home
***
CD-ROM
|
disk
|
FTP
|
other
***
search
/
NASA Climatology Interdisciplinary Data Collection
/
NASA Climatology Interdisciplinary Data Collection - Disc 1.iso
/
readmes
/
readme.islscp_veg
< prev
next >
Wrap
Text File
|
1998-03-04
|
30KB
|
766 lines
[CIDC FTP Data]
[Land Cover IDC Data on FTP]
Data Access
ISLSCP Land Cover Data
[rule]
Readme Contents
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data
Data Access and Contacts
Data Directory
Points of Contact
References
[rule]
Data Set Overview
Phenological differences among vegetation types, reflected in
temporal variations in NDVI derived from satellite data, have been
used to classify land cover at continental scales. This study
explored methodologies for extending this concept to a global
scale (DeFries and Townshend 1994a). A coarse resolution (one by
one degree) data set of monthly NDVI values for 1987 (Los, et al.
1994, Sellers, et al. 1994, 1995b) was used as the basis for a
supervised classification of eleven cover types that broadly
represent the major biomes of the world. Because of missing values
at high latitudes, the Pathfinder AVHRR data set for 1987 (James
and Kalluri, 1994) for summer monthly NDVI and red reflectance
values were used to distinguish the following cover types: tundra,
high latitude deciduous forest and woodland, coniferous evergreen
forest and woodland.
The land cover data set was further modified to be consistent with
the SiB vegetation classes described in Dorman and Sellers,
(1989), Sellers et. al. (1995a) and Sellers et. al. (1995b).
Sponsor
The production and distribution of this data set are being funded
by NASA's Mission To Planet Earth program. The data are not
copyrighted; however, we request that when you publish data or
results using these data please acknowledge as follows:
The authors wish to thank the Distributed Active Archive
Center (Code 902.2) at Goddard Space Flight Center,
Greenbelt, MD, 20771, for producing the data in its
present format and distributing them. The original data
products were produced by Dr. Ruth DeFries and Dr. John
Townshend (University of Maryland at College Park,
Department of Geography), with revisions made by Dr.
James Collatz (Code 923, NASA Goddard Space Flight
Center).
Original Archive
This data was originally published as part of the International
Satellite Land Surface Climatology Project (ISLSCP) Initiative I
CD-ROM set.
Meeson, B.W., F.E. Corprew, J.M.P. McManus, D.M. Myers, J.W.
Closs, K. -J. Sun, D.J. Sunday, P.J. Sellers. 1995. ISLSCP
Initiative I-Global Data Sets for Land-Atmosphere Models,
1987-1988. Volumes 1-5. Published on CD by NASA
(USA_NASA_GDAAC_ISLSCP_001-USA_NASA_GDDAC_ISLSCP_005).
Future Updates
An ISLSCP Initiative II is being planed.
The Data
Characteristics
* Parameters: Global land cover classification.
* Units: This data is unitless. The numeric data values
represent the following land cover classifications:
# Land Cover Classification
__________________________________________
0 water
1 broadleaf evergreen forest
2 broadleaf deciduous forest and woodland
3 mixed coniferous and broad-leaf deciduous forest and
woodland
4 coniferous forest and woodland
5 high latitude deciduous forest and woodland
6 wooded c4 grassland
7 c4 grassland
9 shrubs and bare ground
10 tundra
11 desert, bare ground
12 cultivation
13 ice
14 c3 wooded grassland
15 c3 grassland
* Range: 0 to 15
* Temporal Coverage: The data set is derived from data
collected in 1987.
* Temporal Resolution: Not applicable
* Spatial Coverage: Global
* Spatial Resolution: 1 degree by 1 degree
Source
The global land cover data set was based on AVHRR maximum monthly
composites for 1987 of NDVI values at approximately 8 km
resolution, averaged to one by one degree resolution (Los, et al.
1995) . A Fourier transform was applied to smooth the temporal
profiles and remove aberrant low values (Sellers, et al. 1994,
1995b) . At high northern latitudes, the data set was based on the
AVHRR Pathfinder data set for 1987 (James and Kalluri, 1994),
resampled to a spatial resolution of one by one degree and
composited to obtain maximum monthly NDVI values and corresponding
red reflectance values for summer months.
