEN: Local, committed search for planning

Search in ASPEN has focused on high-speed local search in a committed planspace, using a stochastic combination of aportfolio of heuristics for iterative repair and improvement algorithms.3–5 In this ap-proach, at each choice point in the iterativerepair process, a stochastic choice is madeby ASPEN among a portfolio of heuristics(with probabilities the user can specify).6

This approach has performed well in a widerange of applications.2 The stochastic ele-ment combined with a portfolio of heuristicshelps to avoid the typical pitfalls of localsearch. Using a committed plan representa-tion enables fast search moves and propaga-tion of effects (100 s of operations per CPUsecond on a workstation). To increase effi-ciency, we also use aggregates of activities.7

We’ve focused on an early-commitment,local, heuristic, iterative search approach to

planning, scheduling, and optimization.This approach has several desirable proper-ties for spacecraft operations planning.

First, using an iterative algorithm lets ususe automated planning at any time and onany given initial plan. The initial plan mightbe as incomplete as a set of goals, or it mightbe a previously produced plan with only afew flaws. Repairing and optimizing anexisting plan enables fast replanning whennecessary from manual plan modificationsor from unexpected differences detectedduring execution. Local search planningthus can have an anytime property, in whichit always has a “current best” solution andimproves it as time and other resourcesallow. Refinement search methods don’thave this property.8 Local search can alsoeasily adapt for use in a “mixed initiative”mode for partial ground-based automation.

Also, it’s easier to write powerful heuris-tics that evaluate ground plans. Thesestrong heuristics let us prune the search,ruling out less promising planning choices.

Third, a local algorithm doesn’t incur theoverhead of maintaining intermediate plansor past attempts. This feature lets the plannerquickly try many plan modifications for re-pairing conflicts or improving preferences.However, unlike systematic search algori-thms, we cannot guarantee that our iterativealgorithms will explore all possible combi-nations of plan modifications or that it willnot retry unhelpful modifications. In our ex-perience, these guarantees are not valuablebecause for large-scale problems completesearch is intractable.

Finally, by committing to values for para-meters, such as activity start times and re-

source usages, ASPEN can efficiently computeeffects of a resource usage and the corres-ponding resource profiles. Least-commit-ment techniques retain plan flexibility butcan be computationally expensive for largeapplications.9

The sidebar “Unique Challenges of EO-1Sensorweb Mission Planning” discussessome of the challenges we faced in adapt-ing ASPEN for the EO-1 sensorweb.

Science data accessA sensorweb project goal is to provide

scientists easy access to multiple datasources on a single science event, such as avolcanic eruption or a forest fire (see the“Sensorweb Examples” sidebar). This dataaccess portal for the sensorweb project isstill under construction.

Another goal of the sensorweb effortwas to enable easy tracking of spacecraftoperations. This tracking would let scien-tists understand the images the spacecrafthad acquired and view where science prod-ucts are in request, acquisition, downlink,and processing phases. To accomplish thisgoal, we have an operational Web site forscience team access. We will shortly makethis site publicly available.

Ongoing extensions and deep space applications

Terrestrial dust storms are of signifi-cant science interest and can be detectedusing several sensors, including GOES,AVHRR, and MODIS.10 Growing to be aslarge as hundreds of kilometers long,these storms are important because of theamount of dust they can transport and their

MAY/JUNE 2005 www.computer.org/intelligent 19

Table 1. Science alert systems.

Discipline Source Detector

Volcanoes MODIS (Terra, Aqua) MODVOLC, Univ. of HawaiiGOES GOESVolcPOES AVHRR—VolcanoAir Force Weather Advisory Volcanic ash alertsInternational FAA Volcanic ash advisoriesTunguratua, Eventador In situ instruments, Harvard, UNHHawaiian Volcano Observatory Sensor alertsRabaul Volcano Observatory

Floods QuikSCAT Dartmouth Flood ObservatoryMODIS Dartmouth Flood ObservatoryAMSR Dartmouth Flood Observatory

Cryosphere QuikSCAT Snow/ice, JPLWisconsin Lake Buoys UW Dept. Limnology

Forest fires MODIS (Terra, Aqua) Rapidfire, UMD MODIS, Rapid Response

Dust storms MODIS (Terra, Aqua) Naval Research Laboratory, Monterey

impact on aviation. A dust storm sensorwebwould use low-resolution assets to tracklarge-scale dust storms and autonomouslydirect high-resolution assets such as EO-1to acquire more detailed data. Such datawould improve scientific understanding ofdust initiation and transport phenomena.

