VEG_CLSS.DOC
1. TITLE
1.1 Data Set Identification.
Global land cover classification from satellite data.
(Fixed ; UMD, NASA/GSFC)
1.2 Data Base Table Name.
Not applicable.
1.3 CD-ROM File Name.
\DATA\VEGTATN\VEG_MAP\VEG_CLSS.VGC
Note: capital letters indicate fixed values that appear on the CD-ROM
exactly as shown here, lower case indicates characters (values) that
change for each path and file.
The filename extension (.sfx), identifies the data set content for the
file (see section 8.2) and is equal to .VGC for this data set.
1.4 Revision Date Of This Document.
April 5, 1995.
2. INVESTIGATOR(S)
2.1 Investigator(s) Name And Title.
Dr. Ruth DeFries
Department of Geography
University of Maryland, College Park
Dr. J.R.G. Townshend
Department of Geography
University of Maryland, College Park
2.2 Title Of Investigation.
Global Mapping of Vegetative Land Cover
2.3 Contacts (For Data Production Information).
_________________________________________________________
| Contact 1 | Contact 2 |
______________|____________________|_____________________|
2.3.1 Name | Dr. Ruth DeFries | Dr. John Townshend |
2.3.2 Address | Dept. of Geography | Dept. of Geography |
| 1113 Lefrak Hall | 1113 Lefrak Hall |
City/St.| College Park, MD | College Park, MD |
Zip Code| 20742-8225 | 20742-8225 |
2.3.3 Tel. | 301 405-7861 | 301 405-4050 |
2.3.4 Email | rd63@umail.umd.edu | jt59@umail.umd.edu |
______________|____________________|_____________________|
2.4 Requested Form of Acknowledgment.
Please cite the following publication when ever these data are used:
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.
3. INTRODUCTION
3.1 Objective/Purpose.
The data set was developed to explore the conceptual and methodological
issues that arise when using the Normalized Difference Vegetation Index
(NDVI) as a basis for global classification of vegetative land cover.
The purpose of the study is to use satellite data to improve currently
available information on global land cover for applications to global
change research.
3.2 Summary of Parameters.
The data set describes the geographic distributions of eleven major
cover types based on interannual variations in NDVI (see section
8.2 for listing of cover types included). Vegetation-type dependent
parameters as used in SIB2 are included in this documentation, see
section 11.
3.3 Discussion.
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. 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 eleven cover types were selected primarily to conform with the cover
types required as input to climate models. 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.
The global land cover data set is the result of a maximum likelihood
classification of eleven cover types. 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, and 2) the "tundra" cover type may be inaccurate because of
missing data at high latitudes.
4. THEORY OF MEASUREMENTS
Not available at this revision.
5. EQUIPMENT
5.1 Instrument Description.
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. in press) . 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.
5.1.1 Platform (Satellite, Aircraft, Ground, Person...).
Not applicable.
5.1.2 Mission Objectives.
Not applicable.
5.1.3 Key Variables.
Not applicable.
5.1.4 Principles of Operation.
Not applicable.
5.1.5 Instrument Measurement Geometry .
Not applicable.
5.1.6 Manufacturer of Instrument.
Not applicable.
5.2 Calibration.
5.2.1 Specifications.
Not applicable.
5.2.1.1 Tolerance.
5.2.2 Frequency of Calibration.
Not applicable.
5.2.3 Other Calibration Information.
Not applicable.
6. PROCEDURE
6.1 Data Acquisition Methods.
Not available at this revision.
6.2 Spatial Characteristics.
6.2.1 Spatial Coverage.
The coverage is global. Data in file are ordered from North to
South and from West to East beginning at 180 degrees West and 90
degrees North. Point (1,1) represents the grid cell centered at
89.5 N and 179.5 W (see section 8.4).
For additional information on acquisition and processing of the
data sets that were used to derive global land cover, see (Los,
et al. 1994), (Sellers, et al. 1994, 1995b), and (James and
Kalluri, 1994) .
6.2.2 Spatial Resolution.
The data are given in an equal-angle lat/long grid that has a
spatial resolution of 1 X 1 degree lat/long.
6.3 Temporal Characteristics.
6.3.1 Temporal Coverage.
The data set is derived from data collected in 1987.
6.3.2 Temporal Resolution.
Not applicable.
7. OBSERVATIONS
7.1 Field Notes.
Not applicable.
