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Global Land Cover Characteristics Data Base Version 2.0

PLEASE NOTE: This is the Version 2.0 release of the global land cover characteristics data base. The land cover information has been updated from Version 1.2.  Please read section 9.0 for information about the revision process and what changes have been made to the database. 

Table of Contents

1.0 Introduction

The U.S. Geological Survey's (USGS), the Earth Resources Observation and Science (EROS) Center, the University of Nebraska-Lincoln (UNL) and the Joint Research Centre of the European Commission have generated 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, 2000). 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 database. 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 database was developed on a continent-by-continent basis. All continents in the global database 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. (Please note that while the native projection for the global land cover database is the Interrupted Goode Homolosine, the land cover data are now available in a Geographic projection based on user requirements.)  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:

  • Seasonal land cover regions
  • Global Ecosystems (Olson, 1994a, 1994b)
  • International Geosphere Biosphere Programme Land Cover Classification (Belward, 1996)
  • USGS Land Use/Land Cover System (Anderson and others, 1976)
  • Simple Biosphere Model (Sellers and others, 1986)
  • Simple Biosphere 2 Model (Sellers and others, 1996)
  • Biosphere-Atmosphere Transfer Scheme (Dickinson and others, 1986)
  • Vegetation Lifeforms (Running and others, 1994)

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, 1998); 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 10-day 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 Forest Evergreen Needleleaf Tree Evergreen Needleleaf Trees Evergreen Needleleaf Forest
Oak/pine mixed forest Mixed Forest Mixed Deciduous Conifer Forest Deciduous and Evergreen Trees Mixed Forest


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:

  • Global Ecosystems (Olson, 1994a, 1994b)
  • IGBP Land Cover Classification (Belward, 1996)
  • U.S. Geological Survey Land Use/Land Cover System (Anderson and others, 1976)
  • Simple Biosphere Model (Sellers and others, 1986)
  • Simple Biosphere 2 Model (Sellers and others, 1996)
  • Biosphere Atmosphere Transfer Scheme (Dickinson and others, 1986)
  • Vegetation Lifeform (Running and others, 1995)

The legends for each of these derived data sets can be found in Appendices 1-7.

6.0 Global Land Cover Validation Exercise

Accuracy statistics for one land cover layer from the version 1.2 Global Land Cover Characteristics Database (GLCC), the International Geosphere Biosphere Programme (IGBP) DISCover data set, was established by researchers at the University of California, Santa Barbara (UCSB). The UCSB validation was funded through the NASA Land Cover Land Use Change Program on behalf of the IGBP Land Cover Working Group (LCWG). While the results of this validation exercise do not provide conclusive evidence of the accuracy of the other land cover classifications included in either version 1.2 or version 2.0 GLCC, they provide a general indication of data quality.

IGBP DISCover accuracy figures were derived using a simple random sample stratified by land cover type (Belward and others, 1999). To determine the true cover type, three interpreters independently interpreted either Landsat TM or SPOT images covering each sample. In order for the AVHRR pixel to be called correct, the majority of the three interpreters (2 of 3) had to agree on the land cover type, as interpreted from Landsat or SPOT, for the sample point.  Based on the original IGBP LCWG validation protocol, the overall accuracy figures are (Scepan, 1999):

Sample point accuracy     59.4%

Area-weighted accuracy     66.9%

The area-weighted accuracy weights the importance of each class accuracy based on the land area occupied by that class.  These accuracy figures are based on the assumption that if the three people interpreting the Landsat or SPOT data could not reach a consensus on the "true" cover type (meaning there were three different answers), then the AVHRR classification was declared to be incorrect - event though there was no evidence that it actually was wrong. As a result, the LCWG developed a revised set of accuracy statistics.  These figures, referred to as "Majority Rule' accuracy, is based on the assumption that if the "true" cover for a sample could not be determined by the interpreters, then the sample should be thrown out.  Based on this assumption, the overall accuracy numbers are (Scepan, 1999):

Majority Rule Accuracy    73.5%

Area Weighted Majority Rule    78.7%

It must be noted that there is no statistical validity of these figures because of the reduction of the number of useful validation samples.

Another perspective on DISCover accuracy is provided by Defries and Los (1999).  They investigated the impacts of the accuracy of DISCover for one application, climate modeling.  In their study, they aggregated classes into groups corresponding to two key parameters used in climate models: leaf area index (LAI) and surface roughness.  Based on this aggregation, the applications accuracy of DISCover for estimating those parameter are:

LAI Accuracy    84.5%

LAI Area Weighted Accuracy    90.2%

Surface Roughness Accuracy    82.4%

Surface Roughness Area Weighted Accuracy    87.8%

A comprehensive presentation of all aspects of the validation, including per class accuracy tables, can be found in the September 1999 volume of Photogrammetric Engineering and Remote Sensing: Special Issue on Global Land Cover Validation.

