- ISLSCP Land Cover Data
- Data Set Overview
- Sponsor
- Original Archive
- Future Updates
- The Data
- Characteristics
- Source
- The Files
- Format
- Name and Directory Information
- Companion Software
- The Science
- Theoretical Basis of Data
- Processing Sequence and Algorithms
- Scientific Potential of Data
- Validation of Data
- Contacts
- Points of Contact
Phenological differences among vegetation types, reflected in temporal variations in NDVI derived from satellite data, have been used to classify land cover at continental scales. This study explored methodologies for extending this concept to a global scale (DeFries and Townshend 1994a). A coarse resolution (one by one degree) data set of monthly NDVI values for 1987 (Los, et al. 1994, Sellers, et al. 1994, 1995b) was used as the basis for a supervised classification of eleven cover types that broadly represent the major biomes of the world. Because of missing values at high latitudes, the Pathfinder AVHRR data set for 1987 (James and Kalluri, 1994) for summer monthly NDVI and red reflectance values were used to distinguish the following cover types: tundra, high latitude deciduous forest and woodland, coniferous evergreen forest and woodland.The land cover data set was further modified to be consistent with the SiB vegetation classes described in Dorman and Sellers, (1989), Sellers et. al. (1995a) and Sellers et. al. (1995b).
Sponsor
The production and distribution of this data set are being funded by NASA's Earth Science enterprise. The data are not copyrighted; however, we request that when you publish data or results using these data please acknowledge as follows:
The authors wish to thank the Distributed Active Archive Center (Code 902.2) at Goddard Space Flight Center, Greenbelt, MD, 20771, for producing the data in its present format and distributing them. The original data products were produced by Dr. Ruth DeFries and Dr. John Townshend (University of Maryland at College Park, Department of Geography), with revisions made by Dr. James Collatz (Code 923, NASA Goddard Space Flight Center).Original Archive
This data was originally published as part of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I CD-ROM set.Meeson, B.W., F.E. Corprew, J.M.P. McManus, D.M. Myers, J.W. Closs, K. -J. Sun, D.J. Sunday, P.J. Sellers. 1995. ISLSCP Initiative I-Global Data Sets for Land-Atmosphere Models, 1987-1988. Volumes 1-5. Published on CD by NASA (USA_NASA_GDAAC_ISLSCP_001-USA_NASA_GDDAC_ISLSCP_005).
Future Updates
An ISLSCP Initiative II is being planed.
Characteristics
- Parameters: Global land cover classification.
- Units: This data is unitless. The numeric data values represent the following land cover classifications:
- # Land Cover Classification
- __________________________________________
- 0 water
- 1 broadleaf evergreen forest
- 2 broadleaf deciduous forest and woodland
- 3 mixed coniferous and broad-leaf deciduous forest and woodland
- 4 coniferous forest and woodland
- 5 high latitude deciduous forest and woodland
- 6 wooded c4 grassland
- 7 c4 grassland
- 9 shrubs and bare ground
- 10 tundra
- 11 desert, bare ground
- 12 cultivation
- 13 ice
- 14 c3 wooded grassland
- 15 c3 grassland
- Range: 0 to 15
- Temporal Coverage: The data set is derived from data collected in 1987.
- Temporal Resolution: Not applicable
- Spatial Coverage: Global
- Spatial Resolution: 1 degree by 1 degree
Source
The global land cover data set was based on AVHRR maximum monthly composites for 1987 of NDVI values at approximately 8 km resolution, averaged to one by one degree resolution (Los, et al. 1995) . A Fourier transform was applied to smooth the temporal profiles and remove aberrant low values (Sellers, et al. 1994, 1995b) . At high northern latitudes, the data set was based on the AVHRR Pathfinder data set for 1987 (James and Kalluri, 1994), resampled to a spatial resolution of one by one degree and composited to obtain maximum monthly NDVI values and corresponding red reflectance values for summer months.