8. DATA DESCRIPTION
8.1 Table Definition With Comments.
--------------------------------------------------------------------------------
| 8.2.1 | | | |
|Parameter/Variable Name | | | |
--------------------------------------------------------------------------------
| | 8.2.2 | 8.2.3 | 8.2.4 | 8.2.5 |
| | Parameter/Variable Description |Range |Units |Source |
--------------------------------------------------------------------------------
|LAND_COVER_CLASSIFICATIONS | | | |
| | |Min = 0 |Not |DeFries & |
| | Value Land Cover class |Max = 15 |Applicable |Townshend |
| | ===== ================ | | | |
| | 0 0 water | | | |
| | 1 1 broadleaf evergreen forest | | | |
| | 2 2 broadleaf deciduous forest | | | |
| | and woodland | | | |
| | 3 3 mixed coniferous and broad-| | | |
| | leaf deciduous forest and | | | |
| | woodland | | | |
| | 4 4 coniferous forest and | | | |
| | woodland | | | |
| | 5 5 high latitude deciduous | | | |
| | forest and woodland | | | |
| |6,8 6 wooded c4 grassland | | | |
| | 7 6 c4 grassland | | | |
| | 9 7 shrubs and bare ground | | | |
| |10 8 tundra | | | |
| |11 6 desert, bare ground | | | |
| |12 9 cultivation | | | |
| |13 ice | | | |
| |14 9 c3 wooded grassland | | | |
| |15 9 c3 grassland | | | |
--------------------------------------------------------------------------------
The data values in the first column are consistent with SiB vegetation classes
(Dorman and Sellers, 1989). It was not possible to separate SiB vegetation
classes 6 (broadleaf trees with groundcover) and 8 (broadleaf shrubs with
groundcover) using the classification method described here. Class 6,
therefore, includes both types, and there are no class 8 values in the data
set.
In the SiB2 GCM application of Sellers et. al. (1995a, b) and for the purpose
of producing the NDVI related data sets elsewhere on the CD-ROM, this
classification is simplified to the right-hand column, where most
tropical seasonal biomes are assigned C4 grassland properties and temperate
biomes with c3 ground cover are assigned cultivation properties. For the
FASIR corrections (see FASIR document elsewhere on the CD-ROM) classes 1,6 and
14 were merged and represented classes 1, 6, 8 and 14, classes 2 and 3 were
merged, class 4 represented class 4 and 5 and class 12 represented classes
7,9,10,11,12 and 15.
The cover class descriptions used in DeFries and Townshend (1994a) differ
somewhat from the classes shown in Table 8.2.2. Their method resolved 11
classes which are regrouped into SiB classes as shown in the following Table.
--------------------------------------------------------------------------------
| 8.2.6 | 8.2.7 |
--------------------------------------------------------------------------------
| Classification numbers | DeFries and Townshend |
| from the first column | |
| in Table 8.2.1 | nomenclature |
--------------------------------------------------------------------------------
| | |
| 1 | Broadleaf evergreen trees |
| 2 | Broadleaf deciduous trees |
| 3 | Mixed trees |
| 4 | Needleleaf evergreen trees |
| 5 | High latitude deciduous trees |
| 6,8 | Grass with 10 - 40% woody cover |
| 7 | Grass with <10% woody cover |
| 9 | Shrubs and bare soil |
| 10 | Moss and lichens |
| 11 | Bare |
| 12 | Cultivated |
--------------------------------------------------------------------------------
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).
8.3 Sample Data Base Data Record.
Not applicable.
8.4 Data Format.
The CD-ROM file format is ASCII, and consists of numerical fields of
varying length, which are space delimited and arranged in columns and
rows. Each column contains 180 numerical values and each row contain 360
numerical values.
Grid arrangement
ARRAY(I,J)
I = 1 IS CENTERED AT 179.5W
I INCREASES EASTWARD BY 1 DEGREE
J = 1 IS CENTERED AT 89.5N
J INCREASES SOUTHWARD BY 1 DEGREE
90N - | - - - | - - - | - - - | - -
| (1,1) | (2,1) | (3,1) |
89N - | - - - | - - - | - - - | - -
| (1,2) | (2,2) | (3,2) |
88N - | - - - | - - - | - - - | - -
| (1,3) | (2,3) | (3,3) |
87N - | - - - | - - - | - - - |
180W 179W 178W 177W
ARRAY(360,180)
8.5 Related Data Sets.
For other global land cover data sets, see (Matthews 1983) , (Olson, et
al. 1983), (Wilson and Henderson-Sellers 1985) and vegetation
classification map (Dorman and Sellers 1989, Nobre et al. 1991).
9. DATA MANIPULATIONS
9.1 Formulas.
9.1.1 Derivation Techniques/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.
9.2 Data Processing Sequence.
9.2.1 Processing Steps and Data Sets.
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.
9.2.2 Processing Changes.
Not applicable.
9.3 Calculations.
9.3.1 Special Corrections/Adjustments.
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.
