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GLC2000: a new approach to globalland cover mapping from Earthobservation dataE. Bartholomé Corresponding author & A. S. Belwarda Institute for Environment and Sustainability, EC Joint ResearchCentre, 21020 Ispra (VA), ItalyPublished online: 22 Feb 2007.

To cite this article: E. Bartholomé Corresponding author & A. S. Belward (2005) GLC2000: a newapproach to global land cover mapping from Earth observation data, International Journal ofRemote Sensing, 26:9, 1959-1977, DOI: 10.1080/01431160412331291297

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GLC2000: a new approach to global land cover mapping from Earthobservation data

E. BARTHOLOME* and A. S. BELWARD

Institute for Environment and Sustainability, EC Joint Research Centre, 21020

Ispra (VA), Italy

(Received 4 November 2003; in final form 25 May 2004 )

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.

1. Introduction

The Earth’s land surface is where most of us live most of the time. Vegetation

covering the land provides us with food, fuel and fibre; it is also is a major factor

controlling energy, water and gas exchange with the atmosphere and is a source and

sink in biogeochemical cycles (Sellers et al. 1997). Thus vegetation cover affects

current climate state and plays an important role in climate forcing. At the sametime, climate is the main factor controlling the distribution of natural vegetation,

hence land cover will respond to changing climate. Anthropogenic actions such as

clearing forests to make way for agriculture also affect the distribution of global

land cover, which in turn alters its role in the functioning of the climate system

(Shukla et al. 1990). Reliable information on the state of our planet’s land cover is

thus needed on a regular basis if we are to understand the balance between global

land cover patterns, climate, and changes occurring in either of these.

For climate studies, the surface needs to be described in terms of albedo,

roughness, evapotranspiration, carbon exchange and aerosol emissions. Measuringthese variables consistently on the global scale can be difficult, but they can be

inferred from land cover type, especially if the land cover classification scheme is

constructed to meet this objective. Specific elements to address in this context

include the distribution of evergreen and deciduous canopy types, because estimates

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 26, No. 9, 10 May 2005, 1959–1977

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2005 Taylor & Francis Group Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160412331291297

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of perennial and annual above-ground biomass are required for carbon cycle

dynamics studies and as a means of defining seasonal and regional variations in

surface albedo, surface moisture availability and aerodynamic roughness length.

The distribution of vegetation types according to leaf morphology (needle leaf,

broadleaf and grasses) must be determined for gas exchange characteristics. In

addition, disturbance to cover, especially as a result of fire, needs to be documented

because of associated changes in roughness, albedo, aerosol, water and gas exchange

(Geider et al. 2001). These requirements result in a land cover classification where

divisions between cover types are determined for use in biogeochemical models

(Running et al. 1994). This logic was the major driver of the first effort for global

land cover mapping using data from Earth observing satellites initiated by the

International Geosphere Biosphere Programme (IGBP) in 1990 (Townshend 1992).

The IGBP’s land cover mapping activities were based on data from the Advanced

Very High Resolution Radiometer (AVHRR) with a nominal 1 km resolution,

collected between 1992 and 1993. Data processing, image analysis and final

classification to a single global legend was carried out during the 1990s, and the

validated dataset was published towards the end of the decade (Loveland et al.

1999). The legend was defined on the basis of the philosophy proposed by Running

and co-workers (Running et al. 1994) with subsequent review by those IGBP core

projects that would use the final database (Belward et al. 1999). The IGBP’s core

projects that refined and finalized the legend included Biospheric Aspects of the

Hydrological Cycle (BAHC), Global Change and Terrestrial Ecosystems (GCTE),

International Global Atmospheric Chemistry Project (IGAC) and Land Use Cover

Change (LUCC). The final IGBP legend consisted of 17 classes (Loveland and

Belward 1997) and continues to be used for global land cover maps destined for the

modelling communities today; for example, the Moderate Resolution Imaging

Spectroradiometer Land Discipline Group currently provides land cover map

updates at 1 km resolution using the same land cover legend as IGBP (Friedl et al.

2002).

However, descriptions of global land cover attuned to users other than climate,

earth system and biogeochemical cycle models are required. This is because changes

to climate and changes to land cover also affect the land’s capacity to support

human life and because such changes can alter the biological diversity of our planet.

Multilateral environmental conventions, such as the UN Convention on Biological

Diversity, or the Convention to Combat Desertification are one way in which policy

makers seek to address long-term sustainable development. A number of these

agreements have identified the year 2000 as a benchmark and have involved the

Millennium Ecosystem Assessment (Reid 2000) to provide ecological and economic

analysis for this year in a coordinated fashion.

