Show Summary Details

Page of

PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, CLIMATE SCIENCE (climatescience.oxfordre.com). (c) Oxford University Press USA, 2016. All Rights Reserved. Personal use only; commercial use is strictly prohibited. Please see applicable Privacy Policy and Legal Notice (for details see Privacy Policy).

date: 23 November 2017

Desertification and Re-Greening of the Sahel

Summary and Keywords

In the past 50 years, human activities and climatic variability have caused major environmental changes in the semi-arid Sahelian zone and desertification/degradation of arable lands is of major concern for livelihoods and food security. In the wake of the Sahel droughts in the early 1970s and 1980s, the UN focused on the problem of desertification by organizing the UN Conference on Desertification (UNCOD) in Nairobi in 1976. This fuelled a significant increase in the often alarmist popular accounts of desertification as well as scientific efforts in providing an understanding of the mechanisms involved. The global interest in the subject led to the nomination of desertification as focal point for one of three international environmental conventions: the UN Convention to Combat Desertification (UNCCD), emerging from the Rio conference in 1992. This implied that substantial efforts were made to quantify the extent of desertification and to understand its causes. Desertification is a complex and multi-faceted phenomenon aggravating poverty that can be seen as both a cause and a consequence of land resource depletion. As reflected in its definition adopted by the UNCCD, desertification is “land degradation in arid, semi-arid[,] and dry sub-humid areas resulting from various factors, including climate variation and human activities” (UN, 1992). While desertification was seen as a phenomenon of relevance to drylands globally, the Sahel-Sudan region remained a region of specific interest and a significant amount of scientific efforts have been invested to provide an empirically supported understanding of both climatic and anthropogenic factors involved. Despite decades of intensive research on human–environmental systems in the Sahel, there is no overall consensus about the severity of desertification and the scientific literature is characterized by a range of conflicting observations and interpretations of the environmental conditions in the region. Earth Observation (EO) studies generally show a positive trend in rainfall and vegetation greenness over the last decades for the majority of the Sahel and this has been interpreted as an increase in biomass and contradicts narratives of a vicious cycle of widespread degradation caused by human overuse and climate change. Even though an increase in vegetation greenness, as observed from EO data, can be confirmed by ground observations, long-term assessments of biodiversity at finer spatial scales highlight a negative trend in species diversity in several studies and overall it remains unclear if the observed positive trends provide an environmental improvement with positive effects on people’s livelihood.

Keywords: land degradation, desertification, Sahel syndrome, remote sensing, Earth Observation, ground observations, greening trends, semi-arid, climate change

Introduction

Dryland areas, defined as those areas of the world where water is an important limitation for plant growth, have become the subject of increased interest due to the impacts of current global changes and concern for the sustainability of human lifestyles. Dryland ecosystems provide vital services, such as arable land and grazing areas, as well as energy and forestry products. Following the severe drought in the early 1970s in the Sahel region, the UN spurred an intensive interest in the issue of dryland degradation/desertification, most prominently marked by the UN Conference on Desertification in 1977, followed by the UN Convention to Combat Desertification (emerging from the Rio conference in 1992). This fueled a significant increase in the scientific interest to provide an improved understanding of both climatic and anthropogenic factors involved in the dynamics of drylands in general and in the Sahel in particular.

The key concepts of “desertification” and “land degradation” are used in numerous ways in the literature, and no attempt to provide a complete overview will be given here. The existence of UNCCD, and the fact that much research within this field takes the UNCCD as a point of departure, makes it convenient to use UNCCD definitions of terms, as they are by far the most widely applied. It must be emphasized that these definitions are the outcomes of a political—rather than a scientific—process, and therefore the definitions are not necessarily optimally seen from a scientific perspective. The following extracts from the full definition1 include the most central formulations. According to the United Nations Convention to Combat Desertification (UNCCD), “desertification” means:

‘land degradation in arid, semi-arid[,] and dry sub-humid areas resulting from various factors, including climatic variations and human activities,’ and land degradation means: ‘reduction or loss, in arid, semi-arid[,] and dry sub-humid areas, of the biological or economic productivity and complexity of rain-fed cropland, irrigated cropland, or range, pasture, forest[,] and woodlands. . .’

(UNCCD, 1994)

It is evident that the definition opens for many different interpretations, as identification/monitoring of desertification/land degradation can be based on a wide range of indicators caused by both climate and anthropogenic influence. Such indicators may in some cases point towards different conclusions. For example, there is no reason to assume that economic productivity of land is positively correlated with biological complexity (biodiversity). Thus two studies, based on different indicators (all in line with the above definition), may arrive at contradictory results.

The literature on desertification suffers from the lack of clarity of the use of the concepts of desertification and land degradation (Rasmussen et al., 2016). One particular problem is related to whether or not changes in biological and/or economic productivity of land caused by climate change or variations should be termed “land degradation.” Another problematic issue related to the UNCCD definition is the lack of mentioning of the time horizon required to identify land degradation. The temporal aspect has been discussed extensively by Dearing, Braimoh, Reenberg, Turner, and van der Leeuw (2010), Lynam and Stafford Smith (2004), and Smith et al. (2007), who distinguish between so-called “fast” and “slow” processes of land change, and associate “land degradation” exclusively with the “slow” processes. The strengths and weaknesses of the UNCCD definition—or alternatives to it—will not be further discussed here; yet it is obvious that use of different definitions and terminology may cause confusion when comparing results and conclusions.

The Sahel Context

The Sahel is a climatic zone covering arid and semi-arid biomes and is one of the world’s largest dryland areas bordering the Sahara Desert to the north (Figure 1). The Sahel, stretching from the Atlantic Ocean in the west to the Red Sea in the east, constitutes a transition zone between the arid northern and the humid southern eco-regions. The delineation is traditionally done by average annual rainfall isohyets with northern/southern boundaries of 100 mm and 700 mm, respectively (Lebel et al., 2009). Rainfall is largely determined by the progression of the Intertropical Convergence Zone (ITCZ) during the West African Monsoon (Nicholson & Palao, 1993). In northern Sahel (150–300 mm/year rainfall) landscapes are characterized by large plains, small temporal water ponds surrounded with evergreen, and semi-deciduous dense vegetation cover. Sand dunes and rocky formations cover the remaining lands. The large stretches of plains are mainly used for grazing and subsistence cultivation dominated by dry cereals such as sorghum and millet. Herbaceous cover primarily includes annual grasses and scattered trees dominated by xeric taxa, such as Acacia, Balanites, and Ziziphus (von Maydell, 1986).

Desertification and Re-Greening of the SahelClick to view larger

Figure 1. Sahel delineation (150–700 mm/year precipitation isohyets) and annual average precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP 1982–2015).

Recent scientific findings suggest a decline in biodiversity and a shift to more arid and drought resilient species (Gonzalez, 2001; Gonzalez, Tucker, & Sy, 2012; Herrmann & Tappan, 2013). Southward, the Sahelian zone is characterized by a unimodal rainfall regime during boreal summer with annual average rainfall ranging from 400 to 700 mm. This permits a mosaic of woodland and savanna vegetation with (semi) deciduous trees with evergreen forests bordering perennial water bodies. A considerable part of the zone is under cultivation (e.g., maize, cowpeas, cassava, or groundnuts) and the remaining areas of natural vegetation are used for extraction of commodities and grazing (Hiernaux et al., 2009b; Kandji, Verchot, & Mackensen, 2006). Similarly to the northern Sahelian zone, a general decline in species diversity and a decrease of gallery forests are regarded as signs of land degradation. In contrast to the northern Sahel, a dense cover of perennial grasses and trees reflect a structure of fewer disturbances as compared to more arid areas (Hiernaux et al., 2009a).

Sahel has been referred to as the region showing the largest rainfall anomalies worldwide during the last century (Nicholson, 2000), suffering from recurrent droughts and large inter-annual variations in rainfall and consequently also vegetation productivity (Figure 2). Seasonal rainfall is found to vary significantly at scales of a few tens of km (meso-scale; Nicholson, 2000) and spatial variability at the daily timescales is also high due to the predominantly convective nature of precipitation during the rainy season (Lebel et al., 2003).

Desertification and Re-Greening of the SahelClick to view larger

Figure 2. Sahel Precipitation Index (20-10N, 20W-10E), June through October 1950–November 20162.

Traditionally, livelihoods in the Sahel were based on the production of livestock and grazing the region’s nutritious grasslands during and after the rainy season (Figure 3 a and b). During the dry season, pastoralists migrated southwards in search of forage resources in the Sudanian zone (south of Sahel), a movement known as transhumance. More recently, people have also settled to practice small-scale rain-fed farming for their livelihood (Ickowicz et al., 2012). Due to the rapidly growing population, the global market change, the changing environment, and the spatial and temporal rainfall variability, most parts of the Sahel ecosystems can no longer sustain small-scale rain-fed farming, which has locked people into a negative spiral of poverty and degradation (Enfors, Gordon, Peterson, & Bossio, 2008). This has resulted in important southward internal mass migrations of farmers/livestock in search of arable lands and grazing areas (Ouedraogo et al., 2010).

According to the United Nations Environment Program (UNEP), population growth, deforestation, expansion of cropping lands, overgrazing, climate change (droughts and little rainfall), and poor policies have transformed large parts of the Sahel into barren land during the 20th century (Kandji et al., 2006). Estimations have claimed that 500 million hectares of African land are degraded, including 65% of agricultural land and 30% of the Sahel zone (Kandji et al., 2006; Niemeijer & Mazzucato, 2002; Oldeman, Hakkeling, & Sombroek, 1990). These figures, however, have been severely criticized by the scientific community (Niemeijer & Mazzucato, 2002; Tiffen & Mortimore, 2002; Warren, 2002), as little evidence of this widespread degradation was given to support such numbers (Figure 3 c and d).

Desertification and Re-Greening of the SahelClick to view larger

Figure 3. (a) Livestock farming is the major source of income for most people living in the Sahel. (b) The woody vegetation in Sahel consists of small sized scattered trees and shrubs. (c+d) Degraded soils are a more local phenomenon and rarely widespread.

Photos by M. Brandt, Senegal, October 2015 (a,b,d) and Mali, November 2012.

Mapping Desertification and Land Degradation

Vegetation productivity is of great economic importance because crop and livestock production is the most essential economic activity in many arid and semi-arid regions. Moreover, primary production is an important element in dryland ecosystem services, as defined by the Millennium Ecosystem Assessment (MEA) Desertification Synthesis (Adeel, Safriel, Niemeijer, & White, 2005). Therefore, spatially and temporally consistent, long-term data on changes and trends in vegetation productivity are of great interest for the assessment of environmental conditions and their trends in dryland regions. According to Adeel et al. (2005), at least 10–20% of drylands are already degraded and a recent publication from the UNCCD (Secretariat, 2013) states that global assessments indicate an increase in the percentage of highly degraded land area from 15% in 1991 to 25% by 2011. Many reputable sources rank desertification among the greatest environmental challenges today and a major impediment to meeting basic human needs in drylands (Adeel et al., 2005; Safriel, & Zafar, 2005). But scholars also underline that more elaborate studies are needed to identify where the problems occur and what is their true extent (Prince, 2016).

