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International Journal of Remote Sensing Volume 26, 2005 - Issue 9
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GLC2000: a new approach to global land cover mapping from Earth observation data

E. Bartholomé Corresponding author
&
A. S. Belward
Pages 1959-1977 | Received 04 Nov 2003, Accepted 25 May 2004, Published online: 22 Feb 2007
 
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Abstract

A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data are available free of charge for all non‐commercial applications from http://www.gvm.jrc.it/glc2000.

Acknowledgements

The JRC with the endorsement and support of the VEGETATION programme partners coordinated the GLC2000 project. The S1 data were kindly made available under the terms of the VEGA 2000 initiative. The involvement of all GLC2000 partners is gratefully acknowledged. The number in front of their name refers to the geographic window displayed in . A full list of individuals is provided in Bartholomé et al. (Citation2002). The authors are particularly indebted to the members of the Global Vegetation Unit of the JRC who contributed the GLC2000 project: F. Achard, S. Bartalev, C. Carmona‐Moreno, V. Gond, S. Kolmert, M. Massart, P. Mayaux, M. Merlotti, H. Eva, S. Fritz, B. Glénat, J.‐M. Grégoire, A. Hartley, H.‐J. Stibig, A. Tournier and P. Vogt.

1.

(1) US Geological Survey, Sioux Falls, USA: T. Loveland, Z. Zhu, C. Giri.

2.

(1) Canadian Center for Remote Sensing, Ottawa, Canada: R. Latifovic.

3.

(10) Institute for Remote Sensing Applications, Beijing, China: Wu B, Xu W.

4.

(global) CNES, Toulouse, France: H. Jeanjean, G. Saint.

5.

(3) Lab. de teledeteccion aplicada, Univ. Nacional Agraria, La Molina, Peru: V. Barrena Arroyo.

6.

(global) VITO, Mol, Belgium: D. Van Speybroeck.

7.

(7) Centre AGRHYMET, Niamey, Niger: A. Nonguierma.

8.

(5c) METEO, Toulouse, France: J.‐L. Champeaux.

9.

(7, global) UNEP/GRID, Geneva, Switzerland: R. Witt, C. Ten Oever.

10.

(7) Centre de Suivi Ecologique, Dakar, Senegal: O. Diallo.

11.

(3) INTA, Castelar/Buenos Aires, Argentina: C. di Bella.

12.

(7) CSIR, Pretoria, South Africa: C. Pretorius.

13.

(global) Africover, Nairobi, Kenya: A. di Gregorio.

14.

(5b, 7) Environnemétrie et Géomatique Un. Cath., Louvain‐la‐Neuve, Belgium: P. Defourny, C. Vancutsem, J.‐F. Pekel.

15.

(3) Ecoforca: Campinas/Sao Paulo, Brazil: A. Dorado, E. de Miranda.

16.

(3) CIRAD, Forêts, Cayenne/Guyanne, France: V. Gond.

17.

(12) Institut Pertanian, Bogor, Indonesia: U. R. Wasrin.

18.

(9) Indian Institute for Remote Sensing, Dehradun UP, India: P. S. Roy, S. Gupta.

19.

(global, 17) FAO, Roma, Italy: He C. J. Latham, M. Cherlet.

20.

(8a) Alterra, Wageningen, The Netherland: C. A. Mucher, E. De Badts.

21.

(6) Metria, Stockholm, Sweden: S. Olovsson, B. Olsson, M. Ledwith.

22.

(3) Corolab Humboldt, Caracas, Venezuela: O. Huber.

23.

(5) Instituto de Sciencias de la tierra, Barcelona, Spain: A. Lobo.

24.

(11) CEReS, Chiba, Japan: R. Tateishi.

25.

(10) University of New Hampshire, Durham, USA: X. Xiao.

26.

(7) Tropical Research Institute, Lisbon, Portugal M. J. De Perestrelo, J. Pereira, A. I. Cabral.

27.

(14) Centre for Ecology and Productivity, Moscow, Russia: D. Ershov, A. Isaev.

28.

(5d) Dipartimento di Pianificazione, IUAV, Venice Italy, S. Griguolo.

29.

(3) CREAN, Cordoba, Argentina: A. C. Ravelo.

30.

(11) Geographical Survey Institute, Tsukuba, Japan: H. Sato.

31.

(10) Chinese Academy of Forestry, Beijing, China: Zhao X.

32.

(7) Royal Museum for Central Africa, Tervuren, Belgium: J. Lavreau.

33.

(7) Regional Centre for Mapping of Resources for Development, Nairobi, Kenya: W. K. Ottichilo.

34.

(7) Observatoire du Sahara et du Sahel, Tunis, Tunisia: C. Fezzani, W. Essahli.

35.

(5b) Instituto de Hidràulica, Engenharia Rural e Ambiente, Lisbon, Portugal: A. Perdigão.

36.

(Global, 2, 3, 4, 5d, 7, 8, 13, 15, 16, 17) Global vegetation Monitoring Unit/JRC, Ispra, Italy: E. Bartholomé, A. S. Belward, F. Achard, S. Bartalev, C. Carmona‐Moreno, H. Eva, S. Fritz, A. Hartley, P. Mayaux, H.‐J. Stibig.

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Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Source: Informa UK Limited
Research Progress on Changes in Land Use and Land Cover in the Western Himalayas (India) and Effects on Ecosystem Services
Source: MDPI AG
Coincidence Analysis of the Cropland Distribution of Multi-Sets of Global Land Cover Products
Source: MDPI AG
Climate change impact on the water regime of two great Arctic rivers: modeling and uncertainty issues
Source: Springer Science and Business Media LLC
Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos
Source: MDPI AG
Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
Source: MDPI AG
The carbon balance of Africa: synthesis of recent research studies
Source: HAL CCSD
Comparison of Two Synergy Approaches for Hybrid Cropland Mapping
Source: Multidisciplinary Digital Publishing Institute
Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data
Source: Informa UK Limited
The efficient urban canopy dependency parametrization (SURY) v1.0 foratmospheric modelling: description and application with the COSMO-CLM modelfor a Belgian summer
Source: Copernicus GmbH
Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling
Source: Co-Action Publishing
A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis
Source: Copernicus Publications
Calibration of the Fire Weather Index over Mediterranean Europe based on fire activity retrieved from MSG satellite imagery
Source: CSIRO Publishing
Comparison of country-level cropland areas between ESA-CCI land cover maps and FAOSTAT data
Source: HAL CCSD
Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa
Source: Multidisciplinary Digital Publishing Institute (MDPI)
Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series
Source: MDPI AG
Global economic trade-offs between wild nature and tropical agriculture.
Source: Public Library of Science (PLoS)
Future hotspots of terrestrial mammal loss
Source: The Royal Society

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