The West African Sahel is an area that has experienced recent climatic and environmental changes (e.g., [1
]). After severe droughts in the 1970s and 1980s, large areas were branded as degraded land (e.g., [3
]). However, satellite time series starting in 1981 revealed a significant greening trend, which can only be partly explained by increasing rainfall [4
]. Thus, a new re-greening debate largely replaced the previous degradation paradigm (e.g., [5
]), although evidence of actual greening and increasing tree densities does not always correlate with greening trends derived from satellite time series [7
]. Recently, Brandt et al.
] and Herrmann & Tappan [7
] highlighted how diverse processes on a local scale can be, and that a positive vegetation trend does not necessarily mean an environmental improvement, as a remarkable species impoverishment was detected in both studies.
Time series analyses based on moderate and coarse resolution satellite data are widely used for monitoring vegetation. In regions with high intra- and inter-seasonal vegetation dynamics, mainly caused by rainfall variability, traditional change detection methods fail to succeed, making continuous data over a long time period irreplaceable. Remote sensing products have been tested against each other and found to be highly consistent for the entire Sahel [9
]. Recent studies are scaled from global [11
] to local [7
] dimensions. Short term trends at a moderate scale (250 m–1 km) are studied with SPOT VEGETATION (VGT) (starting 1998) and Moderate Resolution Imaging Spectroradiometer (MODIS) (starting 2000) [12
]. In Africa, continuous moderate resolution data of years prior to 1998 are only regionally available at 1.1 km from LAC (Local Area Coverage) AVHRR (Advanced Very High Resolution Radiometer) receiving stations [14
]. However, poor quality, difficulties in data processing and availability hampers the use in the West African Sahel. Alternatively, the Normalized Difference Vegetation Index (NDVI) Global Inventory Modeling and Mapping Studies (GIMMS) time series starting in the year 1981 with 8 km spatial resolution derived from the AVHRR global GAC (Global Area Coverage) dataset [17
] has been widely used for long-term trends [4
]. The latest version, termed the third generation GIMMS3g dataset has been recently produced for the period July 1981 to December 2011 with AVHRR sensor data from NOAA (National Oceanic and Atmospheric Administration) 7–18 satellites with an improved calibration. In addition to the NDVI, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI) products have been derived from GIMMS3g dataset to provide quantitative information of the state of earth’s vegetation at 8 km resolution and 15-day intervals [21
]. The recently delivered Geoland Version 1 (GEOV1) time series of FAPAR, LAI and Fraction of Vegetation Cover (FCOVER) based on AVHRR LTDR (Long Term Data Record) dataset combined with SPOT VGT offer a spatial resolution of approximately 5 km for the period 1981–1998 and 1 km from 1999 to present [22
]. Although the main problem of these long-term time series is their very coarse resolution, merging heterogeneous processes and characteristics on the ground into one single pixel, the higher spatial resolution of the GEOV1 dataset as compared to GIMMS3g may contribute to improve local trend analysis. In the Sahel, long term vegetation trend studies using GEOV1 have not been conducted so far. GIMMS3g is mostly used at a global scale [24
] and studies are rarely located in the Sahel [9
Ground-truthing of vegetation trends in the Sahel can be extremely difficult, as landscapes and human activities are not uniform and even if the region is well known, (1) the actual causes of trends remain unclear and (2) degradation or greening can be obscured or neutralized by mixed spectral information from changes of adjacent objects. So far, ground-truthing has rarely been linked to long-term trend studies in the Sahel. Only few studies go beyond hypothetical interpretations of the trends. Herrmann & Tappan [7
] use botanic inventory sites over 27 years, while Brandt et al.
] present an interdisciplinary and descriptive approach. Bégué et al.
] related greening trends to land-cover changes, finding some correlations in the Sahel. Dardel et al.
] compared long-term field observations with GIMMS3g NDVI data, finding a good consistency of positive vegetation trends in Mali (R2
= 0.59) and negative trends in Niger (R2
This study uses FAPAR time series instead of NDVI. FAPAR is defined as the fraction of radiation absorbed by the canopy in the 400–700 nm spectral domain under specified illumination conditions [28
]. It is directly related to the photosynthesis and it is used as an input in light use efficiency models. The relationship between NDVI and FAPAR has been found to be linear for green vegetation, particularly in the semi-arid environment of the Sahel. A number of satellite-related (including atmospheric effects and view-sun angle geometry) and canopy-related (including leaf angle distribution, canopy heterogeneity, brown elements and soil color) factors are found to influence the parameters of this linear relation which is site-specific and often only valid when calibrated for a given soil type [13
]. Compared with NDVI, the FAPAR satellite products mitigate the impact of soil background for low vegetated canopies and the saturation effects for high vegetation amount [29
The purpose of this study is to assess local vegetation trends in the Sahel of Mali and Senegal in the period 1982–2010 by combining long-term FAPAR satellite datasets with ground based data. The potential of GEOV1 and GIMMS3g time series for trend detection is assessed and validated with biomass observations, rainfall data and site visits. This study is designed to better understand the processes responsible for satellite derived trends and, thus, to shed more light on the re-greening debate of the Sahel area.
The spatial pattern of trend analysis of GEOV1 and GIMMS3g FAPAR shows significant discrepancies in our Sahelian study areas. Initially it is not clear if trends realistically reflect patterns on the ground or are caused/accentuated by sensor-, processing- or scale issues. A combination of good data-sources, ground-truthing and local knowledge of the area are important factors that facilitate a sound interpretation and explanation of satellite derived trend maps.
Annual rainfall has significantly increased over the studied time period in both study areas, following the overall upward trend of Sahelian rainfall [49
]. At a regional scale, this explains large parts of the observed positive vegetation trends [43
] in the entire Sahel [9
]. However, at a local scale, numerous variations exist, forming a heterogeneous pattern of vegetation trends [8
]. A higher resolution clearly improves the capability to assess these discrepancies.
Our results show that the spatial pattern seen in satellite trend maps show regional differences that can partly be explained by soil and land-cover differences [9
]. The sandy Seno Plain in Mali can be distinguished from the rocky Dogon Plateau. The same applies for the ferrugieous and the sandy Ferlo in Senegal (see Figure 5
). Considering our Senegalese case studies, most of the positive trends are caused by leaf biomass, which has almost doubled at the three monitoring sites (C3L5
) since 1987. Local trend variations, i.e.
, areas of non-change or only weak change, are mostly caused by deforested and degraded areas. The droughts in the 1970s and 1980s caused considerable harm to trees. Additionally, people increasingly cut living trees in times of droughts as an alternative source for income and fodder. Trees and shrubs on sandy soils withstood the stress much better than those on shallow lateritic soils [2
]. Although recovery of trees and shrubs from droughts is obvious in the biomass observations in Figure 9
, Brandt et al.
] state that strict laws, farmer managed protection, reforestation programs and the dispersion of robust species (especially Balanites aegyptiaca
and Acacia raddiana
) contribute to a large scale greening and increase in leaf biomass in both study areas in Mali and Senegal.
Our examples further demonstrate that both greening and degradation are present at a local scale in the West African Sahel, supporting the findings of Dardel et al.
], Spiekermann [30
], Nutini et al.
] and Martinez et al.
]. Neither greening nor desertification can be generalized. Our study detected degraded areas not following the greening trend, which is invoked by rainfall increases. However, neither is degradation irreversible, nor is greening an always positive phenomenon. In Mali, farmers were observed using traditional methods like stonewalls or holes with manure to recapture degraded soils near Fiko. Tree planting programs and farmer managed agro-forestry were observed all over the study areas in Mali and Senegal, confirming reports by Allen [48
] and Reij et al.
]. In addition, greening can mask degradation, as in both regions a remarkable species impoverishment was detected despite positive woody vegetation trends [8
], a fact that coincides with other Sahelian studies [7
]. In addition, areas seriously affected by soil erosion and spreading of bare soils can be concealed behind a greening trend caused by the woody layer (see C3L5
The moderate correlation and major inter-annual discrepancies between biomass and satellite derived greenness data (Figures 7
) confirm the findings of Diouf & Lambin [37
] and Diallo et al.
