sustainability

Article Assessment and Evaluation of the Response of Vegetation Dynamics to Climate Variability in Africa

Vincent Nzabarinda 1,2,3 , Anming Bao 1,2, Wenqiang Xu 1,2,*, Solange Uwamahoro 1,2,3, Liangliang Jiang 4, Yongchao Duan 5, Lamek Nahayo 6 , Tao Yu 1,2,3, Ting Wang 1,2,3 and Gang Long 1,2,3

1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; [email protected] (V.N.); [email protected] (A.B.); [email protected] (S.U.); [email protected] (T.Y.); [email protected] (T.W.); [email protected] (G.L.) 2 Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi 830011, China 3 University of Chinese Academy of Sciences, Beijing 100049, China 4 School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China; [email protected] 5 Binjiang College, Nanjing University of Information Engineering, Nanjing 210044, China; [email protected] 6 Faculty of Environmental Studies, University of Lay Adventists of Kigali, 6392 Kigali, Rwanda; [email protected] * Correspondence: [email protected]

Abstract: Understanding the impacts of climate variability and change on terrestrial ecosystems in Africa remains a critical issue for ecology as well as for regional and global climate policy mak- ing. However, acquiring this knowledge can be useful for future predictions towards improved governance for sustainable development. In this study, we analyzed the spatial–temporal charac-



 teristics of vegetation greenness, and identified the possible relationships with climatic factors and vulnerable plant species across Africa. Using a set of robust statistical metrics on the Normalized Citation: Nzabarinda, V.; Bao, A.; Xu, Difference Vegetation Index (NDVI3g) for precipitation and temperature over 34 years from 1982 to W.; Uwamahoro, S.; Jiang, L.; Duan, Y.; Nahayo, L.; Yu, T.; Wang, T.; Long, 2015, relevant results were obtained. The findings show that, for NDVI, the annual rate of increase −1 −1 G. Assessment and Evaluation of the (0.013 y ) was less than that of decrease (−0.014 y ). In contrast, climate data showed a sharper Response of Vegetation Dynamics to increase than a marked decrease. Temperature is increasing while rainfall is decreasing, both at a Climate Variability in Africa. sharp rate in central Africa. In Africa, tree cover, broadleaved, deciduous, closed to open (>15%) and Sustainability 2021, 13, 1234. https:// shrubland plant species are critically endangered. The tropical vegetation devastated by the climate doi.org/10.3390/su13031234 variability, causes different plant species to gradually perish; some were cleared out from the areas which experienced degradation, while others were from that of improvement. This study provides Received: 9 December 2020 valuable information to African governments in order to improve environmental sustainability and Accepted: 20 January 2021 development that will lead to the sustainability of natural resources. Published: 25 January 2021

Keywords: climate variability; vegetation dynamics; Africa; plant species Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction In the climate system, vegetation cover influences energy, water and gas interactions with the atmosphere by acting as a principal source and sink in biogeochemical interac- tion. Hence, climate determines the natural vegetation distribution [1]. If there are any Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. component changes, the dynamic equilibrium between climate and vegetation will also This article is an open access article possibly be modified. In Africa, the disturbance is likely to appear from June 1997 to May distributed under the terms and 1998. The global climate system was disrupted by the most significant El Niño/Southern conditions of the Creative Commons Oscillation (ENSO) phenomenon observed during this century [2,3]. Regarding the re- Attribution (CC BY) license (https:// sponses of the terrestrial biosphere in Africa, the most anomalous conditions normally creativecommons.org/licenses/by/ occur over equatorial eastern Africa [4]. Further, the high increase in temperature [5], and 4.0/). interannual variability of rainfall resulted in a strong negative impact on vegetation and

Sustainability 2021, 13, 1234. https://doi.org/10.3390/su13031234 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 1234 2 of 22

agriculture, hence the food security status of the region [6], especially in maize production in southern Africa [7]. Thus, the high spatial and temporal rainfall variability endures a daunting challenge for the management of agricultural activities in this continent [8]. Droughts and flooding events occur [9], while the risk of drought in southern Africa has been identified to increase by 12% in El Niño years [10]. A proper understanding of vegetation dynamics, in terms of space and time, can reverse the situation, and lower the impacts and losses already caused in affected areas so as to reduce the sufferings of the vulnerable population. Thus, it is substantial to quantify the vegetation change magnitude to understand and monitor the greenness and ecosystem dynamics [11]. The management of African tropical forests remains a challenge, and requires scientific decisions for its sustainability and preservation. This will be achieved by extensive research to help with vegetation monitoring through different investigations, since climate change is expected to continue. Researchers predict that climate change and its effects will continue to occur, as temperatures are expected to rise at 1.5 to 2 times the rate of the global temperature rise [12]. Rainfall is expected to continue to change, and the frequency and intensity of drought events are expected to increase as well [13]. The El Niño phenomenon should also be considered in future scenarios [14]. Many global and regional studies have focused on African vegetation dynamics and their relation to climate factors. For example, a 6 year regional study (1982–1987) examined the variability of NDVI and its relation to rainfall in Botswana and showed that the efficiency of rain use appears to rely more on the underlying soil than on the formation of vegetation [15]. Chamaille-Jammes, Fritz [16] assessed NDVI and rainfall relationship in the Hwange National park, Zimbabwe, where their findings revealed that the rainfall–NDVI relationship is stronger at the seasonal scale, due to the influence of field features, than at interannual scale. A satellite-derived rainfall and NDVI data for 20 years (1981–2000) were used to detect spatial and temporal interrelationships by applying component analysis. The findings stated that an earlier event of rainfall and subsequent “greening” is believed to have resulted in a moisture flux that encourages the next rainfall event [17]. Georganos, Abdi [18] used Geographically Weighted Regression to examine the NDVI–rainfall relationship in semi-arid Sahel from 2000 to 2012, and their results revealed that humid areas significantly correlated more than in wetlands and irrigated lands. They conclude that the rainfall–NDVI relationship varied temporally due to spatial trend differences. Furthermore, climate change assessment in Africa shows a teleconnection trends be- tween the Pacific Ocean basin climate conditions and vegetation conditions [19]. Kawabata, Ichii [20] analyzed the annual and seasonal vegetation activities for global monitoring of vegetation changes and their relationship with temperature and precipitation over a 9-year period. Their results revealed that in large regions of the northern middle-high latitudes and in tropical regions, particularly in western Africa, the gradual increase in temperature increased the vegetation. However, vegetation decreased in the arid and semi-arid southern hemisphere caused by the decrease in annual rainfall during this pe- riod. The influence of climate change on the productivity and phenology of vegetation in Sub-Saharan Africa was assessed, and the results showed that SSA vegetation is driven by various factors, particularly, rainfall in West Africa and parts of Central Africa, and ENSO as the key driver in Eastern and Southern Africa [21]. A long-term study (1982–2015) by Kalisa, Igbawua [22] assessed the climate impact on vegetation in east Africa, and the results revealed both positive and negative correlations before and after 1998, respectively, while NDVI correlated higher to rainfall than to temperature. However, most previous studies conducted at regional scale were performed at short- term intervals and hardly took the endangered species across the vegetation biomes in Africa into account. There is a lack of accurate and updated information on the current status of African vegetation due to the limited design of effective policies on land degrada- tion, desertification, forest management and food security. Therefore, there is a need for long-term continental studies which can address these research gaps in Africa. To this end, Sustainability 2021, 13, 1234 3 of 22

the objectives of this study are to: (1) investigate, at a continental scale, African spatial– temporal vegetation greenness changes and endangered plant species from 1982 to 2015, (2) assess the climate spatial–temporal changes and their effects on vegetation change, and (3) identify the regions that may be vulnerable to climate change using NDVI as a reflective indicator. Assessing the dynamics of African vegetation with climate change will help deci- sion makers to understand how best to reduce disasters resulting from reduced vegetation so as to ensure the sustainable development of natural resources at continental scale.

2. Materials and Methods 2.1. Study Site and Data Description 2.1.1. Study Area Africa is the largest continent of all tropical land masses and takes second place among the world0s seven continents. It covers an area of 30 million km2, accounting for 20% of the world0s total land area. It extends about 8050 km from its northernmost point in Tunisia to its southernmost point in South Africa, and 7560 km wide, from its westernmost point in Senegal to its easternmost point in Somalia [23]. Africa generally falls under four seasonal temperatures, which are summarized and arranged as Summer (December–January–February), Autumn (March–April–May), Winter (June–July–August) and Spring (September–October–November) [21,24], but vary from country to country. The climate system of the African continent is varied from humid equatorial to the Mediterranean climate system. Africa has five climate zones (Figure1): tropical; arid hot and cold; temperate dry, hot, cold and warm; the boreal warm and cold, and the polar tundra found at the top of high mountains [25]. Rain in African does not fall in all parts at the same level. Some parts receive year-round precipitation (tropical rainforest) while others receive seasonal precipitation. In addition, some parts receive heavy rainfall while others receive very little (<250 mm y−1 in desert). Rain patterns tend to decrease the further you move from the equator. Rain is negligible in the Sahara Desert, at the horn of Africa, and in the Kalahari Desert. African vegetation is also distributed according to rain and climatic zones, where the NDVI average ranged from 0.79 in tropical to 0.24 in arid (Figure2 ). According to the land cover map defined by the European Space Agency (ESA, Paris, France) Climate Change Initiative (CCI, Noordwijk, The Netherlands) project, among the 25 vegetation classes found in Africa, shrubland is the dominant one because it covers more than 20% of all African vegetation cover (Figure3).

2.1.2. NDVI Data The third-generation Normalized Difference Vegetation Index (NDVI3g) time series of Global Inventory Modeling and Mapping Studies (GIMMS) has been available for more than 34 years, since 1981 [21,26]. The spatial and temporal resolution of these data are 0.083◦ and 15 days, respectively. Despite that, the NDVI is more sensitive to the effects of soil background and requires remote sensing calibration [27], and this is our choice because it is widely used and acceptable [28], given that it fulfills all the necessary calibration requirements. The radiometric calibration, atmospheric attenuation, cloud filtering, orbital drifting, sensor degradation and all other effects not related to climate change have been used to confirm this dataset [26]. Therefore, the NDVI3g offers an extraordinary opportunity to examine the vegetation dynamics in depth, as well as the exchanges in response to climate changes over a long period. From the half-monthly NDVI3g datasets, we generated two NDVI time-series: the seasonal NDVI mean and annual maximum (NDVImax) from 1982 to 2015. We further processed the 34 NDVImax maps for 34 years, and 34 maps for each season among the four annual seasons, to one annual composite and four seasonal composites, resulting in 34 time series to be used as inputs for the trend analysis. Sustainability 2021, 13, 1234 4 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 4 of 24

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FigureFigure 1.1. The distribution of of African African climatic climatic zones zones with with Köppen–Geiger Köppen–Geiger classification. classification. Source Source [25]. [25].

FigureFigure 2. Spatial–temporary2. Spatial–temporary differentiation differentiation inin annualannual mean normalized normalized difference difference vegetation vegetation index index (NDVI), (NDVI), precipitation precipitation (mm)(mm) and and temperature temperature (◦ (°C)C) for for five five climate climate zoneszones ofof AfricaAfrica over 34 years. years.

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Sustainability 2021, 13, 1234 5 of 22 Figure 2. Spatial–temporary differentiation in annual mean normalized difference vegetation index (NDVI), precipitation (mm) and temperature (°C) for five climate zones of Africa over 34 years.

Figure 3. Spatial distribution of landcover in Africa in 2015. Source: Climate Change Initiative Land Cover (CCI-LC) maps.

2.1.3. Climate Data The Climate Hazards Group Infra-Red Precipitation and station (CHIRPS) is a rainfall dataset with more than 30 years of history. It streaks from 50◦ S to 50◦ N and covers all longitudes. The datasets are available at ftp://ftp.chg.ucsb.edu/pub/org/chg/products/ CHIRPS/ from 1981 to the present. The 0.05◦ resolution satellite imagery creates gridded rainfall time series for trend analysis and seasonal drought monitoring [29]. CHIRPS has a low systematic and mean absolute errors compared to the Global Precipitation Climatology Centre (GPCC) and high-quality datasets in Africa [30]. Thus, these precipitation datasets have been chosen to be used in this study. The global reanalysis ERA-Interim was produced by the European Center for Medium- Range Weather Forecast (ECMWF) [31] and the dataset is extended from 1979 to 2019. This interpolated to regular Lat/Lon grid open-access dataset is available on ECMWF data archive 2-m monthly mean of the daily mean temperature data with 73 km resolution, and was used in this paper after being downloaded from https://apps.ecmwf.int/Datasets/ data/interim-full-moda/levtype%3Dsfc/.

2.1.4. Land Cover Data To assess the characteristics and evaluate the fluctuations in and extinction of vegeta- tion types, we used a newly improved and consistent Land cover map from 1992 to 2015 of the European Space Agency (ESA) Climate Change Initiative (CCI) project, downloaded from an online open-access platform (https://www.esa-landcover-cci.org). Sustainability 2021, 13, 1234 6 of 22

2.2. Methods 2.2.1. Pre-Processing of the Data Before using different datasets with different spatial resolutions, a resampling was done to a common resolution of 0.083◦ to match their spatial resolution to that of the NDVI time-series. The NDVI values range from about −0.2 to 0.1 for snow, inland water bodies, deserts, exposed soils and sparsely vegetated areas, and from 0.1 to 0.1 for increasing amounts of vegetation [32]. Thus, the NDVI-values lower than 0.1 and desert areas in general were masked. Fractional vegetation cover (FVC) was estimated for each pixel [33]. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 24 2.2.2. Linear Regression Analysis The spatial and temporal variations in annual and seasonal average NDVI, mean maximum temperature and cumulative precipitation were carefully analyzed using a linear The spatial and temporal variations in annual and seasonal average NDVI, mean regression analysis method in this research. To obtain the slope coefficients of trend lines, maximum temperature and cumulative precipitation were carefully analyzed using a lin- the time series values of each pixel were calculated ear regression analysis method in this research. To obtain the slope coefficients of trend lines, the time series values of eachn pixel∑n wereX Y − calculated∑n X ∑ n Y Slope = i=1 i i i=1 i i=1 i (1) 𝑛 ∑n 𝑋 𝑌2 − ∑n 𝑋 ∑2 𝑌 n ∑L=1 X − (∑L=1 X) 𝑆𝑙𝑜𝑝𝑒 = (1) 𝑛 ∑ 𝑋 − ∑ 𝑋 where X and Y are the values of the independent (years) and dependent (NDVI values) variableswhere 𝑋 inand the 𝑌 i areth year,the values respectively, of the independent and n is the (years) cumulative and dependent number of(NDVI years values) (from 1982variables to 2015) in the of ith the year, study respectively, period. Generally, and n is the if cumulative the slope = number 0, the dependent of years (from variable 1982 showsto 2015) a of stability the study while period. when Generally, slope >0, if the the variation slope = 0, in the the dependent dependent variable variable shows shows a anstability increasing while trend,when whileslope >0, when the thevariation slope in is <0,the dependent it shows a decreasingvariable shows trend. an Afterincreasing that, thetrend, slope while (Figure when4a the) was slope multiplied is <0, it shows to its ap -valuedecreasing (Figure trend.4b) After to clearly that, show the slope the spatial(Figure distribution4a) was multiplied of significant to its trendsp-value with (Figure their 4b) strength to clearly (Figure show5a). Thethe integrationspatial distribution of the two of mapssignificant (Figure trends4a,b) with were their presented strength in classes.(Figure 5a). The integration of the two maps (Figure 4a,b) were presented in classes.

(a) (b)

FigureFigure 4.4. Change in the trends of the mean NDVI from 1982 to 2015. (a) annual change trends inin NDVI:NDVI: thethe yellowyellow toto greengreen colorcolor showsshows thethe trendtrend ofof increaseincrease inin vegetation greenness per year,year, whilewhile thethe decreases are marked by yellow to red color; andand ((bb)) significancesignificance ((pp-value)-value) of of the the change change trends trends in in NDVI NDVI are are classified classified as as strong strong significance significance from from 0 to0 to 0.01, 0.01, from from 0.01 0.01 to to 0.05 as significant and 0.05 to 1 as no significant results. 0.05 as significant and 0.05 to 1 as no significant results. Sustainability 2021, 13, 1234 7 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 8 of 24

Figure 5. ChangeFigure in 5. theChange trends in of the the trends mean of NDVI the mean from 1982NDVI to from 2015: 1982 (a) The to 2015: overall (a) trends The overall in five trends types: in slight five degradation, slight improvement,types: slight stable degradation, or without slight vegetation, improvement, significant stable degradation, or without and vegetation, significant significant improvement; degrada- (b) percentage of each in the fourtion, classifiedand significant trends, improvement; where Sl D stands (b) percentage for Slight of Degradation; each in the four Sl I, Slightclassified Improvement, trends, where St andSl Dnv, Stable or non-vegetation;stands S D,for Strong Slight degradationDegradation; and Sl I, SI, Slight Strong Improv Improvement.ement, St and nv, Stable or non-vegetation; S D, Strong degradation and SI, Strong Improvement.