The Files
Format
* File Size:259200 bytes
* Data Format: IEEE floating point notation
* Headers, trailers, and delimiters: none
* Fill value: none
* Image orientation:North to South
Starting position: (179.5W, 89.5N)
End position: (179.5E, 89.5S)
Name and Directory Information Naming Convention
The file naming convention for this data set is
islscp.vegmap.1nnegl.ddd
where
islscp = International Land Surface Climatology Project
vegmap = Global land cover map
1 = number of levels
n = vertical coordinate, n = not applicable
n = temporal period, n = not applicable
e = horizontal grid resolution, e = 1 x 1 degree
gl = spatial coverage, gg = global land
bin = File type, (bin=binary, ctl=GrADS control file)
Directory Path
/data/inter_disc/biosphere/land_cover/
Companion Software
Several software packages have been made available on the CIDC
CD-ROM set. The Grid Analysis and Display System (GrADS) is an
interactive desktop tool that is currently in use worldwide for
the analysis and display of earth science data. GrADS meta-data
files (.ctl) have been supplied for each of the data sets. A GrADS
gui interface has been created for use with the CIDC data. See the
GrADS document for information on how to use the gui interface.
Decompression software for PC and Macintosh platforms have been
supplied for datasets which are compressed on the CIDC CD-ROM set.
For additional information on the decompression software see the
aareadme file in the directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been
made available to read these data. You may also acquire this
software by accessing the software/ directory on each of the CIDC
CD-ROMs
The Science
Theoretical Basis of Data
The cover class descriptions used in DeFries and Townshend (1994a)
resolved 11 classifications, which are listed below:
Classification numbers DeFries and Townshend
1 Broadleaf evergreen trees
2 Broadleaf deciduous trees
3 Mixed trees
4 Needle leaf evergreen trees
5 High latitude deciduous trees
6,8 Grass with 10 - 40% woody cover
7 Grass with greater than 10% woody cover
9 Shrubs and bare soil
10 Moss and lichens
11 Bare
12 Cultivated
The original eleven cover types were selected primarily to conform
with the cover types required as input to climate models.
For descriptions of the functional characteristics of these cover
types, in terms of approximate height of mature vegetation,
percent ground surface covered by vegetation, seasonality, and
leaf type, see Table 1 in DeFries and Townshend (1994a).
The land cover data set was modified to be consistent with the SiB
vegetation classes described in Dorman and Sellers, (1989),
Sellers et. al. (1995a) and Sellers et. al. (1995b) in the
following ways:
1. The Matthews (1983) vegetation map is used as the global
land/ocean mask except for Africa where Kuchler (1983) is
used.
2. Vegetation class 8 (broad leaf shrubs and ground cover) was
not distinguishable from class 6 (wooded grassland) using the
classification methods described here so class 6 includes
both wooded grasslands and shrubs with ground cover
understory. There are no class 8 values in the data set.
3. The original classification data set had 90 missing points in
Arctic that are classified as land points in the land/ocean
mask. These were set to class 11 (bare ground). Two other
points not classified lie in the southwestern Pacific
(latitudinal index, longitudinal index=94,329 and 94,330).
These points are set to class 1 to match an adjoining point
that had been classified.
4. Class 6 (wooded c4 grassland) and class 7 (c4 grasslands)
occurring in regions with climates unfavorable for c4 grasses
were reclassified to class 14 (wooded c3 grassland) and class
15 (c3 grasslands) respectively. The main criteria for
deciding whether the climate is favorable for c4 grasses are
that the following two conditions apply for any month at that
grid point: a) mean monthly temperature is above 22 degree C
and b) mean monthly precipitation is above 25mm. The mean
monthly temperature and precipitation fields were from
Leemans and Cramer (1991).
