Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series

International Journal of Applied Earth Observation and Geoinformation 11 (2009) 62–76

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Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series C. Vancutsem a,*, J.-F. Pekel a, C. Evrard b, F. Malaisse c, P. Defourny a a

Department of Environmental Sciences and Land Use Planning, Universite´ Catholique de Louvain, 2/16 Croix du Sud, B-1348 Louvain-la-Neuve, Belgium Department of Biology, Laboratory of Plant Biology, Universite´ Catholique de Louvain, 5/14 Croix du Sud, B-1348 Louvain-la-Neuve, Belgium c Laboratory of Ecology, Soil, Ecology, Land planning Unit, Agricultural University of Gembloux, passage des De´porte´s 2, 5030 Gembloux, Belgium b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 13 February 2007 Received in revised form 22 July 2008 Accepted 5 August 2008

The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarseresolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover). ß 2008 Elsevier B.V. All rights reserved.

Keywords: RDC Land cover mapping Vegetation GLC2000 Africover Time series Mean compositing

1. Introduction The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized, particularly to analyze the diversity of landscapes and land cover dynamics, and to assess the degradation of habitats and better manage natural resources. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of tropical forest cover of Central Africa and has a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas

* Corresponding author. Fax: +32 10478898. E-mail address: [email protected] (C. Vancutsem). 0303-2434/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2008.08.001

still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. The first regional and continental land cover maps over Central Africa were derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very-High-Resolution Radiometer (AVHRR) data (Tucker, 1985; Mayaux et al., 1997; Laporte et al., 1998; Loveland et al., 1999; Hansen et al., 2000). However, the AVHRR data, originally designed for meteorological applications, have poor geometric accuracy and limited radiometric calibration (Meyer et al., 1995; Cihlar et al., 1997) that reduces the spatial resolution of the imagery and introduces spatial and temporal inconsistencies in the syntheses. Therefore, the classification process is often based on the single use of a vegetation index that is less disturbed by the noise induced by preprocessing. For instance, The International Geosphere Biosphere Programme (IGBP) land cover map (Loveland et al., 1999) was based on a multi-temporal

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unsupervised classification of 12-month NDVI composites. However, this approach prevents efficient discrimination between land cover classes. More dedicated sensors appeared with enhanced spatial and spectral characteristics, such as SPOT-VEGETATION (VGT) and the MODerate resolution Imaging Spectroradiometer (MODIS) on board the Terra platform, even though the spatial resolution of MODIS varies much more than that of VGT. Recent studies confirm the potential of VGT (Fritz et al., 2003; Mayaux et al., 2004) and MODIS data for producing more detailed land cover and vegetation maps (Borak et al., 2000; Zhan et al., 2000; Hansen et al., 2002; Townshend and Justice, 2002) at global and regional scales. However, the use of coarse-resolution optical data is constrained by their performance in the presence of cloud screening and by noise arising due to the compositing process, which limits the spatial consistency of the composite and the temporal resolution. Advanced BRDF correction exhibited limited performance for syntheses using MODIS (Huete et al., 2002) and VGT data (Duchemin et al., 2002; Hagolle et al., 2004; Swinnen, 2004; Vancutsem et al., 2007b). Performance in the presence of cloud screening is also still a constraint in producing cloud-free and consistent syntheses, particularly in equatorial areas (Mayaux et al., 2004; Gond et al., 2005). Another way to produce land cover map involves the use of high-resolution data. In the framework of the Food and Agriculture Organization of the United Nations (FAO) Africover program (Latham, 2001), high-resolution data have been used to realize land cover maps of several countries in Africa, such as the DRC. These products, based on visual interpretation of Landsat images by several experts, present a great level of detail, but suffer from large inconsistencies because of heterogeneity in acquisition dates, images and interpretation from one scene to another. Moreover, this approach hardly takes into account the seasonal variation and phenologic behavior of different vegetation types. The objective of this research was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VGT time series. More specifically, three main issues are addressed: (i) the production of spatially very consistent syntheses for all available reflectance bands using a new compositing methodology; (ii) stratification of the country to deal with various seasonal behaviors; and (iii) the use of ecological knowledge from botanists and reference documents to improve the interpretation step and to characterize the seasonality of various vegetation types. This approach is applied to the DRC, which presents particularly difficult conditions in term of cloud coverage and accessibility. The classification result has been assessed by comparing it with two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover). 2. Data The data set used to produce the land cover map consists of 366 daily (S1 products) SPOT VGT images of the year 2000 at a spatial resolution of 1 km. The VGT instrument was launched on board SPOT-4 in March 1998 and delivers measurements that are specifically tailored to monitor land surface parameters on a global basis (http://www.spot-vegetation.com/). The four spectral bands of the sensor allow characterization of the main features of the plant canopy: (i) the red band centered on the absorption peak of chlorophyll (0.61–0.68 mm); (ii) the near-infrared (NIR) band (0.78–0.89 mm) corresponding to maximum vegetation spectral reflectance; and (iii) the shortwave infrared (SWIR) band centered around 1.65 mm for reflectance related to the water content of

