Atmospheric, Oceanic and Earth Sciences Bioinformatics and Computational Biology Chemistry and Biochemistry Computational and Data.

http://cos.gmu.edu

• Atmospheric, Oceanic and Earth Sciences• Bioinformatics and Computational Biology• Chemistry and Biochemistry • Computational and Data Sciences • Environmental Science and Policy • Geography and Geoinformation Science • Mathematical Sciences • Molecular and Microbiology • Physics and Astronomy

• Climate Dynamics Chair, Part of Nobel-Winning IPCC, 2007

• Remote sensing and GIS have been focuses in College of Science.

• Mason Researcher and his team seek New Ways to Fight AIDS

Highlight:Department:

Combining remote sensing, GIS, model-simulation, ground measurement, and expert knowledge to meet the needs for global and regional environment and climate change and their impact studies.

Ground observation

Environment and ecology

Land use/cover

Climate monitoring

Remote sensingModel simulation

Science: • Global and regional environmental monitoring • Global and regional climate monitoring• Climate change effects and impacts assessment

Technology:• Cross-sensor comparison/validation/calibration• Measurement continuity• Climate data records

Remote Sensing Products of ESTC/EastFIRE Lab

• Soil Moisture • Drought Indexes• Sand and Dust Storm• True Color Images• Vegetation Indices• Vegetation Moisture• Fuel Temperature• Fuel Load• Fuel Moisture• Active Fire Location• Thermal Anomaly• Fire Danger• Burned Area• Air Quality (smoke and pollution)• Biomass• Phenology

Water deficit index trapezoid combining RS VI index Water deficit index trapezoid combining RS VI index and The thermal data (from Susan Ustin)and The thermal data (from Susan Ustin)

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21301640860

21301640860

nmnmnm

nmnmnm

RRR

RRRNMDI

Cw (cm)

0

5

10

15

20

25

30

35

500 900 1300 1700 2100

Wavelength [nm]

Spe

ctra

l Ref

lect

ance

[%]

0.008

0.016

0.024

0.032

0.04

N=1.3Cab=40μg/cm2

Cm=0.1g/cm2

LAI=2

θ=25%

Cw

R1640nm-R2130nm

θ: soil moisture; Cw: leaf water content

Wang, Lingli and John J. Qu, 2007, NMDI: A Normalized Multi-band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing. Geophysical Research Letters, 34, L20405. ( DOI:10.1029/2007GL031021 ).

Red dawn: The Sydney Opera House at sunrise on 09/23/2009, top, as a red sand and dust strom shrouded the city, and, bottom, how the iconic venue normally looks(09/23/2009)

Caused by climate change ???

Impact assessment of agrometeorology ???

Proposed Normalized difference dust index (NDDI) for remotely sensing dust storms

(Qu et al., 2006, IEEE)

Global and regional environmental monitoring

Designed a drought index NMDI (normalized multi-band drought index) for drought, wildland fire monitoring

(Wang, L.and J. Qu., 2007, GRL)

Aug, 2003 Sep, 2003

Aug, 2004 Sep, 2004

(Wang et al., 2008, Agricultural and Forest Meteorology)

Climate change effects and impacts assessment

Proposed an assessment algorithm to identify hurricane caused forest damage

(Wang W. and Qu. J, 2010 )

SM-RMAS (Soil Moisture- Remotely MonitoringMonitoring and Analysis is a near real time operational system including: (1) satellite remote sensing data processing sub-system, (2) ground measurement database; (3), SM computing sub-system, (4) SM prediction sub-system, and (5) SM analysis and DSS sub-system (1, 2 and 3 in progress and 4 and 5 future plan).

• It is a new challenge to bridge satellite remote sensing and CAgM.

• We need to integrate satellite RS, GIS, model simulations, ground measurements and Decision Support System for supporting CAgM.

• GMU will strongly support CAgM/WMO research and training activities.

ESTC web page: http://estc.gmu.edu

EastFIRE Lab: http://eastfire.gmu.edu