The above modifications resulted in the following land cover
classifications:
0 water
1 broadleaf evergreen forest
2 broadleaf deciduous forest and woodland
3 mixed coniferous and broad-leaf deciduous forest and
woodland
4 coniferous forest and woodland
5 high latitude deciduous forest and woodland
6,8 wooded c4 grassland
7 c4 grassland
9 shrubs and bare ground
10 tundra
11 desert, bare ground
12 cultivation
13 ice
14 c3 wooded grassland
15 c3 grassland
Processing Sequence and Algorithms
Maximum likelihood classification based on 12 monthly NDVI values
was used to obtain the global land cover data set. In outline, the
maximum likelihood procedure classifies each pixel to the land
cover type that it most resembles in terms of its remotely sensed
properties. The remotely sensed properties are used to define a
multi-dimensional space within which pixels of each cover type can
be located. The mean vector and variance-covariance matrix for
each cover type are estimated using its worldwide population of
pixels from the training set. Then, using the maximum likelihood
rule (Swain and Davis 1978), the multidimensional space is
partitioned into sub-spaces each uniquely associated with one land
cover type. The whole of the global land mass is then classified
according to the remotely sensed properties of each pixel. Thus,
if a pixel falls within the sub-space associated with cover type
ci, it is labeled ci. If the pixel falls within the sub-space
associated with cover type cj, it is labeled as that cover type,
cj.
To account for phasing of seasons, maximum likelihood
classification was based on monthly NDVI values sequenced from the
peak value at each pixel (see DeFries and Townshend (1994a) for
more detail).
Training sets for each of the eleven cover types were identified
as the areas where three existing ground-based data sets of global
land cover (Matthews 1983, Olson, et al. 1983, Wilson and
Henderson-Sellers 1985) agree that the land cover is present.
Although there is considerable disagreement among these data sets
(DeFries and Townshend 1994b), the locations where the three data
sets agree were selected as those with the greatest confidence
that the cover type actually exists on the ground. The following
steps were taken to ensure that each training set was as
spectrally distinct as possible or to further subdivide the
training set so that each would be spectrally distinct:
1. each training set was split into Northern and Southern
Hemispheres to account for phasing of seasons in the two
hemispheres.
2. the feature space occupied by each training set was visually
examined. Pixels that were obvious outliers were removed, and
clusters were examined to determine if they were falling in
different geographic areas. Where this was the case, the
training set was subdivided. The most obvious example where
subdivision was required was cultivated crops whose spectral
signatures vary considerably among continents.
3. Bhattacharrya Distances--a measure of the separability of the
training sets--and overlaps in the feature space were
examined to determine if some cover types should be combined.
This was the case, for example, for Southern Hemisphere
broadleaf deciduous forest located mainly in Africa and
Southern Hemisphere wooded grassland.
The global land cover data set was modified from the original
maximum likelihood classification result as follows to eliminate
stray pixels that were obviously incorrectly classified: pixels
falling within training areas that were not correctly classified
were changed to the cover type indicated by the training area;
pixels surrounded on all sides by a different cover type were
changed to that cover type; pixels classified as broadleaf
evergreen in mid-latitudes were changed to the wooded grassland
cover type; pixels classified as coniferous evergreen within the
tropics were changed to the broadleaf evergreen cover type; pixels
classified as mixed deciduous and evergreen forest and woodland
within the tropics were changed to the wooded grassland cover
type. In total, these changes altered approximately 10 percent of
the total land surface.
Modifications by G. James Collatz and Sietse Los, (Biospheric
Sciences Branch, Code 923, NASA/Goddard Space Flight Center) were
made to the data (see the Theoretical Basis of Data section of
this readme), so that it was consistent with the SiB vegetation
classes described in Dorman and Sellers, (1989), Sellers et. al.
(1995a) and Sellers et. al. (1995b).
Scientific Potential of Data
The tables below (Time-invariant land surface properties) can be
used in conjunction with the vegetation classification to specify
global parameter fields. Most parameter fields are derived for use
in the Simple Biosphere model (SiB2; see Sellers et al., 1994, and
Sellers et al., 1995a,1995b) and may need to be adapted for use in
other models.
Biome dependent morphological and physiological parameters.
