Transcript www.gri.msstate.edu

Development of Rapid Prototyping Capability to
Evaluate Potential Uses of
NASA Research Products and Technologies to
Estimate Distribution of Mold Spore Levels
over Space and Time
University of Mississippi Medical Center
Science Systems and Applications, Inc. (SSAI)
Mississippi State University
Data Relationship Diagram for
Mold Spore Distribution Estimation
Mold
Spore
Count
NASA / NOAA
Meteorological
data
Ground
Meteorological
data
Ground Monitoring Equipment and
Accessories for Collecting Meteorological
Data
Meteorological Data Logger
•
•
•
•
•
Basic weather station
Silicon pyranometer sensor for solar radiation
Soil moisture sensor
Soil temperature sensor
Leaf wetness sensor
Ground Monitoring Equipment and
Accessories for Collecting Mold spores
Mold Spore Traps (Burkard Manufacturing Co Ltd., UK)
• 7-Day Recording Volumetric Spore Traps12v (solar charger operated)
• 7-Day Recording Volumetric Spore Traps110VAC (electricity operated)
Collection
Drum
Air Intake
Types of Mold We Should Know About
Cladosporium
(1)
The most commonly identified outdoor fungus, but it can easily enter into the house through the
HVAC and other airflow entryways. Cladosporium also has an indoor species that grows on
textiles, wood and other porous, damp areas. Both indoor and outdoor species are triggers for
hay fever and asthma symptoms.
Alternaria
(2)
A large spore mold that can deposit in the nose, mouth and upper respiratory tract causing an
allergic response. Indoors, it is often found in carpets, textiles, house dust and potentially damp
areas like window frames and showers. It can also be found in plant soil.
Stachybotrys
(3)
Pronounced (stack-ee-BOT-ris), this is an especially toxic black mold that produces airborne toxins
(mycotoxins) that can cause serious breathing difficulties, dizziness, flu-like symptoms and
bleeding in the lungs. Stachybotrys requires excessive moisture to thrive (usually running water)
and is a slimy black mold. Fortunately, stachybotrys is not found in homes as often as the other
molds listed above.
Aspergillus
(4)
Usually found in warmer climates in areas of water damage or extreme dampness. Aspergillus
species are also commonly found in house dust. Many species produce mycotoxins which may be
associated with disease in humans and some animals. Also found in building materials and in fall
leaves and other decomposing matter like compost piles.
Penicillium
(5)
A very common mold known to cause allergies, hay fever and asthma. Species may be found
growing on wallpaper, wallpaper glue and decaying fabrics in water-damaged buildings or homes.
It is also found in carpet and in interior fiberglass duct insulation. Some species can produce
mycotoxins.
Number in the parenthesis indicate the relative order of predominance of mold
spores so far identified
End Users
• Mississippi Asthma Coalition
• Mississippi Poison Control Center
• Mississippi State Department of Health
Field Data Collection
Pollen and Mold Sample Locations [Burkard Stations]
Kosciusko
Ground Monitoring Station Locations
Sallis
Humphreys
Sharkey Louise
Attala
Holmes
Eden
Goodman
Pickens
Yazoo City
Yazoo
Red Water IR
Carthage
Leake
Sharkey
Satartia
Carthage
Standing Pine IR
Flora
Bentonia
Madison
Canton
Lena
!.
DWF&P
Flora
!.
Warren
Madison
Ridgeland
DEQ
UMMC
Bolton
Clinton
Edwards
Hinds
Scott
!.
Jackson
Morton
!.
FlowoodJackson IA
Brandon
Pearl
Raymond
Pelahatchie
Richland
Rankin
Learned
Polkville
Florence
Utica
Terry
!.
Terry
Puckett
Smith
Braxton
Crystal Springs
Harrisville
!.
D'lo
Mendenhall
Simpson
Raleigh
Metrological Data Logger
Configuration
Data is logged every 15 minutes (96 readings/day)
Base units collect temperature, humidity, dew point,
rainfall, wind direction and speed (average and max
gust). One or more additional sensors used on all.