Development-aid projects also call for land cover information. These projects

typically include the sustainable management and use of land resources, protecting

biodiversity, forest conservation and restoration, combating desertification,

improving food security and limiting watershed degradation. Policy users need

information on land cover condition to develop policies and strategies at both global

and local levels. They also need this information to measure the impact and

effectiveness of management actions associated with their policies.

Both non-climate environmental conventions and development projects need

descriptions of land cover that infer resource management, biodiversity and land use

attributes, rather than biophysical values; for example, special attention needs to be

1960 E. Bartholome and A. S. Belward

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paid to agricultural land cover classes, forest classes need to take cash crop

plantations such as oil palm into account, and ecologically important/fragile classes

including swamp forests, mangroves, sedge and shrub tundras need to be delineated

in an independent manner rather than grouped into broader functional vegetation

classes.

Science and policy requirements for land cover place somewhat conflicting

constraints on the generation of global datasets. On the one hand, consistent land

surface parameterization is needed to improve our understanding of the state of the

global climate system and its variability; on the other, sustainable development or

issues such as biodiversity call for specific regionally relevant information.

Another difference between science and policy users of global land cover

information concerns the process whereby this information is generated.

Development-aid programmes and the activities of entities such as the United

Nations Environment Programme (UNEP) and UN Food and Agriculture

Organization (FAO) pay particular attention to capacity-building and technology-

transfer activities. Mapping the world’s land cover resources by a single entity using

a uniform approach to a single legend has obvious advantages for global consistency

and automated processing. But such a centralized approach has limited capacity-

building value and can also lead to a lack of ‘local’ acceptance of the resulting

products; nation states can be reluctant to accept observations/measurements

made by third parties without prior agreement or their own involvement in the

measurement process.

The European Commission’s Joint Research Centre (JRC) has just concluded a

project to document global land cover characteristics for the year 2000 (GLC2000)

for both science and policy users. This paper describes the project and the resulting

database.

2. The GLC2000 project strategy

The GLC2000 project objectives were (1) to produce a standardized global land

cover product for the year 2000 explicitly linked to more thematically detailed

regional datasets, (2) to federate the international scientific community in such a

way that they could contribute to the generation of the land cover database and to

the product validation, (3) to bring onboard product definition possibilities to

ensure linkages with national and sub-continental interests, and (4) to implement a

region-by-region product generation, followed by production of a global product

based on the these.

To meet these objectives a partnership of regional experts was put into place to

begin the mapping process, and as previously determined by the IGBP, production

of a consistent global land cover database would rely on data from Earth observing

satellites.

To obtain cloud free imagery for many parts of the world daily observations are

required; and if made throughout a full year such daily observations will capture the

seasonal cycles of plant growth and differentiation. At the end of 1999 there were

three satellites in orbit flying sensors capable of making observations useful for

global land cover mapping; the AVHRRs flying on NOAA’s satellites (as used by

IGBP), the Moderate Resolution Imaging Spectroradiometer (MODIS) on board

the Terra-1 satellite launched on 18 December 1999, and the VEGETATION-1

sensor on board the SPOT-4 satellite launched on 24 March 1998. The AVHRR

data were only used by the IGBP because nothing more suitable was available at this

Global land cover mapping 1961

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time (the AVHRR is primarily a meteorological instrument and its use for land

cover mapping is somewhat serendipitous). Although the data from MODIS are

certainly valuable for global land cover mapping, and indeed are used for just such

purposes (Freidl et al. 2002), at the beginning of 2000 the sensor was still in a

commissioning phase and data could not be distributed to the GLC2000 partners.

The VEGETATION instrument had by this time already been in orbit for 2 years.

As the sensor’s name implies the technical characteristics and performance of the

VEGETATION system were designed for multi-temporal analysis of vegetation

from the outset (Achard et al. 1994). Because the data were appropriate for global

vegetation studies and because an unbroken daily record for the whole of our

reference year, 2000, was potentially available the project based its land cover

mapping on these data.

The flexibility is due in part to the technical advances in Earth observing

instruments, and in part due to the availability of tools that provide traceable links

between different land-cover legends (Di Gregorio and Jansen 2000, McConnell

et al. 2000). Early experience of land cover mapping from VEGETATION-1 data

showed indeed that the high geometric fidelity of any time series opened up new

perspectives in terms of mapping details with coarse resolution satellite sensor data

(Gond and Bartholome 2001). The increased detail in turn offered a potential for

applications at national scale and thus increased interest for local partners.