2.2.3. Vegetation2.2.3. Types Vegetation Analysis Types Analysis Through the slopeThrough analysis the sloperesult analysismap, using result the map, tabulation using themethod, tabulation we computed method, we computed the values of plantthe values species of in plant each species type of in variation each type (Table of variation 1) in 1992 (Table and1) in in 1992 2015. and Then, in 2015. Then, we we were able towere identify able how to identify much howof each much vegetation of each type vegetation was in typeeach wasof the in five each classes of the five classes of of NDVI trends.NDVI Thus, trends. we computed Thus, we the computed changes. From the changes. here, we From were here, able to we identify were able the to identify the vegetation degradationvegetation areas degradation and, within areas th and,e land within cover the map, land we cover assessed map, wethe assessedplant spe- the plant species cies and their transitions,and their transitions, and identified and identifiedthose on the those path on to the destruction path to destruction (extinction). (extinction). Sta- Statistical tistical analysisanalysis of each oftype each of vegetation type of vegetation was carried was out carried to ensure out to the ensure true thetypes true of typesveg- of vegetation etation in danger.in danger.

Table 1. DivisionTable of the 1. degreesDivision of of variat the degreesion in the of NDVI variation change in the trend. NDVI change trend.

Slope SlopeTrend Magnitude Trend Magnitudep-Value p-ValueVariation Variation Positive Positive0.002–0.013 0.002–0.0130–0.01 0–0.01Strong improvement Strong improvement 0.0003–0.002 0.0003–0.0020.01–0.05 0.01–0.05Slight improvement Slight improvement Negative–PositiveNegative–Positive −0.0003–0.0003− 0.0003–0.0003 0.05–1 0.05–1Stable or non-vegetated Stable or non-vegetatedarea area Negative Negative−0.002–−0.0003− 0.002–−0.00030.01–0.05 0.01–0.05 Slight degradation Slight degradation −0.014–−0.002 −0.014–−0.0020–0.01 0–0.01 Strong degradation Strong degradation

2.2.4. NDVI and2.2.4. Key NDVI Meteorologic and Keyal Meteorological Factors Correlation Factors Analysis Correlation Analysis Pearson′s correlationPearson coefficients0s correlation were coefficients precisely werecalculated precisely for calculatedeach pixel, forbetween each pixel, between the mean annualthe NDVI mean as annual a dependent NDVI as variable, a dependent and the variable, mean andannual the temperature mean annual and temperature and accumulative annualaccumulative precipitation annual as precipitation independent as variables. independent To clearly variables. understand To clearly and understand and accurately assessaccurately the effect assess of climate the effect variability of climate on the variability vegetation on thechange, vegetation we also change, calcu- we also calcu- lated this correlation coefficient between the seasonal NDVI and seasonal temperature and precipitation by using Pearson′s correlation coefficients’ equation Sustainability 2021, 13, 1234 8 of 22

lated this correlation coefficient between the seasonal NDVI and seasonal temperature and precipitation by using Pearson0s correlation coefficients’ equation

n ( − )( − ) ∑i−1 xi x yi y rxy = q , (2) n n 2 ∑i=1(xi − x) ∑i=1(yi − y)

where y and x are the predictor (climate) and response (NDVI) variables, respectively. In the correlation coefficient between two variables, with a value ranging from −1 to 1, having a correlation coefficient greater than zero means a positive correlation between the two specific variables and having a negative means a negative correlation between them. This means that a larger absolute value properly indicates a stronger correlation. All the above-defined seasons were considered for 34 years in this study. Thus, during this analysis, the non-vegetated areas were masked to better obtain real information. We also applied the Spearman correlation statistics, which is the most popular non- parametric tool that can be used to remediate autocorrelation in trend analyses where there is a need to detrend or to normalize the distribution of analyzed variables [34,35].

3. Results 3.1. Characteristics of Trends in Vegetation Dynamics from 1982 to 2015 Based onthe linearregressionanalysis, possible changesin averageNDVI (Figures4a,b and5a,b) over the 34 years considered by this study, revealed an upward trend in vegetation green- ness across the northern part of Africa, including some portions in northern hot desert (Sahara), with an annual change up to 0.012 y−1. A significant declining trend in vegetation greenness was detected in the southern parts of central and eastern Africa, along the corri- dors of Angola toward southern Madagascar, with a rate as low as −0.014 y−1. In addition, vegetation in south-central tropical Africa demonstrated a mostly slight degradation and the desert area, like that in Namibia, demonstrated an increase in NDVI for some parts, especially in semi-arid zones. The overall trend of vegetation north of the equator shows an increase; however, the decreasing trends in certain areas cannot be ignored. The south part of the equator generally shows a slight degradation and stable state of vegetation. However, some strong degradation is highlighted along the line of tropical boundary from Angola to Madagascar, just around the southern part of central Africa and that of east Africa. East Africa shows a strong improvement; however, some parts in Tanzania, Kenya and Mozambique show a strong degradation. The rate of vegetation degradation was classified into five classes, and the percentage of each of the five classes of the mean NDVI trends for the 34 years is shown in Figure5a,b. From this, the large part of Africa shows a slight improvement, of 37% of the pixel0s area, and this number is not bigger than the stable or non-vegetated area, which is about 35.9% of the pixels. This class occupied 25.76% of stable vegetation, which tends to increase, and 10.14% of stable vegetation also tends to increase. Further, the number identified as a strong improvement was also high, at 13.4% of the pixel0s area and the number of pixels identified as a slight and significant degradation, at 11.5%, and 2%, respectively. Table2 shows the sequence of countries with a large area in each class of vegetation trends. Regardless of the size of each country, Angola typically experienced significant vegetation degradation to the largest surface area in all African countries, while Sudan ex- perienced the largest strong improvement (Table2a). For both slight degradation and slight improvement, RD Congo was ranked first. For the case of stable or non-vegetated area, the identified north desert countries were headed by Algeria, with the largest surface area. Sustainability 2021, 13, 1234 9 of 22

Table 2. The representation of the 9 countries most identified with high vegetation trends (area in pixel).