The following modifications have been made to the global land
cover data set by:
G. James Collatz and Sietse Los, Biospheric Sciences Branch,
Code 923, NASA/Goddard Space Flight Center, Greenbelt MD 20771.
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) in the
following ways:
a) The Matthews (1983) vegetation map is used as the global
land/ocean mask except for Africa where Kuchler (1983) is used.
b) 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 groundcover understory.
c) 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.
d) 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).
9.4 Graphs and Plots.
See DeFries and Townshend (1994a).
10. ERRORS
10.1 Sources of Error.
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.
10.2 Quality Assessment.
10.2.1 Data Validation by Source.
The data set has not been systematically validated.
10.2.2 Confidence Level/Accuracy Judgment.
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.
10.2.3 Measurement Error for Parameters and Variables.
Not available.
10.2.4 Additional Quality Assessment Applied.
None.
11. NOTES
11.1 Known Problems With The Data.
See section 10.2.
11.2 Usage Guidance.
See section 10.2.
11.3 Other Relevant Information.
The following two 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
Tables: Time-invariant land surface properties. These 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, 1995b and
Sellers et al., 1995a,b and papers referenced) and may need to be
adapted for use in other models. (Parameters are from Sellers et al.,
1995a,b).
______________________________________________________________________________
______________________________________________________________________________
11.3.1 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 area density z_c m 28.0 17.0 15.0 10.0 10.0 0.6
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
factor chi_l - 0.1 0.25 0.13 0.01 0.01 -0.3
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, visible,
live alpha_v,l - 0.1 0.1 0.07 0.07 0.07 0.11
Leaf reflectance, visible,
dead alpha_v,d - 0.16 0.16 0.16 0.16 0.16 0.36
Leaf reflectance, near IR,
live alpha_n,l - 0.45 0.45 0.4 0.35 0.35 0.58
Leaf reflectance, near IR,
dead alpha_n,d - 0.39 0.39 0.39 0.39 0.39 0.58
Leaf transmittance, visible,
live delta_v,l - 0.05 0.05 0.05 0.05 0.05 0.07
Leaf transmittance, visible,
dead delta_v,d - 0.001 0.001 0.001 0.001 0.001 0.22
Leaf transmittance, near IR,
live delta_n,l - 0.25 0.25 0.15 0.1 0.1 0.25
Leaf transmittance, near IR,
dead delta_n,d - 0.001 0.001 0.001 0.001 0.001 0.38
Soil reflectance, visible a_s,n - 0.11 0.11 0.11 0.11 0.11 0.11*
Soil reflectance, near IR a_s,v - 0.225 0.225 0.225 0.225 0.225 0.225*
Maximum rubisco capacity, mol m^-2
top leaf V_max0 s^-1 6e-5 6e-5 6e-5 6e-5 6e-5 3e-5
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 stomatal mol m^-2
conductance b s^-1 0.01 0.01 0.01 0.01 0.01 0.04
Photosynthesis coupling
coefficient beta_ce - 0.98 0.98 0.98 0.98 0.98 0.8
High temperature stress
factor, photosynthesis s_2 K 313 311 307 303 303 313
Low temperature stress
factor, photosynthesis s_4 K 288 283 281 278 278 288
Minimum leaf resistance** r_min s m^-1 80 80 100 120 120 110
_____________________________________________________________________________
11.3.2 Biome dependent parameters continued.
_____________________________________________________________________________
SiB Vegetation Type
Name Symbol Units 7 8 9 10 11 12
_____________________________________________________________________________
Canopy top height z_2 m 1.0 1.0 0.5 0.6 1.0 1.0
Inflection height for
leaf area density z_c m 0.6 0.6 0.3 0.35 0.6 0.6
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
factor chi_l - -0.3 -0.3 0.01 0.2 -0.3 -0.3
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, visible,
live alpha_v,l - 0.11 0.11 0.1 0.11 0.11 0.11
Leaf reflectance, visible,
dead alpha_v,d - 0.36 0.36 0.16 0.36 0.36 0.36
Leaf reflectance, near IR,
live alpha_n,l - 0.58 0.58 0.45 0.58 0.58 0.58
Leaf reflectance, near IR,
dead alpha_n,d - 0.58 0.58 0.39 0.58 0.58 0.58
Leaf transmittance, visible,
live delta_v,l - 0.07 0.07 0.05 0.07 0.07 0.07
Leaf transmittance, visible,
dead delta_v,d - 0.22 0.22 0.001 0.22 0.22 0.22
Leaf transmittance, near IR,
live delta_n,l - 0.25 0.25 0.25 0.25 0.25 0.25
Leaf transmittance, near IR,
dead delta_n,d - 0.38 0.38 0.001 0.38 0.38 0.38
Soil reflectance, visible a_s,n - 0.11* 0.15* 0.3* 0.11 0.3* 0.1
Soil reflectance, near IR a_s,v - 0.225* 0.25* 0.35* 0.23 0.35* 0.15
Maximum rubisco capacity, mol m^-2
top leaf V_max0 s^-1 3e-5 3e-5 6e-5 6e-5 3e-5 6e-5
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 stomatal mol m^-2
conductance b s^-1 0.04 0.04 0.01 0.01 0.04 0.01
Photosynthesis coupling
coefficient beta_ce - 0.8 0.8 0.98 0.98 0.8 0.98
High temperature stress
factor, photosynthesis s_2 K 313 313 313 303 313 308
Low temperature stress
factor, photosynthesis s_4 K 288 288 288 278 288 281
Minimum leaf resistance** r_min s m^-1 110 110 80 80 110 80
_____________________________________________________________________________
*Soil reflectance for areas with bare soil are specified according to ERBE
data which is available elsewhere on this CD ROM.