3. The GLC2000 partnership

Implementing the GLC2000 project through a partnership of regional experts offers

a number of advantages. Past experience of mapping a region helps ensure that

optimum image classification methods are used, makes maximum benefit of regional

expertise for definition of regionally appropriate map legends and spreads the

workload. A less obvious but no less important benefit is the spreading of

responsibility. International partnership results in international ownership, and thus

more widespread acceptance of the final product.

More than 30 research teams participated in the GLC2000 project. Each

identified a geographic region of interest, for which they received VEGETATION-1

data (see §5 below), and each made a commitment to provide a land cover map from

these. The participating organizations are listed in the acknowledgements. Each of

the Earth’s continents was treated as a separate region, and some partners also

analysed sub-continental areas in detail. In total there were 18 of these map

production regions. Figure 1 shows their distribution. The partnership remained

active throughout the project, contributing to the legend definition, data processing,

image classification and product validation steps. Each partner contributed on a

‘best-effort’ basis, using his or her own resources. This of course was a major risk for

project implementation; the success of the project demonstrates that there was a

shared interest among the scientific community to contribute to such an endeavour.

4. The GLC2000 legend

To meet the twin objectives of global consistency and regional flexibility the legend

had to accommodate both complete descriptions of land cover features identified at

national to sub-continental scales and in the meantime ensure consistency between

these scales and the global coverage. In addition because the project was being

implemented through an international partnership in a distributed fashion local land


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cover classes needed to be described in such a way that their equivalent could be

identified in other parts of the world. For these reasons the GLC2000 partners

agreed during the ‘legend’ workshop held at Ispra in 2001 to make use of the FAO

Land Cover Classification System.

The UN Food and Agriculture Organization developed the Land Cover

Classification System (LCCS) to analyse and cross-reference regional differences

in land cover descriptions (Di Gregorio and Jansen 2000, McConnell et al. 2000).

LCCS describes land cover according to a series of pre-identified classifiers and

attributes organized in a hierarchical manner (figure 2). These separate cultivated

and managed lands, natural and semi-natural, vegetated or non-vegetated surfaces,

terrestrial or aquatic/flooded, life-forms, cover, height, spatial distribution, leaf type

and phenology. Each of these classifiers/attributes is associated with a unique code;

as more and more classifiers are added to a particular land cover category its code is

completed, and a ‘standard’ name is given by LCCS. The user is also free to assign a

‘typical’ name to the category, in line with local usage. Thus a category with a

specific set of classifier and attribute values can be given a different name in different

parts of the world. Yet these classifiers and attributes allow one to understand that

the given land cover categories are identical. In a similar way removing some of the

more specific classifiers and attributes allows more complex regional products to be

generalized into a simplified global legend in an explicit and traceable manner.

Each partner had responsibility for establishing the legend that best served their

region’s priorities for land cover information. They then identified the LCCS

descriptors that best described the land cover categories.

The GLC2000 global scale legend (Bartholome et al. 2002) documents 22 general

land-cover types (table 1). These have been chosen to accommodate in a consistent

manner the aggregation of all classes represented in the more detailed regional scale

products, and to provide compatibility with other maps by providing equivalency

with the IGBP classification system (Loveland and Belward 1997).

Specific land cover categories that could be identified and mapped in regional

products are then generalized into the global legend. Table 2 illustrates this process

for the global land cover class named ‘closed broadleaved deciduous tree cover’: the

Figure 1. Location of the 18 production regions divided among the GLC2000 partners. Seelist and corresponding numbers in the Acknowledgements section of this paper.


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number of corresponding classes ranges from zero to seven according to the regional

product.

5. Materials and methods

5.1 Earth Observation data

The bulk of the data used in the project came from a VEGETATION dataset

specifically put together for global assessments at the turn of the Century, the

VEGA 2000 dataset (VEGETATION data for Global Assessment in 2000). This

dataset was assembled by the VEGETATION programme partners (Centre

National d’Etudes Spatiales, Swedish National Space Board, Italian Space

Agency, Belgian Office of Science and Technology and European Commission) as

a contribution to the Millennium Ecosystem Assessment (Reid 2000). It includes 14

months of global daily images acquired by VEGETATION-1 between 1 November

1999 and 31 December 2000. The data are standard S1 daily mosaics whose key

properties are described below.

5.1.1 Instrument. The VEGETATION-1 instrument is carried onboard the SPOT

4 satellite, which is in a sun synchronous orbit and crosses the equator at 10h30 an at

an altitude of 822 km. The sensor has four spectral bands, corresponding each to a

different camera and optical system. The spectral bands are blue (437–480 nm),

red (615–700 nm), near-infrared (772–892 nm) and short-wave infrared

Figure 2. The hierarchical tree from the FAO’s Land Cover Classification System. Adaptedfrom Di Gregorio and Jansen (2000).