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canopy components and to its structure. An additional band in the blue region (0.4 and 0.50 mm) is provided for characterizing the atmospheric state. The main advantages of these S1 SPOT VGT products are: (i) the daily multispectral reflectance measurements are radiometrically calibrated and atmospherically corrected; (ii) the global data set presents high multitemporal co-registration accuracy (Sylvander et al., 2000); and (iii) daily acquisition allows complete temporal characterization of the vegetation. Moreover, three other types of data were used as a support for interpretation, clustering of the land cover classes and the evaluation process: (i) the Landsat Geocover 2000 ETM+ data set (30-m spatial resolution) (ESC, 2004); (ii) Shuttle Radar Topography Mission (SRTM) data (www.srtm.usgs.gov); and (iii) reference vegetation maps and documents, i.e. a vegetation map of the Congo (Devred, 1958a), Lebrun (1936) and Leonard (1953) for the location and description of forest classes in the Congo, Saeger (1956) for a description of the vegetation types in the Garamba area, the Yangambi classification (Trochain, 1957), INEAC vegetation maps (Devred, 1958a; Evrard, 1960; Pecrot and Leonard, 1960; Compe`re), which present and describe the vegetation at a regional scale, Germain (1965) for a description of flooded grassland, Evrard (1968) for a description of the edaphic forests of the Congo, the vegetation map of Schmitz (1977) for the Shaba area, a vegetation map of Africa (White, 1983), and Doumenge (1990) for a description of the Congo forests. Finally, vectorial data for features such as roads, places and rivers from the geographical database on the DRC (Vancutsem et al., 2006a) were superimposed on the classification result for a land cover mapping edition. 3. Methodology The land cover mapping methodology involves the four steps described in this section: (i) data preprocessing; (ii) stratification; (iii) classification; and (iv) evaluation. The three first steps are illustrated in Fig. 1. 3.1. Data processing The main challenge in land cover mapping of this large equatorial area is the production of cloud-free and spatially consistent images, called temporal syntheses or composites. The methodology selected for this first step is the mean compositing (MC) (Vancutsem et al., 2007b), which improves the quality of temporal syntheses by reducing atmospheric and directional variations for the SPOT VEGETATION (Fritz et al., 2003; Mayaux et al., 2004; Gond et al., 2005; Vancutsem et al., 2007b) and MERIS (Vancutsem et al., 2007a) sensors. The MC method involves averaging for each pixel and in each spectral band all the valid reflectance values acquired during the period chosen. Vegetation indices are computed on the basis of these reflectance averages. The main advantage of this strategy is that it uses all of the available and useable information to provide the interpretation step with the most representative signal for each pixel for a given compositing period. In addition, it is very flexible, as the compositing period can be adapted according to the cloud coverage frequency and the target seasonality. The MC strategy requires efficient quality control of the reflectance values as a preliminary step. This includes the removal of clouds and hazes, and all artifact values. After this quality control procedure, the valid reflectance values are averaged for each pixel and each wavelength during the period chosen.

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Fig. 1. First three steps of the land cover mapping methodology. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)

Three syntheses have been generated from the temporal SPOT VGT series acquired during 2000: (i) a seasonal composite for December–January corresponding to the dry season in the north of the country (above the equator); (ii) an annual composite; and (iii) a seasonal composite for May–June corresponding to the dry season in the south (below the equator). Dry season image selection was already successfully applied by Mayaux et al. (1997) and Laporte et al. (1998), as it provides a good contrast between forests and other more seasonal land-cover types. The improvement here involves using syntheses of several months rather than single-date images and adjusting the compositing period according to the specificity of each area. To provide a range, the number of observations taken into account in the annual mean computation varies from four to 150, with an average of 82 for all of the DRC. Moreover, monthly composites were produced to characterize the seasonality of all vegetation types. Two vegetation indices have been used in this study: the normalized difference vegetation index (NDVI) (Eq. (1)) and the normalized difference water index (NDWI) (Eq. (2)). NDVI (Tucker, 1979) is well recognized for characterization of the chlorophyll activity of vegetation cover over time. NDWI (Gao, 1996) is rather related to the vegetation water content (Ceccato et al., 2002). Some recent studies (Xiao et al., 2003; Boles et al., 2004) demonstrated the potential of this index for land cover classification.   s NIR  s RED NDVI ¼ (1) s NIR þ s RED NDWI ¼



s NIR  s SWIR s NIR þ s SWIR



(2)

3.2. Stratification Because of its equatorial location, the DRC has an inversion of seasonality between the north and the south. This makes the classification step particularly complex, as the same land cover types in the north and south of the country do not present the same phenological stage at the same time, and thus it is impossible to classify the whole country in a single way. For this reason, the country has been stratified in three regions: the north, the center and the south, corresponding to the Sudanian and Sudano-Guinean savanna areas in the north, the dense forest area in the center, and the Zambezian savanna and woodland areas in the south. In this way, it was possible to use different composites for each region and

thus optimize the compositing period according to their specific seasonality. To preserve the spatial consistency of the composites, stratification was based on vegetation limits identified using an NDVI threshold based on the annual composite. These limits correspond to boundaries between forest and non-forest areas in the north, in the south and in the east of the Congo Basin. 3.3. Classification Based on the temporal syntheses produced, unsupervised classifications were performed. The two most frequently used algorithms in remote sensing are the k-mean and the ISODATA (Iterative Self-Organizing Data Analysis) clustering algorithms. In this study, the ISODATA algorithm has been selected as it presents some further refinements by splitting and merging of clusters (Jensen, 1996). It allows different number of clusters while the kmeans assumes that the number of clusters is known a priori. Three digital classifications were completed for each region: (i) 20 classes were derived from the December–January seasonal composite in the north; (ii) 25 classes were derived from the annual composite in the center region; and (iii) 35 classes were derived from the May–June seasonal composite in the south. The number of classes varies according to the diversity of each area and the initial number of clusters was determined from a visual image analysis and the Congo vegetation map (Devred, 1958b). For each composite, three of the four wavelength bands were used in the classification process, i.e. the red, NIR and SWIR bands, since the blue band is too sensitive to atmospheric variations. Collaboration with experts with extensive field knowledge of the DRC and the use of several reference maps and documents allowed class interpretation thanks to images of high spatial resolution and temporal profiles. The resulting classes were clustered into 16 final classes. Several workshops were organized with two botanists, C. Evrard and F. Malaisse, and several remote sensing experts, i.e. P. Mayaux, J.-P. Malingreau, J.-F. Pekel, C. De Wasseige and B. Desclee. During these workshops, experts had to interpret the classes jointly behind a large screen showing the classification, the Landsat images and the vegetation profiles. Moreover, two other botanists, P. Bamps and Luc Pauwels, were consulted on specific questions related to their area of expertise, i.e. Katanga and Bas-Congo provinces. Finally, using altitude limits identified by Lebrun (1936) and the SRTM data, the dense moist forest class was split into three classes: lowland dense forest (<1100 m), submontane forest (1100– 1750 m) and mountain forest (>1750 m).