SiB Vegetation Type
Name Symbol Units 1 2 3 4 5 6
Canopy top
height z_2 m 35.0 20.0 20.0 17.0 17.0 1.0
Inflection
height for leaf z_c m 28.0 17.0 15.0 10.0 10.0 0.6
area density
Canopy base
height z_1 m 1.0 11.5 10.0 8.5 8.5 0.1
Canopy cover
fraction V - 1.0 1.0 1.0 1.0 1.0 1.0
Leaf angle
distribution chi_l - 0.1 0.25 0.13 0.01 0.01 0.3
factor
Leaf width l_w m 0.05 0.08 0.04 0.001 0.001 0.01
Leaf length l_l m 0.1 0.15 0.1 0.06 0.04 0.3
Total soil
depth D_t m 3.5 2.0 2.0 2.0 2.0 1.5
Maximum rooting
depth D_r m 1.5 1.5 1.5 1.5 1.5 1.0
1/2 inhibition
water potential psi_c m -200 -200 -200 -200 -200 -200
Leaf
reflectance, alpha_v,l - 0.1 0.1 0.07 0.07 0.07 0.11
visible, live
Leaf
reflectance, alpha_v,d - 0.16 0.16 0.16 0.16 0.16 0.36
visible, dead
Leaf
reflectance, alpha_n,l - 0.45 0.45 0.4 0.35 0.35 0.58
near IR, live
Leaf
reflectance, alpha_n,d - 0.39 0.39 0.39 0.39 0.39 0.58
near IR, dead
Leaf
transmittance, delta_v,l - 0.05 0.05 0.05 0.05 0.05 0.07
visible, live
Leaf
transmittance, delta_v,d - 0.001 0.001 0.001 0.001 0.001 0.22
visible, dead
Leaf
transmittance, delta_n,l - 0.25 0.25 0.15 0.1 0.1 0.25
near IR, live
Leaf
transmittance, delta_n,d - 0.001 0.001 0.001 0.001 0.001 0.38
near IR, dead
Soil
reflectance, a_s,n - 0.11 0.11 0.11 0.11 0.11 0.11*
visible
Soil
reflectance, a_s,v - 0.225 0.225 0.225 0.225 0.225 0.225*
near IR
Maximum rubisco mol
capacity, top V_max0 m^-2 6e-5 6e-5 6e-5 6e-5 6e-5 3e-5
leaf s^-1
Intrinsic
quantum yield epsilon - 0.08 0.08 0.08 0.08 0.08 0.05
Stomatal slope
factor m - 9.0 9.0 7.5 6.0 6.0 4.0
Minimum mol
stomatal b m^-2 0.01 0.01 0.01 0.01 0.01 0.04
conductance s^-1
Photosynthesis
coupling beta_ce - 0.98 0.98 0.98 0.98 0.98 0.8
coefficient
High
temperature
stress factor, s_2 K 313 311 307 303 303 313
photosynthesis
Low temperature
stress factor, s_4 K 288 283 281 278 278 288
photosynthesis
Minimum leaf s
resistance** r_min m^-1 80 80 100 120 120 110
Canopy top
height z_2 m 1.0 1.0 0.5 0.6 1.0 1.0
Inflection
height for leaf z_c m 0.6 0.6 0.3 0.35 0.6 0.6
area density
Canopy base
height z_1 m 0.1 0.1 0.1 0.1 0.1 0.1
Canopy cover
fraction V - 1.0 1.0 0.1 1.0 1.0 1.0
Leaf angle
distribution chi_l - -0.3 -0.3 0.01 0.2 -0.3 -0.3
factor
Leaf width l_w m 0.01 0.01 0.003 0.01 0.01 0.01
Leaf length l_l m 0.3 0.3 0.03 0.3 0.3 0.3
Total soil
depth D_t m 1.5 1.5 1.5 1.5 1.5 1.5
Maximum rooting
depth D_r m 1.0 1.0 1.0 1.0 1.0 1.0
1/2 inhibition
water potential psi_c m -200 -200 -300 -200 -200 -200
Leaf
reflectance, alpha_v,l - 0.11 0.11 0.1 0.11 0.11 0.11
visible, live
Leaf
reflectance, alpha_v,d - 0.36 0.36 0.16 0.36 0.36 0.36
visible, dead
Leaf
reflectance, alpha_n,l - 0.58 0.58 0.45 0.58 0.58 0.58
near IR, live
Leaf
reflectance, alpha_n,d - 0.58 0.58 0.39 0.58 0.58 0.58
near IR, dead
Leaf
transmittance, delta_v,l - 0.07 0.07 0.05 0.07 0.07 0.07
visible, live
Leaf
transmittance, delta_v,d - 0.22 0.22 0.001 0.22 0.22 0.22
visible, dead
Leaf
transmittance, delta_n,l - 0.25 0.25 0.25 0.25 0.25 0.25
near IR, live
Leaf
transmittance, delta_n,d - 0.38 0.38 0.001 0.38 0.38 0.38
near IR, dead
Soil
reflectance, a_s,n - 0.11* 0.15* 0.3* 0.11 0.3* 0.1
visible
Soil
reflectance, a_s,v - 0.225* 0.25* 0.35* 0.23 0.35* 0.15