Since the “Sahel drought” of the 1970s and early 1980s, this zone has been described as a hotspot of land degradation, threatened both by recurrent droughts (Nicholson, 2000) and by human overuse, for example, through overgrazing (Hulme, 2001; Lamb, 1982) which is in contrast to more recent EO findings (Anyamba & Tucker, 2005; Eklundh & Olsson, 2003; Fensholt & Rasmussen, 2011; Herrmann, Anyamba, & Tucker, 2005; Prince, Colstoun, Brown, & Kravitz, 1998; Rasmussen, Fog, & Madsen, 2001).

Much of the research on desertification in the Sahel can be classified in two different categories, either as local-to-regional-scale case studies, relying mostly on field work of both bio-physical and socio-economic character, or as studies based mainly on the use of time-series of satellite images, in some cases supported by field work. In a much-cited meta-study Geist and Lambin (2004) summed up the findings of case studies conducted for global drylands. The main results were that desertification was observed in all these cases, and that a range of “proximate causes” and “drivers” were found to be common to many case studies. These causes and drivers included both human and natural factors, yet human factors, such as population growth, tended to be dominating. In contrast to this, several studies based on time-series of satellite observations have shown increasing “greenness” (known as the re-greening) of the Sahel (Anyamba & Tucker, 2005; Dardel et al., 2014b; Eklundh & Olsson, 2003; Olsson, Eklundh, & Ardo, 2005), and studies relating “greenness” to other variables have shown that “greenness” and annual rainfall are highly correlated (Fensholt & Rasmussen, 2011; Fensholt et al., 2013; Herrmann et al., 2005; Hickler et al., 2005; Huber, Fensholt, & Rasmussen, 2011; Rishmawi, Prince, & Xue, 2016). A pioneer study by Nicholson, Tucker, and Ba (1998) based on satellite “greenness” and rain gauge measurements showed no progressive change of the Saharan boundary or vegetation cover in the Sahel during 1980–1995, and also no systematic reduction of vegetation productivity as assessed by the water-use efficiency of the vegetation was observed.

The Global Assessment of Human Induced Soil Degradation (GLASOD) project was the first comprehensive effort to map land degradation globally based on standardized criteria (Oldeman et al., 1990) and subsequent studies confirmed evidence of land degradation of varying degrees of severity (Ickowicz et al., 2012). However, massive criticism of the reliability and accuracy of GLASOD followed and a more recent approach called GLADA (Global Assessment of Land Degradation and Improvement) was initiated. GLADA was conducted within the Food and Agriculture Organization of the United Nations’ (FAO) Land Degradation Assessment in Drylands (LADA) program and was designed to map land degradation with improved (integrated remote sensing) techniques (Bai, Dent, Olsson, & Schaepman, 2008a). The LADA program made important progress in assessing the causes and impacts of land degradation at global, national, and local levels, in order to detect hot spots and identify intervention strategies (Nachtergaele & Licona-Manzur, 2008). LADA considers land degradation as a multi-faceted phenomenon including biophysical, social, economic, and environmental questions that must be dealt with using a combined set of geo-informational, scientific, and local knowledge tools (Herrmann & Hutchinson, 2005; Reynolds et al., 2007).

These programmatic studies showed that the reduction of available agricultural lands in Africa’s drylands is exacerbated by soil fragility. A variety of processes are at work, and it is clear from a “convergence of evidence” and consensus among the expert community that large areas in the Sahel are affected (Vlek, Le, & Tamene, 2010). Land degradation—as yet poorly quantified and recognized—is amplified by rapid population growth, increased temperature-driven evaporative demand, and increased rainfall variability (Giannini et al., 2013; Roudier, Sultan, Quirion, & Berg, 2011; Seto & Reenberg, 2014). Besides the consequences on the physical environment and ecosystems, it induces a wide range of socio-economic threats, including poverty, food insecurity, water shortage, health problems, and conflicts (Rasmussen, 1999; Reynolds et al., 2007).

Since the adoption of the UNCCD convention, much has been done on various fronts in Africa, including the collaboration of national, sub-regional, and regional entities to combat desertification and improve our understanding of the mechanisms and the effects of the phenomenon. However, despite these significant achievements, considerable challenges remain with the need of a serious and prompt response. Many of the obstacles stem from the availability of data and tools that are used to assess land degradation. Furthermore, contemporary science is characterized by disagreements and the use of different proxies for biophysical and socio-economic indicators to explain the root causes, driving forces, statuses, impacts of and responses to land degradation. Similarly, many controversies exist on the proper usage and development of replicable methods to assess land degradation, or key indicators of essential variables of processes and state of land in the Sahel (Higginbottom & Symeonakis, 2014).

The Sahel Syndrome

The linkages between the biophysical and human subsystems can take the form of positive feedbacks. Soil degradation caused by “overcultivation” is expected to trigger expansion of cropland to compensate for low yields. This, in turn will cause accelerated soil degradation in the newly included marginal land (Greenland, Bowen, Eswaran, Rhoades, & Valentin, 1994) calling for further expansion into marginal lands unsuitable for cropping. Such unsustainable trajectories have been proposed as explanations for the Sahel crisis in the 1980s represented as the archetype of the “Sahel syndrome” (Lüdeke, Petschel-Held, & Schellnhuber, 2004; Schellnhuber et al., 1997). Although these viewpoints have been challenged by several scholars (Mortimore & Turner, 2005; Niemeijer & Mazzucato, 2002), the narrative of Sahel being caught in a vicious cycle of land degradation has remained dominant in the environmental policy documents for decades (Reenberg, 2012; Speirs & Marcussen, 1999). Much literature seems to be based on the assumption that drought is an inescapable fate of the Sahel, and that climate change and variability is a major challenge to livelihoods, food security, and agricultural production (Rasmussen et al., 2016). In contrast, other studies see climate change as only one among many constraints for agriculture in the Sahel (Nielsen & Reenberg, 2010), pointing to the possibility that rainfall might actually increase, at least in the central parts of the Sahel, allowing for quite different adaptation strategies (Mertz et al., 2011).

These contrasting viewpoints reflect the long-standing and still continuing debate on the “Charney hypothesis,” attributing the Sahel drought to a biogeophysical feedback, in which overgrazing plays an important role. The Charney hypothesis, based on the work of Otterman (1974), involves a feedback mechanism from an increase in albedo as a result of a decrease in plant cover (overgrazing, misuse of the environment, etc.), causing a decrease in rainfall because of the reduced temperature from a radiative loss and hence convective potential in the atmosphere. This in turn was suggested to foster subsidence within the troposphere, leading to drier conditions in the Sahel, and therefore less plant growth during the wet season, establishing a positive feedback loop in the form of a self-aggravating process which ultimately would culminate in desertification. From modeling studies (Eltahir & Gong, 1996; Xue & Shukla, 1993) it was, however, found that the albedo changes in the Sahel could not alone cause such a strong positive feedback. From satellite observations it was also pointed out that changes in surface albedo in the Sahel region may range around 0.1 (Nicholson, 2002), which is considerably less than the 0.23 used by Charney. “Overgrazing” was often claimed to be one of the main causes of desertification in the Sahel when discussed during and shortly after the Sahel drought of the 1970s and 1980s. This opinion coincided with the general recognition of “equilibrium theory” (built upon concepts of carrying capacity and stocking rates) within rangeland ecology where pastoral systems were commonly portrayed as overstocked, overgrazed, degraded, and unproductive systems (Lamprey, 1983). The traditional view was that desertification spreads from watering points, be it semi-permanent ponds or wells in pastoral lands because of the pressure from livestock (Le Houerou, 2012). Ultimately, the responsibility for land degradation is placed with the pastoralists themselves and the associated unsustainable management systems (Williams & Balling, 1996). This “paradigm” has been debated intensely and was opposed by the “non-equilibrium theory” stating that livestock populations will have a marginal feedback effect on vegetation fodder resources as total livestock numbers rarely reach an equilibrium point due to the highly fluctuating resource base mainly determined by the rainfall conditions (Wiens, 1984; Hiernaux, Dardel, Kergoat, & Mougin, 2016). It has been shown that most of the variation in grazing land productivity (one of the indicators of “land degradation” that can be measured from satellite) during and after the drought may be explained by rainfall variations (Fensholt & Rasmussen, 2011; Fensholt et al., 2013; Hickler et al., 2005; Huber et al., 2011). Also, traditional pastoral management systems have proved to be more efficient than “modern” alternatives, especially in regions with high inter-annual rainfall variability (Ellis & Swift, 1988; Homewood, 1994). The very concept of desertification has been criticized for decades (Helldén, 1991; Rasmussen, 1999; Rasmussen et al., 2016) and recently it has been stated that scientifically “desertification” is a meaningless and indefinable concept and the standard policies to reverse desertification generally do more harm than good, to environment and to people (Behnke & Mortimore, 2016).

The Satellite Observed Re-Greening of the Sahel, Land Use/Land Cover Changes (LULCC) and Climatic Drivers

As a hot-spot of environmental change, the Sahel region has been central for the development of research methods of EO land degradation studies (review papers; Karlson & Ostwald, 2016; Knauer, Gessner, Dech, & Kuenzer, 2014; Mbow, Brandt, Ouedraogo, de Leeuw, & Marshall, 2015). Satellite images can be categorized as either high or coarse spatial resolution data and both types of data have been used to study desertification/land degradation. However, coarse resolution data have been most widely used since continuous information on changes in vegetation productivity can be derived from such sensor systems in contrast to high resolution data that serve merely as “snapshots” in time. According to the UNCCD definition, vegetation productivity is a key indicator of whether desertification is taking place. High resolution satellite data are useful for identifying changes in land cover and land use, which may have implications for the economic and biological productivity and vegetation complexity in drylands, yet it is far more complex to develop generally applicable and robust indicators of land degradation which may be derived from high resolution satellite images.

Recent coarse spatial resolution EO vegetation index (VI) studies converge on a significant greening trend over the past decades for global drylands in general (Fensholt et al., 2012) and in the Sahel in particular (Dardel et al., 2014b; Fensholt et al., 2013; Herrmann et al., 2005; Olsson et al., 2005). However, conflicting characterization and interpretation of those changes in vegetation cover are often reported in the scientific literature (Herrmann & Hutchinson, 2005). This partly stems from incompatible use of research methods and scales of analysis (Rasmussen et al., 2016) and the relative contributions of different climatic and anthropogenic factors to explain those changes are still debated.

