The GlobCover classification chain

Based on GLC2000 and then GlobCover experiences, the legend (Table 1) contains 22 classes and is strictly based on the LCCS of the UN-FAO (Di Gregorio, 2005). LCCS describes land cover according to a hierarchical series of classifiers in a dichotomous phase (vegetated or non-vegetated, terrestrial or aquatic/flooded, cultivated or natural) and by attributes (life- form, fractional cover, leaf type, phenology, etc.). Each class obtained by the classification process is explicitly described by a thematic label, which is directly associated to a land cover class name and a LCCS code. The use of LCCS reduces the ambiguity in land cover class definition thanks to the standardization of the combination of independent classifiers. These classifiers allow defining a highly flexible and systematic land cover typology, with the capacity to describe the land cover features at a variety of scales and to derive enough classes to cope with the real world diversity (Di Gregorio, 2005).

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ID Value Global Globcover legend (level 1) Color LCCS major descriptor

11 Post-flooding or irrigated croplands (or aquatic) 14 Cultivated and managed areas / Rainfed croplands

20 Mosaic Cropland (50-70 %) / Vegetation (grassland, shrubland, forest) (20-50 %) 30 Mosaic Vegetation (grassland, shrubland, forest) (50-70 %) / Cropland (20-50 %) 40 Closed to open (> 15 %) broadleaved evergreen and/or semi-deciduous forest (> 5 m) 50 Closed (> 40 %) broadleaved deciduous forest (> 5 m)

60 Open (15-40 %) broadleaved deciduous forest/woodland (> 5 m) 70 Closed (> 40 %) needle-leaved evergreen forest (> 5 m) 80 Closed (> 40%) needle-leaved deciduous forest (> 5 m)

90 Open (15-40 %) needle-leaved deciduous or evergreen forest (> 5 m) 100 Closed to open (> 15 %) mixed broadleaved and needle-leaved forest (> 5 m) 110 Mosaic forest or shrubland (50-70 %) / grassland (20-50 %)

120 Mosaic grassland (50-70 %) / forest or shrubland (20-50 %) 130 Closed to open (> 15 %) shrubland (< 5 m)

140 Closed to open (> 15 %) grassland 150 Sparse (< 15 %) vegetation

160 Closed (> 40 %) broadleaved forest regularly flooded - Fresh water

170Closed (> 40 %) broadleaved semi-deciduous and/or evergreen forest regularly flooded - Saline water

180Closed to open (> 15 %) vegetation (grassland, shrubland, woody vegetation) on regularly flooded or waterlogged soil - Fresh, brackish or saline water 190 Artificial surfaces and associated areas (Urban areas > 50 %) 200 Bare areas

210 Water bodies 220 Permanent snow and ice

Artificial surfaces and bare areas Inland waterbodies,

snow and ice Cultivated terrestrial

areas and managed lands

Natural and semi- natural terrestrial

vegetation

Natural and semi- natural aquatic

vegetation Table 1 : The 22 global land cover classes of the GlobCover legend.

Figure 2: GlobCover 22 equal-reasoning strata. Green strata rely on annual composites and yellow strata rely on annual and seasonal composites for the classification.

Being the first automated mapping approach of land cover on a global scale, GlobCover provides a basis for the detailed description of the land surface states needed for climate modelling (Arino et al., 2008). The GlobCover classification chain, developed with the support of ESA, is a fully automated chain that delivers classification using multispectral

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seasonal, annual or multi-annual composites (Bicheron et al., 2008; Bontemps et al., 2011).

Before the classification process, the world is stratified in 22 equal- reasoning regions (strata) which have been delineated taking into account bio-climatic and remote sensing criteria (Figure 2). The purpose is (i) to reduce the land surface reflectance variability in the dataset in order to increase the spectral differentiation among classes and (ii) to allow a regional tuning of the classification parameters to take into account the regional characteristics (vegetation seasonality, cloud coverage, etc.). The great but much controlled flexibility of this strategy allows defining a classification process valid at global scale while tackling both the regional heterogeneity of the land cover characteristics.

The classification module of the GlobCover processing chain consists in transforming the SPOT-VEGETATION cloud-free reflectance composites produced by the pre-processing step into meaningful global land cover maps at 1-km spatial resolution. The algorithms are designed to run independently for each of the 22 strata with specific parameters.

First, a supervised classification is applied to identify the low represented classes, i.e. urban areas and wetland areas. Second, the pixels classified through the supervised classification are masked and an unsupervised classification algorithm, which relies on the Iterative Self- Organizing Data Analysis Technique (ISODATA) clustering technique, is applied to create a large number of clusters of spectrally similar pixels from the selected composites. Using an unsupervised classification algorithm allows reaching a rather high degree of automation while reducing the processing time. It is a classification process based solely on the image statistics, without availability of training data or other a priori knowledge of the area. The ISODATA is an iterative optimization clustering procedure. It is based upon estimating some reasonable assignment of the pixel vectors into candidate clusters and then moving them from one cluster to another in such a way that the sum of squared error is progressively reduced. It assigns a set of pixels into clusters so that the pixels in the same cluster are more similar to each other than to those in other clusters. The assignation is based on natural groupings present in the reflectance values. The basic premise is that reflectance values within a given land cover class should be close together in the measurement space, whereas pixels belonging to different land cover classes should be comparatively well-separated.

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This unsupervised classification step relies on the annual composite when the stratum presents a low seasonality and/or high cloud cover frequency (Figure 2, green strata). For the other strata, a supplementary seasonal or bi-monthly composite is selected, according to the season showing the best contrasts for land cover classes, to be part of the unsupervised classification with the annual composite (Figure 2, yellow strata). The spectral clusters produced by the unsupervised classification in the strata which present a high seasonality are then temporally characterized by the computation of phenological metrics (minimum and maximum of vegetation), derived from the SPOT-VEGETATION time series and spatially averaged for each cluster. The key idea is to combine the spatial consistency of the clusters delineation obtained from well selected multispectral composites with the discrimination power of the temporal profiles analysis to group clusters with similar characteristics in a manageable number of spectro-temporal classes.

Then, the supervised and unsupervised classification results are merged. The unsupervised algorithm generates however separable clusters for which the land cover label is not known and needs to be determined by comparing them to some auxiliary reference dataset. The reference dataset was compiled from 16 global, continental, national or regional land cover maps merged and superimposed on the GLC2000 land cover map, and translated in the GlobCover legend thanks to the LCCS classifiers information. The referenced-based labelling function automatically assigns previously defined LCCS land cover classes (GlobCover classes) to the spectro-temporal clusters according to their correspondence with the reference land cover classes. For each cluster, a histogram of class frequency is computed based on the reference dataset. The most represented classes inside the cluster are identified and ranked using the number of pixels they cover and their label. Several decision rules have been defined with the help of international land cover experts to interpret the class frequency and to derive unique label for each spectro-temporal class.