Format
- File Size:259200 bytes
- Data Format: IEEE floating point notation
- Headers, trailers, and delimiters: none
- Fill value: none
- Image orientation:North to South
- Starting position: (179.5W, 89.5N)
- End position: (179.5E, 89.5S)
Name and Directory Information Naming Convention
- The file naming convention for this data set is
- islscp.vegmap.1nnegl.ddd
- where
- islscp = International Land Surface Climatology Project
- vegmap = Global land cover map
- 1 = number of levels
- n = vertical coordinate, n = not applicable
- n = temporal period, n = not applicable
- e = horizontal grid resolution, e = 1 x 1 degree
- gl = spatial coverage, gg = global land
- bin = File type, (bin=binary, ctl=GrADS control file)
- Directory Path
- /data/inter_disc/biosphere/land_cover/
Companion Software
Several software packages have been made available on the CIDC CD-ROM set. The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is currently in use worldwide for the analysis and display of earth science data. GrADS meta-data files (.ctl) have been supplied for each of the data sets. A GrADS gui interface has been created for use with the CIDC data. See the GrADS document for information on how to use the gui interface.Decompression software for PC and Macintosh platforms have been supplied for datasets which are compressed on the CIDC CD-ROM set. For additional information on the decompression software see the aareadme file in the directory:
- software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been made available to read these data. You may also acquire this software by accessing the software/read_cidc_sftwr directory on each of the CIDC CD-ROMs
Theoretical Basis of Data
The cover class descriptions used in DeFries and Townshend (1994a) resolved 11 classifications, which are listed below:Classification numbers DeFries and Townshend
- 1 Broadleaf evergreen trees
- 2 Broadleaf deciduous trees
- 3 Mixed trees
- 4 Needle leaf evergreen trees
- 5 High latitude deciduous trees
- 6,8 Grass with 10 - 40% woody cover
- 7 Grass with greater than 10% woody cover
- 9 Shrubs and bare soil
- 10 Moss and lichens
- 11 Bare
- 12 Cultivated
The original eleven cover types were selected primarily to conform with the cover types required as input to climate models.
For descriptions of the functional characteristics of these cover types, in terms of approximate height of mature vegetation, percent ground surface covered by vegetation, seasonality, and leaf type, see Table 1 in DeFries and Townshend (1994a).
The land cover data set was modified to be consistent with the SiB vegetation classes described in Dorman and Sellers, (1989), Sellers et. al. (1995a) and Sellers et. al. (1995b) in the following ways:
- The Matthews (1983) vegetation map is used as the global land/ocean mask except for Africa where Kuchler (1983) is used.
- Vegetation class 8 (broad leaf shrubs and ground cover) was not distinguishable from class 6 (wooded grassland) using the classification methods described here so class 6 includes both wooded grasslands and shrubs with ground cover understory. There are no class 8 values in the data set.
- 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.
- Class 6 (wooded c4 grassland) and class 7 (c4 grasslands) occurring in regions with climates unfavorable for c4 grasses were reclassified to class 14 (wooded c3 grassland) and class 15 (c3 grasslands) respectively. The main criteria for deciding whether the climate is favorable for c4 grasses are that the following two conditions apply for any month at that grid point: a) mean monthly temperature is above 22 degree C and b) mean monthly precipitation is above 25mm. The mean monthly temperature and precipitation fields were from Leemans and Cramer (1991).
The above modifications resulted in the following land cover classifications:
- 0 water
- 1 broadleaf evergreen forest
- 2 broadleaf deciduous forest and woodland
- 3 mixed coniferous and broad-leaf deciduous forest and woodland
- 4 coniferous forest and woodland
- 5 high latitude deciduous forest and woodland
- 6,8 wooded c4 grassland
- 7 c4 grassland
- 9 shrubs and bare ground
- 10 tundra
- 11 desert, bare ground
- 12 cultivation
- 13 ice
- 14 c3 wooded grassland
- 15 c3 grassland
Processing Sequence and Algorithms
Maximum likelihood classification based on 12 monthly NDVI values was used to obtain the global land cover data set. In outline, the maximum likelihood procedure classifies each pixel to the land cover type that it most resembles in terms of its remotely sensed properties. The remotely sensed properties are used to define a multi-dimensional space within which pixels of each cover type can be located. The mean vector and variance-covariance matrix for each cover type are estimated using its worldwide population of pixels from the training set. Then, using the maximum likelihood rule (Swain and Davis 1978), the multidimensional space is partitioned into sub-spaces each uniquely associated with one land cover type. The whole of the global land mass is then classified according to the remotely sensed properties of each pixel. Thus, if a pixel falls within the sub-space associated with cover type ci, it is labeled ci. If the pixel falls within the sub-space associated with cover type cj, it is labeled as that cover type, cj.To account for phasing of seasons, maximum likelihood classification was based on monthly NDVI values sequenced from the peak value at each pixel (see DeFries and Townshend (1994a) for more detail).