Stable or Strong Degradation Slight Degradation Slight Improvement Strong Improvement Non-Vegetated Area a NDVI Trends Variation Area (in Pixel) for Each Country Angola 10.21 DR, Congo 44.88 Algeria 168.04 DR Congo 94.12 Sudan 28.44 Tanzania 4.60 Angola 34.39 Libya 146.66 South Africa 63.96 Ethiopia 27.91 Kenya 3.79 Zambia 18.26 Egypt 78.42 Ethiopia 43.30 Chad 26.70 Mozambique 3.50 Mozambique 18.21 Sudan 77.14 Sudan 38.65 South Africa 22.39 Zambia 3.15 14.27 Niger 62.36 Nigeria 38.64 South Sudan 21.72 Madagascar 3.07 Tanzania 13.11 Mauritania 57.63 Namibia 37.88 Nigeria 20.19 DR Congo 2.79 Morocco 10.34 Mali 51.41 Angola 36.70 Mali 16.04 Ethiopia 2.64 Namibia 10.03 DR Congo 40.74 C A R 35.03 Botswana 12.94 Sudan 2.52 Sudan 9.77 Chad 38.84 Tanzania 34.70 Kenya 11.92 b The Percentage of NDVI Trends Based on the Area of Each Country Djibouti 19.63 Djibouti 53.38 Libya 93.99 Eq G 72.57 Senegal 49.89 Angola 9.93 Angola 33.47 Egypt 90.44 C A R 69.22 Sierra Leone 45.11 Kenya 8.48 Madagascar 30.58 Algeria 79.01 Lesotho 68.01 South Sudan 42.27 Eritrea 7.42 Zambia 30.01 Mauritania 64.42 Congo 64.62 Liberia 33.78 Malawi 7.33 Mozambique 29.28 Niger 62.08 Burundi 64.43 Togo 32.94 Tanzania 6.67 Rwanda 26.50 Mauritius 50.00 Liberia 63.85 The Gambia 31.32 Guinea- Madagascar 6.58 Malawi 26.01 Sudan 49.29 Swaziland 63.03 31.14 Bissau Mozambique 5.63 Tunisia 25.80 Mali 48.32 Gabon 62.89 Guinea 31.12 Zambia 5.18 DR Congo 24.25 Tunisia 43.43 Guinea 62.69 Ethiopia 30.68 In the column of Slight Improvement, table a line 8 and table b line 2, C A R stands for Central African Republic. In the same column, table b line 1, Eq G stands for Equatorial Guinea. In same column, table a line1, DR, Congo stands for Democratic Republic of the Congo.

Based on the results of the percentages of total variations in each country (Table2b), Djibouti was identified to have experienced the most degraded vegetation, as its area of significant vegetation degradation is greater than its remaining parts, and no part of it has ever seen a strong improvement. Although Madagascar was not identified in the first five strongly degraded countries, the area from its central part to its southern part revealed a strong degradation. Furthermore, Sierra Leone and Liberia did not experience any strong degradation, but a great improvement. The island of Mauritius neither experienced a strong improvement nor a strong degradation. Further, based on the surface area of the countries, Djibouti is also the first with slight degradation, (Table2b).

3.2. Trend Dynamics Per Vegetation Type After assessing the rate of vegetation increase and decrease, the authors detected the areas where changes were recorded. Based on an area (in %), occupied by each vegetation type and other land cover in 1992, changes in the vegetation in this area until 2015 were calculated according to the vegetation rate resulting from NDVI trend analysis for 34 years (1982–2015). The results in Figure6 show the vegetation fluctuation in each of the five classified vegetation trends of the annual mean NDVI from 1982 to 2015. Considering the percentage of vegetation cover changes between 1992 and 2015 for each vegetation type, the type of “Tree cover, Broadleaved, Deciduous, closed to open (>15%) (TBDCO) and Shrubland” were the most severely degraded in Africa, yet they were in the opposite classes. The TCBDCO accounted for more than 74%, 37.3%, 18.4% and 4.1% of cleared vegetations in the area that experienced strong, slight degradation, stable or non-vegetated area, and slight improvement respectively. They also increased by 8.7% in terms of strong improvement, whereas shrubland had about 63.1%, 47.2%, 30.6% and 23.5% of the cleaned vegetation of strong, slight improvement, stable or non-vegetated area and slight degra- dation, respectively, with a 1.1% increase in strong degradation. Figure6 also shows that the “Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%)” and other types of vegetation only experienced degradation in areas where NDVI had Sustainability 2021, 13, 1234 10 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 11 of 24

eitherpercentages strongly are improved in the negative. or strongly These decreased.types of vegetation This is explainedwere replaced by the by factthose that with their an percentagesincrease in such are in areas, the negative. such as These“Herbaceous types of cover” vegetation and were“Rainfed replaced cropland”. by those All with these an increasetypes of in ravaged such areas, vegetation such as “Herbaceouscover are found cover” in regions and “Rainfed known cropland”. for recurrent All these flooding, types ofdroughts, ravaged agriculture vegetation activities, cover are logging found in and regions an increase known in for population. recurrent flooding,Thus, stakeholders droughts, agricultureare urged to activities, take action logging on andflood an mitigati increaseon in programs, population. logging Thus, stakeholdersand family planning, are urged tostrictly take actionfor the on prevention flood mitigation of ecosystems, programs, using logging the efforts and family of local planning, communities strictly and for local the preventiongovernment. of ecosystems, using the efforts of local communities and local government.

