PLEASE NOTE: This is the Version 1.2 release of the global land cover characteristics data base. It has not yet been validated and is subject to review. Following validation, the data base will be modified based on the lessons learned. Comments from users of this data set are encouraged and will be considered for incorporation in later versions.

Table of Contents

1.0 Introduction

The U.S. Geological Survey's (USGS) Earth Resources Observation System (EROS) Data Center, the University of Nebraska-Lincoln (UNL) and the Joint Research Centre of the European Commission are generating a 1-km resolution global land cover characteristics data base for use in a wide range of environmental research and modeling applications (Loveland and others, in press). The land cover characterization effort is part of the National Aeronautics and Space Administration (NASA) Earth Observing System Pathfinder Program and the International Geosphere-Biosphere Programme-Data and Information System focus 1 activity. Funding for the project is provided by the USGS, NASA, U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, U.S. Forest Service, and the United Nations Environment Programme.

The data set is derived from 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning a 12-month period (April 1992-March 1993) and is based on a flexible data base structure and seasonal land cover regions concepts. Seasonal land cover regions provide a framework for presenting the temporal and spatial patterns of vegetation in the data base. The regions are composed of relatively homogeneous land cover associations (for example, similar floristic and physiognomic characteristics) which exhibit distinctive phenology (that is, onset, peak, and seasonal duration of greenness), and have common levels of primary production.

Rather than being based on precisely defined mapping units in a predefined land cover classification scheme, the seasonal land cover regions serve as summary units for both descriptive and quantitative attributes. The attributes may be considered as spreadsheets of region characteristics and permit updating, calculating, or transforming the entries into new parameters or classes. This provides the flexibility for using the land cover characteristics data base in a variety of models without extensive modification of model inputs.

The analytical strategy for global land cover characterization has evolved from methods initially tested during the development of a prototype 1-km land cover characteristics data base for the conterminous United States (Loveland and others, 1991, 1995; Brown and others, 1993). In the U.S. study, multitemporal AVHRR data, combined with other ancillary data sets, were used to produce a prototype land cover characteristics data base.

2.0 Implementation Approach

The global land cover characteristics data base is being developed on a continent-by-continent basis. All continents in the global data base share the same map projection (Interrupted Goode Homolosine), have 1-km nominal spatial resolution, and are based on 1-km AVHRR data spanning April 1992 through March 1993. While each continental data base has unique elements based on the salient geographic aspects of the specific continent, there are a common set of derived thematic maps produced through the aggregation of seasonal land cover regions. The thematic maps include:

Following the completion of the global data set, other attributes may be defined that would include region characteristics such as elevation (mean, median, mode, minimum, maximum, variance), representative soil characteristics, biome membership, and multitemporal NDVI statistics (mean, median, mode, minimum, maximum, variance) for each monthly composite period. All data used or generated during the course of the project (source, interpretations, attributes, and derived data), unless protected by copyrights or trade secret agreements, will be distributed.

3.0 Source Data

One-kilometer AVHRR NDVI composites are the core data set used in land cover characterization. In addition, other key geographic data include digital elevation data, ecoregions interpretations, and country or regional-level vegetation and land cover maps. See Brown and others (1993) for a detailed discussion of the role of ancillary data for land cover characterization.

3.1 AVHRR Data

The base data used are the International Geosphere Biosphere Programme (IGBP) 1-km AVHRR 10-day composites for April 1992 through March 1993 (Eidenshink and Faundeen, 1994). Multitemporal AVHRR NDVI data are used to divide the landscape into land cover regions, based on seasonality. While the primary AVHRR data used in the classification is NDVI, the individual channel data sets are used for postclassification characterization of certain landscape properties. A data quality evaluation was conducted and is reported by Zhu and Yang (1996).

3.2 Digital Elevation Model (DEM) Data

DEM data are used to model the ecological factors governing natural vegetation distribution, and are important for identifying land cover types and stratifying seasonal regions representing two or more disparate vegetation types.

3.3 Ecoregions Data

Ecological regions data are used to identify regions with disparate land cover types and for stratifying seasonal regions representing two or more disparate vegetation types. Both continental and country level ecoregions data are used in this process.

3.4 Map Data

Maps and atlases of ecoregions, soils, vegetation, land use, and land cover are used in the interpretation phase of the study and serve as reference data to guide class labeling.

4.0 Technical Description of Characterization Methods

The methods used can be described as a multitemporal unsupervised classification of NDVI data with post-classification refinement using multi-source earth science data. Monthly AVHRR NDVI maximum value composites for April, 1992 through March, 1993 are used to define seasonal greenness classes. Past investigations have demonstrated that classes developed from multitemporal NDVI data represent characteristic patterns of seasonality and correspond to relative patterns of productivity (Loveland and others, 1991; Brown and others, 1993).

The translation of the seasonal greenness classes to seasonal land cover regions require post-classification refinement with the addition of digital elevation, ecoregions data and a collection of other land cover/vegetation reference data. The interpretation is based on extensive use of computer-assisted image processing tools (Brown and others, in press); however, the classification process is not automated and more closely resembles a traditional manual image interpretation philosophy. There is a reliance on the skills of the human interpreter to make the final decisions regarding the relationship between spectral classes defined using unsupervised methods and landscape characteristics that are used to make land cover definitions.

4.1 AVHRR Data Preparation

The initial step in the process involves the preparation of the AVHRR NDVI data for use in the unsupervised classification. This requires recompositing the 10day composites into monthly data sets. The use of monthly rather than 10day composites represents a compromise between temporal frequency and the need for cloudfree data (Moody and Strahler, 1994). It also provides a means to reduce data volume while maintaining annual phenological information. Experience has shown that composites representing a longer period are more suitable for image classification due to the substantial improvement in composite quality (Zhu and Yang, 1996).

Masks representing water bodies, snow and ice, and barren or sparsely vegetated areas are developed to eliminate NDVI data from the composites for those areas where the meaning of the NDVI values is ambiguous. In addition, the masked data set has a reduced overall variance and the classes defined using unsupervised classifications are therefore more representative of landscape patterns. The water mask is developed through the interpretation of single-date AVHRR channel 2 (near-infrared) images supplemented with water body information taken from the Digital Chart of the World (Defense Mapping Agency, 1992). Snow and ice, barren, and sparsely vegetated masks are produced from a 12-month maximum value NDVI composite threshold values that vary according to continental characteristics.

4.2 Unsupervised Classification

The initial segmentation of the 12-month NDVI composites into seasonal greenness classes is performed using unsupervised clustering. This classification method is often used for studies in which the location and characteristics of specific classes are unknown. Unsupervised classification uses clustering to identify "natural" groupings of pixels with similar NDVI properties. In this case, the clusters correspond to annual sequences of greenup, peak, and senescence. The specific clustering algorithm used is CLUSTER, a variation of the K-Means algorithm that has been optimized for use with large data sets (Kelly and White, 1993). It is an iterative statistical clustering algorithm that defines clusters or groups of NDVI values with similar properties. The clustering is controlled by predetermined parameters for number of iterations and number of resulting clusters. The clusters are defined by channel mean vectors and covariance matrices. The specific number of clusters for each continent was based on an empirical evaluation of several clustering trials.

4.3 Preliminary Labeling

The purposes of the preliminary labeling step are to provide a general understanding of the characteristics of each cluster (or seasonal greenness class) and to determine which classes have two or more disparate land cover classes represented within their spatial distribution (e.g., a class may include a mixture of both broadleaf deciduous trees/shrubs and cropland). Preliminary labeling involves inspecting the spatial patterns and spectral or multitemporal statistics of each class, comparing each class to reference data, and making decisions concerning land cover types.