Training sets for each of the eleven cover types were identified as the areas where three existing ground-based data sets of global land cover (Matthews 1983, Olson, et al. 1983, Wilson and Henderson-Sellers 1985) agree that the land cover is present. Although there is considerable disagreement among these data sets (DeFries and Townshend 1994b), the locations where the three data sets agree were selected as those with the greatest confidence that the cover type actually exists on the ground. The following steps were taken to ensure that each training set was as spectrally distinct as possible or to further subdivide the training set so that each would be spectrally distinct:
- each training set was split into Northern and Southern Hemispheres to account for phasing of seasons in the two hemispheres.
- 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.
- Bhattacharrya Distances--a measure of the separability of the training sets--and overlaps in the feature space were examined to determine if some cover types should be combined. This was the case, for example, for Southern Hemisphere broadleaf deciduous forest located mainly in Africa and Southern Hemisphere wooded grassland.
The global land cover data set was modified from the original maximum likelihood classification result as follows to eliminate stray pixels that were obviously incorrectly classified: pixels falling within training areas that were not correctly classified were changed to the cover type indicated by the training area; pixels surrounded on all sides by a different cover type were changed to that cover type; pixels classified as broadleaf evergreen in mid-latitudes were changed to the wooded grassland cover type; pixels classified as coniferous evergreen within the tropics were changed to the broadleaf evergreen cover type; pixels classified as mixed deciduous and evergreen forest and woodland within the tropics were changed to the wooded grassland cover type. In total, these changes altered approximately 10 percent of the total land surface.
Modifications by G. James Collatz and Sietse Los, (Biospheric Sciences Branch, Code 923, NASA/Goddard Space Flight Center) were made to the data (see the Theoretical Basis of Data section of this readme), so that it was consistent with the SiB vegetation classes described in Dorman and Sellers, (1989), Sellers et. al. (1995a) and Sellers et. al. (1995b).
Scientific Potential of Data
The tables below (Time-invariant land surface properties) can be used in conjunction with the vegetation classification to specify global parameter fields. Most parameter fields are derived for use in the Simple Biosphere model (SiB2; see Sellers et al., 1994, and Sellers et al., 1995a,1995b) and may need to be adapted for use in other models.
Biome dependent morphological and physiological parameters.
SiB Vegetation Type Name Symbol Units 1 2 3 4 5 6 Canopy top height z_2 m 35.0 20.0 20.0 17.0 17.0 1.0 Inflection height for leaf 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, top leaf V_max0 mol m^-2
s^-16e-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 conductance b mol m^-2
s^-10.01 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 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, top leaf V_max0 mol m^-2
s^-13e-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 conductance b mol m^-2
s^-10.04 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 on the ISLSCP Initiative I CD ROM set. This CD-ROM set can be ordered through the Goddard DAAC home page.**Minimum leaf resistance is the light saturated, unstressed resistance to water vapor diffusion through the leaf surface. It is calculated using table values of V_max and m and the photosynthesis and stomatal models described in Collatz et. al. 1991. The total canopy resistance can be calculated using the minimum leaf resistance scaled by environmental conditions and integrated over all the leaves in the canopy. A simple way to perform the integration would be to multiply the environment-modified minimum leaf resistance by the leaf area index (LAI) or by the fraction of incident PAR that is absorbed by the canopy (FPAR). Global fields of LAI and FPAR are available on the ISLSCP Initiative I CD-ROM set, which can be ordered through the Goddard DAAC home page.