**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, Agric. For. Meteor., 54:107-136. 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 elsewhere on this CD-ROM.
______________________________________________________________________________
Biome independent parameters
______________________________________________________________________________
Name symbol units value
______________________________________________________________________________
Ground roughness length z_s m 0.05
Augmentation factor for momentum G_1 - 1.449
Transition height factor for momentum G_4 - 11.785
Depth of surface soil layer D_1 m 0.02
Rubisco Michaels-Menten constant for CO2 K_c Pa 30*2.1^Qt
Rubisco inhibition constant for oxygen K_o Pa 30,000*1.2^Qt
Rubisco specificity for CO2 relative to S - 2,600*0.57^Qt
oxygen
Q10 temperature coefficient Qt - (T-298)/10
Photosynthesis coupling coefficient beta_ps - 0.95
High temperature stress factor, photosynthesis s_1 K^-1 0.3
Low temperature stress factor, photosynthesis s_3 K^-1 0.2
High temperature stress factor, respiration s_5 K^-1 1.3
High temperature stress factor, respiration s_6 K 328
Leaf respiration factor f_d - 0.015
______________________________________________________________________________
______________________________________________________________________________
12. REFERENCES
12.1 Satellite/Instrument/Data Processing Documentation.
None.
12.2 Journal Articles and Study Reports.
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).
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, 1995a. 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, 1995b. 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.
12.3 Archive/DBMS Usage Documentation.
Contact the EOS Distributed Active Archive Center (DAAC) at NASA Goddard
Space Flight Center (GSFC), Greenbelt Maryland (see Section 13 below).
Documentation about using the archive or information about access to the
on-line information system is available through the GSFC DAAC User
Services Office.
13. DATA ACCESS
13.1 Contacts for Archive/Data Access Information.
GSFC DAAC User Services
NASA/Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Phone: (301) 286-3209
Fax: (301) 286-1775
Internet: daacuso@eosdata.gsfc.nasa.gov
13.2 Archive Identification.
Goddard Distributed Active Archive Center
NASA Goddard Space Flight Center
Code 902.2
Greenbelt, MD 20771
Telephone: (301) 286-3209
FAX: (301) 286-1775
Internet: daacuso@eosdata.gsfc.nasa.gov
13.3 Procedures for Obtaining Data.
Users may place requests by accessing the on-line system, by sending
letters, electronic mail, FAX, telephone, or personal visit.
Accessing the GSFC DAAC Online System:
The GSFC DAAC Information Management System (IMS) allows users to
ordering data sets stored on-line. The system is open to the public.
Access Instructions:
Node name: daac.gsfc.nasa.gov
Node number: 192.107.190.139
Login example: telnet daac.gsfc.nasa.gov
Username: daacims
password: gsfcdaac
You will be asked to register your name and address during your first
session.
Ordering CD-ROMs:
To order CD-ROMs (available through the Goddard DAAC) users should
contact the Goddard DAAC User Support Office (see section 13.2).
13.4 GSFC DAAC Status/Plans.
The ISLSCP Initiative I CD-ROM is available from the Goddard DAAC.
14. OUTPUT PRODUCTS AND AVAILABILITY
14.1 Tape Products.
None.
14.2 Film Products.
None.
14.3 Other Products.
None.
15. GLOSSARY OF ACRONYMS
AVHRR Advanced Very High Resolution Radiometer
CD-ROM Compact Disk (optical), Read Only Memory
DAAC Distributed Active Archive Center
EOS Earth Observing System
GCM General Circulation Model of the atmosphere
GSFC Goddard Space Flight Center
IDS Inter-disciplinary Science
ISLSCP International Satellite Land Surface Climatology Project
NASA National Aeronautics and Space Administration
NDVI Normalized Difference Vegetation Index