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Table 1 GLC2000 legend for the Democratic Republic of Congo No.

User’s label

LCCS label

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Closed evergreen forest Submontane forest Mountain forest Swamp forest Mosaic forest–cropland Mosaic forest–savanna Closed deciduous woodland (Miombo) Open deciduous woodland Deciduous shrubland with sparse trees Open deciduous shrubland Closed grassland Open grassland with sparse shrubs Open grassland Sparse grassland Swamp bushland and grassland Cropland Inland waters Cities

Closed (>70%) evergreen lowland (<900 m) forest Closed (>70%) evergreen submontane (900–1500 m) forest Closed (>70%) evergreen montane (>1500 m) forest Closed (>70%) evergreen forest on regularly flooded land Closed (>40%) evergreen forest/cropped areas (15–40%) Closed (>40%) evergreen and semi-deciduous forest/shrubland (>40%) Closed (>40%) deciduous woodland (forest) Open (15–40%) deciduous woodland with shrubs (>15%) and grassland (>15%) Closed to open (>15%) deciduous shrubland with grassland (>40%) and sparse trees (<15%) Closed to open (>15%) deciduous shrubland with grassland (>40%) and sparse trees (<5%) Closed (>40%) grassland with sparse trees and shrubs (<20%) Open (15–40%) grassland with sparse shrubs (<20%) Open (5–15%) grassland Sparse (1–5%) grassland Shrubland (>15%) regularly flooded/grassland (>40%) with sparse trees (5–15%) regularly flooded Croplands (>50) Inland waters Cities

All the 18 classes are described in an explanatory note to the land cover map, which includes the spatial distribution of each class, the floristic composition, the physiognomy, and a complete description using the Land Cover Classification System (LCCS) developed by the FAO to analyze and cross-reference regional differences in land cover descriptions (Gregorio and Jansen, 2000). Moreover, the temporal profiles of several vegetation types are presented and discussed. Finally, cartographic vector information derived from the General map of the DRC (Vancutsem et al., 2006a) has been added to the classification to produce a land cover map at the 1: 3,000,000 scale (Vancutsem et al., 2006b). 3.4. Evaluation methodology The evaluation step is particularly delicate for low-scale regional maps, as it is difficult to proceed to any classical ground validation at an acceptable cost. The only possibility thus involves a comparison with one or several existing maps or with highresolution images, such as Landsat images. For the DRC, two land cover maps have recently been produced, GLC2000 (Mayaux et al., 2004) and Africover (Latham, 2001). The first is based on four types of remotely sensed data: SPOT VGT data for 2000, radar (ERS and JERS) images, Defence Meteorological Satellite Program (DMSP) data, and a digital elevation model,

processed using different methods (Mayauxet al., 2004). Nineteen classes have been produced for the DRC and described using the LCCS system (Table 1). The second map is based on interpretation of Landsat images acquired at different dates (from 1990 to 2004). The level of detail of this map is higher than the map produced; 94 classes were produced and described using the LCCS system. An aggregate legend corresponding to the thematic level of the GLC2000 legend is available in Table 2 that includes the LCCS labels and the Africover codes. The evaluation process relies on a quantitative and qualitative comparison between the land cover map produced and these two recent maps. The quantitative evaluation was carried out in two steps. First, the land cover map produced (VGT map) was compared to the Africover map, generalized at the same spatial resolution as the SPOT-VGT data for several levels of aggregation, i.e. 2, 4, 5, 6 and 7 classes (Table 3). These levels of aggregation are required because classes of each map are thematically different and thus aggregation into a lower number of classes allows comparison between them. Confusion matrices were computed for each level of aggregation (Section 4.3.1). The same approach was applied to the GLC2000 map for comparison to the accuracy of the VGT map based on SPOT-VGT data. The confusion matrices are based on a sampling of 500–1000 windows of 3  3 pixels for each Africover class. Second, to determine the divergence observed in the confusion matrices,

Table 2 Africover aggregated legend for the Democratic Republic of Congo User’s label

LCCS label

Africover codesa

1

Swamp forest

4TC*, 4TP*

2 3 4 5

Swamp grassland Swamp shrubland Evergreen forest Semi-deciduous forest

Closed (>65%) broadleaf forest on permanently (>4 m) or temporarily (2–4 m) flooded land Herbaceous VGT on permanently or temporarily flooded land Shrubs on permanently or temporarily flooded land Evergreen closed broadleaf forest (>65%) Semi-deciduous or deciduous or semi-evergreen closed broadleaf forest (>65%)

Woodland with shrubs Open woodland Very open woodland Shrubland Grassland Bare soil Water bodies Cropped areas

Open (65–15%) deciduous woodland (14–7 m) with open high shrubs (65–15%) Open (65–40%) deciduous woodland (14–7 m) Open (40–15%) deciduous woodland (14–7 m) Open (65–15%) shrubland with open (65–15%) herbaceous layer or thickets (>65%) Closed (>65%) medium to tall grassland (3–0.3 m) Industrial areas or urban areas or extraction sites or bare soil Water bodies Cropped areas

No.

6 7 8 9 10 11 12 13 a

The asterisk replaces one or several characters.