]. As this study uses only three monitoring sites, the obtained relationships are weaker (see Figure 8a,b
). Furthermore, the spatial resolution is much coarser (about 5 km compared to 1.1 km). However, the biomass data at the observed sites gives clear evidence that the direction, the spatial discrepancies as well as the magnitude of FAPAR trend maps are largely realistic in this area. It further shows that woody vegetation is the main driver of positive FAPAR trends seen in Figure 5
Although the two data products show good spatial consistency at an annual and regional scale (see Figure 2
), the local pattern and magnitude of trends strongly differs. Degrading GEOV1 to 8 km resolution (results not shown for brevity) reduces the details but keeps the spatial pattern with apparent differences to GIMMS3g trend maps. Both datasets are created by sampled 1.1 km AVHRR data which are resampled to a 8 km (5 km) grid cell by selecting subsets, while omitting other subsets [13
]. The whole processing line of the FAPAR data causes significant variations and the choice of the dataset may have significant effects on the results [33
]. In GEOV1 FAPAR data, the values of the VGT period are sometimes higher in densely vegetated areas than the AVHRR period, causing trends to be overestimated. On the contrary, trends in GIMMS3g FAPAR are too weak and significantly underestimated. The base levels of GIMMS3g FAPAR are much higher than those of GEOV1 FAPAR. Our comparison with ground data showed that the reality lies in between the two products, but closer to GEOV1. These differences in the processing line may influence the magnitude of trend analysis and bias the significance test, but the spatial pattern of GEOV1 trends shows agreement with ground observations (Table 3
This study focused on the local vegetation trends in drylands of Western Africa (Sahel of Mali and Senegal) over the 1982–2010 period. Two long-term satellite datasets of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from Advanced Very High Resolution Radiometer (AVHRR) data were considered: Geoland Version 1 (GEOV1) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g). Biomass ground measurements and rainfall data supported a quantitative validation of detected trends in satellite products. Auxiliary information and expert knowledge of the study areas allowed a qualitative validation and interpretation of the local observed trends.
Our results show that the choice of the dataset has significant impact on the results. The study seems to indicate that, compared to GIMMS3g, the spatial pattern of GEOV1 trends show a better agreement with ground data, rainfall pattern, land-cover, land management of the two study areas in the Sahel of Mali and Senegal. The differences in the processing lines (input reflectances and retrieval algorithms) seem to play a role in the observed differences rather beside the differences in their spatial resolution. Note however that our conclusions on the accuracy of GEOV1 and GIMMS3g time series for trend detection analysis are limited to the study areas. An extensive validation and comparison of both datasets at global scale should be addressed in a forthcoming study. This study shows the potential of GEOV1 for local trend detection. However, some inconsistencies have been detected in the GEOV1 dataset and are being to be corrected. Correction will be achieved through a second version of Geoland Version 2 (GEOV2) products from VEGETATION (VGT) and AVHRR sensors which is expected to contribute to global climate monitoring and earth science modelling applications.
The inter-annual correlation between FAPAR and annual rainfall is significant over the study areas, explaining around 50% of the variability in vegetation changes. Spatial discrepancies are mainly caused by land- and tree-cover, which are controlled by soil, human and drought resilience. As precipitation in the Sahel was very low when the time series started in 1982 and gradually increased, a positive greening trend is mostly observed in the study area. However, deforested and degraded areas clearly stand out in GEOV1 trend maps while they are hardly visible in GIMMS3g. The positive trends of the three case study sites in Senegal (C3L5, C2L5 and C2L4) are caused by the woody layer recovering from droughts/dry periods and its consequences.
These local patterns have shown that both greening and degradation are present in the Sahel of Mali and Senegal, but also greening can hide degradation. Neither the re-greening nor the desertification paradigm can be generalized as both are present at a local level.