Figure 4 shows a large dust storm in thePersian Gulf as imaged by MODIS in Novem-ber 2003. Ground-based instrumentation—such as operated by the US Department ofAgriculture in the American Southwest andthe People’S Republic of China’S network ofsites in the Gobi Desert—can also serve todetect these storms. Detection and trackingof dust storms is also of considerable inter-est on Mars where such storms can grow tocover the entire planet.

The sensorweb concept also appliesdirectly to deep-space science applications,Sun-Earth connection science, and astro-

physics applications.11 On Mars, for exam-ple, surface instruments could detect ortrack active, transient atmospheric, and geo-logic processes such as dust storms. Alreadyin place on Mars is a wide range of comple-mentary assets. The Mars Global Surveyor(MGS) spacecraft is flying the Thermal Em-ission Spectrometer, which can observe duststorms at a global scale. We could use thisinstrument to detect dust storm initiationevents, calling in higher-resolution imagingdevices including THEMIS on Mars Odyssey,the MGS-MOC camera on MGS, and theHiRise camera on Mars Reconaissance Or-biter (scheduled to arrive in 2006).

Additionally, we could integrate surfaceassets such as the Mars Exploration Rovers(currently deployed) and the Phoenix Lan-der (2007) into a Mars sensor network. Inthe future, we expect even more assets to beon Mars, enabling even more integratedobservation campaigns. An integrated net-work of Mars instruments is particularlycritical to support extended Mars surfacemissions (particularly manned missions) as

envisioned by the new NASA explorationinitiative.

The automated sensorweb concept hasbroad application beyond planetary science.For example, in space weather, sun-pointedinstruments could detect coronal mass ejec-tions and alert Earth-orbiting magnetos-pheric instruments to reconfigure to maxi-mize science data. This solar activity wouldalso have ramifications on manned explo-ration on the Moon or Mars. Such an auto-mated tracking system would be critical toensuring the safety of such missions.

AcknowledgmentsPortions of this work were performed at the

Jet Propulsion Laboratory, California Institute ofTechnology, under a contract with the NationalAeronautics and Space Administration. Weacknowledge the important contributions ofNghia Tang and Michael Burl of JPL, ThomasBrakke, Lawrence Ong, and Stephen Ungar ofGSFC, Jerry Hengemihle and Bruce Trout ofMicrotel LLC, Jeff D’Agostino of the Hammers

20 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

The EO-1 sensorweb application presented a number ofinteresting challenges for automated planning, includingprioritization, file system modeling, momentum manage-ment and maneuvers, and coordination of planners.

PrioritiesIn prioritization, the sensorweb application requires that

the mission-planning element reason about relative priori-ties on observations as well as how their supporting activi-ties relate to the goal observation priority. Within the EO-1mission operations, we developed a strictly ordered set ofpriorities. In this scheme, each observation is assigned avalue from 1 to 1,000 (with lower values denoting higherpriority). Different user types can submit observation re-quests within an allotted range of priorities, with many of the users’ ranges overlapping: a high-priority observa-tion from user 1 might preempt a low-priority observationfrom user 2, but not a high priority observation from user2. ASPEN respects these priorities by the nature of the en-coded search heuristics.

These search strategies first prefer plan repair operatorsthat don’t delete observations. However, if forced to deleteobservations, these heuristics prefer deleting lower-priorityobservations. In this scheme, priority levels are strictly dom-inating. For example, one observation of priority 500 willbe preferred to two observations of priority 700.

File systemFor file system modeling, one degree of scheduling flexi-

bility involves separating science data processing fromdata acquisition. After a scene is acquired onboard, sen-sorweb can analyze it to detect science events (this appliesonly for certain types of science images: volcanoes, floods,and cryosphere). Sensorweb can then rapidly downlinkthis event summary to give scientists a snapshot of theactivity; the complete image will take longer to downlinkand process.