4H*, 4F* 4S* 2TCL177, 2TCL187 2TCL217, 2TCL128, 2TC28, 2TCL28, 2TCM28 2TP* 2TO* 2TV* 2S* 2H* 5*, 6* 7WP, 8W* H*, T*, S*

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Table 3 Number of land cover classes for each land cover map (see the corresponding labels in Tables 1, 2 and 4) corresponding to the seven aggregated classes used for the accuracy assessment Aggregated classes

VGT-map

GLC2000

Africover

Terra firma forest Edaphic forest Rural complex and agriculture Closed deciduous forest, woodland and tree savanna Shrubland Grassland, steppe savanna and bare soil Aquatic and swamp savanna

4, 5, 6, 8, 17, 18 2 7, 13 12, 14, 15 9, 11 9, 11 2, 3

1, 2, 3, 6 4 5, 16 7, 8 9, 10 11, 12, 13, 14, 18 15

4, 5 1 13 6, 7, 8 9 10, 11 2, 3

Fig. 2. Samples of the annual mean composite (SWIR, NIR, red) produced from SPOT VEGETATION time series for 2000 for the Democratic Republic of Congo. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)

the areas of agreement and disagreement between the three products were computed and visualized for a level of aggregation of six common classes (Section 4.3.2). Reference maps and documents are used in discussing the results. Finally, qualitative evaluation of the VGT map in comparison to the other two land cover maps, the annual SPOT VGT composite and the Landsat images provides an appreciation of the spatial consistency and spatial details contained in the three maps. A total of 20 samples of 160 km  160 km were randomly selected in key ecosystems, of which seven are shown for illustration.

d), whereas in the south, gallery forests penetrate into savanna areas (in green in Fig. 2e). Vast savanna areas are also evident in the forest domain, either as large patches surrounding the forests or as small islands of grasslands enclosed within the forest (in pink in Fig. 2e and f).

4. Results and analysis 4.1. Compositing The annual composite (Fig. 2) is remarkable for its spatial consistency in the red, NIR and SWIR spectral bands over the entire central African region and is absent of clouds and haze. The major visible features of the dense moist forest biome are the presence of swamp forest along the rivers (in dark green in Fig. 2a) and the ribbons of secondary forest formations along the road network (in light green in Fig. 2a and d). It is also possible to distinguish rural complex areas surrounded by secondary forest (in very light green in Fig. 2c and d). In the north of the Congo Basin, the transition between forest and savanna is quite abrupt (in pink in Fig. 2b and

Fig. 3. Seasonal mean composite (SWIR, NIR, red) for May–June produced from SPOT VEGETATION time series for 2000 in the Katanga area. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Fig. 4. Temporal NDVI (dashed lines) and NDWI (solid lines) profiles of the dense moist forest (black lines) and the Miombo woodland (gray lines) for 2000.

The 2-month composites are particularly interesting for the north and south parts of the country. The contrast between land cover types is more pronounced for these regions compared to the annual temporal synthesis. In particular, the May–June composite allows very good identification of the Miombo woodland (in light green in Fig. 3). Finally, 1-month NDVI and NDWI temporal series were used to characterize the temporal profile of each land cover type. However, NDWI was preferred to NDVI because of the stability of the signal and better discrimination between land cover types (Fig. 4). This stability is probably due to the low sensitivity of the index to atmospheric conditions in comparison to NDVI (Xiao et al., 2003).

4.2. Map description The VGT map produced over the DRC retains the geometric characteristics of the VGT data, with a spatial resolution of 1 km. The map, originally in Plate carre´e, Ellipsoid WGS-84, was projected in the new geographical reference proposed by a consortium of experts (http://www.geoweb.rug.ac.be/sygiap/ index.asp), i.e. the Mercator projection secant to parallels 58N and 58S, Ellipsoid GRS80, Datum WGS84. The land cover map product of the DRC is presented in Fig. 5 on a reduced scale. The LCCS description of each class is presented in Table 4. Moreover, a complete description of the land cover classes and their localization and a complete characterization of

Fig. 5. The land cover map produced and the 18 land cover classes.

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Table 4 User and LCCS legends for the land cover map produced No.

User’s label

LCCS label

1 2

Water bodies Edaphic forest

3 4

Aquatic grassland Dense moist forest

5

Old secondary forest

6

Young secondary forest

Water bodies Closed (>65%) broadleaf semi-deciduous high (14–30 m) forest on permanently (>4 m) or temporarily (2–4 m) flooded land Closed (>65%) tall (0.8–3 m) grassland on permanently or temporarily flooded land Closed (>65%) multi-layer broadleaf semi-deciduous high (14–35 and 5–14 m) forest with closed (>65%) shrubs (1–5 m) with open (40–65%) herbaceous layer (<1 m) Open (40–65%) multi-layer broadleaf semi-deciduous high (14–30 and 5–14 m) forest with open (40–65%) shrubs (3–5 m) with closed (>65%) herbaceous layer (1–3 m) Open (15–65%) multi-layer broadleaf semi-deciduous medium (14–20 and 3–7 m) forest, with closed (>65%) shrubs (<3 m) with closed (>65%) herbaceous layer (<1 m) Open (15–65%) broadleaf semi-deciduous low (7–14 m) forest with herbaceous and tree crops (plantations) Mosaic closed (>65%) semi-deciduous high (>14 m) forest–closed grassland with sparse trees Closed (>65%) medium (0.3–0.8 m) grassland/herbaceous crops) Broadleaf deciduous shrubland (3–7 m) (15–40%) with closed (>65%) herbaceous layer Closed (>65%) tall (3–0.8 m) grassland with sparse trees (7–14 m) (5–15%) Broadleaf deciduous woodland (>14 m) (>60%) with open (>65%) short herbaceous layer (<0.8 m) Permanently cropped area with rain-fed broadleaf tree crops (plantations) or rain-fed herbaceous crops or bare soil Broadleaf deciduous woodland (7–14 m) (25–60%) with closed (>65%) tall herbaceous layer (0.8–3 m) Broadleaf deciduous woodland (7–18 m) (15–40%) with closed (>65%) tall herbaceous layer (0.8–3 m) Closed (>65%) tall (0.8–3 m) grassland on waterlogged soil Closed (>65%) broadleaf semi-deciduous (>14 m) forest, altitude 1100–1750 m Closed (>65%) broadleaf semi-deciduous (>14 m) forest, altitude >1750 m

