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Automated hot spot and smoke detections will beprovided by the Automated Biomass Burning Algo-rithm (ABBA) and the Fire Identification, Mappingand Monitoring Algorithm (FIMMA), developed byNESDIS' Office of Research and Applications (ORA).Smoke trajectories will be calculated by the HY-brid Single-Particle Lagrangian Integrated TrajectoryMode l (HYSPLIT), developed by NOAA • s Air Re-sources Laboratory. Satellite imagery and model out-puts will be displayable as multiple data layers in theHMS updated half hourly, and analysts will generate acomposite image and graphic product disseminated onthe Internet. The product will be geolocated and becomp atible for display by commonly used GeographicInformation Systems. Users will be able to choose geo-graphic areas displayed.

An operational demonstration is scheduled for earlySumm er , 2001, with subsequent further development,including the possible incorporation of data from theNASA's MODIS instrument and satellite derived vege-tation index and fire potential maps.•

641C-10 1105hMethods based on remote sensing data

for environmental monitoring tosupport National and RegionalProtection Agencies

Vincenzo Cuomo' (+39-0971-427208;cuorno0unibas.it)

Rosa Lasaponara 2 (+39-0971-427268;lasaponaraOimaaa.pa.crir.it )

Francesca Maria Macchiato 3 (+39-081-676459;macchiatoOna.infn.it )

Tiziana Simoniello 4 (+39-0971-427264;simonie0imaaa.pz.cne.it )

'IMAAA-CNR, C/da S. Loja, Tito Scale, PZ 85050,Italy

2 1/BEA-Un iversity of Basilicata, C/da Macchia Ro-manana, Potenza, PZ 85100, Italy

3 DSF-Univers ity of Naples, via Cintia, Naples, NA80126, Italy

4 1nfo-Com University of Rome, via Eudossaina,8,Rome, RM 00100, ItalyRemote sensing provides useful data for environ-

ments] monitoring nevertheless, efforts are required totest and evaluate methods and techniques to be appliedfor operational applications.

Since 1994, in the context of several projectsfounded by the Italian Environment Protection Agency(ANPA) and Environmental Department of BasilicataRegion, we have experienced the use of remote sens-ing for environmental monitoring in operative contexts.Particularly, we have developed and tested methodolo-gies based on the integration of remote sensed data

med at: estimations of space/tempoial dynamics of,vtuilace parameters (such as temperature and vegeta-l/la index.), forest fire detection and danger estima-tipin risk assessment, change detection, desertification,!pine is monitoring, etc. Some examples are brieflyulidnariz

e ed below.

The action C of Timoran projects was devoted tofeat fire monitoring. We devised a dynamic short-`-e fire forecasting basted on the integration of m-

a sensing and GIS. A daily fire sueceptibilihr 69-sliSent was performed, from NOAA-AVHRR exploit-the cross analysis of the temporal evolution of

MI, / and the middle-infrared channel. Four dangerawes have been obtained (low, moderate, high and

high). We also estimated the expected fire severityand integrating different danger variables,

he (1) fire susceptibility . (water stress) performedsg satellite AVHRR data, (2) fuel type, (3) incidenceidography, (4) wind forecast, obtained from meteo-giCal models. Potential and limitations of AVHRFI.re detection were evaluated in the Italian ecosys-

At present we are working on evaluating the ef-venesa of Landsat-TM imagery for mapping burnedin heterogeneous regions, characterized by differ-

ver types, rough topography and complex ecosys-the context of "Devising of environmental indi-s based on remote sensing data" project, funded

NEA. , we investigated on an AVHRR time series1085 to 2000 to perform change detectiOn analy-d to monitor desertificatiian process in the Italian

.4nda . First at all, we quantified the systematic441s due to satellite orbit drift and calibration resid-

af channels. After the accurate removal-Iii lertions, performed at the pixel level, we appliedII,IPh er of change detection analyses based on NDVIM a lized Difference VegetatiOn Index). Results andnnances of the different methods were evaluatedgierPar with ground data in selected test areas.:O, we definedned a change detection indicator basedine in MVC (Maximum Value Composite)-,1 behavior expressed as percentage variation anduted pixel by pixel. At present we are exp enenc-114 rel iably of the indicator performing further*t ics at a higher spatial (Landsat-TM, SAR, field') resolution in areas classified as more vulnera-rfth. the AV HRR-based analysis.If