Metrological Data Logger
Spectrum Technology Inc, WatchDog 2700
Opitional Sensor Port Assignments
Location
UMC
Flora
DWF&P
Harrisville
Terry
Sensor
A
B
C
D
Solar
Leaf
Wettness
Leaf
Wettness
Leaf
Wettness
Leaf
Wettness
NA
Soil
Moisture
Soil
Moisture
Soil
Moisture
Soil
Moisture
NA
2" Soil
Temp
NA
4" Soil
Temp
NA
NA
NA
NA
NA
NA
Units
Min
Max
Watts/m2
1
Leaf Wetness
none
0
Soil Moisture
Soil Temp
cbar
o
C
0
-30
Solar
Type/Note
E
Battery
Voltage
Battery
Voltage
Battery
Voltage
Battery
Voltage
Battery
Voltage
Model
Silicon
Pyranometer,
1,250 300-1,100 nm
36701
0=dry,
15
15=wet
3666
Watermark,
0=Saturated,
200
200=Dry
6420WD20
100
3667-20
F
Motor
Voltage
Motor
Voltage
Motor
Voltage
Motor
Voltage
Motor
Voltage
Metrological Data Logger
In addition, data are obtained from NOAA/NWS Jackson
International Airport site for comparison. Their barometric
pressure used for standard pressure correction of daily
collected air volume.
Data are reduced to hourly and daily levels/avg. Hourly
temp data used to match to NWS bp and integrate STP
volumes for daily corrected totals.
Typical Station Installation
Problems Encountered and Solutions
Originally informed power requirements for DC powered spore
trap was 12v at 150 mA and systems configured around those
spec’s.
Energy analysis suggested 15W (1 Amp) panel would supply
needed power with 2X safety factor in the worst of weather.
1st problem. Since solar powered with deep cycle battery,
voltage goes up and down with sun and hence the air pump
motor and resulting flow rate and volume varied.
Problems Encountered and Solutions
Solution to varying voltage was addition of regulator.
Worked extremely well with tight regulation.
Down side was extra current draw. With regulation, current
draw jumped to 250ma.
11/11/ 00:00
11/10/ 12:00
11/10/ 00:00
11/9/ 12:00
11/9/ 00:00
11/8/ 12:00
11/8/ 00:00
11/7/ 12:00
11/7/ 00:00
11/6/ 12:00
11/6/ 00:00
11/5/ 12:00
11/5/ 00:00
11/4/ 12:00
11/4/ 00:00
11/3/ 12:00
11/3/ 00:00
11/2/ 12:00
11/2/ 00:00
Battery
Problems Encountered and Solutions
Battery
1.32
1.31
1.30
1.29
1.28
1.27
1.26
1.25
1.24
11/11 00:00
11/10 12:00
11/10 00:00
11/9 12:00
11/9 00:00
11/8 12:00
11/8 00:00
11/7 12:00
1.32
1.29
1.28
0.7
1.27
0.6
1.26
1.25
0.5
1.24
0.4
Motor
Battery
11/7 00:00
11/6 12:00
11/6 00:00
11/5 12:00
11/5 00:00
11/4 12:00
11/4 00:00
11/3 12:00
11/3 00:00
11/2 12:00
11/2 00:00
Battery
Problems Encountered and Solutions
Motor
1.0
1.31
0.9
1.30
0.8
Problems Encountered and Solutions
When the short and cold winter days appeared, batteries
started dropping and occasionally lost a cell.
An extra battery was obtained and sites were lasting about
7-10 days between rotating changes.
We have learned deep cycle batteries are inadequate in
regards to charging efficiency at about 70%, that is, they
need almost 1.5 amp hours charge in for every one out.
Capacity also dropped at least 20% with cold weather.
Problems Encountered and Solutions
Solutions: Higher efficiency dc/dc regulators obtained.
Extra 5w (1/3 amp) panel added to one stand.
Longer days and warmer weather biggest help. Batteries now
lasting at least a month and appear to be leveling off.
Problems Encountered and Solutions
Solutions: Since loggers are recording battery and motor
voltage on spare channels, flow at suboptimal voltage can be
determined.
We have obtained a good quality flow meter and the
regulators are variable. Flow at different voltages can now
be obtained and volumes corrected according.
Problems Encountered and Solutions
“STP”
Learned from Burkard that their
supplied custom flowmeters are
calibrated at 20oC and 760 mm Hg
Problems Encountered and Solutions
“STP”
Samplers were set to 10 l/min at
20oC and ~ 760mm Hg
Results in 14.4 m3 of air per day
Problems Encountered and Solutions
“STP”
With hourly temperature from
loggers and barometric pressure
from NWS, the volume of air is
being corrected.