7 8 9 10 11 12 13 14 15 16 17 18

Rural complex (forest area) Forest–savanna mosaic Steppe savanna–crop mosaic Shrubland Grassland Woodland (Miombo) Agriculture Savanna woodland Tree savanna Swamp grassland Submontane forest Mountain forest

the vegetation types in terms of their temporal behavior are presented in the explanatory note for the land cover map, available at http://www.uclouvain.be/enge-cartesRDC. Aquatic grassland and swamp grassland represent the smallest land cover classes for the DRC (Table 7). Aquatic grassland is evident where flooding persists and drainage conditions are unfavorable. It is mainly distributed along the Congo and Ubangui Rivers, at the periphery of the edaphic forest and between the Congo and Ubangui Rivers. Swamp grassland (Cyperus papyrus), which is more dependent on soil than aquatic grassland (Schmitz, 1977), is only evident in the Upemba region, around Lufira Lake and to the south of Lake Moero in Katanga. The temporal profiles presented in Fig. 6 highlight seasonality differences between the two formations, which are mainly due to their different localizations within the country. Indeed, aquatic grassland located in the north-west of the country is marked by two small dry seasons (of 3 and 1 months), whereas the swamp grassland, mainly located in

the south-east of the country, is subject to one longer dry season (approx. 5 months). The edaphic forest class is mainly present along the rivers of the Congo Basin. It includes several types, depending on the flooding period and the richness of the environment, i.e. temporarily flooded forest, swamp forest and riparian forest (Evrard, 1968). The predominant class is dense moist forest, which mainly includes semi-deciduous forest and several isolated patches of evergreen forest. Both vegetation types cover the majority of the Congo Basin and 30% of the area of the entire country. Temporal profiles of dense moist forest for several locations in the DRC (Fig. 7) exhibit a seasonality that varies from one region to another according to pluviometry. Old secondary forest is evident between dense moist forest and young secondary forest. This formation represents a reconstitution step just before adult forests in the evolutionary series (Evrard, 1968).

Fig. 6. SPOT VEGETATION NDWI temporal profiles for aquatic grassland in the north-west of the Democratic Republic of Congo (dashed gray line) compared to swamp grassland in the south-east of the country (solid black line) for 2000.

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Fig. 7. SPOT VEGETATION NDWI temporal profiles of dense moist forest in the south-east of the Democratic Republic of Congo, near Lusambo (dashed black line), in the northwest near Gbadolite (solid black line) and in the center of the Congo Basin near Boende (solid gray line) for 2000.

As clearly observed in the annual composite, young secondary forest is mainly apparent along the road network and around villages, covering 5.3% of the area of the country. Villages and agricultural areas in the forest are represented by the rural complex class. It is formed by a complex of secondary regrowth, fallow, home gardens, food crops and village plantations (Vandenput, 1981). The forest–savanna mosaic class mainly appears at the periphery of dense moist forest, but is also located near the boundary with the Sudan, in the west of Garamba Park, along the gallery forests of Bandundu, and at the periphery of dense moist forest in the Demba area (Kasai occidental province), and in the Kibombo and Kabambare areas (Maniema province). The Miombo woodland is one of the three woodland associations present in the DRC. It appears in the Katanga area, where it

covers 26% of the province. Fig. 8 clearly shows the difference in seasonality between this deciduous woodland and the semideciduous forest encountered in the Congo Basin. The end of cold nights around the middle of August mainly contributes to vegetation regrowth at this time. However, this event can be shifted, depending on the occurrence of fires and the type of species (Malaisse, 1978). As observed from the temporal profiles, the decrease in photosynthetic activity arises later and more slowly than for savanna classes. All the savanna formations are represented from savanna woodland to steppe savanna. The savanna woodland class marks the transition between woodland and tree savanna. The trees are smaller and the tree cover is more open (25–60%) in comparison to the Miombo woodland. It is only located in the Katanga, where it covers 9% of the province.

Fig. 8. SPOT VEGETATION NDWI temporal profiles of five land cover types in Katanga province: (i) Miombo woodland (solid black line), (ii) woodland savanna (solid gray line), (iii) tree savanna (dashed gray line), and (iv) steppe savanna of the plateaus of Kundelungu (dashed black line) for 2000.

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Tree savanna presents more open tree cover than savanna woodland (15–40%). This class is mainly distributed in the Katanga area, where it covers 29% of the province. The phenology of these formations in the Katanga area is presented in Fig. 8. It is clearly evident that the lower the tree density, the greater is the decrease in vegetation around April–May and the lower is the vegetation index. Grasslands are distributed both in the forest domain and at the periphery of the forest. Maintenance of this class is ensured by frequent fires. Grasslands also dominate the landscapes of BasCongo province (Compe`re, 1970). Finally, the steppe savanna–crop mosaic class covers 13.3% of the country. Steppe savanna comprises a closed stratum of low grasses (<80 cm). This class covers the plateaus of Feshi-Mukosso and penetrates the Kwango valleys (Devred, 1958b). It is also present in Katanga province, and on the plateaus of Muhila, Mrungu, Kibara, Kundelungu, Biano and Manika. The agriculture class is mainly located in the forest domain (Bumba, Kisangani, Lubutu, Kindu). It includes industrial plantations in the forest (hevea, coffee trees, palm trees), the cities and the villages, as well as agriculture areas (pastures and crops) in the east of the country, such as in the Masisi and Beni areas (Nord-Kivu province). The submontane forest and mountain forest classes are located in the east of the country, where the altitude is higher than 1100 m (Lebrun, 1936). 4.3. Evaluation of the map 4.3.1. Confusion matrices Fig. 9 shows that the global accuracy obtained for several levels of aggregation using Africover as a reference is better for the VGT map than for GLC2000, particularly for levels of five and six classes. Up to six classes, the VGT map has an overall minimum accuracy of 76.9%, whereas this is only 67.5% for GLC2000. However, by discriminating several types of savanna, the accuracy decreases to less than 58.0% for both maps. The discrimination between forest and non-forest areas (two aggregated classes) strongly agrees with Africover (98.5%). In particular, omission errors are very low for the VGT map, i.e. 0.3% versus 6.2% for GLC2000. For an aggregation level of four classes, i.e. forest, agriculture and rural complex, woodland and savanna, and aquatic savanna, the global accuracy decreases to 81.0% for both maps. In fact, the aquatic vegetation type is strongly underestimated for both maps (approx. 56.0% of omission errors), which considerably reduces the total accuracy. The better resolution of the data used to produce the Africover map allows better identification of this land cover