The preliminary labeling step includes two primary tasks. The first is the generation of statistics and graphics for each class, describing their relationship to the available ancillary data (for example, graphs profiling the temporal sequence of NDVI, graphs of class elevation ranges, and tabular summaries comparing the seasonal greenness classes to nominal data sets). The second task is the interpretation of the summaries, graphs, and reference data to determine the general land cover type or types associated with each seasonal greenness class and to identify the classes that represent two or more disparate land cover types. Typically, a minimum of three interpreters label each class. Where differences exist, the interpreters compare decisions and consult reference materials in order to arrive at a consensus.

4.4 Postclassification Stratification

Post-classification stratification is used to separate classes containing two or more disparate land cover types. Experience has shown that at least 70% of the seasonal greenness classes represent multiple land cover types (Brown and others, 1993; Running and others, 1995). Most of these types of problems are the result of spectral similarities between natural and agricultural land cover. These problems can usually be solved by developing criteria based on the relationship between the confused seasonal greenness classes and selected ancillary data sets. Elevation and ecoregions data have proven to be the most useful ancillary variables for post-classification stratification (Brown and others, 1993).

There are two tasks involved in the post-classification stratification step. The first is to determine the ancillary variables and preliminary decision rules that separate the classes identified in the preliminary labeling step as having multiple land cover types. The second task is to implement and refine the decision rules. Generally, this is an iterative process in which the initial criteria are tested, refined, and finally used to permanently modify the original class. This results in a number of new seasonal greenness classes, that through the following step, become the final map units (seasonal land cover regions). A complete history of the processing of each class is maintained.

4.5 Final Land Cover Characterization

Following the generation of the seasonal land cover regions in the postclassification stratification step, the remaining steps in data base development are: (1) generate final attributes; (2) determine the land cover type or types for each class; and (3) derive thematic data sets.

As with the preliminary labeling step, the final land cover characterization involves generating a suite of attributes that describe the characteristics of each seasonal land cover region. Both statistics and contingency tables are created between the final seasonal land cover regions layer and the respective ancillary variables (NDVI, AVHRR channels 15, elevation, ecoregions, etc.). The attributes are part of the global land cover characteristics database and, in addition, are used as evidence in the determining the final land cover types.

A convergence of evidence approach is used to determine the land cover type for each seasonal land cover class. All available documentation, including the region attributes, image maps, atlases, other existing land cover/vegetation maps, and any other relevant materials are consulted and compared to the spatial distribution of each region. As before, at least three interpreters are used to insure consistency.

The seasonal land cover regions are then translated into the Global Ecosystem framework (1994a, 1994b). Olson has defined 94 ecosystem classes that are based on their land cover mosaic, floristic properties, climate, and physiognomy. The Global Ecosystem framework provides a mechanism for tailoring data to the unique landscape conditions of each continent, while still providing a means for summarizing the data at the global level. The Global Ecosystem types have been cross-referenced to land cover classes in the Simple Biosphere Model (SiB), Simple Biosphere 2 Model, the Biosphere Atmosphere Transfer Scheme (BATS), International GeosphereBiosphere Programme (IGBP), and USGS/Anderson (see Table 1).

Table 1. Example translation table to derived data legends.

Cluster Label   Global Ecosystem   BATS Scheme      SiB Scheme     IGBP Scheme      

pine forest     coniferous        evergreen        evergreen      needleleaf       
                evergreen forest  needleleaf       needleleaf     trees and        
                                                   trees          shrubs           

oak/pine mixed  mixed forest      mixed forest     broadleaf      mixed trees and  
forest                                             deciduous      shrubs           

The final task associated with this step is the generation of the derived data sets, including land cover and seasonal measures. In this step, the seasonal land cover regions are aggregated (or renumbered) into the appropriate classes of the output classification legends. Urban areas, extracted from the Digital Chart of the World (Defense Mapping Agency, 1992)are added to three of the derived data sets: Global Ecosystems, IGBP Land Cover, and the USGS Land Use/Land Cover system.