FigureFigure 6.6. Vegetation exchange in % from 1992 to 2015 classifiedclassified in the NDVI trend result of the vegetationvegetation dynamicsdynamics (1982–2015)(1982–2015) where,where, 10:10: rainfedrainfed cropland;cropland; 11:11: herbaceous cover; 12: tree or shrub shrub cover;cover; 20: cropland, cropland, irrigated irrigated or or post post flooding; flooding; 30: 30: mosaic mosaic croplands croplands (>50%)/natural (>50%)/natural vegetation vegetation (tree, shrub, herbaceous cover) (<50%); 40: mosaic natural vegetation (tree, shrub, herbaceous cover) (tree, shrub, herbaceous cover) (<50%); 40: mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%); 50: tree cover, broadleaved, evergreen, closed to open (>15%); 60: tree (>50%)/cropland (<50%); 50: tree cover, broadleaved, evergreen, closed to open (>15%); 60: tree cover, broadleaved, deciduous, closed to open (>15%); 61: tree cover, broadleaved, deciduous, cover,closed broadleaved, (>40%); 62: tree deciduous, cover, broadleaved, closed to open deci (>15%);duous, 61: open tree (1540%); cover, broadleaved, 70: tree cover, deciduous, needleleaved, closed (>40%);evergreen, 62: treeclosed cover, to open broadleaved, (>15%); 80: deciduous, tree cover, open needleleaved, (1540%); 70: treedeciduous, cover, needleleaved, closed to open evergreen, (>15%); closed90: tree to opencover, (>15%); mixed 80: treeleaf cover,type needleleaved,(broadleaved deciduous,and needleleaved); closed to open 100: (>15%); mosaic 90: tree cover,and mixedshrub(>50%)/herbaceous leaf type (broadleaved cover and(<50%); needleleaved); 110: mosaic 100:herbaceous mosaic cover tree and(>50%)/tree shrub(>50%)/herbaceous and shrub (<50%); cover120: shrubland; (<50%); 110: 122: mosaic deciduou herbaceouss shrubland; cover 130: (>50%)/tree grassland; and150: shrubsparse (<50%);vegetation 120: (tree, shrubland; shrub, her- 122: deciduousbaceous cover) shrubland; (<15%); 130: 151: grassland; sparse tree 150: (<15%) sparse ;152: vegetation sparse (tree,shrub shrub, (<15%); herbaceous 153: sparse cover) herbaceous (<15%); cover (<15%) ;160: tree cover, flooded, fresh or brakish water; 170: tree cover, flooded, saline water; 151: sparse tree (<15%) ;152: sparse shrub (<15%); 153: sparse herbaceous cover (<15%) ;160: tree 180: shrub or herbaceous cover, flooded, fresh/saline/brakish water; 190: urban areas; 200: bare ar- cover, flooded, fresh or brakish water; 170: tree cover, flooded, saline water; 180: shrub or herbaceous eas; 201: consolidated bare areas; 202: unconsolidated bare areas; 210: water bodies. Following the cover,legend flooded, of the global fresh/saline/brakish Land Cover map water; using 190: the urbanUnited areas; National 200: Land bare areas;Cover 201: Classification consolidated System bare areas;(UN-LCCS), 202: unconsolidated which is quite barecompatible areas; 210:with water the plant bodies. functional Following types the used legend in many of the models. global Land Cover map using the United National Land Cover Classification System (UN-LCCS), which is quite compatible3.3. Climate with Variability the plant Trends functional from types 1982 usedto 2015 in many models. 3.3. ClimateFigure Variability7 shows the Trends spatial from distribution 1982 to 2015 of annual climate change trends from 1982 to 2015. Except for the southwest Sahara, which shows a slight increase in temperature, the Figure7 shows the spatial distribution of annual climate change trends from 1982 significant rate of increase rises up to 0.0734 °C y−1 of the temperature identified through- to 2015. Except for the southwest Sahara, which shows a slight increase in temperature, out the eastern Sahara Desert and strongly along the Sahel zone. Likewise, we identified the significant rate of increase rises up to 0.0734 ◦C y−1 of the temperature identified a similar trend in northern Ethiopia, eastern Kenya and western Uganda. The significant throughout the eastern Sahara Desert and strongly along the Sahel zone. Likewise, we decrease rate to −0.047 °C y−1 is identified in the western part of west Africa, in southeast identified a similar trend in northern Ethiopia, eastern Kenya and western Uganda. The Somalia, west Kenya and in the southern part of Africa. The eastern-north central and significant decrease rate to −0.047 ◦C y−1 is identified in the western part of west Africa, eastern Africa generally showed a slight decrease. in southeast Somalia, west Kenya and in the southern part of Africa. The eastern-north central and eastern Africa generally showed a slight decrease. Sustainability 2021, 13, 1234 11 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 12 of 24

(a) (b)

Figure 7. Trends in annual rainfall and temperature over Africa from 1982 to 2015 based on Chirps and European Center for Medium-RangeMedium-Range Weather ForecastForecast Re-AnalysisRe-Analysis (ERA)-Interim/European(ERA)-Interim/European center center for medium-range weather forecast (ECMWF). (a), Spatial distribution of annual mean temperature. ( b), Spatial Spatial distribution distribution annual annual accumulated accumulated precipitation precipitation from 1982 to 2015. from 1982 to 2015.

The precipitation variability during during the 34 years of the study showed a significantsignificant increaseincrease atat thethe raterate ofof 21.17 21.17 mm mm y −y−11 inin somesome parts,parts, like like that that along along the the Sahel Sahel from from Senegal Senegal to toEthiopia, Ethiopia, yet yet did did not not occur occur inDjibouti in Djibouti and and western western part part of Eritrea. of Eritrea. This This increase increase was seenwas seenagain again in the in middle the middle part of part Madagascar of Madagascar and northern and northern part of Tunisia,part of AlgeriaTunisia, and Algeria Morocco, and Morocco,and in the and high in mountainsthe high mountains of Ethiopia, of Ethi Southopia, Sudan South and Sudan northern and northern Uganda. Uganda. It further It furtherwas identified was identified in the northern in the northern and central and ce partntral of southernpart of southern Africa andAfrica south-central and south-cen- and traleastern and Africa.eastern The Africa. observed The observed significant significant decrease, todecrease, the rate to of the−14.1126 rate of mm −14.1126 y−1, occurred mm y−1, occurredstrongly instrongly central in Africa, central especially Africa, especially in the DR Congo,in the DR Angola, Congo, Gabon Angola, and Gabon south Cameroon.and south Cameroon.The same scenario The same occurred scenario in occurred southwestern, in southwestern, eastern and east in east-southernern and in east-southern Africa. Af- rica. The spatial distribution of the seasonal climate and vegetation change trends from 1982The to 2015 spatial is shown distribution in Figure of8 .the The seasonal NDVI rate climate strongly and increased vegetation in thechange tropical trends northern from 1982hemisphere to 2015 is and shown decreased in Figure in the 8. The southern NDVI hemisphere rate strongly during increased the springin the tropical (SON) season, north- ernwhile hemisphere the opposite and trend decreased is seen in in the autumn southern (MAM). hemisphere Vegetation during below the the spring Sahel (SON) area has sea- a son,strong while increasing the opposite rate during trend is summer seen in (DJF),autumn but (MAM). was gradually Vegetation reduced below in the autumn Sahel area and hasincreased a strong back increasing to the Sahel rate area during during summer winter (DJF), (JJA). but Furthermore, was gradually central reduced and west in autumn African andvegetation increased strongly back to decreased the Sahel during area during spring winter and summer, (JJA). Furtherm while, duringore, central winter, and that west in Africancentral andvegetation southwestern strongly Africa decreased showed during a strongly spring increasing and summer, pattern. while, during winter, that inThe central temperature and southwestern reflected different Africa showed spatial a trends strongly in every increasing season, pattern. withthe greatest increase generally observed in the northern part of Africa during the different seasons. During the spring season (SON), the temperature increased almost throughout the conti- nent, with a significant increase occurring in northeast Africa, the Sahel zone and western Madagascar. The rate of change was from −0.062 to 0.093 ◦C y−1. Even though this season does not show the greatest increase in temperature, a significant increase was displayed in most parts of Africa. However, during this season, the sub-Saharan areas are predominated by a slight increase in temperature. In summer, the temperature shows a decrease, mostly northwest of the Sahara Desert and in part of Southern Africa, while an increase occurred Sustainability 2021, 13, 1234 12 of 22

Sustainability 2021, 13, x FOR PEER REVIEWin central and north-west Africa. During this season, the rate of change was from −130.077 of 24

to 0.085 ◦C y−1.

Figure 8. Spatial distribution of NDVI, temperature and precipitationprecipitation seasonal variationvariation raterate fromfrom 19821982 toto 2015.2015.

TheFurther, temperature during the reflected autumn different (MAM), thespatial temperature trends in continuedevery season, to decrease, with the mostly greatest in increasethe sub-Sahara, generally especially observed in in the the high northern mountains, part of while Africa an during increase the is recordeddifferent northseasons. of Duringthe Sahara, the spring precisely season along (SON), Sahel, the and temper in Algeria,ature Tunisiaincreased and almost Morocco. throughout The winter the conti- (JJA) nent,shows with a wide a significant area of decrease increase in occurring temperature; in northeast however, Africa, its rate the of decrease Sahel zone (−0.047 and western◦C y−1) Madagascar.is very small The compared rate of change to rest ofwas the from three −0.062 seasons to 0.093 and it°C possesses y−1. Even though the biggest this rateseason of doesincrease not (0.105show ◦theC y greatest−1). While increase most partsin temperature, of the continent a significant show a decreaseincrease inwas temperature, displayed incentral-western most parts of SahelAfrica. has However, a strong during increase. this season, the sub-Saharan areas are predomi- natedAlthough by a slight temperature increase in andtemperature. precipitation In summer, are recorded the temperature differently shows over the a decrease, seasons, mostlythey both northwest seem to of be the similar Sahara in Desert some areas. and in In part winter of Southern (JJA), precipitation Africa, while occurred an increase in a veryoccurred small in partcentral of Africa,and north-west with a significant Africa. During rate of this both season, increase the rate and of decrease change (17.5was from and −1 −0.0778.194 to mm 0.085 y °C, respectively). y−1. This precipitation increases only in areas below the Sahel line andFurther, the Equator, during the especially autumn in (MAM), the high the mountains temperature of Ethiopia. continued It is to in decrease, the same mostly season inthat, the especially sub-Sahara, in especially the West Africanin the high countries mountains, bordering while an the increase ocean from is recorded Guinea north Bissau of theto Gabon, Sahara, a precisely significant along decrease Sahel, in and precipitation in Algeria, isTunisia also observed. and Morocco. These The rates winter of change (JJA) showscontinue a wide spreading area of in decrease the spring, in temperature; especially eastward. however, The its raterate ofof increasedecrease is (− strong0.047 °C in y the−1) isHorn very of small Africa, compared and a sharp to declinerest of spreadsthe three strongly seasons throughout and it possesses Central Africa,the biggest particularly rate of increasein Cameroon, (0.105 Gabon, °C y−1). Democratic While most Republic parts of the of the continent Congo show and the a decrease Central Africain temperature, Republic. central-westernIn summer (DJF),Sahel thehas ratea strong of increase increase. in precipitation over the 34 years in this study was −1 observedAlthough only intemperature the southern and part precipitation of Africa, with are arecorded rate of 10.658 differently mm y over. The the significant seasons, theyincreases both turned seem to out be to similar be the continuationin some areas. of springIn winter (SON) (JJA), in theprecipitation southeast direction.occurred in It isa − −1 veryin this small season part that of aAfrica, small ratewith of a decreasesignificant in precipitationrate of both increase ( 5.401 mmand ydecrease) was observed.(17.5 and Central African countries generally have a problem of reduced rainfall occurrence in all −8.194 mm y−1, respectively). This precipitation increases only in areas below the Sahel line and the Equator, especially in the high mountains of Ethiopia. It is in the same season that, especially in the West African countries bordering the ocean from Guinea Bissau to Ga- bon, a significant decrease in precipitation is also observed. These rates of change continue Sustainability 2021, 13, x FOR PEER REVIEW 14 of 24