Fig. 9. Accuracy obtained for the land cover map produced (solid black line) and the GLC2000 map (dashed gray line) using Africover as reference data for several levels of aggregation of land cover classes.

type, which involves small and linear features. This mainly explains the inaccuracy obtained for SPOT VGT data. By discriminating swamp forest from terra firma forest (level of five classes), better accuracy is observed for the VGT map compared to GLC2000 (83.0% versus 74.0%). This difference is mainly due to omission errors for the edaphic forest class, i.e. 42% for GLC2000 versus 22% for the VGT map. The confusion matrix obtained for a level of aggregation of six common classes (Tables 5 and 6), whereby woodland and savanna are discriminated, still shows better accuracy for the VGT map than for GLC2000 (77.0% versus 65.5%). In particular, edaphic forest and savanna exhibit much greater omission errors for GLC2000 than for the VGT map. The land cover map produced also exhibits fewer contamination errors for all classes except for savanna. Finally, discrimination between shrubland and grassland/ steppe savanna (level of seven classes) considerably reduces the accuracy for both maps. This emphasizes the diversity of interpretation for the savanna classes. All these results highlight the complexity of the validation step. Data have to be interpreted cautiously, as some results can be interpreted as omission or contamination errors when they are actually differences in interpretation. Therefore, such differences have to be spatially analyzed, as detailed in the following section. 4.3.2. Spatial correspondence Areas of agreement and disagreement among the VGT map, Africover, and GLC2000 for an aggregation level of six classes are presented in Fig. 10. Computation of these areas revealed that the three maps agree for 53.8% of the country. On the other hand, only 7.1% of the

Table 5 Matrix for the land cover map produced (VGT map) and Africover VGT-map

Africover Terra firma forest

Terra firma forest Edaphic forest Rural complex and agriculture Closed deciduous forest, woodland and tree savanna Savanna Aquatic and swamp savanna Total Accuracy

Edaphic forest

Rural complex and agriculture

Closed deciduous forest, woodland and tree savanna

Savanna

Aquatic and swamp savanna

Total

Accuracy

958 28 10 0

155 788 0 0

22 0 724 0

11 0 0 691

8 0 1 7

64 96 0 1

1218 912 735 699

78.6 86.4 98.5 98.9

4 0

57 0

254 0

298 0

984 0

369 470

1966 470

50.1 100

1000 78.8

1000 72.4

1000 69.1

1000 98.4

1000 47

1000 95.8

6000 76.9

85.4 76.9

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Table 6 Matrix for the GLC2000 map and Africover GLC2000

Africover Terra firma forest

Terra firma forest Edaphic forest Rural complex and agriculture Closed deciduous forest, woodland and tree savanna Savanna Aquatic and swamp savanna Total Accuracy

Edaphic forest

Rural complex and agriculture

Closed deciduous forest, woodland and tree savanna

Savanna

Aquatic and swamp savanna

Total

Accuracy

930 30 35 5

429 559 7 4

136 0 796 53

110 0 0 885

187 0 8 453

50 154 0 227

1842 743 846 1627

50.5 75.2 94.1 54.4

0 0

1 0

14 1

5 0

351 1

39 530

410 532

85.6 99.6

1000 35.1

1000 47

6000 67.5

76.6 67.5

1000 93.0

1000 55.9

1000 79.6

country exhibits total disagreement between the three maps. The remaining 39.1% corresponds to areas of agreement between only two of the three maps: 21.9% agreement between the VGT map and Africover, 10.5% between the VGT map and GLC2000, and 6.7% between Africover and GLC2000. Thus, the land cover map produced agrees with at least one of the two existing maps for 86.2% of the area, with agreement for 71% of the country with GLC2000 and for 75.7% of the country with Africover. The areas of total disagreement are mainly located in the savanna domain, but also at the periphery of the forest and along the rivers. In fact, these areas are mainly interpreted as swamp savanna or edaphic forest for Africover, whereas GLC2000 and the VGT map identify terra firma forest, savanna or woodland instead. The better resolution of Landsat images allows more accurate

1000 88.5

delineation of swamp vegetation than SPOT VGT data, which probably explains these differences in interpretation. A main area of disagreement between Africover and the two other maps is located between the Ubangui and Congo Rivers, where Africover identifies large areas of terra firma forest (7% of the country), whereas this is edaphic forest according to Devred (1958b) and Evrard (1968), as well as the VGT map and GLC2000. Other areas of disagreement are located in the Congo Basin along the secondary forest, which is interpreted as terra firma forest for GLC2000 and the VGT map, whereas it is edaphic forest according to Africover. The areas of disagreement between the VGT map and the other two maps are mainly located in the south of the country in the savanna domain. In particular, areas identified by Africover and