5.0 Derived Data Sets

The following derived data sets are included in the global land cover data base:

The legends for each of these derived data sets can be found in Appendices 1-6.

6.0 Data Format and Access

The global land cover characteristics data are in a flat, headerless raster format. The raster images contain class number values for each pixel that correspond to the appropriate land cover classification scheme legend. Data are distributed as compressed and uncompressed single-band images. The files can be obtained either by anonymous file transfer protocol (ftp) or downloaded from the EDC Distributed Active Archive Center (DAAC) World Wide Web site: ( The instructions for accessing these files are contained in the following two sections.

6.1 Anonymous FTP for PC or UNIX Users

  1. Contact the FTP server. At your command prompt, use the command: ftp
  2. Login as an anonymous user and use your email address as the password.
  3. Change to the directory pub/data/glcc
  4. Change to the global subdirectory, globe
  5. Locate the file of interest (a complete file listing is located in section 8.0 of this readme). The image files were compressed using the GNU 'gzip' software and can be identified by the ".gz" file extension. These files must be uncompressed before use with the 'gunzip' utility, a part of the 'gzip' software. Directions for acquiring the gzip software from GNU can be found on the WWW on the GNU home page ( If you do not have access to gzip, the ftp server can uncompress the files as they are retrieved.
  6. To FTP compressed files:
  7. To FTP uncompressed files:

6.2 Downloading from the World Wide Web Site

On the Global Land Cover page contains links to all documentation files and the image files (both compressed and uncompressed). NOTE: World Wide Web browsers can vary in how the files will be downloaded. On PCs, some browsers will allow a user to interactively select the location where the file will be saved and to edit the file name. However, on certain browsers files may be automatically downloaded to a default storage location on the local system.

  1. Select either button, compressed or uncompressed, for the file of interest.
  2. A pop-up screen may appear, showing information on the download procedure. At this point, choose the directory in which to save the file.
  3. For compressed files, the .gz extension must be added to the filename before downloading. However, if the browser will not accept adding an extension, continue with the download, and then rename the file with the .gz extension when the download is complete.
  4. For uncompressed files, leave the .gz extension off. If a pop-up screen shows the filename with a .gz extension either edit the filename, or proceed with the download and rename the file without the extension after the procedure is complete.

7.0 Geometric Characteristics

The land cover characteristics data base is available for each of five continental areas and for the entire globe. The continental land cover characteristics data bases are available in two different map projections: the Interrupted Goode Homolosine and the Lambert Azimuthal Equal Area (see Steinwand, 1994, and Steinwand and others, 1995, for a complete description of these projections). The geometric characteristics for each continent are described in the individual documentation files for each continental data set. The global data are available in one projection only--the Interrupted Goode Homolosine projection.

The data dimensions of the Interrupted Goode Homolosine projection for the global land cover characteristics data set are 17347 lines (rows) and 40031 samples (columns) resulting in a data set size of approximately 695 megabytes for 8-bit (byte) images. The following is a summary of the map projection parameters used for the global data sets:

Projection Type: Interrupted Goode Homolosine

8.0 File Listing


/pub/data/glcc/globe                                                                      Globe

globedoc1_2.txt                                     ascii                                   Global readme text
bats.leg                                                  ascii                                    BATS legend
igbp.leg                                                  ascii                                    IGBP legend
oge.leg                                                   ascii                                    Global Ecosystems legend
sib.leg                                                    ascii                                    SIB legend
sib2.leg                                                  ascii                                    SIB2 legend
usgs.leg                                                  ascii                                    USGS land use/land cover legend
gbats1_2.img.gz                                     byte                                    BATS image
gigbp1_2.img.gz                                     byte                                    IGBP image
goge1_2.img.gz                                      byte                                    Global Ecosystems image
gsib1_2.img.gz                                       byte                                    SIB image
gsib21_2.img.gz                                     byte                                    SIB2 image
gusgs1_2.img.gz                                     byte                                    USGS land use/land cover image

9.0 References

Anderson, J.R., Hardy, E.E., Roach J.T., and Witmer R.E., 1976, A land use and land cover classification system for use with remote sensor data: U.S. Geological Survey Professional Paper 964, 28 p.