Problems Encountered and Solutions
Resulting Daily Volume After STP Correction
15.2
3
Daily Air Volume (m)
15.1
15.0
14.9
14.8
14.7
14.6
14.5
14.4
14.3
14.2
11/1
11/8
11/15
11/22
11/29
12/6
12/13
12/20
12/27
1/3
1/10
1/17
1/24
1/31
Mold Spore Sampling, Count
and Identification
•As of April 25, 2008, we have collected 1055 daily samples from 6 sites.
•Since the tape attached to the rotating drum inside the Burkard air sampler
has to be cut vertically for removal during the weekly collection, one of the
days in a week is not a complete 24 hours.
•We have elected not to count the spores on incomplete days (Wednesday).
•Each 24-hour section of tape is cut using a standardized grid and mounted
on a glass slide for coverslip staining as specified in a standard protocol by
the National Allergy Bureau (NAB).
•With the exclusion of Wednesday from the daily samples, we have
collected 908 samples for mold spore enumeration.
•We have conducted identification of weekly predominant species including
quantification of Alternaria species and qualitative assessment of the other
frequently observed species.
•To date, we have counted 188 samples from 6 sites.
•There are 12 weeks of mold spore species identification data including at
least one week per site.
We have already found all seven of the clinically relevant molds identified by
the AAAAI Immunotherapy Committee
•Cladosporium cladosporioides
•Cladosporium herbarum
•Alternaria alternata
•Epicoccum nigrum
•Helminthosporium
•Aspergillus
•Penicillium
Examples of Identified Mold Spores
Bipolaris, 1000X
Cladosporium, 1000X
Helicomyces, 1000X
Arthrinium, 1000x
Stachybotrys, 1000X
Alternaria, 1000X
Preliminary Analysis and
Results
Regression Analyses
• Regression analyses are being performed to
investigate the strength of the relationship
between measurements of spores/m3, NDMI
(Normalized Difference Moisture Index), and
various weather-related variables.
• The goals are to identify which variables play the
largest role in predicting spores/m3 and to
develop a model for estimation.
April 2008
35
MODIS NDMI Values
• NDMI (Normalized Difference Moisture Index)
– Similar to NDVI (Normalized Difference Vegetation
Index) except SWIR and NIR reflectances are used
instead of Red band and NIR reflectances
– NDMI = (NIR–SWIR) / (NIR+SWIR)
• Time series of daily NDMI values
– Generated using TSPT (Time Series Product Tool)
and 500 m MODIS MOD09 data (8-day reflectance
composites)
– Extracted for the monitoring sites and used in the
regression analysis
MODIS: Moderate Resolution Imaging Spectroradiometer
SWIR: shortwave infrared
NIR: near-infrared
April 2008
36
Remote Sensing
TSPT (Time Series Product Tool)
•
•
•
•
TSPT software custom-designed for NASA at Stennis Space Center
Developed in MATLAB®
Purpose: To rapidly create and display various MODIS or simulated VIIRS
(Visible/Infrared Imager/Radiometer Suite) products as single-band and bandcombination time series, such as NDVI or NDMI images, for wide-area crop
surveillance and other time-critical applications.
Typical MODIS input datasets:
–
–
–
–
•
Output/display options:
–
–
–
•
MOD02 Planetary Reflectance (Swath)
MOD09 Surface Reflectance (Tile)
MOD13 Vegetation Indices
MOD43 Nadir BRDF-Adjusted Reflectances
Single time frame and multitemporal change images
Daily time series plots at a selected location
Temporally processed image videos
Features:
–
Noise removal and temporal processing techniques
•
•
–
MODIS metadata is used to find and optionally to remove bad, cloudy, and suspect data.
TSPT also filters out variance due to atmosphere, sensor geometry, etc.