For example, if an image A and an image B are X minutesapart, there isn’t enough time to process the science datafrom image A before the imaging of B. Playing back theimage from the solid-state recorder (where it’s streamedduring data acquisition) into RAM to analyze it requires useof the SSR; acquiring image B also requires use of the SSR.However, image A must be analyzed prior to downlink andfile deletion on the SSR.

Representing this properly within ASPEN requires the abilityto model a file system, which we’ve demonstrated in ASPEN inthe Generalized Timelines module. This capability isn’t partof the core ASPEN that has been used in many applications. Toreduce risk, we require that images be analyzed before thenext image is acquired and represent this as a simple protec-tion in planning terminology. This representation decisionslightly reduces the efficiency of ASPEN-generated plans.

Unique Challenges of EO-1 Sensorweb Mission Planning

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Corp., Robert Bote of Honeywell Corp., Jim VanGaasbeck and Darrell Boyer of ICS, MichaelGriffin and Hsiao-Hua Burke of MIT LincolnLabs, Ronald Greeley and Thomas Doggett ofArizona State University, and Victor Baker andJames Dohm of the University of Arizona.

References

1. S. Chien et al., “The EO-1 Autonomous Sci-ence Agent,” Proc. Autonomous Agents andMultiagent Systems Conf. (AAMAS 2004),ACM Press, 2004, pp. 420–427.

2. S. Chien et al., “Aspen: Automating SpaceMission Operations Using Automated Plan-ning and Scheduling,” Proc. 6th Int’l Conf.Space Operations, 2000; http://ai.jpl.nasa.gov.

3. Huberman et al., “An Economics Approachto Hard Computational Problems,” Science,vol. 275, 1997, pp. 51–54.

4. C. Gomes and B. Selmon “Algorithm Portfo-lios,” Artificial Intelligence, vol. 126, 2001,pp. 43–62.

Momentum management and maneuversManeuver and momentum management presents particu-

lar challenges to EO-1 operations. EO-1 uses reaction wheelsto point the spacecraft in imaging targets and downlinkingdata. Because EO-1 has only three reaction wheels, it must usemagnetic torquers and the Earth’S magnetic field to dumpmomentum from the reaction wheels. Without this process,the reaction wheels might either be unable to achieve thedesired attitude (because they’re already spinning as fast asthey can, called momentum saturation) or a wheel mightchange spin direction (zero crossing) during an image, whichcauses jitter in the spacecraft and ruins the image.

However, desaturating the wheels (zero biasing) is time-consuming, so often there isn’t enough time between imagesto perform this step. ASPEN attempts to acquire all imageswith zero biasing before and after each image. Still, if zerobiasing for an image will not fit, ASPEN will still acquire theimage: acquiring an image with only a small chance of beingruined is better than not acquiring the image at all. ASPEN

implements this strategy in its search heuristics. If it has diffi-culty fitting an image into the schedule, it will first considerremoving the momentum management activities for the low-est-priority image participating in the conflict. If it can still fitthe image without these activities, it will retain the image.

This approach leads to another complication: the associationof myriad activities the image requires. If after searching, ASPEN

determines that an observation will not fit, it must remove allthe associated activities. It does so by annotating them with

the scene ID of the image requiring their presence in the planand cleaning up the plan appropriately when the image isremoved. As another complication, parameters of the momen-tum management activities depend on how ASPEN handled theimmediately preceding scene, as that scene determines the reac-tion wheels’ initial momentum. ASPEN handles this eventualitywith an explicit dependency between the momentum manage-ment activities for an observation and the momentum state atthe end of the prior observation. Again, this could be directlymodeled in the Generalized Timelines module, but for expedi-ence we modeled it with a state and parametric dependency.

Coordinating plannersAnother challenge of sensorweb planning is the coordination

of the onboard and ground planners. In the sensorweb, theonboard planner might have changed the plan since uplinkbecause of execution variances (such as additional images beingscheduled). To correctly handle this eventuality, the groundplanner guesses, on the basis of the previously uploaded plan,whether a scene will be possible. If it is, the goal requesting theobservation uploads. The onboard planner then receives thisrequest and might add the observation (and any necessary sup-porting activities) to the onboard plan, deleting scenes asrequired (consistent with scene priorities). Because this replan-ning might require considerable search and hence onboardcomputing time—and the maneuvers commence 45 minutesprior to a scene—the ground ASPEN only uplinks observationrequests that occur at least two hours after the uplink window.