Biome independent parameters
Name symbol units value Ground roughness length z_s m 0.05 Augmentation factor for 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 oxygen S - 2,600*0.57^Qt 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 The tables (Biome dependent and Biome independent parameters) were compiled by G. James Collatz, Code 923, NASA/GSFC, Greenbelt MD 20771, phone: 301-286-1425, e-mail: jcollatz@biome.gsfc.nasa.gov
Validation of Data
Wintertime NDVI values were missing for large areas in high latitudes in the primary data set used for this study (Los, et al., 1994) . For these areas, results from a maximum likelihood classification using AVHRR Pathfinder data (James and Kalluri, 1994) for summertime monthly NDVI and red reflectance values were used.The data set has not been systematically validated. Cursory validation indicates that the user should be aware of the following problems:
- 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.
- the "tundra" cover type may be inaccurate because of missing data at high latitudes.
Points of Contact
For information about or assistance in using any DAAC data, contact
- EOS Distributed Active Archive Center(DAAC)
- Code 902.2
- NASA Goddard Space Flight Center
- Greenbelt, Maryland 20771
- Internet: daacuso@daac.gsfc.nasa.gov
- 301-614-5224 (voice)
- 301-614-5268 (fax)
Collatz, G.T., J.T. Ball, C. Grivet, J.A. Berry. 1991. Physiological and environmental - regulation of stomatal conductance, photosynthesis and transpiration - A model that includes a laminar boundary- layer, Agric. For. Meteor., 54:107-136.DeFries, R. S. and J. R. G. Townshend, 1994a, NDVI-derived land cover classification at global scales. International Journal of Remote Sensing, 15:3567-3586. Special Issue on Global Data Sets.
DeFries, R. S. and J. R. G. Townshend, 1994b. Global land cover: comparison of ground-based data sets to classifications with AVHRR data. In Environmental Remote Sensing from Regional to Global Scales, edited by G. Foody and P. Curran, Environmental Remote Sensing from Regional to Global Scales. U.K.: John Wiley and Sons.
Dorman, J.L., and Sellers, P.J., 1989. A Global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the simple biosphere model (SiB). Journal of Applied Meteorology, 28:833-855.
James, M. E. and S. N. V. Kalluri, 1994. The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. International Journal of Remote Sensing, Special Issue on Global Data Sets. 15(17):3347-3363.
Kuchler, A.W., 1983, World map of natural vegetation. Goode's World Atlas, 16th ed., Rand McNally, 16-17.
Leemans, R., and W. P. Cramer, 1991, The IIASA database for mean monthly values of temperature, precipitation and cloudiness on a global terrestrial grid, technical report, International Institute for Applied Systems Analysis, Laxenburg, Austria.
Los, S.O., C.O. Justice, C.J. Tucker, 1994. A global 1 by 1 degree NDVI data set for climate studies derived from the GIMMS continental NDVI data. International Journal of Remote Sensing, 15(17):3493- 3518.
Matthews, E., 1983. Global vegetation and land use: new high resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22: 474-487.
Olson, J. S., Watts, J. and L. Allison, 1983. Carbon in live vegetation of major world ecosystems. W-7405-ENG-26, U.S. Department of Energy, Oak Ridge National Laboratory.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, and D.A. Randall, 1994. A global 1*1 degree NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, 15(17):3519-3545.
Sellers, P.J., D.A. Randall, C.J. Collatz, J.A. Berry, C.B. Field, D.A. Dazlich, C. Zhang, and C.D. Collelo, 1996a. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1: Model formulation. submitted to Journal of Climate.
Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, and D.A. Randall, 1996b. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 2: The generation of global fields of terrestrial biophysical parameters from satellite data. submitted to Journal of Climate.
Swain, P. H. and S. M. Davis, (ed.), 1978. Remote Sensing: The Quantitative Approach. (New York: McGraw-Hill Book Company).
Wilson, M. F. and A. Henderson-Sellers, 1985. A global archive of land cover and soils data for use in general circulation models. Journal of Climatology, 5: 119-143.
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Last update:Fri Aug 22 18:11:16 EDT 1997