Capability of fusing data from the MODIS instruments onboard the Aqua and Terra satellites,
which nearly doubles the effective temporal resolution
April 2008
37
TSPT – Data Flow
TSPT Data Flow
Inputs
• Science Datasets
• Sensor Zenith Angle
• Cloud & Quality Data
Inputs…
• may be obtained from
DAACs or other sources
• may be subsetted and/or
reprojected with tools
such as the MODIS
Reprojection Tool
(http://edcdaac.usgs.
gov/landdaac/tools/
modis/index.asp)
• may be simulated with
Application Research
Toolbox (Ross et al.,
2006)
April 2008
Multiple, temporally processed images can be created
from single sensors or from fused Aqua and Terra data.
TSPT Internal Processing
Create Daily
Fused Product
Compute Ideal
NDVI, NDMI,
or other product
Process Temporally
(Median Filter, Savitzky-Golay,
etc.)
Create Geographic
Gridded Data from
MODIS Swath or Tile
(GUI Only)
Generate
Visualization
Products
Visualization Products
• 1-D time series plots given a latitude, longitude, and date range
• 2-D images given a region and a date
38
• 3-D time series videos given a region and a date range
TSPT – Output Example
• Example time series:
– MODIS NDVI time series for Mobile Bay area with filtering and
cloud removal applied using the TSPT.
April 2008
39
Methodology for Preliminary
Regression Analysis
•
The preliminary regression analysis involved the following:
– Weather data, mold spore counts, and NDMI values from 4 of the 6 sites:
DWFP, Terry, UMC, and Flora
– Timeframe: 11/3/2007–12/4/2007, because of the availability of mold spore
count data for those days
– “Global” analysis as opposed to site-specific analysis – data from all 4 sites were
included
•
Steps:
– Compile dataset for analyses (including data for 4 of the 6 sites)
• Weather data
– Compute daily average, maximum, and minimum for each variable, and compute other values,
such as:
» 2-day average max temperature (identified in literature review)
» 7-day cumulative rainfall (identified in literature review)
» Hours of relative humidity >= 80%
– Use only those variables that are common to all 4 sites
• Mold spore count data (available for most days during the 11/3/07–12/4/07 timeframe)
• NDMI from MODIS data time series
April 2008
– Remove data from days for which there was no mold count data (once every ~7
days within the 11/3/07–12/4/07 timeframe)
– Generate Pearson’s correlation coefficient, r, to show strength of relationship
between spores/m3 and each of the weather variables and NDMI
– Select the top 4 weather variables based on “r” values to include with spores/m3
and NDMI in the regression analysis
40
– Perform preliminary regression analyses (using Microsoft Excel)
Spores/m3 vs. variable:
rh_gte80
0.4443
AvgHMD
0.4313
MinHMD
0.4139
NDMI
-0.3638
MaxHMD
0.2744
MinDEW
0.2677
MaxDEW
0.2596
rh_lt50
-0.2582
AvgDEW
0.2358
7-day cumulative RNF
0.2253
MinTMP
0.1952
MaxWND
-0.1545
AvgRNF
0.1533
SumRNF
0.1533
MinWNG
0.1177
MinWND
0.1014
AvgWND
-0.0732
Avg 2-day max TMP
-0.0664
MaxRNF
0.0585
MaxTMP
-0.0571
AvgWNS
0.0497
wrun_km
0.0497
MinWNS
0.0456
AvgTMP
0.0333
MaxWNG
-0.0244
AvgWNG
0.0140
MaxWNS
0.0098
April 2008
Strength of relationship
between Spores/m3 and
Variables – Pearson’s “r”
r
•
•
The table (left) shows Pearson correlation
coefficients (r values) between daily values of
spores/m3 and each weather variable (for all 4 sites
combined). Variables are sorted by r values.
The following variables are listed:
–
Average (Avg), maximum (Max), and minimum (Min),
and other daily values for:
•
•
•
•
•
•
•
–
•
HMD = relative humidity (%), including
– rh_gte80 (Hours of relative humidity >= 80%)
– rh_lte50 (Hours of relative humidity < 50%)
DEW = dew point (degrees C)
RNF = rainfall (mm), including
– Daily sum of values (SumRNF)
– 7-day cumulative rainfall
TMP = air temperature (degrees C), including
– Average 2-day maximum temperature
WND = wind direction (degrees)
WNG = wind gust (km/hr)
WNS = wind speed (km/hr)
– wrun_km = wind run (km/day)
NDMI from MODIS data time series
The top 4 variables, one per measurement
type/category (i.e., humidity, temperature, etc.), with
the strongest/highest “r” values were chosen for the
regression analysis. These variables are in bold
41
and are highlighted blue in the table.
