Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region

The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.Optical and Laser Remote Sensin

Mapping land use land cover transitions at different spatiotemporal scales in West Africa

Post‐classification change detection was applied to examine the nature of Land Use Land Cover (LULC) transitions in West Africa in three time intervals (1975–2000, 2000–2013, and 1975– 2013). Detailed analyses at hotspots coupled with comparison of LULC transitions in the humid and arid regions were undertaken. Climate and anthropic drivers of environmental change were disentangled by the LULC transitions analyses. The results indicated that human‐managed LULC types have replaced the natural LULC types. The total vegetation cover declined by −1.6%. Massive net gains in croplands (107.8%) and settlements (140%) at the expense of natural vegetation were detected in the entire period (1975–2013). Settlements expanded in parallel with cropland, which suggests the effort to increase food production to support the increasing population. Expansion of artificial water bodies were detected in the humid regions during the period of 1975–2000. Nonetheless, shrinking of water bodies due to encroachment by wetlands and other vegetation was observed in the arid regions, coupled with net loss in the whole of West Africa. The results indicate deforestation and degradation of natural vegetation and water resources in West Africa. Underlying anthropic drivers and a combination of anthropic and climate drivers were detected. LULC transitions in West Africa are location specific and have both positive and negative implications on the environment. The transitions indicate how processes at the local level, driven by human activities, lead to changes at the continental level and may contribute to global environmental change.Optical and Laser Remote Sensin

Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region

The potential drivers of vegetation changes in the Sudano-Sahelian region of Africa remain poorly understood due to complex interactions between climatic and anthropogenic processes. In this study, we analyzed the vegetation greenness trends in relation to rainfall variability that we considered the essence of climatic effects on vegetation in a well-known water-limited environment by using time series of satellite data in the Sudano-Sahelian region during 2001–2020. We quantified in more detail the relative contributions of rainfall variability (climatic factor), land use/land cover (LULC) change, and fire occurrence change (non-climatic factors) to vegetation greenness trends in selected sub-regions. The results showed that vegetation greening was widespread (26.9% of the total study area), while vegetation browning was more clustered in central West Africa (5% of the total study area). About half of the vegetation greening area can be explained by long-term rainfall variability during 2001–2020, but most of the area characterized by a browning trend was unrelated to rainfall variability. An analysis of the relative importance showed that LULC changes had significant local effects on vegetation greenness and that these changes were characterized by a strong spatial heterogeneity in specific sub-regions. Gains in cropland and natural vegetation related to positive land management were probably the dominant drivers of greening in Senegal and Ethiopia. Also, the combined impacts of rainfall variability and LULC changes contributed to greening trends in the arid zone, particularly in Mali and Sudan. In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss associated with extensive agricultural production activities. Furthermore, we found that repeated fires for agricultural expansion in central West Africa intensified vegetation browning. These results advanced our understanding of vegetation dynamics in response to climatic and non-climatic factors in Sudano-Sahelian drylands characterized by increasing pressures on land resources.Optical and Laser Remote Sensin

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

Introduction: The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region. Methods: This study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework. Results: The study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.Optical and Laser Remote Sensin

Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R2 > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85.Optical and Laser Remote Sensin

Quantifying spatial reallocation of land use/land cover categories in West Africa

Past Land Use Land Cover (LULC) transitions analysis at the sub-continental scale of West Africa revealed spatial reallocation, i.e., simultaneous losses and gains of the LULC categories at different locations. We applied the component analysis approach to separate the total change into three major components, i.e., quantity (net change), exchange and shift (allocation change) as a way to analyse such spatial reallocation and identify the paired categories that accounted for the largest exchange and shift through time. Quantity change is the absolute value of the category's gross gains minus the category's gross losses. An exchange occurs when for example, a natural vegetation patch evolves to cropland at a location concurrently with an equal extent of cropland evolving into natural vegetation at a different location. A shift occurs when the LULC categories involved in the exchange are more than two. The amount of exchange and shift and locations that these exchanges occurred are very useful information for land policies appraisal and the long term contested re-greening of Africa as it may signal simultaneous regrowth and degradation of natural vegetation at different locations in the same landscape and also possible misclassification errors. The results revealed large exchanges in the landscape of West Africa between 1975 and 2000 for arid and humid eco-regions in West Africa. Overall, the exchange and shift components between wetland, water bodies and some other LULC categories such as forestland, other vegetation and cropland were the highest. The exchange between natural vegetation and cropland was considerable, which confirms regrowth despite the massive degradation revealed by the previous studies. Here, the large exchange in 1975–2000 highlighted large spatial reallocation of the LULC categories. The highest net change was experienced in the period between 2000 and 2013 at all spatial aggregations. Settlement and cropland experienced the highest positive net change whilst forestland and other vegetation experienced the highest negative net change. Shift was absent in the category of settlements indicating persistence over time. This analysis provided useful information on the contested re-greening of West Africa.Optical and Laser Remote Sensin