Vegetation Monitoring from Space

EO has been an increasingly valuable tool for assessing and quantifying changes in vegetation cover, especially in areas with scarce ground data. Time series of EO-based estimates of vegetation productivity from the series of AVHRR (Advanced Very High Resolution Radiometer) satellites (Tucker et al., 2005) have recently improved our ability to monitor and understand variations in vegetation productivity and land degradation from regional to global scale (Fensholt et al., 2015). Vegetation indices like NDVI (Normalized Difference Vegetation Index; Tucker, 1979) suggested nearly 40 years ago measure the differential absorption and scattering by green leaf material of solar radiation in the visible (red) and near infrared (NIR) wavebands and are still the most widely used proxy for vegetation vigour due to the calculation simplicity and the availability of three decades of EO data fulfilling the index calculation. Because of the central role of vegetation cover as an indicator of the land surface condition in drylands, time series analysis of EO-based vegetation metrics (NDVI, LAI (Leaf Area Index), and fAPAR (fraction of Absorbed Photosynthetically Active Radiation)) from the coarse spatial resolution AVHRR data (8 km spatial resolution) remains the main tool for detecting trends of land degradation using remote sensing (Fensholt et al., 2012) despite well-known shortfalls related to the satellite sensor design (Pinzon & Tucker, 2014). It is challenging however, to translate EO measurements of reflected or emitted radiation into information on relevant variables, such as vegetation productivity, land use/cover, or species type. It should always be kept in mind that EO data can only provide degradation indicators; a negative or positive trend in vegetation productivity does not necessarily mean widespread and irreversible degradation or an improvement in ecological services as both livelihoods and vegetation greenness depend on many factors. In this context, Herrmann, Sall, and Sy (2014) showed that people’s perceptions differ sometimes significantly from research outcomes: while living in a pixel with a positive trend in vegetation greenness, the population in areas of Sahel might still perceive a degradation of their environment and ecological services. A study in Senegal (Herrmann & Tappan, 2013) found impoverishment of woody vegetation and a shift towards dominance of shrubs and more arid-tolerant species for an area identified as greening by remote sensing and argued that the interpretation of satellite-based greening trends as improvement is not necessarily justified. This indicates that the concepts of “greening” and “land degradation” in the Sahel are not mutually exclusive as discussed by Dardel et al. (2014a), since differences between quantitative and qualitative aspects of vegetation needs to be considered (Herrmann & Sop, 2016). Hence, monitoring and detecting desertification and land degradation in the Sahel using satellite data is a contentious topic, despite the improvement in data and analytical methods. One of the constraining issues is the agreement among scientists on the most robust methods/indicators for monitoring and assessing land dynamics (Wessels, Van Den Bergh, & Scholes, 2012).

Pettorelli et al. (2005) presented a review of the use of NDVI in ecological studies, which concludes that NDVI is an extremely useful tool for better understanding vegetation dynamics, for example the effects of disasters such as drought or fire, and in research of temporal and spatial trends of vegetation growth. Some caveats are also noted in the review including: data of low quality for cloudy areas, pixels including both vegetation and water bodies (e.g., lakes and rivers), and to avoid comparing NDVI values between pixels, as the same NDVI value might represent different conditions for different vegetation communities. For a region such as Sahel, with extensive areas of rangeland and inter-annual variation of plant species composition, this last noted caveat is challenging as the relation between ground observations of NDVI, species composition, and biomass have shown large variability for the same location (Senegal) between years (Mbow, Fensholt, Rasmussen, & Diop, 2013).

The literature on use of NDVI is rich, yet the exact methods and indicators used vary substantially. Some scholars use daily values calculated from the original data acquisitions, others use “composites” over several days (up to a month; Holben, 1986) and yet others integrate these daily NDVI-values or composites to annually or seasonally “summed” or “integrated” NDVI-values (Anyamba & Tucker, 2005; Eklundh & Olsson, 2003; Jamali, Seaquist, Eklundh, & Ardö, 2014). Once the parameterization of a growing season NDVI curve is established, it is possible to extract other descriptive variables, such as the length of the growing season or the “seasonal integral” (the so-called small integral) of NDVI. An example of the of the seasonal integral of NDVI serving as a proxy for the annual productivity is given in Figure 4 showing the pronounced latitudinal gradient in vegetation productivity increasing from the north towards the south.

Desertification and Re-Greening of the SahelClick to view larger

Figure 4. Seasonal integral (SIN) of NDVI (average value of the 2000–2015 growing seasons based on MODIS satellite data.

The trends in maximum NDVI, annually/seasonally integrated NDVI are not necessarily identical, and care must be taken when interpreting and comparing NDVI-trends derived from these different variables (Fensholt et al., 2015). While the “seasonal integral” appears conceptually attractive as a proxy for NPP, it should be noted that it might be sensitive to the methods used to determine the starting and ending dates of the integral. Figure 5 shows the linear trend (Theil-Sen slope indicator) in seasonal NDVI during the period 1982–2015 and reveal a pronounced greening throughout the entire Sahel region during the period of analysis.

Desertification and Re-Greening of the SahelClick to view larger

Figure 5. Trend analysis for NDVI linear trend 1982–2015 based on the growing season NDVI integral (GIMMS3g seasonal integral (SIN)).

As the start of the analysis period coincides with the major drought years of the early 1980s (the AVHRR satellite data are available since mid-1981), it can be argued that this is likely to bias such trend analysis towards positive trends in vegetation productivity. In Figure 6 the trend analysis for NDVI for various periods of at least 10 years in length (average of the entire Sahel) during 1982–2015 is conducted. It can be seen that by far the most combinations of period of analysis yields a positive trend and a considerable part of these statistically significant (p<0.05). Only very few combinations of time periods produce negative trends and none of these are found to be significant.

Desertification and Re-Greening of the SahelClick to view larger

Figure 6. Trend analysis for NDVI (GIMMS3g seasonal integral (SIN); average of the entire Sahel) for various periods of at least 10 years in length during 1982–2015. The x- and y-axes indicate the start and ending year, respectively. The scale indicates the magnitude of the trend based on Mann–Kendall’s tau coefficient while black dots mark a significant trend (p<0.05).

Changes in Ecosystem Composition and Functioning

Trees and shrubs are an important element of the Sahelian savanna ecosystems for livelihoods dependent on fuel-wood supply. During the drought years, a significantly reduced woody cover was frequently reported, in line with the common perception of ongoing land degradation/desertification, caused by the droughts, savanna clearing to expand cropped lands, and by increasing demand for charcoal and wood fuel in urban centers (Kandji et al., 2006; Tappan, Sall, Wood, & Cushing, 2004; Vincke, Diedhiou, & Grouzis, 2010). Several case studies based on field observations/questionnaires and/or high spatial resolution remote sensing documenting the continuation of this downward trend in woody cover have been reported from various locations in Sahel (Gonzalez et al., 2012; Wezel & Lykke, 2006). Based on changes in watershed-scale, vegetation conditions in four regions of the Sahel (Kaptué, Prihodko, & Hanan, 2015) identified strong regional differences in the extent and direction of NDVI change, and in the apparent role of woody and herbaceous components in driving the temporal trend.

Time-series of EO NDVI covering the entire Sahel have proven to hold information on other properties of the vegetation cover than the NPP (Net Primary Productivity) or GPP (Gross Primary Productivity), and by identifying changes in the pattern of annual variation of NDVI, information on changes in ecosystems and plant functional types can be derived. Recent EO studies have used the differences in phenophases of dryland woody and herbaceous plants to estimate the dry season foliage density as a proxy for woody cover and its dynamics in the Sahel (Brandt et al., 2016a, 2016b). This constitutes an advance in assessment of trees outside forests (TOFs), which are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in most dryland regions. It has been estimated that globally 40% of all agricultural land has a tree cover of more than 10% (Zomer et al., 2014). The accuracy of currently available global scale woody cover datasets is rather poor and by no means reflects the ecosystem services of woody vegetation in drylands. By combining this method with in-situ observations from western Sahel, a much higher woody cover density in Sahel is revealed (almost one order of magnitude) as compared to the widely used MODIS global reference dataset (Brandt et al., 2016a, 2016b). Also, a significant increase in the woody foliage mass and woody cover was found since the 1980s, closely following the long-term NDVI greening trend in Senegal (Brandt et al., 2016a, 2015). However, although the overall trend is positive and regeneration after the drought years is observed in many places, woody cover changes are spatially heterogeneous with the greatest increases happening away from populated areas (Brandt et al., 2016a). Moreover, these increases are seemingly accompanied with a massive loss in biodiversity amongst woody species with a tendency towards shrubs at the expense of taller trees (Herrmann & Tappan, 2013). In relation to the greening it can be concluded that field data could be linked with long-term NDVI trends for different landscapes comprising both herbaceous (Dardel et al., 2014b) and woody vegetation (Brandt et al., 2015).

Changes in Land Cover and Land Use

The Sahel region is particularly reliable on rain-fed agriculture, and it is vital to understand the coupling between the observed re-greening and land use/land cover change (LULCC) in the agrarian Sahel. However, due to the small size of agricultural plots and the tradition of shifting cultivation characterized by a mosaic of cropland, and fallow and natural grassland, existing global and regional LULCC datasets based on low to medium resolution satellite imagery are known to perform poorly in the Sahel (Lambert, Waldner, & Defourny, 2016). One of the enduring challenges is the lack of suitable historical LULCC products documenting the extent of and changes in cropland at the Sahel scale over time (Vintrou et al., 2012). The impact of land management on vegetation changes (changes in cropping practices or livestock grazing intensity) has been studied using local scale EO-based methods (Ruelland, Tribotte, Puech, & Dieulin, 2011; Tappan et al., 2004) but has proven difficult to relate to the coarse spatial resolution regional/global scale observed trends. In addition, fallow fields are always considered as cropland in existing regional and global scale land cover classifications (Lambert et al., 2016) hampering an assessment of the direct linkage between cropping and NDVI trends. A digital atlas, Landscapes of West Africa: A Window on a Changing World (Cotillon, 2017) has been produced as a result of USAID-sponsored (United States Agency for International Development) work, in collaboration with CILSS (Comité permanent inter-État de lutte contre la sécheresse au Sahel) and AGRHYMET (Agriculture, Hydrology, Meteorology), and many Sahelian national institutions. High accuracy in the LULCC maps was achieved by careful visual interpretation of satellite imagery providing LULCC of West Africa from three different epochs covering 1975, 2000, and 2013, for the first time allowing a quantification of changes in the area under agriculture for the entirety of West Africa (Figure 7).

Desertification and Re-Greening of the SahelClick to view larger

Figure 7. Cropland expansion in West Africa (Cotillon, 2017). In 1975, cropland was widely scattered among the natural landscapes, covering 563,000 km2 (10.7 percent) of the mapped area. By 2013, areas under agriculture doubled, reaching a total of 1,100,000 km2, or 22.4% of the land surface (Cotillon, 2017).

Not surprisingly, a general increase in agriculture can be observed over the period from 1975 to 2013, with encroachment into marginal lands, for example in Niger. However, considerably large areas are also characterized by an abandonment of agriculture. Such LULCC assessment does not allow for a separation of cropped and fallow fields and are therefore not suited for mapping of agricultural intensification or extensification by changes in the crop/fallow cycle.

Conflicting hypotheses of the role of agricultural expansion on the observed re-greening have been reported, speculating that both an expansion (into native savanna) and intensification (fertilizer) of cropland cultivation and on the other hand agricultural land abandonment, caused by, for example, the civil war in Sudan (Fuller, 1998; Olsson et al., 2005), influence on the observed NDVI trends. Still at present, little scientific empirical evidence for the link between NDVI trends and cropland changes is provided for the Sahel region.

In line with Cotillon (2017), several local scale studies showed a general increase in cropped areas since the 1950s as reviewed by van Vliet, Reenberg, and Rasmussen (2013). In recent years, a few studies have indicated that cropland has a higher annual NDVI than savanna in Sahel, suggesting a possible link between cropland expansion/cropping intensification and the observed positive trends in NDVI (Bégué, Vintrou, Ruelland, Claden, & Dessay, 2011; Nutini, Boschetti, Brivio, Bocchi, & Antoninetti, 2013).

The linkage between NDVI trends and the role of fallow fields, as a part of the crop-fallow rotation cycle, should however be taken into account and better studied. In the Sahel, shifting cultivation is the traditional cropping system, by which the fields are cultivated for a given period and then left as fallow for a series of years to restore soil fertility (De Rouw & Rajot, 2004). Fallow fields are dominated by annual herbaceous plants interspersed with bushes and a sparse tree cover, which is functionally comparable to the vegetation composition of natural savanna. Cropped fields and fallow fields therefore have different seasonal greenness signals, with the greenness signal of fallow fields approximating that of savanna. As current classification schemes of the land cover class “cropland” encompass fallow fields there is a need to determine the impact of fallow fields on the NDVI signal and related trends as analyzed and discussed in Tong et al. (2017). Tong et al. developed a new EO based approach for mapping cropped fields and agricultural intensification/abandonment in low productive agrarian systems in developing countries (based on sub-pixel analysis of species specific vegetation phenology in the Sahel). Surprisingly, results from this research indicate that areas characterized by an increase in fallow periods also have shown a significant greening trend.

The observed greening from NDVI data has been interpreted as an increase in biomass, the existing vegetation becoming greener, or a combination of both. During the period of re-greening of the Sahel following the severe droughts of the 1970s and 1980s, the region has experienced a population boom and an expected corresponding increase in numbers of livestock (according to national statistics collected by FAOSTAT (Food and Agriculture Organization Corporate Statistical Database)). However, it is also poorly understood how commonly applied remote sensing techniques reflect the extensive influence of grazing on rangeland vegetation. Olsen, Miehe, Ceccato, & Fensholt (2014) found that ungrazed plots in the northern Senegal are found to have different species composition and greater NPP, especially during wetter than normal years, as compared to areas subjected to controlled grazing. The NDVI/NPP relationships for grazed and ungrazed plots are found to vary depending on grazing. This means that the same NDVI values for grazed and ungrazed plots on average represent far less fresh biomass at a grazed plot even when taking livestock consumption into account. The differences clearly observed in field data are however not seen in MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI. It is concluded that the higher NDVI for less biomass of grazed plots, combined with the large increase in livestock of the Sahel, means that the greening of the Sahel cannot uncritically be interpreted as a positive trend in vegetation productivity. It cannot be ruled out that in some places the greening trend also includes grazing-induced changes in species composition with no clear links to trends in biomass production.

Climatic Drivers of the Sahel Re-Greening

Due to a lack of linkages to ground studies, only little is known about the underlying drivers of changes in ecosystem functioning and long-term changes in vegetation abundance (Fensholt et al., 2015). Only recently a few studies confirmed the greening by tracking back ground-based (in situ) biomass observations over several decades (Dardel et al., 2014b; Diouf et al., 2015) but results vary across different landscapes and success in combining methods has been relatively limited, mainly due to the fact that continuous field measurements are only rarely conducted in drylands of developing countries like Sahel. Time-series of EO NDVI has been inter-compared with climatic variables like rainfall, air temperature, and incoming shortwave radiation to reveal geo-biophysical causes for observed changes in vegetation greenness across global drylands (Fensholt et al., 2012). Also the effect of CO2 fertilization has been suggested as a driver of the greening of global drylands (Donohue, Roderick, McVicar, & Farquhar, 2013), but should be balanced against the desiccation effect of temperature increases in semi-arid grassland (Morgan et al., 2011).

The productivity of the semi-natural grasslands of the Sahel is to a considerable extent controlled by water availability, and rainfall is generally assumed to be the most important determinant of vegetation growth in the Sahelian zone (Herrmann et al., 2005; Hickler et al., 2005; Hielkema, Prince, & Astle, 1986; Huber et al., 2011; Malo & Nicholson, 1990; Prince et al., 1998). Plant nutrient (and in particular phosphorous) availability becomes increasingly important in the sub-humid areas in the southern fringes of Sahel (De Ridder, 1982). Analyses of trends in precipitation based on rain gauge measurements (Frappart et al., 2009; Lebel & Ali, 2009; Maidment et al., 2015; Nicholson, 2005; Panthou et al., 2014; Sanogo et al., 2015), as well as on global precipitation datasets (Fensholt & Rasmussen, 2011) show that precipitation has increased in the Sahel since the mid-1980s. Thus the overall greening, observed in the field and by use of time series of satellite images, is not surprising. Regression analysis of various NDVI datasets against gridded or point-based precipitation data have documented that rainfall explain a considerable amount of the inter-annual variability found in satellite-based vegetation estimates (Fensholt et al., 2013; Herrmann, Anyamba, & Tucker, 2005; Huber et al., 2011). However, rainfall alone does not completely explain the dynamics, indicating an anthropogenic contribution to/a human component influencing the trend through changes in land use (e.g., agriculture expansion and intensification), forest management, production strategies, and conservation efforts (see section “Changes in Land Cover and Land Use”).

Sea Surface Temperature and Vegetation Productivity Teleconnections

Vegetation productivity across different dryland regions is known to be affected locally by changes in precipitation as discussed in the section “Climatic Drivers of the Sahel Re-Greening.” The cause of inter-annual precipitation variability has been found to be related to the regional climate driven by SST (Sea Surface Temperature) patterns (Giannini, 2016; Giannini, Saravanan, & Chang, 2003). In the Sahel, the reasons for the large inter-annual and decadal fluctuations in rainfall are still to be fully understood, but early studies by Folland, Palmer, and Parker (1986); Lamb (1978); and Palmer (1986) already found a relationship (teleconnection) with regional and global SST conditions. Rainfall in Sahel and the associated SST patterns have been related to the ENSO (El Niño Southern Oscillation) and NAO (North Atlantic Oscillation; Biasutti, Held, Sobel, & Giannini, 2008; Palmer, 1986; Shanahan et al., 2009; Ward, 1998). Several studies have reported limited correlations (Anyamba & Eastman, 1996; Anyamba & Tucker, 2005; Anyamba, Tucker, & Eastman, 2001; Philippon, Jarlan, Martiny, Camberlin, & Mougin, 2007; Propastin, Fotso, & Kappas, 2010), yet others have shown stronger relationships (Camberlin, Janicot, & Poccard, 2001; Oba, Post, & Stenseth, 2001; Ward, 1998). Oba et al. (2001) explained large parts of the inter-annual variation in vegetation productivity to the NAO during the 1980s, but Wang (2003), on the other hand, did not find a consistent relationship. Other studies, for example, Brown, de Beurs, and Vrieling (2010) have found a relationship between the Pacific Decadal Oscillation (PDO) and start of season as well as seasonal integrated NDVI in West Africa and Williams and Hanan (2011), found the Indian Ocean Dipole Mode Index (IOD) and the Multivariate ENSO Index (MEI) to be related to rainfall individually, but the interacting effects of the two indices removed the correlations to rainfall. The relationship between SST anomalies and Sahelian vegetation productivity has also been demonstrated empirically from time-series of EO data. For example, Huber and Fensholt (2011) studied the relationship between the Sahelian vegetation variability and large-scale ocean–atmosphere phenomena causing changes in SST patterns. Over the last 3 decades, significant correlations were found between global climate indices/SST anomalies and Sahelian productivity, however with different characteristics in western, central, and eastern Sahel.

Assessing Land Degradation Based on the Rain Use Efficiency

If the greening of Sahel is primarily caused by increased precipitation, it has been argued that this may disguise continued degradation caused by other factors, such as excessive cultivation and overgrazing (Hein et al., 2011). Several approaches have been suggested to eliminate the effect of rainfall change (by an appropriate normalization procedure) on biological productivity, in order to elucidate the impact of non-rainfall related changes, for example. human impacts (Evans & Geerken, 2004; Prince et al., 1998; Wessels et al., 2007). The concept of Rain-Use Efficiency (RUE), defined as the ratio of ANPP (aboveground net primary productivity) to annual precipitation is an attempt to do so (Le Houérou, 1984; Le Houérou, 1989; Prince et al., 1998). Changes in Rain Use efficiency (RUE) have been used as an integral measure for evaluating land degradation and desertification (by assessing non-precipitation related land degradation), yet this is obviously in conflict with the UNCCD definition, which also includes land degradation due to rainfall change.

RUEs have been estimated entirely or partly from EO data or using only ground measurements (Bai, Dent, Olsson, & Schaepman, 2008b; Hein & De Ridder, 2006; Hein et al., 2011; Prince et al., 1998; Prince, Wessels, Tucker, & Nicholson, 2007). The scarcity of ground measurements makes EO-based methods the only means of acquiring a Sahel-wide overview. EO-based RUE approaches have been debated vigorously in the scientific literature (Bai et al., 2008b; Hein & De Ridder, 2006; Prince et al., 2007). The assumption involved in the use of RUE is that ANPP is proportional to precipitation in the absence of human-induced land degradation. However, if this assumption of proportionality does not hold, the normalization for precipitation does not isolate non-rainfall factors (Veron, Oesterheld, & Paruelo, 2005). In EO-based studies ANPP preservation of such proportionality can be compromised by substituting NPP by EO-derived vegetation indices (often NDVI) since NDVI is not zero in the absence of vegetation. Care must therefore be taken that the assumed precipitation normalization is successful as otherwise trends in RUE will reflect the precipitation trend.

Assuming that the isolation of non-rainfall factors is successful, temporal changes in RUE can be assessed on a regional scale given the advances in the development of long-term EO vegetation and precipitation datasets. This has been done to evaluate land degradation in the Sahel (Fensholt & Rasmussen, 2011) without any clear evidence of widespread land degradation (Dardel et al., 2014a; Fensholt et al., 2013).

The RUE concept is based on the assumption that water availability is the most important factor limiting ANPP. This raises the question concerning of at which annual rainfall level other factors become constraining, such as nutrients and incoming solar radiation. Hein and De Ridder (2006) used ground data from a variety of semi-arid rangelands in the Sahel and elsewhere and Hein et al. (2011) as well as Hein (2006) argued that at RUE will tend to decrease in dryland areas of moderately high precipitation levels, because other production factors than water availability become limiting. The amount of annual precipitation below which proportionality may be assumed is debated and varies with vegetation, soil, and climate. Hein et al. (2011) cited Breman and De Wit (1983) for suggesting a value around 300 mm of rainfall per year. Hein and De Ridder (2006) argued that at RUE will tend to decrease in dryland areas of moderately high precipitation levels, because other production factors than water availability become limiting.

Other scholars also based on ground measurements conclude that biome-specific RUE values should be applied depending on the rainfall regime (Huxman et al., 2004; Paruelo, Lauenroth, Burke, & Sala, 1999; Ruppert et al., 2012). This has implications for interpreting values of regional scale EO-based RUE in both the temporal and spatial domain since RUE values might not be directly inter-comparable across space for drylands receiving different amounts of rainfall (Prince et al., 1998). Ratzmann, Gangkofner, Tietjen, and Fensholt (2016) show how the functional response of vegetation to rainfall is affected by below and above average rainfall in Sahel for a period covering 1983 to 2011. The differential response of vegetation to above or below average rainfall questions the idea of a constant value of RUE in space and time raising concern about RUE as an integral measure for evaluating land degradation and desertification in various semiarid and arid zones across the globe.

Conflicting Evidence on Land Degradation

The UNCCD definition of land degradation cited in the introduction points to several indicators (e.g., biological productivity, economic productivity, and ecosystem complexity). The apparent disparity between desertification and greening is likely to be caused by the broad scope of the UNCCD definition (as e.g., production and species diversity can be negatively correlated) in combination with the methods applied.

One particular problem is related to whether or not changes in biological and/or economic productivity of land caused by climate change or variations should be termed “land degradation.” According to the UNCCD definition (given in the introduction) it should, yet many authors (e.g., Prince et al., 1998, 2007) choose to use the term “land degradation” to describe changes not related to climate. Actually, many studies, using the concept of “rain use efficiency” (RUE), imply that changes in vegetation productivity are “normalized” for rainfall change, considering only the non-rainfall related vegetation productivity changes as “land degradation” (or the opposite).

In a meta-study by Geist and Lambin (2004) the authors summed up the findings of relevant studies for global drylands, both in terms of observation of land degradation and identification of its causes. It can be argued that most of the case-studies may have been selected exactly because some process of land degradation/desertification was known to be represented within the area, making the sample selection unsuited for assessment of the prevalence of land degradation. Yet, such case studies are of high relevance in understanding the causes/drivers of desertification, which is arguably the main focus of Geist’s book (Geist, 2005) and the Geist and Lambin’s meta-study (2004). The indicators used in the case studies vary considerably. Geist (2005) categorizes these as either “meteorological”, “ecological,” or “socio-economic.” Frequency of occurrence of these indicators are extracted and used to assess their importance. Their relevance, seen relative to the above “official” definition of land desertification, is, however, not discussed. While the UNCCD suggests NPP or GPP as relevant indicators of land degradation, few of the case studies actually measure this, and more often focus is on changes in woody cover. Geist and Lambin (2004) categorize the “explanations” of desertification given in the case studies according to whether they may be seen as “proximate causes” or as “drivers.” The end-product of the analysis is a quantitative assessment of the relative importance of different causes/drivers, measured on the basis of how frequently they are mentioned in the case studies. The overall conclusion is that human factors are the most frequently mentioned drivers. These include population increase, failure of land tenure systems and failure of national institutions. The proximate causes are mainly overgrazing and “overcultivation,” loss of tree-cover due to increasing need for fuel wood and/or expansion of cultivated areas.

The (ir)reversibility of land degradation/desertification is a key issue in the desertification debate. The short duration of most of the studies used by Geist and Lambin makes it hard to establish whether land degradation/desertification processes are actually irreversible, as often claimed. Rasmussen et al. (2001) actually observe a reversal of certain land degradation processes in Burkina Faso. This issue has been discussed extensively by Smith et al. (2007), distinguishing between “fast” and “slow” processes of land change, and associating “land degradation” exclusively with the “slow” processes. This is relevant when comparing outcomes of in-situ studies, often carried out over a few years, to analysis of time-series of satellite images, which may cover up to 40 years. This question may be closely related to the issue of choice of land degradation indicators: If the most direct indicator of desertification, as defined by UNCCD, is NPP or GPP, and the most important driver turned out to be rainfall variations, there are no strong theoretical reasons to suggest that desertification must be irreversible. If instead woody cover is used as an indicator, it is more likely that reversibility would take longer to detect. When assessing whether vegetation productivity has been increasing or decreasing over time as derived from time-series analysis (e.g., Figure 5) of remotely sensed data, it is also important to keep the spatial scales of the analysis/pixel resolution into consideration. The majority of scholars documenting the re-greening of the Sahel is derived from EO data where individual pixels cover approximately 64 km2 (AVHHR GAC data being the only viable source of data with a continuous temporal cover going back to the 1980s). However, much more nuanced spatial patterns of trends in productivity can be obtained from newer types of satellite data available since 2000 (MODIS data, with a pixel resolution of 0.25 km2). This is illustrated in Figure 8, covering an area from northern Burkina Faso, where distinct trends in vegetation productivity can be observed in the MODIS data, showing negative vegetation trends for the plateaus (orange clusters in the center of the image subset) intersected by a system of valleys characterized by positive trends in vegetation (bluish colors). Such patterns providing indicators of diverging trends to be associated with mechanisms interlinked with topography are obviously not identifiable in the trend map based on coarser resolution data.

Desertification and Re-Greening of the SahelClick to view larger

Figure 8. For an area in northern Burkina Faso distinctly different trends in vegetation productivity (2000–2015) are observed between plateaus and valleys when conducted based on MODIS 250 m data (red oval left panel) whereas such patterns tends to be averaged out when conducting the same analysis based on AVHRR based data.

Overall, analyses based on time-series of satellite data show that the basis for generalization about decadal time-scale land degradation trends at the regional scale is extremely weak, even if the case-studies were selected in an unbiased way. The political and practical implications, especially as concerns the design of policies and programs to combat desertification, are obvious: If desertification is not ongoing, development policies do not need to address it. Further, if environmental change, such as changes in vegetation productivity, was mainly caused by climate change rather than by human factors, emphasis should be placed on mitigating undesirable climate change. It is argued in Rasmussen et al. (2016) that there is no basis for claiming that one category or the other produce false results, and it is shown that the observed differences are likely to be the combined result of:

  • In situ studies constitute a very small sample of global drylands, and that study sites may have been selected exactly because they provide examples of various processes of desertification, leading to a bias towards desertification/land degradation, rather than the opposite, making generalization on this basis hazardous.

  • In situ studies seldom cover a period long enough to observe and explain the slow processes of land degradation.

  • Satellite-based studies are usually limited in either temporal or spatial scale and observations remain proxies/indicators of degradation leaving room for diverging interpretations.

  • Indicators used to differ greatly both within and between the two categories of analysis (in situ and satellite-based studies) and indicators are often without clear relation to the UNCCD definition of desertification/land degradation.

Future Vegetation Trends in the Sahel

Accurate information on future climate change impacts on the vegetation resource base in the Sahel is vital for the development of adequate adaptation and mitigation strategies including land use and land management change. To achieve information on the future state of the Sahel vegetation involves the use of coupled state-of-the-art global and regional climate/Earth system, vegetation/ecosystem and land use models. Dynamic vegetation models (DVMs) can be used both diagnostically for improved understanding of the drivers of observed EO-based trends (hindcast simulations). If such process-based model descriptions of vegetation growth are developed to simulate accurately past changes then DVMs can also be used for modeling future scenarios of the Sahel vegetation state (prognostic mode) forced with climate scenarios (e.g., IPCC AR5 CMIP5). Climate models, however, have provided a rather uncertain outlook for the future rainfall conditions in the Sahel (Biasutti et al., 2008; Druyan, 2011; Giannini, Biasutti, Held, & Sobel, 2008a; Giannini, Biasutti, & Verstraete, 2008b). Current CMIP5 models under the Representative Concentration Pathway (RPC) 8.5 emission scenario diverge substantially in their predictions on the future evolution of the West African Monsoon and the associated rainfall, some pointing towards a wetter Sahel and some towards a drier one also with differences between eastern and western Sahel (Monerie, Sanchez-Gomez, & Boe, 2017). Also the rainfall seasonal distribution is expected to change in a warming climate and an increase in number of extreme events and inter-annual variability in Sahel over recent decades has already been documented (Taylor et al., 2017; Zhang, Brandt, Guichard, Tian, & Fensholt, 2017). The impacts of such changes in the rainfall regime is likely to have an impact on vegetation growth conditions in the Sahel, but is currently not well understood.

Conclusion

The Sahel (one of the world’s largest dryland areas) is characterized by large inter-annual variations in vegetation productivity and has suffered from recurrent droughts over recent decades. The United Nations Convention to Combat Desertification’s (UNCCD) definition of desertification (degradation in dryland areas) highlights that change in vegetation productivity is a key indicator (but not the only one) of land degradation. Long-term data on vegetation productivity is therefore of great interest for the assessment of changes in environmental conditions in the Sahel and Earth Observation (EO) satellite data provide the only suitable means of consistent monitoring of changes at such large spatial scales. Recurrent generalizations, claiming irreversible and widespread land degradation in Sahel are not supported by satellite-based analysis of vegetation. Here, the opposite is found with positive trends at the regional scale over the last three decades and only smaller pockets of degraded land are observed. Generally, no clear relationship between changes in livelihoods/climate and degradation can be inferred.

At this present state it remains unclear if the observed re-greening of the Sahel provides an environmental improvement with positive effects on people’s livelihood. Only recently have a few studies confirmed the greening by tracking back in-situ biomass observations of herbaceous and woody vegetation over several decades, and new results combining time-series of both EO data and in-situ observations from local scale studies in western Sahel suggest a loss in biodiversity amongst tree-species during the period of observed greening. However, success in combining field observations and remote sensing methods has been relatively limited because the process of obtaining field evidence is a cumbersome task and is only rarely conducted continuously in drylands of developing countries. Several other case-based studies support an ongoing degradation and the results of the two categories of studies (ground-based vs. EO-based) thus seem to diverge considerably, both with respect to whether land degradation is occurring and with respect to what causes/drivers are the most prominent.

Trends in vegetation productivity may be related to climatic as well as non-climatic causes of change (e.g., changes in land management), and it is of great policy relevance to better understand the drivers and causal mechanisms of observed productivity trends. However, one of the main challenges in dryland vegetation research remains resolving and disentangling the impact from climate and human induced land use change respectively. This apparent divergence in the scientific literature is not just a matter of academic interest: It has considerable implications for the attempts to mitigate land degradation, which is the overall goal of UNCCD. It does make a difference to mitigation whether land degradation is actually dominating the Sahel, and whether the main drivers of change are associated with local natural resource management practices, or whether it is mainly controlled by rainfall and thus by global climate change, mainly resulting from GHG-emissions (Greenhouse Gas) outside the region.

In-situ and satellite-based studies of desertification/land degradation each have their merits and limitations: The in-situ studies allow detailed studies of processes and perceptions of the local population, while time-series of satellite images allow identification of trends over tens of years and statistical analysis of causal factors. What is required is that the two types of approaches are efficiently integrated, so that local-scale studies over short periods of time are planned and analyzed in the context of the overview and longer time-perspective offered by regional-scale studies. In this way the apparent contradictions, and the following confusion as concerns the proper policies, can be reduced.

Further Reading

Behnke, R., & Mortimore, M. (Eds.). (2016). Introduction: The end of desertification? In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing Environmental Change in the Drylands (pp. 1–34). Berlin: Springer.Find this resource:

Fensholt, R., Horion, S., Tagesson, T., Ehammer, A., Grogan, K., Tian, F., . . . Rasmussen, K. (2015). Assessing drivers of vegetation changes in drylands from time series of Earth Observation data. In C. Küenzer, S. Dech, & W. Wagner (Eds.), Remote sensing time series: Remote land surface dynamics (pp. 183–202). Berlin: Springer.Find this resource:

Herrmann, S. M., & Hutchinson, C. F. (2005). The changing contexts of the desertification debate. Journal of Arid Environments, 63, 538–555.Find this resource:

Kusserow, H. (2017). Desertification, resilience and re-greening in the African Sahel—A matter of the observation period? Manuscript submitted for publication.Find this resource:

Mbow, C., Brandt, M., Ouedraogo, I., de Leeuw, J., & Marshall, M. (2015). What four decades of Earth Observation tell us about land degradation in the Sahel? Remote Sensing, 7, 4048–4067.Find this resource:

Mertz, O., Mbow, C., Reenberg, A., Genesio, L., Lambin, E. F., D’haen, S., . . . Sandholt, I. (2011). Adaptation strategies and climate vulnerability in the Sudano‐Sahelian region of West Africa. Atmospheric Science Letters, 12, 104–108.Find this resource:

Rasmussen, K., D’haen, S., Fensholt, R., Fog, B., Horion, S., Ostergaard Nielsen, J., . . . Reenberg, A. (2016). Environmental change in the Sahel: Reconciling contrasting evidence and interpretations. Regional Environmental Change, 16, 673–680.Find this resource:

Reenberg, A. (2012). Insistent dryland narratives: portraits of knowledge about human-environmental interactions in Sahelian environment policy documents. West African Journal of Applied Ecology, 20, 97–111.Find this resource:

References

Adeel, Z., Safriel, U., Niemeijer, D., & White, R. (2005). Ecosystems and human well-being: Desertification synthesis: A report of the Millennium Ecosystem Assessment. Washington, DC: World Resources Institute.Find this resource:

Anyamba, A., & Eastman, J. R. (1996). Interannual variability of NDVI over Africa and its relation to El Niño/Southern Oscillation. International Journal of Remote Sensing, 17, 2533–2548.Find this resource:

Anyamba, A., & Tucker, C. J. (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63, 596–614.Find this resource:

Anyamba, A., Tucker, C. J., & Eastman, J. R. (2001). NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. International Journal of Remote Sensing, 22, 1847–1859.Find this resource:

Bai, Z., Dent, D., Olsson, L., & Schaepman, M. (2008a). Global assessment of land degradation and improvement: 1. Identification by remote sensing (Report 2008/01). ISRIC − World Soil Information: Wageningen, the Netherlands.Find this resource:

Bai, Z. G., Dent, D. L., Olsson, L., & Schaepman, M. E. (2008b). Proxy global assessment of land degradation. Soil Use and Management, 24, 223–234.Find this resource:

Bégué, A., Vintrou, E., Ruelland, D., Claden, M., & Dessay, N. (2011). Can a 25-year trend in Soudano-Sahelian vegetation dynamics be interpreted in terms of land use change? A remote sensing approach. Global Environmental Change, 21, 413–420.Find this resource:

Behnke, R., & Mortimore, M. (Eds.). (2016). Introduction: The end of desertification? In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing Environmental Change in the Drylands (pp. 1–34). Berlin: Springer.Find this resource:

Biasutti, M., Held, I. M., Sobel, A. H., & Giannini, A. (2008). SST forcings and Sahel rainfall variability in simulations of the twentieth and twenty-first centuries. Journal of Climate, 21, 3471–3486.Find this resource:

Brandt, M., Hiernaux, P., Rasmussen, K., Mbow, C., Kergoat, L., Tagesson, T., . . . Fensholt, R. (2016a). Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics. Remote Sensing of Environment, 183, 215–225.Find this resource:

Brandt, M., Hiernaux, P., Tagesson, T., Verger, A., Rasmussen, K., Diouf, A. A., . . . Fensholt, R. (2016b). Woody plant cover estimation in drylands from Earth Observation based seasonal metrics. Remote Sensing of Environment, 172, 28–38.Find this resource:

Brandt, M., Mbow, C., Diouf, A. A., Verger, A., Samimi, C., & Fensholt, R. (2015). Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Global Change Biology, 21, 1610–1620.Find this resource:

Breman, H., & De Wit, C. (1983). Rangeland productivity and exploitation in the Sahel. Science, 221, 1341–1347.Find this resource:

Brown, M. E., de Beurs, K., & Vrieling, A. (2010). The response of African land surface phenology to large scale climate oscillations. Remote Sensing of Environment, 114, 2286–2296.Find this resource:

Camberlin, P., Janicot, S., & Poccard, I. (2001). Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO. International Journal of Climatology, 21, 973–1005.Find this resource:

Charney, J. G. (1975). Dynamics of deserts and drought in the Sahel. Quarterly Journal of the Royal Meteorological Society, 101, 193–202.Find this resource:

Cotillon, S. E. (2017). The landscapes of West Africa—40 years of change (Fact Sheet 2017-3005). U.S. Geological Survey: Sioux Falls, SD.Find this resource:

Dardel, C., Kergoat, L., Hiernaux, P., Grippa, M., Mougin, E., Ciais, P., & Nguyen, C.-C. (2014a). Rain-use-efficiency: What it tells us about the conflicting Sahel greening and Sahelian paradox. Remote Sensing, 6, 3446–3474.Find this resource:

Dardel, C., Kergoat, L., Hiernaux, P., Mougin, E., Grippa, M., & Tucker, C. J. (2014b). Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sensing of Environment, 140, 350–364.Find this resource:

De Ridder, N. (1982). Productivity of Sahelian rangelands: Study of the soils, the vegetation and the exploitation of that natural resource. Wageningen, the Netherlands: Wageningen Agricultural University.Find this resource:

De Rouw, A., & Rajot, J.-L. (2004). Nutrient availability and pearl millet production in Sahelian farming systems based on manuring or fallowing. Agriculture, Ecosystems & Environment, 104, 249–262.Find this resource:

Dearing, J. A., Braimoh, A. K., Reenberg, A., Turner, B. L., & van der Leeuw, S. (2010). Complex land systems: The need for long time perspectives to assess their future. Ecology and Society, 15, 21.Find this resource:

Diouf, A. A., Brandt, M., Verger, A., El Jarroudi, M., Djaby, B., Fensholt, R., . . . Tychon, B. (2015). Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series. Remote Sensing, 7, 9122–9148.Find this resource:

Donohue, R. J., Roderick, M. L., McVicar, T. R., & Farquhar, G. D. (2013). Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophysical Research Letters, 40, 3031–3035.Find this resource:

Druyan, L. M. (2011). Studies of 21st-century precipitation trends over West Africa. International Journal of Climatology, 31, 1415–1424.Find this resource:

Eklundh, L., & Olsson, L. (2003). Vegetation index trends for the African Sahel 1982–1999. Geophysical Research Letters, 30, 1430.Find this resource:

Ellis, J. E., & Swift, D. M. (1988). Stability of African pastoral ecosystems: Alternate paradigms and implications for development. Journal of Range Management Archives, 41, 450–459.Find this resource:

Eltahir, E. A. B., & Gong, C. L. (1996). Dynamics of wet and dry years in West Africa. Journal of Climate, 9, 1030–1042.Find this resource:

Enfors, E., Gordon, L., Peterson, G., & Bossio, D. (2008). Making investments in dryland development work: Participatory scenario planning in the Makanya catchment, Tanzania. Ecology and Society, 13, 42.Find this resource:

Evans, J., & Geerken, R. (2004). Discrimination between climate and human-induced dryland degradation. Journal of Arid Environments, 57, 535–554.Find this resource:

Fensholt, R., Horion, S., Tagesson, T., Ehammer, A., Grogan, K., Tian, F., . . . Rasmussen, K. (2015). Assessment of vegetation trends in drylands from time series of Earth Observation data. In C. Küenzer, S. Dech, & W. Wagner (Eds.), Remote sensing time series: Revealing land surface dynamics (pp. 159–182). Berlin: Springer.Find this resource:

Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S. D., Tucker, C., . . . Wessels, C. (2012). Greenness in semi-arid areas across the globe 1981–2007—An Earth Observing satellite based analysis of trends and drivers. Remote Sensing of Environment, 121, 144–158.Find this resource:

Fensholt, R., & Rasmussen, K. (2011). Analysis of trends in the Sahelian “rain-use efficiency” using GIMMS NDVI, RFE and GPCP rainfall data. Remote Sensing of Environment, 115, 438–451.Find this resource:

Fensholt, R., Rasmussen, K., Kaspersen, P., Huber, S., Horion, S., & Swinnen, E. (2013). Assessing land degradation/recovery in the African Sahel from long-term earth observation based primary productivity and precipitation relationships. Remote Sensing, 5, 664–686.Find this resource:

Folland, C. K., Palmer, T. N., & Parker, D. E. (1986). Sahel rainfall and worldwide sea temperatures, 1901–1985. Nature, 320, 602–607.Find this resource:

Frappart, F., Hiernaux, P., Guichard, F., Mougin, E., Kergoat, L., Arjounin, M., . . ., Lebel, T. (2009). Rainfall regime across the Sahel band in the Gourma region, Mali. Journal of Hydrology, 375, 128–142.Find this resource:

Fuller, D.O. (1998) Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987–1993. International Journal of Remote Sensing, 19, 2013–2018.Find this resource:

Geist, H. (2005). The causes and progression of desertification. Aldershot, U.K.: Ashgate.Find this resource:

Geist, H. J., & Lambin, E. F. (2004). Dynamic causal patterns of desertification. Bioscience, 54, 817–829.Find this resource:

Giannini, A. (2016). 40 years of climate modeling: The causes of late-20th century drought in the Sahel. In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing environmental change in the drylands (pp. 265–291). Berlin: Springer.Find this resource:

Giannini, A., Biasutti, M., Held, I. M., & Sobel, A. H. (2008a). A global perspective on African climate. Climatic Change, 90, 359–383.Find this resource:

Giannini, A., Biasutti, M., & Verstraete, M. M. (2008b). A climate model-based review of drought in the Sahel: Desertification, the re-greening and climate change. Global and Planetary Change, 64, 119–128.Find this resource:

Giannini, A., Salack, S., Lodoun, T., Ali, A., Gaye, A., & Ndiaye, O. (2013). A unifying view of climate change in the Sahel linking intra-seasonal, interannual and longer time scales. Environmental Research Letters, 8, 024010.Find this resource:

Giannini, A., Saravanan, R., & Chang, P. (2003). Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302, 1027–1030.Find this resource:

Gonzalez, P. (2001). Desertification and a shift of forest species in the West African Sahel. Climate Research, 17, 217–228.Find this resource:

Gonzalez, P., Tucker, C. J., & Sy, H. (2012). Tree density and species decline in the African Sahel attributable to climate. Journal of Arid Environments, 78, 55–64.Find this resource:

Greenland, D. J., Bowen, G., Eswaran, H., Rhoades, R., & Valentin, C. (1994, May). Soil, water, and nutrient management research: A new agenda. Paper presented at the mid-term meeting of the Consultative Group on International Agricultural Research, New Delhi, India.Find this resource:

Hein, L. (2006). The impacts of grazing and rainfall variability on the dynamics of a Sahelian rangeland. Journal of Arid Environments, 64, 488–504.Find this resource:

Hein, L., De Ridder, N. (2006). Desertification in the Sahel: A reinterpretation. Global Change Biology, 12, 751–758.Find this resource:

Hein, L., de Ridder, N., Hiernaux, P., Leemans, R., de Wit, A., & Schaepman, M. (2011). Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images. Journal of Arid Environments, 75, 1164–1172.Find this resource:

Helldén, U. (1991). Desertification: Time for an assessment? Ambio, 20(8), 372–383.Find this resource:

Herrmann, S. M., Anyamba, A., & Tucker, C. J. (2005). Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change, 15, 394–404.Find this resource:

Herrmann, S. M., & Hutchinson, C. F. (2005). The changing contexts of the desertification debate. Journal of Arid Environments, 63, 538–555.Find this resource:

Herrmann, S. M., Sall, I., & Sy, O. (2014). People and pixels in the Sahel: A study linking coarse-resolution remote sensing observations to land users’ perceptions of their changing environment in Senegal. Ecology and Society, 19, 29.Find this resource:

Herrmann, S. M., & Sop, T. K. (2016). The map is not the territory: How satellite remote sensing and ground evidence have re-shaped the image of Sahelian desertification. In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing environmental change in the drylands (pp. 117–145). Berlin: Springer.Find this resource:

Herrmann, S. M., & Tappan, G. G. (2013). Vegetation impoverishment despite greening: A case study from central Senegal. Journal of Arid Environments, 90, 55–66.Find this resource:

Hickler, T., Eklundh, L., Seaquist, J. W., Smith, B., Ardö, J., Olsson, L., . . . Sjöström, M. (2005). Precipitation controls Sahel greening trend. Geophysical Research Letters, 32, L21415.Find this resource:

Hielkema, J., Prince, S., & Astle, W. (1986). Rainfall and vegetation monitoring in the savanna zone of the Democratic Republic of Sudan using the NOAA Advanced Very High Resolution Radiometer. International Journal of Remote Sensing, 7, 1499–1513.Find this resource:

Hiernaux, P., Diarra, L., Trichon, V., Mougin, E., Soumaguel, N., & Baup, F. (2009a). Woody plant population dynamics in response to climate changes from 1984 to 2006 in Sahel (Gourma, Mali). Journal of Hydrology, 375, 103–113.Find this resource:

Hiernaux, P., Dardel, C., Kergoat, L., & Mougin, E. (2016). Desertification, adaptation and resilience in the Sahel: Lessons from long term monitoring of agro-ecosystems. In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing environmental change in the drylands (pp. 147–178). Berlin: Springer.Find this resource:

Hiernaux, P., Mougin, E., Diarra, L., Soumaguel, N., Lavenu, F., Tracol, Y., & Diawara, M. (2009b). Sahelian rangeland response to changes in rainfall over two decades in the Gourma region, Mali. Journal of Hydrology, 375, 114–127.Find this resource:

Higginbottom, T. P., & Symeonakis, E. (2014). Assessing land degradation and desertification using vegetation index data: Current frameworks and future directions. Remote Sensing, 6, 9552–9575.Find this resource:

Holben, B. N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7, 1417–1434.Find this resource:

Homewood, K. M. (1994). Range ecology at disequilibrium—New models of natural variability and pastoral adaptation in African savannas—Behnke, R. H., Scoones, I., Kerven, C. Africa, 64, 581–583.Find this resource:

Huber, S., & Fensholt, R. (2011). Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel. Remote Sensing of Environment, 115, 3276–3285.Find this resource:

Huber, S., Fensholt, R., & Rasmussen, K. (2011). Water availability as the driver of vegetation dynamics in the African Sahel from 1982 to 2007. Global and Planetary Change, 76, 186–195.Find this resource:

Hulme, M. (2001). Climatic perspectives on Sahelian desiccation: 1973–1998. Global Environmental Change, 11, 19–29.Find this resource:

Huxman, T. E., Smith, M. D., Fay, P. A., Knapp, A. K., Shaw, M. R., Loik, M. E., . . . Williams, D. G. (2004). Convergence across biomes to a common rain-use efficiency. Nature, 429, 651–654.Find this resource:

Ickowicz, A., Ancey, V., Corniaux, C., Duteurtre, G., Poccard-Chappuis, R., Touré, I., . . . Wane, A. (2012). Crop-livestock production systems in the Sahel—Increasing resilience for adaptation to climate change and preserving food security. Building Resilience for Adaptation to Climate Change in the Agriculture Sector, 23, 261.Find this resource:

Jamali, S., Seaquist, J., Eklundh, L., & Ardö, J. (2014). Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote Sensing of Environment, 141, 79–89.Find this resource:

Kandji, S. T., Verchot, L., & Mackensen, J. (2006). Climate change and variability in the Sahel region: Impacts and adaptation strategies in the agricultural sector. World Agroforestry Centre (ICRAF): Nairobi, Kenya.Find this resource:

Kaptué, A. T., Prihodko, L., & Hanan, N. P. (2015). On regreening and degradation in Sahelian watersheds. Proceedings of the National Academy of Sciences, 112, 12133–12138.Find this resource:

Karlson, M., & Ostwald, M. (2016). Remote sensing of vegetation in the Sudano-Sahelian zone: A literature review from 1975 to 2014. Journal of Arid Environments, 124, 257–269.Find this resource:

Knauer, K., Gessner, U., Dech, S., & Kuenzer, C. (2014). Remote sensing of vegetation dynamics in West Africa. International Journal of Remote Sensing, 35, 6357–6396.Find this resource:

Lamb, P. J. (1978). Large-scale tropical Atlantic surface circulation patterns associated with Sub-Saharan weather anomalies. Tellus, 30, 240–251.Find this resource:

Lamb, P. J. (1982). Persistence of Subsaharan drought. Nature, 299, 46–48.Find this resource:

Lambert, M.-J., Waldner, F., & Defourny, P. (2016). Cropland mapping over Sahelian and Sudanian agrosystems: A knowledge-based approach using PROBA-V time series at 100-m. Remote Sensing, 8, 232.Find this resource:

Lamprey, H. (1983). Pastoralism yesterday and today: The over-grazing problem. In F. Bourliere (Ed.), Ecosystems of the world (Vol. 13, pp. 643–666). Amsterdam, the Netherlands: Elsevier.Find this resource:

Le Houérou, H. N. (1984). Rain use efficiency—A unifying concept in arid-land ecology. Journal of Arid Environments, 7, 213–247.Find this resource:

Le Houérou, H. N. (1989). The grazing land ecosystems of the African Sahel. Berlin: Springer.Find this resource:

Le Houerou, H. N. (2012). The grazing land ecosystems of the African Sahel. Springer Science & Business Media.Find this resource:

Lebel, T., Cappelaere, B., Galle, S., Hanan, N., Kergoat, L., Levis, S., . . . Seguis, L. (2009). AMMA-CATCH studies in the Sahelian region of West-Africa: An overview. Journal of Hydrology, 375(1–2), 3–13.Find this resource:

Lebel, T., Diedhiou, A., Laurent, H. (2003). Seasonal cycle and interannual variability of the Sahelian rainfall at hydrological scales. Journal of Geophysical Research: Atmospheres, 108, 1401–1411, 8389.Find this resource:

Lüdeke, M. K., Petschel-Held, G., & Schellnhuber, H.-J. (2004). Syndromes of global change: The first panoramic view. GAIA-Ecological Perspectives for Science and Society, 13, 42–49.Find this resource:

Lynam, T. J., & Stafford Smith, M. (2004). Monitoring in a complex world—Seeking slow variables, a scaled focus, and speedier learning. African Journal of Range and Forage Science, 21, 69–78.Find this resource:

Maidment, R. I., Allan, R. P., Black, E. (2015). Recent observed and simulated changes in precipitation over Africa. Geophysical Research Letters, 42, 8155–8164.Find this resource:

Malo, A. R., & Nicholson, S. E. (1990), A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. Journal of Arid Environments, 19, 1–24.Find this resource:

von Maydell, H.-J. (1986). Trees and shrubs of the Sahel, their characteristics and uses. Weikersheim, Germany: Verlag Josef Margraf.Find this resource:

Mbow, C., Brandt, M., Ouedraogo, I., de Leeuw, J., & Marshall, M. (2015). What four decades of Earth Observation tell us about land degradation in the Sahel? Remote Sensing, 7, 4048–4067.Find this resource:

Mbow, C., Fensholt, R., Rasmussen, K., & Diop, D. (2013). Can vegetation productivity be derived from greenness in a semi-arid environment? Evidence from ground-based measurements. Journal of Arid Environments, 97, 56–65.Find this resource:

Mertz, O., Mbow, C., Reenberg, A., Genesio, L., Lambin, E. F., D’haen, S., . . . Sandholt, I. (2011). Adaptation strategies and climate vulnerability in the Sudano-Sahelian region of West Africa. Atmospheric Science Letters, 12, 104–108.Find this resource:

Monerie, P. A., Sanchez-Gomez, E., & Boe, J. (2017). On the range of future Sahel precipitation projections and the selection of a sub-sample of CMIP5 models for impact studies. Climate Dynamics, 48, 2751–2770.Find this resource:

Morgan, J. A., LeCain, D. R., Pendall, E., Blumenthal, D. M., Kimball, B. A., Carrillo, Y., . . . West, M. (2011). C-4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature, 476, 202–205.Find this resource:

Mortimore, M., & Turner, B. (2005). Does the Sahelian smallholder’s management of woodland, farm trees, rangeland support the hypothesis of human-induced desertification? Journal of Arid Environments, 63, 567–595.Find this resource:

Nachtergaele, F. O., & Licona-Manzur, C. (2008). The Land Degradation Assessment in Drylands (LADA) Project: Reflections on indicators for land degradation assessment. In C. Lee & T. Schaaf (Eds.), The future of drylands (pp. 327–348). Berlin: Springer.Find this resource:

Nicholson, S. (2000). Land surface processes and Sahel climate. Reviews of Geophysics, 38, 117–139.Find this resource:

Nicholson, S. E. (2002). What are the key components of climate as a driver of desertification? In J. F. Reynolds & D. M. Stafford Smith (Eds.), Global desertification: Do humans cause deserts? (pp. 41–58). Berlin: Dahlem University Press.Find this resource:

Nicholson, S. E. (2005). On the question of the “recovery” of the rains in the West African Sahel. Journal of Arid Environments, 63, 615–641.Find this resource:

Nicholson, S. E., & Palao, I. M. (1993), A re-evaluation of rainfall variability in the Sahel. Part I. Characteristics of rainfall fluctuations. International Journal of Climatology, 13, 371–389.Find this resource:

Nicholson, S., Tucker, C., & Ba, M. (1998). Desertification, drought, and surface vegetation: An example from the West African Sahel. Bulletin of the American Meteorological Society, 79(5), 815–829.Find this resource:

Nielsen, J. Ø., & Reenberg, A. (2010). Temporality and the problem with singling out climate as a current driver of change in a small West African village. Journal of Arid Environments, 74, 464–474.Find this resource:

Niemeijer, D., & Mazzucato, V. (2002). Soil degradation in the West African Sahel: How serious is it? Environment, 44, 20.Find this resource:

Nutini, F., Boschetti, M., Brivio, P., Bocchi, S., & Antoninetti, M. (2013). Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images. International Journal of Remote Sensing, 34, 4769–4790.Find this resource:

Oba, G., Post, E., & Stenseth, N. C. (2001). Sub-saharan desertification and productivity are linked to hemispheric climate variability. Global Change Biology, 7, 241–246.Find this resource:

Oldeman, L., Hakkeling, R., & Sombroek, W. G. (1990). World map of the status of human-induced soil degradation: An explanatory note. Wageningen, the Netherlands: International Soil Reference and Information Centre.Find this resource:

Olsen, J., Miehe, S., Ceccato, P., & Fensholt, R. (2014). Does vegetation parameterization from EO NDVI data capture grazing induced variations in species composition and biomass in semi-arid grassland savanna? Biogeosciences Discussions, 11, 16309–16347.Find this resource:

Olsson, L., Eklundh, L., & Ardo, J. (2005). A recent greening of the Sahel—Trends, patterns and potential causes. Journal of Arid Environments, 63, 556–566.Find this resource:

Otterman, J. (1974). Baring high-albedo soils by overgrazing—Hypothesized desertification mechanism. Science, 186, 531–533.Find this resource:

Ouedraogo, I., Tigabu, M., Savadogo, P., Compaoré, H., Odén, P., & Ouadba, J. (2010). Land cover change and its relation with population dynamics in Burkina Faso, West Africa. Land Degradation & Development, 21, 453–462.Find this resource:

Palmer, T. N. (1986). Influence of the Atlantic, Pacific and Indian Oceans on Sahel Rainfall. Nature, 322, 251–253.Find this resource:

Panthou, G., Vischel, T., Lebel, T. (2014). Recent trends in the regime of extreme rainfall in the Central Sahel. International Journal of Climatology, 34, 3998–4006.Find this resource:

Paruelo, J. M., Lauenroth, W. K., Burke, I. C., & Sala, O. E. (1999). Grassland precipitation-use efficiency varies across a resource gradient. Ecosystems, 2, 64–68.Find this resource:

Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20, 503–510.Find this resource:

Philippon, N., Jarlan, L., Martiny, N., Camberlin, P., & Mougin, E. (2007). Characterization of the interannual and intraseasonal variability of West African vegetation between 1982 and 2002 by means of NOAA AVHRR NDVI data. Journal of Climate, 20, 1202–1218.Find this resource:

Pinzon, J. E., & Tucker, C. J. (2014). A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sensing, 6, 6929–6960.Find this resource:

Prince, S. D. (2016). Where does desertification occur? Mapping dryland degradation at regional to global scales. In R. Behnke & M. Mortimore (Eds.), The end of desertification? Disputing environmental change in the drylands (pp. 225–263). Berlin: Springer.Find this resource:

Prince, S. D., Colstoun, D., Brown, E., & Kravitz, L. (1998). Evidence from rain‐use efficiencies does not indicate extensive Sahelian desertification. Global Change Biology, 4, 359–374.Find this resource:

Prince, S. D., Wessels, K. J., Tucker, C. J., & Nicholson, S. E. (2007). Desertification in the Sahel: A reinterpretation of a reinterpretation. Global Change Biology, 13, 1308–1313.Find this resource:

Propastin, P., Fotso, L., & Kappas, M. (2010). Assessment of vegetation vulnerability to ENSO warm events over Africa. International Journal of Applied Earth Observation and Geoinformation, 12, S83–S89.Find this resource:

Rasmussen, K. (1999). Land degradation in the Sahel-Sudan: The conceptual basis. Geografisk Tidsskrift, Special Issue 2, 151–159.Find this resource:

Rasmussen, K., D’haen, S., Fensholt, R., Fog, B., Horion, S., Ostergaard Nielsen, J., . . . Reenberg, A. (2016). Environmental change in the Sahel: Reconciling contrasting evidence and interpretations. Regional Environmental Change, 16, 673–680.Find this resource:

Rasmussen, K., Fog, B., & Madsen, J. E. (2001). Desertification in reverse? Observations from northern Burkina Faso. Global Environmental Change, 11, 271–282.Find this resource:

Ratzmann, G., Gangkofner, U., Tietjen, B., & Fensholt, R. (2016). Dryland vegetation functional response to altered rainfall amounts and variability derived from satellite time series data. Remote Sensing, 8, 1026.Find this resource:

Reenberg, A. (2012). Insistent dryland narratives: Portraits of knowledge about human-environmental interactions in Sahelian environment policy documents. West African Journal of Applied Ecology, 20, 97–111.Find this resource:

Reynolds, J. F., Smith, D. M. S., Lambin, E. F., Turner, B. L., II., Mortimore, M., Batterbury, S. P., . . . Walker, B. (2007). Global desertification: Building a science for dryland development. Science, 316, 847–851.Find this resource:

Rishmawi, K., Prince, S. D., & Xue, Y. K. (2016). Vegetation responses to climate variability in the northern arid to sub-humid zones of Sub-Saharan Africa. Remote Sensing, 8, 910.Find this resource:

Roudier, P., Sultan, B., Quirion, P., & Berg, A. (2011). The impact of future climate change on West African crop yields: What does the recent literature say? Global Environmental Change, 21, 1073–1083.Find this resource:

Ruelland, D., Tribotte, A., Puech, C., & Dieulin, C. (2011). Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment. International Journal of Remote Sensing, 32, 1747–1777.Find this resource:

Ruppert, J. C., Holm, A., Miehe, S., Muldavin, E., Snyman, H. A., Wesche, K., Linstadter, A. (2012). Meta-analysis of ANPP and rain-use efficiency confirms indicative value for degradation and supports non-linear response along precipitation gradients in drylands. Journal of Vegetation Science, 23, 1035–1050.Find this resource:

Safriel, U., & Zafar, A. (2005). Dryland systems. In Millennium Ecosystem Assessment (Ed.), Ecosystems and human well-being: Current state and trends (pp. 623–662). Washington, DC: Island Press.Find this resource:

Sanogo, S., Fink, A. H., Omotosho, J. A., Ba, A., Redl, R., Ermert, V. (2015). Spatio-temporal characteristics of the recent rainfall recovery in West Africa. International Journal of Climatology, 35, 4589–4605.Find this resource:

Schellnhuber, H.-J., Block, A., Cassel-Gintz, M., Kropp, J., Lammel, G., Lass, W., . . . Reusswig, F. (1997). Syndromes of global change. GAIA-Ecological Perspectives for Science and Society, 6, 18–33.Find this resource:

Secretariat. (2013). A stronger UNCCD for a land-degradation neutral world (Issue brief). UNCCD: Bonn, Germany.Find this resource:

Seto, K. C., & Reenberg, A. (2014). Rethinking global land use in an urban era. Cambridge, MA: MIT Press.Find this resource:

Shanahan, T. M., Overpeck, J. T., Anchukaitis, K. J., Beck, J. W., Cole, J. E., Dettman, D. L., . . . King, J. W. (2009). Atlantic forcing of persistent drought in West Africa. Science, 324, 377–380.Find this resource:

Smith, D. M. S., McKeon, G. M., Watson, I. W., Henry, B. K., Stone, G. S., Hall, W. B., & Howden, S. M. (2007). Learning from episodes of degradation and recovery in variable Australian rangelands. Proceedings of the National Academy of Sciences, 104, 20690–20695.Find this resource:

Speirs, M., & Marcussen, H. S. (1999). Environmental planning in Africa-action or words? Geografisk Tidsskrift, Special Issue 2, 81–92.Find this resource:

Tappan, G. G., Sall, M., Wood, E. C., & Cushing, M. (2004). Ecoregions and land cover trends in Senegal. Journal of Arid Environments, 59, 427–462.Find this resource:

Taylor, C. M., Belusic, D., Guichard, F., Parker, D. J., Vischel, T., Bock, O., . . . Panthou, G. (2017). Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature, 544, 475–478.Find this resource:

Tiffen, M., & Mortimore, M. (2002). Questioning desertification in dryland sub–Saharan Africa. Natural Resources Forum, 26(3), 218–233.Find this resource:

Tong, X., Brandt, M., Hiernaux, P., Herrmann, S. M., Tian, F., Prishchepov, A. V., & Fensholt, R. (2017). Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: A case study in western Niger. Remote Sensing of Environment, 191, 286–296.Find this resource:

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.Find this resource:

Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., . . . El Saleous, N. (2005). An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26, 4485–4498.Find this resource:

UN (1992). Agenda 21: The United Nations Plan of Action from Rio. New York: United Nations.Find this resource:

UNCCD. (1994). United Nations Convention to Combat Desertification in those Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa. Oct. 14, 1994.Find this resource:

van Vliet, N., Reenberg, A., & Rasmussen, L. V. (2013). Scientific documentation of crop land changes in the Sahel: A half empty box of knowledge to support policy? Journal of Arid Environments, 95, 1–13.Find this resource:

Veron, S. R., Oesterheld, M., & Paruelo, J. M. (2005). Production as a function of resource availability: Slopes and efficiencies are different. Journal of Vegetation Science, 16, 351–354.Find this resource:

Vincke, C., Diedhiou, I., & Grouzis, M. (2010). Long term dynamics and structure of woody vegetation in the Ferlo (Senegal). Journal of Arid Environments, 74, 268–276.Find this resource:

Vintrou, E., Desbrosse, A., Bégué, A., Traoré, S., Baron, C., & Seen, D. L. (2012). Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products. International Journal of Applied Earth Observation and Geoinformation, 14, 83–93.Find this resource:

Vlek, P., Le, Q., & Tamene, L. (2010). Assessment of land degradation, its possible causes and threat to food security in Sub-Saharan Africa. In R. Lal & B. A. Stewart (Eds.), Food security and soil quality (pp. 57–86). Boca Raton, FL: CRC Press.Find this resource:

Wang, G. L. (2003). Reassessing the impact of North Atlantic Oscillation on the sub-Saharan vegetation productivity. Global Change Biology, 9, 493–499.Find this resource:

Ward, M. N. (1998). Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales. Journal of Climate, 11, 3167–3191.Find this resource:

Warren, A. (2002). Land degradation is contextual. Land Degradation & Development, 13, 449–459.Find this resource:

Wessels, K., Van Den Bergh, F., & Scholes, R. (2012). Limits to detectability of land degradation by trend analysis of vegetation index data. Remote Sensing of Environment, 125, 10–22.Find this resource:

Wessels, K. J., Prince, S. D., Malherbe, J., Small, J., Frost, P. E., & VanZyl, D. (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments, 68, 271–297.Find this resource:

Wezel, A., & Lykke, A. M. (2006). Woody vegetation change in Sahelian West Africa: Evidence from local knowledge. Environment, Development and Sustainability, 8, 553–567.Find this resource:

Wiens, J. A. (1984). On understanding a non-equilibrium world: Myth and reality in community patterns and processes. In D. R. Strong, D. Simberloff, L. G. Abele, & A. B. Thistle (Eds.), Ecological communities: Conceptual issues and the evidence (pp. 439–458).Find this resource:

Williams, C. A., & Hanan, N. P. (2011). ENSO and IOD teleconnections for African ecosystems: Evidence of destructive interference between climate oscillations. Biogeosciences, 8, 27–40.Find this resource:

Williams, M. A., & Balling, R. C., Jr. (1996). Interactions of desertification and climate. London: Edward Arnold, Hodder Headline, PLC.Find this resource:

Xue, Y. K., & Shukla, J. (1993). The influence of land-surface properties on Sahel climate. 1. Desertification. Journal of Climate, 6, 2232–2245.Find this resource:

Zhang, W., Brandt, M., Guichard, F., Tian, Q., & Fensholt, R. (2017). Using long-term daily satellite based rainfall data (1983–2015) to analyze spatio-temporal changes in the Sahelian rainfall regime. Journal of Hydrology, 550, 427–440.Find this resource:

Zomer, R. J., Trabucco, A., Coe, R., Place, F., van Noordwijk, M., & Xu, J. (2014). Trees on farms: An update and reanalysis of agroforestry’s global extent and socio-ecological characteristics (Working Paper 179). Bogor, Indonesia: World Agroforestry Centre.Find this resource:

Notes:

(1.) The full definitions may be found on the homepage of UNCCD (http://www.unccd.int/en/about-the-convention/Pages/Text-overview.aspx).

(2.) Available online.