Strength of relationship
between Spores/m3 and
Variables – Pearson’s “r”
Pearson’s r
R2
F-test overall
p-value
rh_gte80
0.4443
0.1974
24.5970
0.000003
AvgHMD
0.4313
0.1860
22.8532
0.000006
MinHMD
0.4139
0.1714
20.6786
0.000015
NDMI
-0.3638
0.1323
15.2495
0.000171
MaxHMD
0.2744
0.0753
8.1403
0.005262
MinDEW
0.2677
0.0717
7.7215
0.006519
MaxDEW
0.2596
0.0674
7.2281
0.00841
rh_lt50
-0.2582
0.0666
7.1408
0.008799
AvgDEW
0.2358
0.0556
5.8859
0.017055
7-day cumulative RNF
0.2253
0.0508
5.3478
0.022798
MinTMP
0.1952
0.0381
3.9600
0.049324
MaxWND
-0.1545
0.0239
2.4446
0.121089
AvgRNF
0.1533
0.0235
2.4070
0.123961
SumRNF
0.1533
0.0235
2.4070
0.123961
MinWNG
0.1177
0.0138
1.4040
0.23888
MinWND
0.1014
0.0103
1.0382
0.310699
AvgWND
-0.0732
0.0054
0.5383
0.464855
Avg 2-day max TMP
-0.0664
0.0044
0.4433
0.507067
MaxRNF
0.0585
0.0034
0.3430
0.559422
MaxTMP
-0.0571
0.0033
0.3272
0.568596
AvgWNS
0.0497
0.0025
0.2471
0.620216
wrun_km
0.0497
0.0025
0.2471
0.620216
– Average (Avg), maximum (Max), and minimum
(Min), and other daily values for:
• HMD = relative humidity (%), including
– rh_gte80 (Hours of relative humidity >=
80%)
– rh_lte50 (Hours of relative humidity <
50%)
• DEW = dew point (degrees C)
• RNF = rainfall (mm), including
– Daily sum of values (SumRNF)
– 7-day cumulative rainfall
• TMP = air temperature (degrees C),
including
– Average 2-day maximum temperature
• WND = wind direction (degrees)
• WNG = wind gust (km/hr)
• WNS = wind speed (km/hr)
– wrun_km = wind run (km/day)
MinWNS
0.0456
0.0021
0.2083
0.649093
– NDMI from MODIS data time series
AvgTMP
0.0333
0.0011
0.1111
0.739706
MaxWNG
-0.0244
0.0006
0.0595
0.807788
AvgWNG
0.0140
0.0002
0.0197
0.888661
MaxWNS
0.0098
0.0001
0.0096
0.922145
Spores/m3 vs. variable:
• The table (left) shows Pearson correlation
coefficients (r values), R2, F-test, and pvalues between daily values of spores/m3
and each weather variable (for all 4 sites
combined). Variables are sorted by rvalues.
• The following variables are listed:
• The top 4 variables, one per measurement
type/category (i.e., humidity, temperature,
etc.), with the strongest/highest “r” values
were chosen for the regression analysis.
These variables are in bold and are
highlighted blue in the table.
Site-Specific Comparison of
Spores and Predictive Variables
• Pearson’s correlation coefficient, r, was
computed using data from the 4 combined sites
and then using site-specific data for comparison.
r – 4 sites
combined
r – UMC
r – Terry
r – Flora
r – DWFP
NDMI
-0.3638
-0.1158
-0.0914
-0.1298
-0.1881
rh_gte80
0.4443
0.7327
0.3159
0.2111
0.5186
MinTMP
0.1952
0.1177
0.3371
0.4149
0.2431
7-day RNF
0.2253
0.5061
-0.0804
0.5408
0.4964
MinDEW
0.2677
0.1793
0.3200
0.4974
0.2755
Spores/m3 vs.
variable:
April 2008
43
Preliminary Results of Regression
Analysis
• Sorted by R2
• Number of observations
= 102
*S = spores/m3
NDMI = Normalized Difference Moisture Index
RH = hours of relative humidity >= 80%
RNF = 7-day cumulative rainfall
D = minimum dew point
T = minimum air temperature
Overall pvalue
p-value
p-value
p-value
p-value
p-value
p-value
minDEW
0.23
R
Square
F
Signif. F
Int.