Belward, A.S., ed., 1996, The IGBP-DIS global 1 km land cover data set (DISCover)-proposal and implementation plans: IGBP-DIS Working Paper No. 13, Toulouse, France, 61 p.

Brown, J.F., Loveland, T.R., Merchant, J.W., Reed, B.C., and Ohlen, D.O., 1993, Using multisource data in global land cover characterization: concepts, requirements and methods: Photogrammetric Engineering and Remote Sensing, v. 59, p. 977-987.

Brown, J.F., Reed, B.C, and Huewe L., in press. Advanced strategy for multi-source analysis and visualization in land cover characterization, in Proceedings, Pecora 13, Human Interactions with the Environment: Perspectives From Space.

Defense Mapping Agency, 1992, Development of the Digital Chart of the World: Washington, D.C., U.S. Government Printing Office.

Dickinson, R.E., Henderson-Sellers, A., Kennedy, P.J., and Wilson, M.F., 1986, Biosphere-atmosphere transfer scheme (BATS) for the NCAR community climate model: NCAR Technical Note NCAR/TN275+STR, Boulder, CO. 69 p.

Eidenshink, J.C. and Faundeen, J.L., 1994, The 1 km AVHRR global land data set-first stages in implementation: International Journal of Remote Sensing, v. 15, no. 17, p. 3,443-3,462.

Kelly, P.M. and White, J.M., 1993, Preprocessing remotely sensed data for efficient analysis and classification, in SPIE Vol. 1963, Applications of artificial intelligence, 1993--knowledge-based systems in aerospace and industry, p. 2,430-2,439

Loveland, T.R., Merchant, J.W., Brown, J.F., Ohlen, D.O., Reed, B.C., Olson, P., and Hutchinson, J., 1995, Seasonal land-cover regions of the United States: Annals of the Association of American Geographers, v. 85, no. 2, p. 339-355.

Loveland, T.R., Merchant, J.W., Ohlen, D.O. and Brown, J.F., 1991, Development of a land-cover characteristics database for the conterminous U.S.: Photogrammetric Engineering and Remote Sensing, v. 57, no. 11, p. 1,453-1,463.

Loveland, T.R., Ohlen, D.O., Brown, J.F., Reed, B.C., Zhu, Z., Merchant, J.W., and Yang, L., in press. Western hemisphere land cover-progress toward a global land cover characteristics database, in Proceedings, Pecora 13, Human Interventions with the Environment: Perspectives From Space.

Moody, A., and Strahler, A.H., 1994, Characteristics of composited AVHRR data and problems in their classification: International Journal of Remote Sensing, v. 15, no. 17, p. 3,473-3,491.

Olson, J.S., 1994a, Global ecosystem framework-definitions: USGS EROS Data Center Internal Report, Sioux Falls, SD, 37 p.

_____ 1994b, Global ecosystem framework-translation strategy: USGS EROS Data Center Internal Report, Sioux Falls, SD, 39 p.

Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., and Ohlen, D.O., 1994, Variability of land cover phenology in the United States: Journal of Vegetation Science, v. 5, p. 703-714.

Running, S.W., Loveland, T.R., Pierce, L.L., R.R. Nemani, and Hunt, E.R., 1995, A remote sensing based vegetation classification logic for global land cover analysis: Remote Sensing of Environment, v. 51, p. 3,948-3,952.

Sellers, P.J., Mintz, Y., Sud, Y.C., and Dalcher A., 1986, A simple biosphere model (SiB) for use within general circulation models: Journal of Atmospheric Science, v. 43, p. 505-531.