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Table 1. The GLC2000 legend of the global product complying to LCCS standards, withcorrespondence to the IGBP legend (Loveland and Belward 1997).

GLC2000 global classes IGBP equivalent Comments

1. Tree cover, broadleaved,evergreen

Evergreen broadleavedforest, woody savannasand savannas (in part)

Tree5woody perennial plant witha single, well defined stem,height .3 m, cover .15%

(IGBP forest: .65% tree cover,.2 m height)

2. Tree cover, broadleaved,deciduous, closed

Deciduous broadleavedforest

Closed cover .40%

3. Tree cover, broadleaved,deciduous, open

Woody savannas andsavanna

40% .open cover .15%, e.g.deciduous woodland types

4. Tree cover, needle-leaved,evergreen

Evergreen needleleavedforest

5. Tree cover, needle-leaved,deciduous

Deciduous needleleavedforest

6. Tree cover, mixed leaf type Mixed forests7. Tree cover, regularly

flooded, fresh andbrackish water

Evergreen broadleaved ‘Swamp forest’

8. Tree cover, regularlyflooded, saline water

Evergreen broadleaved Tree height .3 m, tree cover.15%

‘Mangrove forest’ (dailyvariation of water level).

9. Mosaic: tree cover/othernatural vegetation

Shrubland Tree cover dominant (e.g.fragmented forest cover), withor without croppingcomponent.

10. Tree cover, burnt – For burnt forests mainly inboreal zone where actualvegetation cover unknown.

11. Shrub cover,closed–open,evergreen

Shrubland—open—closed Shrub5woody perennial plantwithout defined main stem,,5 m. Examples of sub-classesat regional level: with sparsetree layer.

12. Shrub cover,closed–open, deciduous

Savannas Shrub5woody perennial plant,5 m.

Examples of sub-classes at reg.level: with sparse tree layer

13. Herbaceous cover,closed—open

Grasslands Herbaceous: plants withoutpersistent stem or shoots aboveground.

Examples of sub-classes atregional level:

(i) natural, (ii) pasture, (iii) withsparse trees or shrubs.

14. Sparse herbaceous orsparse shrub cover

–

15. Regularly flooded shruband/or herbaceouscover

Persistent wetlands May include bogs and sparse treelayer.


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(1600–1692 nm). The total field of view is 101u, corresponding to a 2250 km ground

swath. As the sensor uses push-broom technology the footprint of the instantaneous

field of view varies very little across swath. The range is 1.165 km at nadir and

1.7 km at 50u. The Modulation Transfer Function is better than 30% at half the

Instantaneous Field Of View frequency (Viallefont-Robinet and Henry 2000). The

orbiting repeat cycle is such that there is a 375 km gap at the equator between two

successive passes; all gaps are covered by the acquisitions of the successive day,

leading to a coverage frequency of 21/26 days at this latitude. All data over land are

systematically acquired, stored on the on-board solid-state memory, and down-

loaded while the satellite is in line-of-sight with the Kiruna receiving station in

Sweden. Data are then transferred via a telecommunication link to the

VEGETATION central processing facility in Mol (Belgium) where they are

processed and archived (Saint 1994).

5.1.2 Data geometry. The VEGA 2000 database consists of ‘S1’ daily global

mosaics remapped into lat.–long. projection (figure 3). Pixel resolution in this

product is 1/112u, which corresponds to 1 km at the equator. Absolute location

accuracy is with a rms error of 300 m, the maximum being 465 m, while

multitemporal registration is to 325 m rms. with an absolute maximum error of

675 m (Silvander et al. 2001). To achieve these levels of performance data are

geolocated using an orbital model and a library of ground control points (Passot

2001). Images are orthorectified with reference to the ETOPO5 global elevation

dataset and resampled to the final map projection using a bi-cubic convolution.

GLC2000 global classes IGBP equivalent Comments

16. Cultivated and managedareas

Croplands Examples of sub-classes atregional level:

(i) terrestrial; (ii) aquatic(5flooded during cultivation),(iii) tree crop and shrubs(perennial), (iv) herbaceouscrops (annual), non-irrigated,(v) herbaceous crops (annual),irrigated. Note tree cropsinclude fruit tree plantations,orchards, vineyards.

17. Mosaic: cropland/treecover/other naturalvegetation

Cropland/othervegetation mosaic

Cropland dominant in bothclasses of the mosaic.

Other natural vegetation mayinclude regrowth (e.g. onabandoned cropland), shrubcover, grass cover.

18. Mosaic: cropland/shrubor grass cover

19. Bare areas Barren or sparselyvegetated

Includes both the hot and colddeserts.

20. Water bodies Water Natural and artificial21. Snow and ice Snow and ice Natural and artificial22. Artificial surfaces and

associated areasUrban and built-up areas

Table 1. (Continued.)


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5.1.3 Data radiometry. Absolute sensor calibration is in the order of 5%, while

temporal variation is less than 2% for visible and near-infrared bands. This is

achieved by using the onboard calibration lamp together with measurements in

specific conditions over reference targets (Henry and Meygret 2001). Equalization

Figure 3. Example of a global S1 product in lat.–long. projection (21 June 2000). The colourcomposite has the following colour coding: short-wave infrared (SWIR) in red, near-infrared(NIR) in green, Red in blue.

Table 2. Example for one land cover class of the equivalency between regional legend and theGLC2000 global product (adapted from Fritz et al. 2003).

Tree cover, broadleaved, deciduous, closed

South America Closed deciduous forestTemperate closed deciduous broadleaf forestsForest plantations (Llanos of Venezuela)Montane forests 500–1000 m—closed deciduousMontane forests 500–1000 m—closed temperate deciduousMontane forests .1000 m—closed deciduousMontane forests .1000 m—closed temperate deciduous

Africa Closed deciduous forest (Miombo)Northern Eurasia Deciduous broadleaf forestAsia Broadleaf deciduous forestSouth Asia Tropical moist deciduous

Tropical dry deciduousTemperate broadleaved

South-East Asia Tree cover, broadleaved, deciduous, mainly open (including dryDipterocarp forests)

North-East Europe Tree cover, broadleaved, deciduous, closedEurope Closed deciduous broadleaved forestNorth-West Europe Deciduous forestSouthern Europe Tree cover—mixed leaf type (mostly broadleaved 60–80%)

Tree cover—closed deciduous, broadleaved forestChina Broadleaved deciduous forestNorth America Tropical or sub-tropical broadleaved deciduous forest—closed

canopyTemperate or sub-polar broadleaved deciduous forest—closed

canopyAustralia Closed forest (Eucalyptus)New Zealand missingGreenland missingIceland missing


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between detectors in each array is monitored every 2 weeks and equalization

function parameters are uploaded to the instrument processor for on-board

correction. While this process is fully satisfactory for visible and near-infrared

channels, it is not sufficient for the short-wave infrared camera whose detectors are

randomly damaged by proton impacts. As a result short-wave infrared images often

display stripes corresponding to detectors damaged shortly after the updating of the

on-board equalization functions (Passot 2001).

5.1.4 Data production. The S1 data used in the project are top-of-canopy

reflectance values. Data were processed between 75uN and 56u S, provided that

sun azimuth angle was ,80u. Above 35u latitude images of adjacent swaths overlap

and for each pixel of the S1 mosaic a single set of spectral values pixels is retained

for all channels according to the maximum Normalized Difference Vegetation Index

(NDVI) recorded. Top-of-canopy reflectance values were computed using the

SMAC model (Rahman and Dedieu 1994). Input to SMAC included: water vapour

(from short term forecast produced four times per day by Meteo-France), ozone

climatology, and aerosol generated by a simple static model (Passot 2001).

Discrepancies might thus occur, in particular for aerosols, with actual atmospheric

situation at imaging time. The four spectral channels were delivered in 16 bits-per-

pixel format, with a scaling ranging from 0 to 2000. Additional files were provided

that include NDVI, sun and viewing azimuth and elevation, a time grid and a per-

pixel status map including a simple cloud mask in addition to per channel quality

flags. In total 16 bytes of data were provided for each image pixel. As a result the

total VEGA 2000 dataset is some four terabytes in size.

5.1.5 Other datasets. In addition to the core dataset a number of other sources

have been used to sort out the identification of specific land cover classes. This is the

case in particular for South America where Along-Track Scanning Radiometer

thermal images have been used in the Amazon basin (Eva et al. 2004), for Central

Africa where swamp forest could be detected on ERS radar images and not on

VEGETATION data (Mayaux et al. 2004), and for the delineation of urban areas

where the combination of Defense Meteorological Satellite Program’s Operational

Linescan System night syntheses and VEGETATION data proved to be very

efficient (Eva et al. 2004).