7.0 Details on Version 2.0 Database Revision

The first version of the global land cover database was completed and released to the public in November, 1997.  We applied the feedback we received from the users of this database (Brown and others, 1999) and broad lessons learned from the validation exercise of the IGBP DISCover land cover data (Scepan, 1999; Muchoney and others, 1999) to the development of this revised version of the database.  Version 2.0 of the Global Land Cover Database contains updated land cover and water classes.  The revised version is based on the land cover regions that are found in Version 1.2.  Consequently, this version of the database is still based on the 1992-1993 AVHRR time series, and therefore, represents the land cover patterns for that period.  Detailed information about the revised seasonal land cover regions can be found in a set of ascii tab-delimited text files, one for each continental database:

This graphic shows the areas that were revised in black.  Land cover classes over 10% of the earth's land area were revised for Version 2.0.  Key revision focus areas included:

  • Boreal forest transition zone
  • Forest/cropland separation in Europe
  • Miombo forest in Africa
  • Amazon rainforest

8.0 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 LP Distributed Active Archive Center (DAAC) World Wide Web site. The instructions for accessing these files are contained in the following two sections.

8.2 Downloading from the Web

On the Global Land Cover page contains links to all documentation files and the image files (both compressed and uncompressed). NOTE: 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.

9.0 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 now available in two projections--the Interrupted Goode Homolosine projection and the Geographic (or Plate Carre) projection.

9.1 Interrupted Goode Homolosine Projection Parameters

The data dimensions of the Interrupted Goode Homolosine projection for the global land cover characteristics data set are 17,347 lines (rows) and 40,031 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 Interrupted Goode Homolosine projection:

Projection Type: Interrupted Goode Homolosine

  • Units of measure: meters

    Pixel Size: 1000 meters

    Radius of sphere: 6370997 m.

    XY corner coordinates (center of pixel) in projection units (meters):

    • Lower left: (-20015000, -8673000)

      Upper left: (-20015000, 8673000)

      Upper right: (20015000, 8673000)

      Lower right: (20015000, -8673000)

9.2 Geographic Projection Parameters

The data dimensions of the Geographic projection for the global land cover characteristics data set are 21,600 lines (rows) and 43,200 samples (columns) resulting in a data set size of approximately 933 megabytes for 8-bit (byte) images. The following is a summary of the map projection parameters used for the Geographic projection:

Projection Type: Geographic

  • Units of measure: arc seconds

    Pixel Size: 30 arc seconds

    Radius of sphere: 6370997 m.

    XY corner coordinates (center of pixel) in projection units (arc seconds):

    • Lower left: (-647985, -323985)

      Upper left: (-647985, 323985)

      Upper right: (647985, 323985)

      Lower right: (647985, -323985)

Please Note:  The geographic projection is not an equal-area projection, in contrast with the Interrupted Goode Homolosine projection.  Users should be advised that area statistics calculated from data in the Geographic projection will not be correct because of the areal distortion inherent to this projection.

10.0 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.

Belward, A.S., Estes, J.E., and Kline, K.D., 1999, The IGBP-DIS 1-Km Land-Cover Data Set DISCover: A Project Overview: Photogrammetric Engineering and Remote Sensing , v. 65, no. 9, p. 1013-1020.

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., Loveland, T.R., Ohlen, D.O., Zhu, Z., 1999, The Global Land-Cover Characteristics Database: The Users' Perspective. Photogrammetric Engineering and Remote Sensing , v. 65, no. 9, p. 1,069-1,074.

Brown, J.F., Reed, B.C, and Huewe L., 1998., Advanced strategy for multi-source analysis and visualization in land cover characterization, in Proceedings of the Pecora 13 Symposium, Human Interactions with the Environment: Perspectives From Space, August, 1996, Sioux Falls, South Dakota, p. 367-382 (CD-ROM).

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

Defries, R.S. and Los, S.O., 1999,  Implications of Land-Cover Misclassification for Parameter Estimates in Global Land-Surface Models: An Example from the Simple Biosphere Model (SiB2): Photogrammetric Engineering and Remote Sensing , v. 65, no. 9,  p. 1083-1088.

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, inSPIE 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., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, J, Yang, L., and Merchant, J.W., 2000, Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1-km AVHRR Data:International Journal of Remote Sensing , v. 21, no. 6/7, p. 1,303-1,330.

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.

Muchoney, D., Strahler, A., Hodges, J., and LoCastro, J., 1999,  The IGBP DISCover Confidence Sites and the System for Terrestrial Ecosystem Parameterization: Tools for Validating Global Land-Cover Data: Photogrammetric Engineering and Remote Sensing , v. 65, no. 9, p. 1061-1067.

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.

Scepan, J., 1999,  Thematic Validation of High-Resolution Global Land-Cover Data Sets: Photogrammetric Engineering and Remote Sensing , v. 65, no. 9, p. 1051-1060.

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. 65, no. 9,, 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
95 Evergreen Tree Crop
96 Deciduous Tree Crop
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data


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
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data


Appendix 3.

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
99   Interrupted Areas (Goodes Homolosine Projection)
100   Missing Data


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
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data


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
11 Ice/Snow
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data


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 Forest/Field Mosaic
20 Water and Land Mixtures
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data


Appendix 7.

Vegetation Lifeforms Legend

Value Description
1 Evergreen Needleleaf Vegetation
2 Evergreen Broadleaf Vegetation
3 Deciduous Needleleaf Vegetation
4 Dedicuous Broadleaf Vegetation
5 Annual Broadleaf Vegetation
6 Annual Grass Vegetation
7 Non-Vegetated Land
8 Water Bodies
99 Interrupted Areas (Goodes Homolosine Projection)
100 Missing Data