Sellers, P.J., Randall, D.A., Collatz, G.J., Berry, J.A., Field, C.B., Dazlich, D.A., Zhang, C., Collelo, G.D., and Bounoua, L., 1996, A revised land surface parameterization (SiB2) for atmospheric GCMs - Part I-model formulation: Journal of Climate, v. 9, p. 676-705.

Steinwand, D.R., 1994, Mapping raster imagery to the Interrupted Goode Homolosine projection: International Journal of Remote Sensing, v. 15, no. 17, p. 3,463-3,472.

Steinwand, D.R., Hutchinson, J.A., and Snyder, J.P. ,1995, Map projections for global and continental data sets and an analysis of pixel distortion caused by reprojection: Photogrammetric Engineering and Remote Sensing, v. 61, p. 1,487-1,497.

Zhu, Z., and Yang, L., 1996, Characteristics of the 1-km AVHRR data set for North America: International Journal of Remote Sensing, v. 17, p. 1,915-1,924.

Appendix 1.

Global Ecosystems Legend

Value Description

1 Urban

2 Low Sparse Grassland

3 Coniferous Forest

4 Deciduous Conifer Forest

5 Deciduous Broadleaf Forest

6 Evergreen Broadleaf Forests

7 Tall Grasses and Shrubs

8 Bare Desert

9 Upland Tundra

10 Irrigated Grassland

11 Semi Desert

12 Glacier Ice

13 Wooded Wet Swamp

14 Inland Water

15 Sea Water

16 Shrub Evergreen

17 Shrub Deciduous

18 Mixed Forest and Field

19 Evergreen Forest and Fields

20 Cool Rain Forest

21 Conifer Boreal Forest

22 Cool Conifer Forest

23 Cool Mixed Forest

24 Mixed Forest

25 Cool Broadleaf Forest

26 Deciduous Broadleaf Forest

27 Conifer Forest

28 Montane Tropical Forests

29 Seasonal Tropical Forest

30 Cool Crops and Towns

31 Crops and Town

32 Dry Tropical Woods

33 Tropical Rainforest

34 Tropical Degraded Forest

35 Corn and Beans Cropland

36 Rice Paddy and Field

37 Hot Irrigated Cropland

38 Cool Irrigated Cropland

39 Cold Irrigated Cropland

40 Cool Grasses and Shrubs

41 Hot and Mild Grasses and Shrubs

42 Cold Grassland

43 Savanna (Woods)

44 Mire, Bog, Fen

45 Marsh Wetland

46 Mediterranean Scrub

47 Dry Woody Scrub

48 Dry Evergreen Woods

49 Volcanic Rock

50 Sand Desert

51 Semi Desert Shrubs

52 Semi Desert Sage

53 Barren Tundra

54 Cool Southern Hemisphere Mixed Forests

55 Cool Fields and Woods

56 Forest and Field

57 Cool Forest and Field

58 Fields and Woody Savanna

59 Succulent and Thorn Scrub

60 Small Leaf Mixed Woods

61 Deciduous and Mixed Boreal Forest

62 Narrow Conifers

63 Wooded Tundra

64 Heath Scrub

65 Coastal Wetland, NW

66 Coastal Wetland, NE

67 Coastal Wetland, SE

68 Coastal Wetland, SW

69 Polar and Alpine Desert

70 Glacier Rock

71 Salt Playas

72 Mangrove

73 Water and Island Fringe

74 Land, Water, and Shore (see Note 1)

75 Land and Water, Rivers (see Note 1)

76 Crop and Water Mixtures

77 Southern Hemisphere Conifers

78 Southern Hemisphere Mixed Forest

79 Wet Sclerophylic Forest

80 Coastline Fringe

81 Beaches and Dunes

82 Sparse Dunes and Ridges

83 Bare Coastal Dunes

84 Residual Dunes and Beaches

85 Compound Coastlines

86 Rocky Cliffs and Slopes

87 Sandy Grassland and Shrubs

88 Bamboo

89 Moist Eucalyptus

90 Rain Green Tropical Forest

91 Woody Savanna

92 Broadleaf Crops

93 Grass Crops

94 Crops, Grass, Shrubs

Note 1: In Version 1.2, all water is mapped to one digital value (14).

Appendix 2.

IGBP Land Cover Legend

Value Description

1 Evergreen Needleleaf Forest

2 Evergreen Broadleaf Forest

3 Deciduous Needleleaf Forest

4 Deciduous Broadleaf Forest

5 Mixed Forest

6 Closed Shrublands

7 Open Shrublands

8 Woody Savannas

9 Savannas

10 Grasslands

11 Permanent Wetlands

12 Croplands

13 Urban and Built-Up

14 Cropland/Natural Vegetation Mosaic

15 Snow and Ice

16 Barren or Sparsely Vegetated

17 Water Bodies

4.4 USGS Land Use/Land Cover System Legend (Modified Level 2)

Value  Code        Description

1      100         Urban and Built-Up Land

2      211         Dryland Cropland and Pasture

3      212         Irrigated Cropland and Pasture

4      213         Mixed Dryland/Irrigated Cropland and Pasture

5      280         Cropland/Grassland Mosaic

6      290         Cropland/Woodland Mosaic

7      311         Grassland

8      321         Shrubland

9      330         Mixed Shrubland/Grassland

10     332         Savanna

11     411         Deciduous Broadleaf Forest

12     412         Deciduous Needleleaf Forest

13     421         Evergreen Broadleaf Forest

14     422         Evergreen Needleleaf Forest

15     430         Mixed Forest

16     500         Water Bodies

17     620         Herbaceous Wetland

18     610         Wooded Wetland

19     770         Barren or Sparsely Vegetated

20     820         Herbaceous Tundra

21     810         Wooded Tundra

22     850         Mixed Tundra

23     830         Bare Ground Tundra

24     900         Snow or Ice

Appendix 4.

Simple Biosphere Model Legend

Value Description

1 Evergreen Broadleaf Trees

2 Broadleaf Deciduous Trees

3 Deciduous and Evergreen Trees

4 Evergreen Needleleaf Trees

5 Deciduous Needleleaf Trees

6 Ground Cover with Trees and Shrubs

7 Groundcover Only

8 Broadleaf Shrubs with Perennial Ground Cover

9 Broadleaf Shrubs with Bare Soil

10 Groundcover with Dwarf Trees and Shrubs

11 Bare Soil

12 Agriculture or C3 Grassland

17 Persistent Wetland

18 Dry Coastal Complexes

19 Water

20 Ice Cap and Glacier

Appendix 5.

Simple Biosphere 2 Model Legend

Value Description

1 Broadleaf Evergreen Trees

2 Broadleaf Deciduous Trees

3 Broadleaf and Needleleaf Trees

4 Needleleaf Evergreen Trees

5 Needleleaf Deciduous Trees

6 Short Vegetation/C4 Grassland

7 Shrubs with Bare Soil

8 Dwarf Trees and Shrubs

9 Agriculture or C3 Grassland

10 Water, Wetlands, Ice/Snow

Appendix 6.

Biosphere Atmosphere Transfer Scheme Legend

Value Description

1 Crops, Mixed Farming

2 Short Grass

3 Evergreen Needleleaf Trees

4 Deciduous Needleleaf Tree

5 Deciduous Broadleaf Trees

6 Evergreen Broadleaf Trees

7 Tall Grass

8 Desert

9 Tundra

10 Irrigated Crops

11 Semidesert

12 Ice Caps and Glaciers

13 Bogs and Marshes

14 Inland Water

15 Ocean

16 Evergreen Shrubs

17 Deciduous Shrubs

18 Mixed Forest

19 Interrupted Forest

20 Water and Land Mixtures

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