near IR
Maximum rubisco mol
capacity, top V_max0 m^-2 3e-5 3e-5 6e-5 6e-5 3e-5 6e-5
leaf s^-1
Intrinsic
quantum yield epsilon - 0.05 0.05 0.08 0.08 0.05 0.08
Stomatal slope
factor m - 4.0 4.0 9.0 9.0 4.0 9.0
Minimum mol
stomatal b m^-2 0.04 0.04 0.01 0.01 0.04 0.01
conductance s^-1
Photosynthesis
coupling beta_ce - 0.8 0.8 0.98 0.98 0.8 0.98
coefficient
High
temperature
stress factor, s_2 K 313 313 313 303 313 308
photosynthesis
Low temperature
stress factor, s_4 K 288 288 288 278 288 281
photosynthesis
Minimum leaf s
resistance** r_min m^-1 110 110 80 80 110 80
*Soil reflectance for areas with bare soil are specified according
to ERBE data which is available on the ISLSCP Initiative I CD ROM
set. This CD-ROM set can be ordered through the Goddard DAAC home
page.
**Minimum leaf resistance is the light saturated, unstressed
resistance to water vapor diffusion through the leaf surface. It
is calculated using table values of V_max and m and the
photosynthesis and stomatal models described in Collatz et. al.
1991. The total canopy resistance can be calculated using the
minimum leaf resistance scaled by environmental conditions and
integrated over all the leaves in the canopy. A simple way to
perform the integration would be to multiply the
environment-modified minimum leaf resistance by the leaf area
index (LAI) or by the fraction of incident PAR that is absorbed by
the canopy (FPAR). Global fields of LAI and FPAR are available on
the ISLSCP Initiative I CD-ROM set, which can be ordered through
the Goddard DAAC home page.
Biome independent parameters
Name symbol units value
Ground roughness
length z_s m 0.05
Augmentation
factor for G_1 - 1.449
momentum
Transition height
factor for G_4 - 11.785
momentum
Depth of surface
soil layer D_1 m 0.02
Rubisco
Michaels-Menten K_c Pa 30*2.1^Qt
constant for CO2
Rubisco inhibition
constant for K_o Pa 30,000*1.2^Qt
oxygen
Rubisco
specificity for
CO2 relative to S - 2,600*0.57^Qt
oxygen
Q10 temperature
coefficient Qt - (T-298)/10
Photosynthesis
coupling beta_ps - 0.95
coefficient
High temperature
stress factor, s_1 K^-1 0.3
photosynthesis
Low temperature
stress factor, s_3 K^-1 0.2
photosynthesis
High temperature
stress factor, s_5 K^-1 1.3
respiration
High temperature
stress factor, s_6 K 328
respiration
Leaf respiration
factor f_d - 0.015
The tables (Biome dependent and Biome independent parameters) were
compiled by G. James Collatz, Code 923, NASA/GSFC, Greenbelt MD
20771, phone: 301-286-1425, e-mail: jcollatz@biome.gsfc.nasa.gov
Validation of Data
Wintertime NDVI values were missing for large areas in high
latitudes in the primary data set used for this study (Los, et
al., 1994) . For these areas, results from a maximum likelihood
classification using AVHRR Pathfinder data (James and Kalluri,
1994) for summertime monthly NDVI and red reflectance values were
used.
The data set has not been systematically validated. Cursory
validation indicates that the user should be aware of the
following problems:
1. the distinction between "cultivated" and "grassland" cover
types may be inaccurate because the NDVI temporal profiles of
these two cover types are not significantly distinct.
