Supplementary Material. A - Population density

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1 Gond, V. et al. Vegetation structure and greenness in Central Africa from MODIS multitemporal data Phil Trans Roy Soc B 368 doi: /rstb Supplementary Material A - Population density Figure S1. The density of the human population is very low in the study area (<10 inhabitants per km²) except in the surroundings of Bangui; source: B - Remote sensing data and processing The National Aeronautics and Space Administration (NASA) Terra-MODIS sensor dataset was used as a source. This sensor has many advantages: a good set of calibration; radiometric, atmospheric and geometric corrections; narrow spectral bands to avoid atmospheric absorption windows; a wide field of view; a broad spectral range; a high temporal resolution; and a high spatial resolution (250 to 1000-meter), which is perfectly suited to cover huge areas of tropical forests (Justice et al. 1998). We used the Enhanced Vegetation Index (EVI) from the 16-Day L3 Global 500 m product (MOD13A1 c5) from January 2000 to December EVI values were derived according to the equation provided by Huete et al. [27] (dataset available at ρnir ρred EVI = G. (ρnir + C1. ρred C2. ρblue + L)

2 Where ρnir [ nm], ρred [ nm] and ρblue [ nm] are the reflectance of near infrared, red and blue bands, G is the gain factor, L is the canopy background adjustment (that addresses non-linear, differential NIR and red radiant transfer through a canopy) and C1, C2 are the coefficients of the aerosol resistance terms (which use the blue band to correct aerosol influences in the red band). The coefficients adopted in the MODIS-EVI algorithm are: L=1, C1 = 6, C2 = 7.5 and G (gain factor) = 2.5. This index is directly related to photosynthetic activity [15, 28]. The EVI is considered to be closely related to canopy structure and architecture. Compared to other vegetation indices, such as NDVI which saturates for high values of chlorophyll activity, it also provides improved sensitivity for forests characterized by a high biomass such as tropical forests [14, 27]. This index also reduces atmospheric effects and is thus ideal for tropical areas. We also used the Surface Reflectance 8-Day L3 Global 500 m product (MOD09A1 c5) to calculate the Shortwave Infrared Water Stress Index (SIWSI) for the same period of time. Each file includes estimates of surface spectral reflectance for bands 1 7 (red, near infrared, blue, green and three bands for the shortwave infrared). SIWSI values were calculated according to the following equation [29]: SIWSI = ρnir ρswir ρnir + SWIR Where ρnir [ nm] and ρswir [ nm] are the reflectance of near and shortwave infrared bands. SIWSI is related to leaf water content. This 8-day SIWSI composite allows us to detect changes in leaf water content. In this study, MODIS processing level 3 (L3) products were used. The advantage is that each pixel of L3 data is precisely geo-located, radiometrically calibrated, atmospherically corrected and surface reflectance have been produced. Usually each pixel has also been temporally composited or averaged (such as products used in this study). Thus, pre-processing was not needed and MODIS products with sinusoidal projection were analysed such as. Nevertheless, a visual check was performed on each data in order to detect anomalies, artefacts and inconsistencies. The BRDF corrected data proposed by NASA were not used because of the cloudiness of the study area in regard of previous analysis (Vancutsem et al., 2007). To identify the spatial patterns of vegetation structure and greenness in Central Africa, we first reconstructed a 10-year time-series mosaic and then performed a two-step classification approach of the newly built EVI and SIWSI data sets. During remote sensing processing, even when composite images are used to reduce the atmospheric artefacts, contaminated pixels can persist and lead to strong misinterpretation [31]. To eliminate remnant clouds in the 16-day EVI across a 10-year time period data set, we computed the average value of the 10 satellite images available for each 16-day period (Figure S2). We thus obtained a mean EVI seasonal profile across a synthetic year (23 periods of 16-day images). A test showed that only few MODIS data were affected by clouds after the processing (only three classes were affected in the south-west part of the study area, table 1).

3 Figure S2: example of the average calculation of the 10 satellite images available for the first 16-day period of MODIS-EVI data. However, this process was not sufficient for the 8-day SIWSI data set. That is why we replaced the previous algorithm by another one. Thus, for each spectral band for each period (7 bands and 46 periods) we retained the minimum pixel value among the 10-year dataset because this pixel is the least likely to be affected by a cloud or atmospheric artefacts. Schematically this synthesis image corresponds to a 8-day SIWSI across a 10-year period patchwork. Then, to improve the spectral information, we performed a temporal smoothing designed to remove and replace pixels that were still contaminated (remnant cloud shadows). The aim was to reconstruct variations in leaf moisture over a period of 1 year; we thus computed a minimum SIWSI seasonal profile (46 periods of 8-day images). The algorithm is based on a simple linear interpolation designed to be as close as possible to the signal and thus to cause the least possible disturbance of the information. The two process developed are based on the hypothesis that there was no long-term trend in forest phenology over the studied decade.