Fig. 10. Areas of agreement (color) and disagreement (gray) among the VGT map, Africover and GLC2000 for an aggregation level of six classes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Fig. 11. Comparison of Landast color composites, the Africover map, our SPOT VGT MC annual composite (2000), our classification based on SPOT VGT, and the GLC2000 map for seven locations (windows of 160 km  160 km): (a) Mitwaba (Katanga); (b) Kibombo (Maniema); (c) Bosobolo (Equateur); (d) Befale (Equateur); (e) Bumba (Equateur); (f) Gungu (Bandundu); and (g) Djolu (Equateur). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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GLC2000 as woodland is savanna according to the VGT map and vice versa. The areas of disagreement between GLC2000 and the other two maps are more scattered. In the savanna domain in the north of the country, areas identified by GLC2000 as forest–savanna mosaic are shrubland according to Africover and the VGT map. According to Evrard (1960), Lebrun (1936) and Devred (1958b), this is shrubland in the north of the Ubangui area and in the north-east of the country. Other areas of disagreement occur in the Congo Basin, where areas identifies by GLC2000 as rural complex or agriculture correspond to degraded or secondary forest according to Africover and the VGT map. In the same region, an area identified as terra firma forest by GLC2000 is identified as edaphic forest by the other two maps. 4.3.3. Visual comparison Visual comparison (Fig. 11) confirms that Africover better identifies linear features such as edaphic forest and small features such as swamp savanna than the VGT map and GLC2000. However, the visual comparison demonstrates that the VGT map better delineates these elements than GLC2000, which exhibits several spatial discontinuities (Fig. 10d, e and g). For instance, the edaphic forest class of GLC2000 stops abruptly upstream from Lisala, whereas it continues along the Congo River for the VGT map and Africover (Fig. 10e), which is consistent with the Landsat images. Similar observations have been made in the Congo Basin, where the network of edaphic forest is less detailed and discontinuous than for the VGT map and Africover. Similarly, GLC2000 exhibits a lack of continuity in ribbons of young secondary forest, whereas the VGT map shows good continuity (Fig. 10g). In the forest domain and at the forest periphery, the VGT map exhibits spatial patterns that are similar to Africover and a good level of detail, whereas GLC2000 presents few details and a lack of continuity. In the savanna and woodland domain, GLC2000 exhibits more details and better spatial consistency than in the forest domain, whereas Africover sometimes presents inconsistencies between two tracks, i.e. the network of edaphic forest or swamp savanna is very detailed on one track in comparison to the other tracks (Fig. 12). This can be explained by differences in

Fig. 12. Landsat tracks (red lines) superimposed on the Africover map in the Kaniama area (Katanga province). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

interpretation and/or the use of images from different seasons (and different years) from one scene to another. 4.4. Discussion Comparative analysis of the land cover map produced with the Africover and GLC2000 maps highlights the complexity of such evaluations. Whereas the classification system (LCCS) used is the same, the data, processing methodology, interpretation and description of the land cover classes are specific to each land cover map. However, the results demonstrate that the VGT data have good detail in comparison to the Africover map, even though linear

Fig. 13. Comparison of Landsat images acquired on 1 June 2004 (a), 2 December 2001 (b), and 7 February 2003 (c) with the land cover map produced (d), the Africover map (e), and the GLC2000 map (f) for Garamba National Park in the north-east of the Democratic Republic of Congo.

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C. Vancutsem et al. / International Journal of Applied Earth Observation and Geoinformation 11 (2009) 62–76 Table 7 Estimation of land cover areas

Fig. 14. SPOT VEGETATION NDWI temporal profiles of grassland (solid black line) compared to steppe savanna (dashed gray line) in Garamba National Park.

features such as swamp vegetation and rivers are better identified on the Africover map because of the higher spatial resolution. On the other hand, the temporal dimension of the VGT data is essential to discriminate land cover types that exhibit seasonality, such as the savanna classes. For instance, in Garamba National Park, the VGT map discriminates grassland in the north of the park from steppe savanna in the south of the park (Fig. 13d), whereas Africover identifies areas inside and outside the park as shrubland (Fig. 13e) and GLC2000 indicates the same class for all of the park (Fig. 13f). This difference between the south and north is clearly visible in Landsat images during the dry season (Fig. 13c), i.e. February–March, and in temporal profiles (Fig. 14). Similarly, the VGT map discriminates shrubland from grassland in the Ubangui area, as described by Evrard (1960), Lebrun (1936) and Devred (1958b), whereas both formations are confused by the other two maps. This emphasizes the importance of exploiting seasonal variations, whereas Africover is based on only one single-date image and strongly depends on the phenological stage of the vegetation on this date. Moreover, inclusion of temporal behavior allows better characterization of each vegetation class, which is an obvious advantage for full understanding and monitoring of vegetation types at a national scale. While the VGT map and GLC2000 both use SPOT VGT data for 2000, the VGT map appears to be more spatially consistent and more detailed, particularly for the forest domain. This improvement is due to four factors: (i) use of the MC strategy; (ii) the contribution of experts with extensive knowledge of the area; (iii) a reduction in classification area from a continental to a national scale and (iv) stratification of the study area before classification. The spatial consistency provided by the mean compositing strategy is mainly due to the averaging effect, as previously observed (Vancutsem et al., 2007a). In fact, the MC algorithm considers residual noise in time series after preprocessing (radiometric and atmospheric corrections) and cloud screening as a combination of directional and atmospheric effects, and thus as randomly and uniformly distributed. The averaging process can thus reduce this unpredictable noise. Moreover, use of all the available information within the compositing period rather than selection of only one or a few maximum values makes the MC composite much less sensitive to directional effects than other popular strategies such as the Maximum Value Composite (MVC) NDVI (Holben, 1986). 4.4.1. Land cover statistics Land cover area estimates for the 18 classes of the VGT map presented in Table 7 indicate that dense forest and the agriculture– steppe savanna mosaic are the major classes.

User’s label

Area (km2)

Percentage

Water bodies Edaphic forest Aquatic grassland Dense moist forest Old secondary forest Young secondary forest Rural complex (forest area) Forest–savanna mosaic Steppe savanna–crops mosaic Shrubland Grassland Woodland (Miombo) Agriculture Savanna woodland Tree savanna Swamp grassland Submontane forest Mountain forest

41.156 102.452 5.261 703.671 155.491 125.338 69.912 84.713 313.020 160.836 190.267 144.124 18.052 42.846 154.239 2.053 26.786 6.602

1.75 4.37 0.22 29.98 6.63 5.34 2.98 3.61 13.34 6.85 8.11 6.14 0.77 1.83 6.57 0.09 1.14 0.28

Table 8 Estimation of forest cover areas Source a

IUCN (1992) FAO-FORISb (1993) TREES (1998) Africover (1990–2004) GLC2000 VGT map (2000) a b

Land cover classes

km2

%

Closed forest Closed broadleaf forest Evergreen and semi-deciduous forest Evergreen and semi-deciduous forest and edaphic forest Closed evergreen forest and swamp forest Swamp forest, terra firma forest and secondary forests

1,190,740 1,035,330 1,141,470

51.0 44.0 49.0

1 112 965

47.4

1,245,660

53.1

1,120,340

47.7

Sayer et al. (1992). FAO (1993).