2. the "tundra" cover type may be inaccurate because of missing
data at high latitudes.
Data Access and Contacts
FTP Site
Data Directory
The data on each of the CIDC CD-ROMs resides in the data/
directory.
Points of Contact
For information about or assistance in using any DAAC data,
contact
EOS Distributed Active Archive Center(DAAC)
Code 902.2
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
References
Collatz, G.T., J.T. Ball, C. Grivet, J.A. Berry. 1991.
Physiological and environmental - regulation of stomatal
conductance, photosynthesis and transpiration - A model that
includes a laminar boundary- layer, Agric. For. Meteor.,
54:107-136.
DeFries, R. S. and J. R. G. Townshend, 1994a, NDVI-derived land
cover classification at global scales. International Journal of
Remote Sensing, 15:3567-3586. Special Issue on Global Data Sets.
DeFries, R. S. and J. R. G. Townshend, 1994b. Global land cover:
comparison of ground-based data sets to classifications with AVHRR
data. In Environmental Remote Sensing from Regional to Global
Scales, edited by G. Foody and P. Curran, Environmental Remote
Sensing from Regional to Global Scales. U.K.: John Wiley and Sons.
Dorman, J.L., and Sellers, P.J., 1989. A Global climatology of
albedo, roughness length and stomatal resistance for atmospheric
general circulation models as represented by the simple biosphere
model (SiB). Journal of Applied Meteorology, 28:833-855.
James, M. E. and S. N. V. Kalluri, 1994. The Pathfinder AVHRR land
data set: An improved coarse resolution data set for terrestrial
monitoring. International Journal of Remote Sensing, Special Issue
on Global Data Sets. 15(17):3347-3363.
Kuchler, A.W., 1983, World map of natural vegetation. Goode's
World Atlas, 16th ed., Rand McNally, 16-17.
Leemans, R., and W. P. Cramer, 1991, The IIASA database for mean
monthly values of temperature, precipitation and cloudiness on a
global terrestrial grid, technical report, International Institute
for Applied Systems Analysis, Laxenburg, Austria.
Los, S.O., C.O. Justice, C.J. Tucker, 1994. A global 1 by 1 degree
NDVI data set for climate studies derived from the GIMMS
continental NDVI data. International Journal of Remote Sensing,
15(17):3493- 3518.
Matthews, E., 1983. Global vegetation and land use: new high
resolution data bases for climate studies. Journal of Climate and
Applied Meteorology, 22: 474-487.
Olson, J. S., Watts, J. and L. Allison, 1983. Carbon in live
vegetation of major world ecosystems. W-7405-ENG-26, U.S.
Department of Energy, Oak Ridge National Laboratory.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich,
G.J. Collatz, and D.A. Randall, 1994. A global 1*1 degree NDVI
data set for climate studies. Part 2: The generation of global
fields of terrestrial biophysical parameters from the NDVI.
International Journal of Remote Sensing, 15(17):3519-3545.
Sellers, P.J., D.A. Randall, C.J. Collatz, J.A. Berry, C.B. Field,
D.A. Dazlich, C. Zhang, and C.D. Collelo, 1996a. A revised land
surface parameterization (SiB2) for atmospheric GCMs. Part 1:
Model formulation. submitted to Journal of Climate.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich,
G.J. Collatz, and D.A. Randall, 1996b. A revised land surface
parameterization (SiB2) for atmospheric GCMs. Part 2: The
generation of global fields of terrestrial biophysical parameters
from satellite data. submitted to Journal of Climate.
Swain, P. H. and S. M. Davis, (ed.), 1978. Remote Sensing: The
Quantitative Approach. (New York: McGraw-Hill Book Company).
Wilson, M. F. and A. Henderson-Sellers, 1985. A global archive of
land cover and soils data for use in general circulation models.
Journal of Climatology, 5: 119-143.
------------------------------------------------------------------------
[NASA] [GSFC] [Goddard DAAC] [cidc site]
NASA Goddard GDAAC CIDC
Last update:Fri Aug 22 18:11:16 EDT 1997
Page Author: James McManus -- mcmanus@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov
