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

Figures & data

Figure 1. Study area (African Sahel-Sudano-Guinean region): (a) land cover map, (b) bioclimatic zone. The land cover information is extracted from the ESA CCI-LC map of 2018. The bioclimatic zone is defined by the Aridity Index (AI) following the classification developed by FAO. The Aridity Index (AI) data is derived as the reciprocal of the aridity index layer extracted from the Global Aridity dataset released by CGIAR Consortium for Spatial Information (CGIAR-CSI). The AI is defined as the ratio of precipitation to reference evapotranspiration.

Table 1. Lookup table applied to reclassify land cover types from the ESA CCI land cover products.

LC classes reclassified in this study	Land types used in the original CCI-LC dataset
1. Agriculture	10, 11, 12	Rainfed cropland
	20	Irrigated cropland
	30	Mosaic cropland (>50%)/natural vegetation (tree, shrub, herbaceous cover) (<50%)
	40	Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%)
2. Natural vegetation	50	Tree cover, broadleaved, evergreen, closed to open (>15%)
	60, 61, 62	Tree cover, broadleaved, deciduous, closed to open (>15%)
	70, 71, 72	Tree cover, needleleaved, evergreen, closed to open (>15%)
	80, 81, 82	Tree cover, needleleaved, deciduous, closed to open (>15%)
	90	Tree cover, mixed leaf type (broadleaved and needleleaved)
	100	Mosaic tree and shrub (>50%)/herbaceous cover (<50%)
	160	Tree cover, flooded, fresh or brakish water
	170	Tree cover, flooded, saline water
	110	Mosaic herbaceous cover (>50%)/tree and shrub (<50%)
	130	Grassland
	120, 121, 122	Shrubland
	140	Lichens and mosses
	150, 151, 152, 153	Sparse vegetation (tree, shrub, herbaceous cover)
3. Non-vegetation	200, 201, 202	Bare areas
	190	Urban
	210	Water
	180	Shrub or herbaceous cover, flooded, fresh-saline or brakish water

Figure 2. Workflow of data pre-processing and analysis.

Figure 3. Flow-chart of the method used to estimate the relative importance of each driver variable.

Figure 4. Trends in vegetation greenness in the Sahel-Sudano-Guinean Region: (a) spatial pattern of the significant vegetation greenness trends (Mann–Kendal test, p ≤ 0.05) at 0.05° × 0.05° grid cells; (b) fractional abundance of greening, browning, and no significant trends in the entire region; (c) fractional abundance of greening, browning and no significant trends in each bioclimatic zone. (b) and (c) share the same legend, i.e. green color for “greening”, brown color for “browning”, and grey color for “not-significant”.

Figure 5. Dominant drivers of the significant vegetation greenness trends in the Sahel-Sudano-Guinean Region: (a) latitudinal profile of the number of the pixels with significant vegetation greenness trends caused by each dominant driving factor; (b) spatial pattern at 0.05° × 0.05° grid cells; (c) longitudinal profile of the number of the pixels with significant vegetation greenness trends caused by each dominant driving factor.

Figure 6. Contributions of underlying driving factors to the significant trends (greening and browning) in each bioclimatic zone (i.e. hyper-arid, arid, semi-arid, dry sub-humid, and humid bioclimatic zone) in the Sahel-Sudano-Guinean Region. The contribution is defined as the fraction of pixels with significant greening trends in a certain bioclimatic zone caused by each dominant driver. The statistics for the areas with a significant browning trend are plotted on the left side of the central red line, while on its right side are the statistics for the areas with a significant greening trend.

Figure 7. Trends in temperature and precipitation and natural vegetation and agriculture at locations identified by a significant (Mann-Kendall trend test, p ≤ 0.05) on NDVI trends: (a) temperature; (b) precipitation; (c) natural vegetation; (d) agriculture.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.