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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
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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
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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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