Dr. Ruth DeFries
Department of Geography
University of Maryland, College Park
Dr. J.R.G. Townshend
Department of Geography
University of Maryland, College Park
_________________________________________________________ | 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