Figure 4. Dust storm in the Persian Gulf as captured by MODIS in November 2003.

22 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

Sensorweb has been used in such appli-cations as wildfire and flood control, vol-canology, and cryosphere monitoring.

The wildfire sensorweb We have demonstrated the sensor-

web concept using the MODIS active FireMapping System.1 Both the Terra andAqua spacecraft carry the MODIS instru-ment, providing morning, afternoon,and two night overflights of each loca-tion on the globe per day (coveragenear the poles is even more frequent).The active fire mapping system usesdata from the GSFC Distributed ActiveArchive Center (DAAC), specifically thedata with the predicted orbital ephem-eris, which is approximately three to sixhours from acquisition.

The active fire mapping algorithmdetects hot spots using MODIS thermalbands with absolute thresholds:

T4 > 360K, 330K (night) orT4 > 330K, 315K (night) and T4 – T11 > 25K, 10K (night)

where T4 is the fourth band of the dataand t11 is the eleventh band. This algo-rithm also uses a relative-threshold al-gorithm that requires six nearby cloud-,smoke-, water-, and fire-free pixels upto 21 � 21 square. This triggers if thethermal reading is three standard devi-ations above the surrounding area.

T4 > mean(T4) + 3 std dev (T4)and T4 – T11 > median (T4 – T11) + 3std dev (T4 – T11)

Figure A shows the active fire mapfrom October 2003 fires in Southern Cali-fornia. Figure B shows a context map ofSouthern California with the triggeredsensorweb observation taken below it.

The flood sensorwebThe flood sensorweb uses the Dart-

mouth Flood Observatory Global ActiveFlood Archive to identify floods in re-mote locations automatically, based on satellite data. The DFOflood archive generates flood alerts based on MODIS, QuikSCAT,and AMSR-E satellite data.2 The DFO produces the DFO archive

in collaboration with JPL. The flood sensorweb uses the DFOQuikSCAT atlas because it’s not affected by cloud cover overflooded areas.

Figure A. Active fire alerts for the October 2003 Southern California fires. Red indicates activefires. The light blue box illustrates the background region used in the relative threshold detection.

Figure B. Sensorweb trigger images for the October 2003 Southern California fires. Above isthe MODIS Active Fire Map display. Below is the EO-1 Hyperion image acquired via sensorwebtrigger of the Simi/Val Verde fire area used in Burned Area Emergency Reclamation.

Sensorweb Examples

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The DFO produces the DFO archivein collaboration with the JPL QuikSCATteam. In this process, the QuikSCATscatterometer data help us assess sur-face water conditions.3,4 Specifically,the VV/HH ratio serves to assess surfacewater properties of the areas in 0.25latitude/longitude degree bins. Thesensorweb uses the seven-day runningmean to dampen effects of short-dura-tion rainfall over urban areas. It thencompares these data to the seasonal(90-day) average of the previous yearseason to screen out seasonal wetlands,publishing the screened alerts to a DFOWeb site. Figure C shows an example ofa global flood alert.

In the flood sensorweb, indications of active flooding alertstrigger EO-1 observations at sites called gauging reaches. Theseare river locations whose topography is well understood. Wecan use flood discharge measurements at gauging reaches tomeasure the amount of water passing through a flooded re-gion, comparing them with remotely sensed data. Ultimately,the flood sensorweb increases the amount of high-resolutionremote sensing data available on flooding events in prime loca-tions of interest (gauging reaches) and times of interest, such aswhen active flooding occurs. Figure D shows imagery from anAugust 2003 flood sensorweb demonstration capturing flood-ing in India’s Brahmaputra River.

The volcano sensorweb In the volcano sensorweb, MODIS,

GOES, and AVHRR sensor platforms oper-ate to detect volcanic activity. Thesealerts then trigger EO-1 observations.The EO-1 Hyperion instrument is idealfor studying volcanic processes becauseof its great sensitivity range in the in-frared spectrum.