The estimated forest area (Table 8) in the DRC corresponds to 47.7% of the area of the country, i.e. 1,120,340 km2. This percentage includes all the terra firma forest (lowland, submontane and mountain forest, old and young secondary forest) and edaphic forest. Comparison with Africover (1,136,503 km2) and GLC2000 (1,084,313 km2) demonstrates that our estimate is very close to the Africover result. However, by excluding young secondary forest from our estimate, forest cover decreases to 42.7% of the country. None of the land cover maps, including Africover, precisely estimates the agriculture area. Indeed, the Africover map only estimates this area as 51,200 km2, whereas the FAOSTAT database (http://www.faostat.fao.org/) states that 228,000 km (10%) of the DRC is devoted to agriculture. 5. Conclusions A new land cover map of the DRC (available at http:// www.uclouvain.be/enge-cartesRDC) has been produced based on data of high temporal resolution. This product provides a synoptic and consistent view of this huge territory. The resulting 18 classes and their detailed descriptions imply a high information content of the map, which is useful for national development and environmental programs. This study demonstrates that the MC strategy provides great spatial consistency for composites and consequently for classification products. For the first time, it has been possible to produce complete cloud-free and consistent composites from optical sensor data for all wavelengths over the DRC. The high contrast between different land cover types provides a detailed and consistent

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classification. Another benefit of the strategy is the adaptability of the compositing period to regional conditions. Owing to the temporal dimension of SPOT VGT data, the phenological behavior of each land cover type has been characterized, which allows better understanding of the different vegetation types of this large territory. This step is obviously necessary for monitoring vegetation and detecting possible land cover changes, which are crucial for policy, economic and ecological decisions. Comparative analysis of the land cover map produced in relation to the Africover and GLC2000 maps revealed that it is possible to produce a land cover map based on data of high temporal resolution. The VGT map exhibits a level of detail almost similar to that of Africover, with great spatial consistency and better discrimination between some savanna classes. Moreover, the high temporal frequency and semi-automated nature of the methodology allows frequent monitoring of land cover, in contrast to photographic interpretation of Landsat images. Finally, the close collaboration of botanic experts was critical in the interpretation process, as well as in the continuous calibration of the product. This emphasizes the importance of extensive field knowledge of vegetation for a better understanding and true interpretation of satellite images. Acknowledgements We gratefully acknowledge Paul Bamps, Michel Schaijes and Luc Pauwels for their comments on the land cover product and for the field documents provided. We are also grateful to the Joint Research Center for providing SPOT VGT daily data within the framework of the Global Land Cover 2000 program. Finally, we would like to acknowledge the financial support of the FRIA, and Baudouin Desclee, Gregory Duveiller, Julien Radoux, Marie-Aline Wibrin, Carlos de Wasseige, and Philippe Mayaux for their relevant comments. References Boles, S., Xiao, X., Liu, J., Zhang, Q., Munkhutya, S., Chen, S., 2004. Land cover characterization of Temperate East Asia using multi-temporal image data of VEGETATION sensor. Remote Sensing of Environment 90 (1), 477–489. Borak, J., Lambin, E., Strahler, A., 2000. The use of temporal metrics for land cover change detection at course spatial scales. International Journal of Remote Sensing 21, 1415–1432. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S., 2002. Designing a spectral index to estimate vegetation water content from remote sensing data (Part 1: theoretical approach). Remote Sensing of Environment 82 (1), 188–197. Cihlar, J., Ly, H., Li, Z., Chen, J., Pokrant, H., Huang, F., 1997. Multitemporal, multichannel AVHRR data sets for land biosphere studies-artefacts and corrections. Remote Sensing of Environment 60 (1), 35–57. Compe`re, P., 1970. La carte des sols et de la vegetation du Congo belge et du RuandaUrundi: 25. Bas-Congo. Notice explicative, Publication de l’Institut National pour l’Etude Agronomique du Congo belge (INEAC). Bruxelles Devred, R., 1958a. La carte des sols et de la vegetation du Congo belge et du Ruanda-Urundi:10. Kwango. Notice explicative, Publication de l’Institut National pour l’Etude Agronomique du Congo belge (INEAC), Bruxelles. Devred, R. (1958b). La vegetation foresti‘ere du Congo Belge et du Ruanda-Urundi, Bulletin de la Societe Royale Foresti‘ere de Belgique, 65, 409–468. Doumenge, C., 1990. La Conservation des Eco-Syst‘emes Forestiers du Zaı¨re, Gland, Switzerland: The world consevation union (IUCN). Duchemin, B., Berthelot, B., Dedieu, G., Leroy, M., Maisongrande, P., 2002. Normalisation of directional effects in 10-day global syntheses derived from VEGETATION/SPOT. II. Validation of an operational method on actual data sets. Remote Sensing of Environment 81 (1), 101–113. ESC, 2004. Landsat GeoCover 2000ETM+ and 1990TM Edition Mosaics, US Geological Survey, Sioux Falls, South Dakota. Evrard, C., 1960. La carte des sols et de la vegetation du Congo belge et du RuandaUrundi: 11. Oubangui. Notice explicative, Publication de l’Institut National pour l’Etude Agronomique du Congo belge (INEAC), Bruxelles. Evrard, C., 1968. Recherches ecologiques sur le peuplement forestier des sols hydromorphes de la Cuvette centrale congolaise. 110, Publication de l’Institut National pour l’Etude Agronomique du Congo belge (INEAC), Bruxelles.

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