B42A CC: 310 Thursday 1330hCO2 Flux Measurements from theGround Up II (joint with A, GS)Presiding: S Frolking, Univ. of New

Hampshire; P Crill, Univ. of NewHampshire

B42A-01 1330h INVITEDEstimating Uncertainty of

Ground-based Estimates of NetEcosystem Production: An ExampleUsing an Old-growth Ecosystem

Mark E. Harmon (541-753-9086;mark.harmonOorst.ed u)

Department of Forest Science, Oregon State Univer-sity 210. Richardson Hall, Corvallis, OR 97331-5752,United StatesUnderstanding the rate forest ecosystems exchange

CO 2 with the atmosphere is a critical step in manag-ing the carbon cycle. Unfortunately this exchange isdifficult to estimate and contains nurnerous uncertain-ties, the bounds of which are rarely stated. Flux-towerbased measurements provide spatial integration andhigh temporal resolution. Ground-based measurement*tre complementary in that they allow examination ofspecific mechanisms and spatial heterogeneity. Mak-ingthese estimates, however, requires the combinationmany sub ecosystem-level fluxes from disparate pro-cesses and pools each with a different uncertainty term.To circumvent this problem, Monte Carlo methods wereused to estimate the Uncertainty in estimates of poolStores, net primary production (NPP), heterotrophicrespiration (R h ), and net ecosystem production (NE?).Long-term measurements of tree growth and mortality,litterfall, short-term direct measurements of respira-tion, and long-term estimates of decomposition rateswere coupled with data on detritus and soil stores froman old-growth forest at Wind River Experimental For-est, Washington to illustrate this methodology. Therange of uncertainty of estimates is reported as themean 2 standard errors of the mean. Total carbonstores at the site were estimated to be 615 Mg C ha-1(range=58I- to 647 Mg C Total NPP, includ-ing grazing and deducting for heart rot losses was es-timated to be 5.58 Mg Cha_ I year -1 , with a range

of 5.14 to 6.02 Mg C ha -1 year -1 . Rh , including therespiration of grazers was estimated to be 5.39 Mg Cha -1 year -1 with a range of 4.63 to 6.15 Mg C ha-1year -1 Combining the NPP and Rh estimates indi-cates that over the long-term NEP at the old-growthis +0.19 Mg C ha. -1 year -1 with a range of .-0.67 to+1.05 Mg C ha -1 year -1 . This indicates the standmight be a small sink if heart rots are not too exten-sive in the stand. This estimate contrasts with the 1.9to 2.2 Mg C ha -1 year -1 sink calculated at the samesite using eddy flux tower methods. Several hypothe-ses might explain this discrepancy including: 1) un-detected biomass increases, 2) underestimates of NPPin the form of grazing, 3) undetected and large lossesvia dissolved organic carbon, 4) a temporal mismatchbetween the long-term ground-based versus short-termflux tower measurements, and 5) an underestimate ofecosystem respiration by the flux tower systemm. Thelatter two hypotheses appear to be most likely andtherefore warrant additional attention.

B42A-02 1350h INVITED

Net Ecosystem Exchange in a Tall TowerFootprint: Reconciling Observations,Modeling, and Remote Sensing

Bobby Braswel/ 1•2 (603-862-2264;rob.braswellOunh.edu )

Galina Churkina2 (churkinatObgc-jena.mpg.de )David Schimel 2 (dschimelObgc-jena.mpg.de )Kenneth Davis 3 (davisOessc.psu.edu )'Institute for the Study of Earth Oceans and Space,

Morse Hall, University of New Hampshire, Durham,NH 03824, United States

2 Max Planck Institute for Biogeochemistry, Kahlaia-che Strasse 10, Jena 07745, Germany

3 Departrnent of Meteorology, 512 Walker Building,Pennsylvania State University, University Park, PA16802, United StatesIn this study we investigate thecontrols on

monthly-to-interannual terrestrial NEP within thefootprint of the VV. LEF tall tower. This footprint islarge enough (>1 km 2 ) to be considered asa singleexample of a global ecosystem model or global remotesensing grid cell. We compare the observed NEE from1997-1999 with predictions of the Biome-BGC modeland attempt to associate discrepancies with specificprocesses, including plant phenology and soil respi-ration. The remote sensing observations are used inthis exercise to help diagnose in terannu al variability ingrowing season length and to characterize the distribu-tion of landcover within the footprint. We consider alsoinherent limitations in the data, especially the uncer-tainty associated with data gap-filling methods.