The GOES and AVHRR alert systemsprovide excellent temporal resolutionand rapid triggering based on thermalalerts.5 The GOES-based system looks forlocations that are hot, have high con-trast from the surrounding area, and arenot visibly bright. Additionally, the sys-tem screens hits for motion (to eliminatecloud reflections) and persistence (toremove instrument noise). The GOESalert can provide a Web or email alertwithin one hour of data acquisition.

The MODIS alert system offers highinstrument sensitivity but lower tempo-ral resolution (MODIS generally has atleast four overflights per day). MODVOLC

derives the normalized thermal index

(NTI) from MODIS raw radiance values by computing (R22 –R32)/(R22 + R32), where Ri indicates use of the radiance valuefrom MODIS band i. The system compares the NTI to a thresholdto indicate alerts, generally making it available online withinthree to six hours of acquisition. We’ve also linked into in situsensors to monitor volcanoes. We are working with a number ofteams to integrate such sensors into our sensorweb. The Hawai-ian Volcano Observatory has deployed numerous instruments inHawaii’s Kilauea region. These instruments include tiltmeters,gas sensors, and seismic instrumentation. These sensors can pro-vide indications that collectively point to a high-probabilitynear-term eruption, thereby triggering a request for high-reso-lution EO-1 imagery. The University of Hawaii has also deployedinfrared cameras to a number of volcanic sites worldwide,

Figure C. Dartmouth Flood Observatory global flood alerts for October 2003.

Figure D. Examples of low-resolution MODIS imagery (left) and high-resolution EO-1 imagery(right) from the Flood Sensorweb capturing Brahmaputra River flooding in India, August 2003.

EO-1 Hyperion Image Brahmaputra Aug 6, 2003

MODIS Image Brahmaputra Aug 6, 2003

5. M. Zweben et al., “Scheduling and Resched-uling with Iterative Repair,” Intelligent Sched-uling, Morgan Kaufmann, 1994.

6. G. Rabideau et al., “Iterative Repair Planningfor Spacecraft Operations in the Aspen Sys-tem,” Int’l Symp. AI Robotics and Automationin Space, ESA Press, June 1999.

7. R. Knight et al., “Computing Valid Intervalsfor Collections of Activities with SharedStates and Resources, Int’l Conf. AI PlanningSys., AAAI Press, 2000.

8. A. Jonsson et al., “Planning in interplanetarySpace: Theory and Practice,” Int’l Conf. AIPlanning Sys., AAAI Press, 2000, pp.177–186.

9. S. Chien, et al., “Automated Planning andScheduling for Goal-Based AutonomousSpacecraft,” IEEE Intelligent Systems, vol.13, no. 5, 1998, pp. 50–55.

10. S. Miller, “A Consolidated Technique forEnhancing Desert Dust Storms with MODIS,”Geophysical Research Letters, vol. 30, no. 20,2003, pp. 2071–2074.

11. A.G. Davies et al., ASC Science Report, JetPropulsion Lab., Aug. 2001; http://ase.jpl.nasa.gov.

24 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

including Kilauea, Hawaii; Erte Ale, Ethiopia; Sourfiere Hills,Montserrat; and Colima and Popocatepetl, Mexico.7 Theseinfrared cameras can provide a ground-based detection of lavaflows based on thermal signatures, also alerting the sensorweb.

Cryosphere sensorweb The Earth’s cryosphere consists of freezing water in the form

of snow, lake and sea Ice, and the corresponding thawing ofthese. Because it plays such a central role in creating the Earth’sclimate, a wide range of scientists are interested in studying thecryosphere. Planetary scientists also want to study cryospherephenomena on other planets in the solar system; studying theEarth’s cryosphere is an analogue for other planets’ cryospheresand vice versa.

Using the EO-1 sensorweb, we can study numerous phenom-ena, including glacial ice breakup; sea ice breakup, melting,and freezing; lake ice freezing and thawing; and snowfall andsnowmelt.

Using QuikSCAT data, we’re tracking snow and ice forma-tion and melting, automatically triggering higher-resolutionimaging such as with EO-1. In collaboration with the Univer-sity of Wisconsin–Madison’s Center for Limnology, we havealso linked into data streams from the Trout Lake stations sothat temperature data can trigger imaging of the sites to cap-ture transient freezing and thawing processes. These linkages

let us request high-priority imaging from EO-1 to study short-lived—thaw, melt, breakup, snowfall, or ice formation—cryosphere phenomena.