5.2 Image classification

The range of ecological and physical conditions encountered on the global scale

means that no one image classification method is optimum for all regions, although

such an approach has usually been adopted (Loveland et al. 1999, Friedl et al. 2002,

Hansen et al. 2000). This approach is justified on the grounds that consistent

procedures offer advantages in terms of repeatability. But it will not guarantee the

best possible results everywhere. Using different sub-sets of the global satellite image

archive either in terms of time periods and/or spectral bands, with ad hoc image

classification methods can lead to improved regional classifications (Achard et al.

2001).

The GLC2000 project has adopted a ‘regionally tuned’ approach where each

continental or regional product is produced independently with the lead scientists

taking responsibility for the choice and implementation of image post processing

and classification methods.


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5.2.1 Data preparation. Following this scheme each user-defined geographic

region of interest was extracted from the global dataset at JRC and provided to

the partner. Depending on the region and partners background various procedures

were tested to reduce data volume and further improve spectral quality. Regionally

speaking the situation can be described as follows. Over temperate regions and mid

latitudes in general where the seasonal signal is an important element of land cover

identification the compositing methods need to generate cloud-free monthly to 3-

monthly syntheses of spectral channels. Over high latitudes angular effects need to

be accounted for because of the wide range of illumination conditions. The ‘useful’

season is very short because snow can stay until late in the summer. Over tropical

regions the difficulty is mainly due to almost permanent cloud in particular over the

large rainforest domains of South America, Africa and Southeast Asia: over some

specific areas, such as the coastal area between the Congo River and Mt Cameroon,

the Andean Cordillera between Peru and Colombia, the hinterland between the

Orinoco and the Amazon, and the Indonesian archipelago the GLC2000 experience

shows that it is difficult to produce more than one good quality cloud-free synthesis

per year for these regions. Over arid and semi-arid regions the signal due to soil

spectral properties dominates over vegetation, which typically grows during a very

short period of time.

Starting from daily spectral bands monthly and seasonal syntheses were built

either using statistical averaging technique after improved cloud screening (Bartalev

et al. 2003, Vancutsem et al. 2003), or with the inclusion of conditional spectral

properties (Cabral et al. 2003) or also by applying principal component analysis to

single channel time series (Ledwith 2003). Ten-day and monthly syntheses have been

produced after standardization of Bidirectional Reflectance effects (Han et al. 2004,

Latifovic et al. 2004), although this approach was not straightforward because of the

processing already applied to the data at the central processing facility. Over arid

and semi-arid regions of Africa and the Middle East the NDVI temporal signal (10-

day maximum value composite) was used to identify areas of low density/short

growing cycle vegetation, after per-pixel removal of soil effects, transformation to

Table 3. Detailed legend for the GLC2000 Africa continental map (see also figure 4).

Classno. Definition

Classno. Definition

1 Closed evergreen lowland forest(,900 m)

15 Open grassland

2 Degraded evergreen lowland forest 16 Sparse grassland3 Submontane forest (900–1500 m) 17 Swamp bushland and grassland4 Montane forest (.1500 m) 18 Cropland (.50%)5 Swamp forest 19 Cropland with open woody vegetation6 Mangrove 20 Irrigated cropland7 Mosaic forest—cropland 21 Tree crops8 Mosaic forest—savannah 22 Sandy desert and dunes9 Closed deciduous forest (Miombo) 23 Stony desert10 Deciduous woodland 24 Bare rock11 Deciduous shrubland with sparse trees 25 Salt hardpan12 Open deciduous shrubland 26 Waterbodies13 Closed grassland 27 Cities14 Open grassland with sparse shrubs


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cover percentage and removal of abrupt signal drops due to atmospheric effects.

Spline and harmonic analysis of time series (HANTS, Roerink et al. 2000)

algorithms were compared on NDVI time series over temperate Europe (de Badts

2002) and HANTS was also used over China (Wu et al. 2002).

5.2.2 Data classification. As can be seen for the above the regional complexities of

our planet lead to a wide range of approaches to data processing. In contrast the

approaches to the digital image classification procedure was quite homogeneous. As

with the IGBP land cover product (Loveland et al. 1999) all used the unsupervised

classifiers, such as ISODATA. Classification was applied either to multispectral and

multitemporal datasets (Eva et al. 2003, Bartalev et al. 2003, Han et al. 2003,

Ledwith 2003, Pekel et al. 2003; Latifovic et al. 2004, Mayaux et al. 2004), on NDVI

(de Badts 2002, Wu et al. 2002, Agrawal et al. 2003, Tateishi et al. 2003), on derived

fractional cover percentage (Mayaux et al. 2004), or on a combination of

multispectral and multitemporal data with additional indicators derived from the

time series, such as snow cover duration (Bartalev et al. 2003). The land cover label

of each class was then assigned by each partner taking into account personal

knowledge of the environment in each region of interest, as well as available

reference sources.

5.3 Product validation

In order to eliminate macroscopic errors in the regional maps a systematic

verification method was developed (Mayaux 2003). This established a product

quality history for each region. The principle behind this is to browse the classified

image in a systematic manner following a pre-determined 2u62u grid and to report

on product consistency in a standardized manner. This is made using ground

observations, previous land cover maps and high-resolution satellite imagery (e.g.

Achard et al. 2001). The exercise was carried out for the map production regions at a

JRC workshop held in March 2002.

In addition to the systematic quality assessment step some regional maps have

been subject to other validation exercises, comparing the GLC2000 products to

national forest statistics (Bartalev et al. 2003), statistical samples of Landsat imagery

(Tateishi 2002, Cihlar et al. 2003) or through comparison with other high resolution

sampling exercises in the forest domain (Eva et al. 2004)

The results of the product quality history and regional map validation exercises

have already been incorporated into current releases of both the global and regional

land cover maps. Furthermore the regional products are distributed with their own

indicators of accuracy. To complete the validation process however quantitative

assessments of the global land cover map’s accuracy is ongoing. This will provide

statistical statements concerning the accuracy of each class. The principle is derived

from the scheme adopted by IGBP (Scepan et al. 1999). Surrogates for ground

verification are provided through the interpretation of Landsat Thematic Mapper

(TM) imagery or SPOT HRV acquired during the year 2000, or as close as possible

to that period for sites where image quality was not sufficient in 2000. In total 250

images have been selected and will provide around 1250 3 km63 km reference

segments. This will constitute the reference database for verification of the

GLC2000 global map.


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6. The GLC2000 database

The partners for each region have separately produced the detailed regional maps

(figure 1). The global product has been created by merging these into a new mosaic

by using the LCCS to generalize each of the regional legends into the GLC2000

global legend. The final database includes both products; it is thus possible, for each

region of the world, to access both the global synthesis prepared at JRC (Fritz et al.

2003) and the regional product provided by individual partners. Figure 4 and tables

1 and 3 show an example of the detail contained in the regional map and retained in

the corresponding global land cover map.

The products are delivered in the original input format, i.e. in lat.–long. projection

and with a 1/112u pixel resolution. The dataset is freely available for scientific

Figure 4. Example, for West Africa, of a regional GLC2000 product (left) and thecorresponding area in the global product (right) with the 22-class legend. The effect of classre-grouping can be noticed in a limited number of situations (outlined by arrows). For thelegend to the regional product (left): see table 3; for the legend to the global product (right):see table 1.


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applications after registration at the following web address: http://www.gvm.jrc.it/

glc2000.

The European Commission, in association with UNEP and FAO have also

published the global maps in the Interrupted Goode Homolosine projection at scales

of 1 : 25 500 000 and 1 : 40 000 000 (European Commission 2004). Table 4 shows the

area (in km2) for each class and lists the percentage of the terrestrial surface

accounted for as extracted from the digital database.

The class area statistics show that around a quarter of our planet’s terrestrial

surface has very little or no vegetation cover. Slightly less than 25% is made up of

deserts (barren land), snow and ice, and artificial surfaces—mainly urban areas. Of

these the artificial surfaces (home to around half the planet’s 6 billion inhabitants)

account for less than 0.2%. The planet is still remarkably tree-covered; collectively

the various forest classes account for over 28% of the land cover, though grasslands

and shrublands cover a very similar 27.5%. Cultivated and managed areas account

for over 11%, with an additional 5–6% of the land surface being a mosaic of

cultivation with either grasslands or trees and shrubs. Finally the fragile, but

important wetlands (important for their rich biodiversity, their role in the water

Table 4. Land cover area (in km2) for each of the land cover classes represented in theGLC2000 database. The table also gives the percentage of the total land surface occupied by

each class.

GLC2000 land cover class Area (km2)% landsurface

1 Tree cover, broadleaved, evergreen 12 373 713 8.382 Tree cover, broadleaved, deciduous, closed 6551 943 4.443 Tree cover, broadleaved, deciduous, open 3800 516 2.574 Tree cover, needle-leaved, evergreen 9165 116 6.215 Tree cover, needle-leaved, deciduous 3809 377 2.586 Tree cover, mixed leaf type 3214 113 2.187 Tree cover, regularly flooded, fresh 569 427 0.398 Tree cover, regularly flooded, saline (daily variation) 111 429 0.089 Mosaic: tree cover/other natural vegetation 2427 317 1.6410 Tree cover, burnt 304 538 0.2111 Shrub cover, closed–open, evergreen (with or without