B42A-03 1410h INVITED

Scaling carbon dynamics: models forsynthesis and prediction

George C Hurtt i (603-862-1792;george.hurttOunh.edu ); Paul R Moorcroft2(609-258-6886; paulOeno.princeton.edu ); StephenW Pacala 2 (609-258-6885;stevenieno.princeton.edu ); John P Caspersen2(609-258-686; jpcOeno.princeton.edu ); ElenaShevliakova 2 (609-258-2594;elena0eno.princeton.edu ); Berrien Moore'(603-862-1792; b.rnooreOunh.edu )

l University of New Hampshire, Complex Systems Re-search Center, Durham, NH 03824, United States

2 Princeton University, Department of Ecology andEvolutionary Biology, Princeton, NJ 08544-1003,United StatesCarbon stocks and fluxes vary at a wide range of

spatial and temporal scales. The empirical challengeof making measurements across these scales is beingmet with a range of creative approaches. As more mea-surements from different locations, at different times,and at different scales are made, the challenge of inter-preting these measurements in a consistent frameworkgrows. New ecosystem models that explicitly addressissues of scaling may help with the synthesis, and pro-vide tools for making projections into the future. Atthe same time, by explicitly scaling they are open totesting and parameterization with more data from morescales than ever before. Here I will discuss several im-portant issues related to the challenges of scaling andinterpreting of carbon flux measurements, and providean overview of a new ecosystem model designed to ad-dresses some of these issues, the Ecosystem Demogra-phy Model (ED).

We also • developed procedure based on GIS in or-der to integrate data banks, gathering and elaboratingthe information coming from different sources and toperform a ready update of data and models.

B41C-11 1120hUse of Space Technology in Flood

Mitigation (Western Province,Zambia)

Allan Mulando (00-260-1-251889;a_mulancloOyahoo.corn)

Remote Sensing Unit, Zambia Meteorological Depart-ment, P.O. Box 30200, Lusaka, ZambiaDisasters, by definition are events that appear sud-

denly and with little warning. They are usually shortlived, with extreme . events bringing death, injury anddestruction of buildings and communications. Their af-termath can be as damaging as their physical effectsthrough destruction of sanitation and water supplies,destruction of housing and breakdown of transport forfood, temporary shelter and emergency services.

Since floods are one of the natural disaster's whichendanger both life and property, it becomes vital toknow its extents and where the hazards exists. Flooddisasters manifest natural processes on a larger scaleand infoimation provided by Remote Sensing is a mostappropriate input to analysis of actual events and in-vestigations of potential risks. An analytical and quali-tative image processing and interpretation of RemotelySensed data as well as other data such as rainfall, popu-lation, settlements not to mention but a few should beused to derive good mitigation strategies. Since mit-igation is the cornerstone of emergency management,it therefore becomes a sustained action that will re-duce or eliminate long term risks to people and prop-erty from natural hazards such as floods and their ef-fects. This will definitely involve keeping of homesand other sensitive structures away from flood plains.Promotion of sound land use planning based on thisknown hazard, "FLOODS" is one such form of mitiga-tion that cn be applied in flood affected are. withinflood plain.

a Therefore future mitigation technologies

and procedures should increasingly be based on the useof flood extent information provided by Remote SensingSatellites like the NOAA AVHRR as well as informationon the designated flood hazard and risk areas.

Cite abstracts as: Eos. Trans. AGU, 82(20), Spring Meet. Suppl., Abstract #####-##, 2001.

2001 Spring MeetingMay 29- June 2, 2001Boston, Massachusetts

Published as a supplement toEos, Transactions, American Geophysical Union

Vol. 82, No. 20, May 15, 2001