References1. C. Justice et al., ‘the Modis Fire Products,” Remote Sensing of Envi-

ronment, vol. 83, 2002, pp. 244–262.

2. S.V. Nghiem, Advanced Scatterometry for Geophysical RemoteSensing, JPL Document D-23048, Jet Propulsion Lab, Calif. Inst.Tech., Mar. 2001.

3. B. Brakenridge et al., “Flood Warnings, Flood Disaster Assessments,and Flood Hazard Reduction: The Roles of Orbital Remote Sens-ing,” Proc 30th Int’l Symp. Remote Sensing of Environment, 2003;http://isrse.pdc.org.

4. S.V. Nghiem, W.T. Liu, and X. Xie, “Polarization Reversal overFlooded Regions and Applications to Flood Mapping with Space-borne Scatterometers,” Proc. Int”l Geoscience and Remote SensingSymp., IEEE Computer Society Press, 1999; www.ewh.ieee.org/soc/grss/igarss.html.

5. A. Harris et al., “Web-Based Hot Spot Monitoring Using GOES: WhatIt Is and How It Works,” Advances in Environmental Monitoringand Modelling, vol. 1, no. 3, 2002, pp. 5–36.

6. R. Wright et al., “Automated Volcanic Eruption Detection UsingMODIS,” Remote Sensing of Environment, vol. 82, 2002, 135–155.

7. A. Harris et al., “Ground-Based Infrared Monitoring Provides NewTool for Remote Tracking of Volcanic Activity,” EOS, vol. 84, no. 402003; www.agu.org/pubs/eos.html.

Steve Chien is technical group supervisor ofthe AI Group at JPL, where he leads efforts inresearch and development of automated plan-ning and scheduling systems. He is the princi-pal investigator of the Earth Observing Sensor-web Project and the Autonomous SciencecraftExperiment. He’s also an adjunct associate pro-fessor in the Department of Computer Science atthe University of Southern California. Contacthim at steve.chien@jpl.nasa.gov.

Benjamin Cichy is a member of the technicalstaff in JPL’s Flight Software Development andTechnology Group, where he develops, autonomysoftware for NASA’s New Millenium Program.Contact him at benjamin.cichy@jpl.nasa.gov.

Ashley Davies is a research scientist in JPL’sEarth and Space Sciences Division and leadscientist of the Earth Observing Sensorweb.Contact him at ashley.davies@jpl.nasa.gov.

Daniel Tran is a member of the technical staffin JPL’s AI Group, where he’s working ondeveloping automated planning and schedul-ing systems for onboard spacecraft command-ing. Contact him at daniel.tran@jpl.nasa.gov.

Gregg Rabideau is a member of the technicalstaff in JPL’s AI Group, where he researchesand develops planning and scheduling systemsfor automated spacecraft commanding.Con-tact him at gregg.rabideau@jpl.nasa.gov.

Rebecca Castaño supervises the MachineLearning Systems group at JPL and is alsothe team lead of the Onboard AutonomousScience Investigation System (OASIS).Contact her at rebecca.castano@jpl.nasa.gov.

Rob Sherwood is manager, Earth ScienceInformation Systems. Contact him at rob.sherwood@jpl.nasa.gov.

Dan Mandl is the mission director for theEarth Observing One Mission At GoddardSpace Flight Center. Contact him atdan.mandl@gsfc.nasa.gov.

Stuart Frye is the Lead Systems Engineer forthe Earth Observing One Mission at GoddardSpace Flight Center. Contact him at stuart.frye@gsfc.nasa.gov.

Seth Shulman is the Operations Lead for theEarth Observing One Mission at GoddardSpace Flight Center. Contact him at seth.e.shulman.1@gsfc.nasa.gov.

Jeremy Jones was a software engineer atGoddard Space Flight Center.

Sandy Grosvenor is a software engineer inthe Advanced Architectures and AutomationBranch of Goddard Space Flight Center. Con-tact her at sandy.grosvenor@gsfc.nasa.gov.