NDMI
RH
RNF
minTMP
NDMI-RH-RNF-T-D
0.362
10.87
2.6E-08
0.88
6.7E-06
0.0010
0.025
0.065
NDMI-RH-RNF-T
0.352
13.16
1.3E-08
0.16
1.13E-05
0.0018
0.036
0.10
NDMI-RH-RNF
0.334
16.38
1.0E-08
0.018
2.97E-05
5.9E-05
0.082
NDMI-RH
0.313
22.55
8.5E-09
0.012
9.1E-05
1.62E-06
4wx
0.211
6.46
0.00012
0.53
0.00085
0.29
0.41
0.61
Plot of Actual vs. Predicted Spores/m3
for 4 Sites
R2 = 0.3615
y = -775.4 + (-83179.3*NDMI) + (984.7*rh_gte80) + (-500.3*minDEW) + (251.0*7dayRNF) + (1040.5*minTMP)
Actual vs. Predicted Spores/m3
100,000
Actual spores/m3
80,000
UMC
60,000
Terry
Flora
DWFP
40,000
Reference
20,000
0
-20,000
0
20,000
40,000
60,000
Predicted spores/m3
Equation/model is from regression that yielded the highest R2 value that used data from 4 combined sites. Each
site is plotted individually here to see if any patterns or differences can be observed.
Observations about Preliminary
Regression Results
• Using the combined data for 4 sites, NDMI was involved in all of the topranking regressions based on R2 values.
• November (late Fall) is not known to be a month with high mold spore
counts, so better results are expected with the Spring data
• Literature for correlation studies of mold spore counts with weather
variables typically show lower R2 values (i.e., ~0.45 or less for specific
spores). Therefore, the low R2 values in this study (i.e., R2 = 0.36) are
not surprising, especially when considering the mold spore count data
was from November.
• Literature typically identifies temperature and moisture (rainfall, dew
point, relative humidity) as variables that are highly correlated with mold
spore counts. The month of November was relatively dry (total rainfall
~49 mm or ~1.9 inches) and cool (average temp ~14 degrees C).
46
April 2008
References
•
•
•
•
•
•
•
•
Bruno, A.A., L. Pace, B. Tomassetti, E. Coppola, M. Verdecchia, G. Pacioni, and G. Visconti,
2007. Estimation of fungal spore concentrations associated to meteorological variables.
Aerobiologia 23: 221-228.
Burge, H.A., 2002. An update on pollen and fungal spore aerobiology. Current Reviews of
Allergy and Clinical Immunology. Journal of Allergy and Clinical Immunology 110(4): 544-552.
Mitakakis, T.Z., A. Clift, and P.A. McGee, 2001. The effect of local cropping activities and
weather on the airborne concentration of allergenic Alternaria spores in rural Australia. Grana
40(4-5): 230-239.
O’Hara, C.G., R. Moorhead, D. Shaw, B. Shrestha, K.W. Ross, D. Prados, J. Russell, R.E. Ryan,
2006. Integrated use of tools and technologies for rapidly prototyping simulated data products of
future NASA observing systems for evaluation in application of national importance. Eos
Transactions AGU, 87(52), Fall Meeting Supplement, Abstract IN32A-05. (presentation, Session
IN32A, Wednesday, Dec. 13, 11:20)
Prados, D., R.E. Ryan, and K.W. Ross, 2006. Remote Sensing Time Series Product Tool. Eos
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Ross, K.W., J. Russell, and R.E. Ryan, 2006. Simulating Visible/Infrared Imager Radiometer
Suite Normalized Difference Vegetation Index data using Hyperion and MODIS. Eos
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the daily variation of airborne fungal spores in Granada (southern Spain). International Journal
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Troutt, C., and E. Levetin, 2001. Correlation of spring spore concentrations and meteorological
conditions in Tulsa, OK. International Journal of Biometeorology 45: 64-74.
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April 2008
Contact
Fazlay S. Faruque
Director of GIS and Remote Sensing
The University of Mississippi Medical Center
2500 North State Street
Jackson, MS 39216-4505
Phone: 601-984-4993
E-mail: [email protected]
48
April 2008