sparse tree layer)2082 326 1.41

12 Shrub cover, closed–open, deciduous (with or withoutsparse tree layer)

11 401 869 7.72

13 Herbaceous cover, closed–open 13 286 744 9.0014 Sparse herbaceous or sparse shrub cover 13 835 588 9.3715 Regularly flooded shrub and/or herbaceous cover 1710 035 1.1616 Cultivated and managed areas 17 196 292 11.6517 Mosaic: cropland/tree cover/other natural

vegetation3533 063 2.39

18 Mosaic: cropland/shrub and/or grass cover 3120 396 2.1119 Bare areas 19 962 696 13.5220 Water bodies (natural and artificial) 2557 905 1.7321 Snow and ice (natural and artificial)—with Antarctica 16 354 103 11.0822 Artificial surfaces and associated areas 280 701 0.1923 No data 1293 0.00

Total 147 650 500 100.00


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cycle and their role as the largest source of the greenhouse gas methane) account for

well under 2% of the planet’s land surface.

7. Conclusions

The year 2000 has been identified as a benchmark for environmental assessment by a

number of institutions that put forward specific environmental assessment activities,

such as the Millennium Ecosystem Assessment (Reid 2000) or the Land

Degradation in Arid Lands initiative (FAO et al. 2002), without mentioning the

Forest Resource Assessment (FRA 2000) carried out by FAO (FAO 2001).

The GLC2000 was able to provide the scientific community with a new global

land cover dataset within 2 years of the end of the data acquisition phase. This could

be accomplished thanks to the establishment of a broad partnership at international

level, a process very much in line with the Global Land Cover Network set up by

FAO and UNEP (FAO 2002).

The GLC2000 land cover database makes use of LCCS for the establishment ofthe legend, a system that was endorsed as a unique and universal standard for

classification of land cover (FAO 2002).

GLC2000 is a departure from the previous approaches to global land cover

mapping. The resulting regional maps will serve users who have not benefited from

previous global products. The global aggregation offers an update to those products

based on the 1992/93 AVHRR archive, benefits from the improved spatial and

spectral characteristics offered by the VEGETATION data and provides a detailedview of global land cover conditions at the turn of the millennium.

By March 2004 over 2300 individuals had registered at the GLC2000 web site and

downloaded the global database. In a web survey we found uses ranging from the

thoroughly esoteric such the study of snake distributions throughout Africa, to the

more expected such as use in Numerical Weather Prediction models.

The GLC2000 project again underscores the fact that global land cover mapping

is a far from trivial undertaking, yet the wide range of users involved in the

programme underscore the continued and growing demands for such products.

Because of methodological choices GLC2000 will not be easily replicable in the

future. On the other hand, ease of replication does not automatically equate with

high quality. In any event a number of lessons have been learnt, such as specific

requirements for data pre-processing, the importance of traceability when specifying

legends and the effectiveness of involving key end-users in the process from thebeginning. These lessons will be useful for the implementation of future projects

focused on map update, accuracy improvement of difficult land cover classes, and

land cover change detection.

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 figure 1. A full list of individuals is provided in

Bartholome et al. (2002). The authors are particularly indebted to the members ofthe Global Vegetation Unit of the JRC who contributed the GLC2000 project:

F. Achard, S. Bartalev, C. Carmona-Moreno, V. Gond, S. Kolmert, M. Massart,


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P. Mayaux, M. Merlotti, H. Eva, S. Fritz, B. Glenat, J.-M. Gregoire, A. Hartley,

H.-J. Stibig, A. Tournier and P. Vogt.

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

C. Giri.

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

R. Latifovic.

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

Xu W.

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

(3) Lab. de teledeteccion aplicada, Univ. Nacional Agraria, La

Molina, Peru: V. Barrena Arroyo.

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

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

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

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

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

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

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

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

(5b, 7) Environnemetrie et Geomatique Un. Cath., Louvain-la-Neuve,

Belgium: P. Defourny, C. Vancutsem, J.-F. Pekel.

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

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

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

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

Roy, S. Gupta.

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

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

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

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

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

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

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

(7) Tropical Research Institute, Lisbon, Portugal M. J. De Perestrelo,

J. Pereira, A. I. Cabral.

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

A. Isaev.

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

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

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

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

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

(7) Regional Centre for Mapping of Resources for Development,

Nairobi, Kenya: W. K. Ottichilo.

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

Essahli.

(5b) Instituto de Hidraulica, Engenharia Rural e Ambiente, Lisbon,

Portugal: A. Perdigao.


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