4 C - Forest inventories Figure S3. Location of the 37, ha plots across the study area. A total of 19 forest concessions have been sampled following a systematic design : Alpicam (449), AlpicamSud (585), BETOU (2377), CIBC, (1079), IFB (1392), Ipendja (2597), Kabo (1556), Kiefer (434), LOPOLA (2016), Loundoungou, (2127), MISSA (2083), MOKABI (5619), NGOMBE (3866), PikoundaNord (447), Pokola (2039), SCAF (2800), SOFOKAD (1853), TCA (3148), Toukoulaka (1431) D Letouzy s map Letouzey analyzed variations within mixed lowland terra-firma forest in terms of degradation and deciduousness. In a first step, he based the phyto-geographic map on topographic maps and photo-interpretation of aerial photographs. In a second step, he carried out field work to collect geographical observations and botanical samples. Finally, he synthesized all field and ancillary data to establish the phyto-geographic map [4], which we compared with the results of our analyses. The vegetation types identified with MODIS data were concordant with the vegetation types recognized by Letouzey in Cameroon (Table S1): Savannas classes are well identified (up to 50% of representativeness) for classes 7 and 9 identified as Herbaceous savannas with Imperata cylindrical (147) in Letouzey s report. These classes are located in the north of the

5 study area close to Bertoua and Batouri. Classes 4 and 5 fitted very well with the Letouzey map (Inner and outer forest savannas with grass Pennisetum purpureum (149)). These two classes are rare in Cameroon (22 and 27 km² only) and located close to CAR where they cover larger areas. Classes 6 and 13 are located mainly along the northern forest margin (class 10 is more associated with savannas like a degraded forest edge). These two classes are characterized from Letouzey as Pronouncedly degraded evergreen forest (208) and Semideciduous forest regrowth in cultivated forest zones, herbaceous and shrubby savannas, with relictual forested islands more or less modified (172) respectively. This group of classes characterizes an intermediate zone between forest and savannas where agricultural fragmentation provides a series of heterogeneous landscapes. Classes 14, 48, 44, 43, 45 and 41 correspond mainly with Letouzey s definition of Semi-deciduous forest with Sterculiaceae (Sterculiaceae sub-family in the Malvaceae) and Ulmaceae (159). Class 49 identified as Mixed semi-deciduous forest and Dja evergreen forest dominated by semideciduous elements (190), could be associated with the previous classes. All these classes are located within the Sangha River Interval and correspond to the stands affected by historical fragmentation and subject to past and present human activities (agriculture and logging; [30]). Classes 38, 39, 40, 42 and 46 correspond to Dja evergreen forest on wet soils (with valley with Uapaca paludosa) and dry soils (185) from Letouzey s report. This identification also applies to swamps and swamp forests (classes 12 and 11) when these classes are mainly located along the Dja River. These classes are located in the western part of the Cameroonian part of the study area. It is relevant to the Dja evergreen forest. Table S1. Contingency matrix resulting from the comparison between MODIS classes and the vegetation map of Cameroun. Numbers correspond to vegetation description in the map of Letouzey (1985). Values are relative frequencies with 100% for each MODIS class. forests savannas degraded semideciduous evergreen MODIS classes pixels km² Sudano-Guinean savannas 7 9,488 2, ,384 4, included savannas

6 6 1, savanna-forest edge 10 12,990 3, very open forests 13 12,363 3, ,207 1, open semi-deciduous forests 48 37,279 9, dense semi-deciduous forests 44 28,459 7, , dense evergreen forests 45 17,492 4, ,621 1, , , ,739 2, open evergreen forests 46 8,598 2, , swamp forests 12 4,797 1, ,880 3,

7 E Light intensity Figure S4. Relationship between EVI seasonal profiles (full line) and seasonality of light intensity (dashed line) measured in the three meteorological stations. Colours of symbols correspond to Figure 1. F - References Justice, C. Vermote, E. Townshend, J. Defries, R. Roy, D. Hall, D. Salomonson, V. Privette, J. Riggs, G. Strahler, A. Lucht, W. Myneni, R. Knyazikhin, Y. Running, S. Nemani, R. Wan, Z. Huete, A. van Leeuwen, W. Wolfe, R. Giglio, L. Muller, J. Lewis, P. Barnsley, M The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research, IEEE Transactions on Geoscience and Remote Sensing 36, Vancutsem, C. Bicheron, P. Cayrol, P. Defourny, P An assessment of three candidate compositing methods for global MERIS time series Can. J. Remote Sensing 33,