Sustainability 2021, 13, 1234 13 of 22 spreading in the spring, especially eastward. The rate of increase is strong in the Horn of Africa, and a sharp decline spreads strongly throughout Central Africa, particularly in Cameroon, Gabon, Democratic Republic of the Congo and the Central Africa Republic. seasons, except in autumn (March to May), where some of its parts experience an increase In summer (DJF), the rate of increase in precipitation over the 34 years in this study in rainfall. This is consistent with the apparent decrease in vegetation and the increase in was observed only in the southern part of Africa, with a rate of 10.658 mm y−1. The signif- temperature in this region. icant increases turned out to be the continuation of spring (SON) in the southeast direc- 3.4.tion. Correlation It is in this between season NDVI that a andsmall Climate rate of Variability decrease in precipitation (−5.401 mm y−1) was observed. Central African countries generally have a problem of reduced rainfall occur- The spatial analysis of correlation coefficients between the annual average NDVI and rence in all seasons, except in autumn (March to May), where some of its parts experience the annual cumulative precipitation and the annual average temperature in the entire an increase in rainfall. This is consistent with the apparent decrease in vegetation and the African continent is presented in Figures9 and 10. Moreover, to find the relationship betweenincrease in vegetation temperature dynamics in this region. and climatic variability, the correlation coefficients were found at the annual and seasonal levels. The mean NDVI revealed a positive correlation 3.4. Correlation between NDVI and Climate Variability coefficient with precipitation in most vegetated areas, especially in arid steppe hot areas and itsThe surrounding spatial analysis desert, of hotcorrelation zone. However, coefficients we cannotbetween ignore the annual some partsaverage which NDVI showed and negativethe annual correlations, cumulative likeprecipitation Serra Leone, andGuinea, the annual the average eastern temperature part of Democratic in the entire Republic Af- ofrican the continent Congo, Malawi, is presented East Zambia, in Figures west 9 Mozambique,and 10. Moreover, Burundi to find and the west relationship Tanzania. Thebe- NDVItween andvegetation precipitation, dynamics correlation and climatic coefficient variability, rates duringthe correlation the 34 year coefficients period was were up tofound 0.930 at atthe 69% annual and and down seasonal to −0.774 levels. at 31%The mean of the NDVI vegetated revealed area. a positive The part correlation along the Sahelcoefficient presented with precipitation a high positive in most correlation vegetated coefficient, areas, especially while the in partarid insteppe central hot Africaareas showedand its surrounding a slight negative desert, correlation hot zone. between However, annual we mean cannot NDVI ignore and some cumulative parts annualwhich precipitationshowed negative (Figure correlations,9a). The significance like Serra test Leon (Figuree, Guinea,9c) showed the eastern that there part is of no Democratic significant correlationRepublic of between the Congo, mean Malawi, NDVI East and cumulativeZambia, west annual Mozambique, precipitation, Burundi given and that west only Tan- 20% (positivezania. The 12.7% NDVI and and 7.3% precipitation, of negative) correlation shows a coefficientp-value between rates during−0.05 the to 0.05.34 year The period main part,was up i.e., to 61.2% 0.930 showsat 69% aand slight down positive to −0.774 correlation, at 31% of while the vegetated the slight area. negative The correlationpart along wasthe Sahel 23.7%. presented a high positive correlation coefficient, while the part in central Africa showedThe a correlation slight negative coefficient correlation rate between between the annual annual mean NDVI NDVI and and the cumulative mean temperature annual isprecipitation shown in Figure (Figure9b, 9a). and The represents significance a higher test negative (Figure rate,9c) showed which isthat negatively there is no correlated signifi- atcant−0.873 correlation and positively between correlated mean NDVI at 0.844. and Acumulative very strong annual negative precipitation, correlation given coefficient that (only−0.873) 20% was (positive identified 12.7% in and the 7.3% northern of negative) part of Southern shows a p Africa,-value especiallybetween − around0.05 to 0.05. Namibia The andmain Botswana, part, i.e., 61.2% in south shows Madagascar, a slight positive west of correlation, Ethiopia and while the middlethe slight parts negative of Somalia. correla- tion was 23.7%.

(a) (b)

Figure 9. Cont. Sustainability 2021, 13, 1234 14 of 22 Sustainability 2021, 13, x FOR PEER REVIEW 15 of 24

Figure 9. A 34 year correlation coefficient between NDVI and climate variables and their p-values. (a,c): NDVI and precipi- tationFigure correlation 9. A 34 year and correlation their p-value coefficient distribution, between (b, dNDVI): NDVI and and climate temperature variables correlation and their p and-values. their (pa-value,c): NDVI distribution and pre- from1982cipitation to correlation 2015. Notably, and their−0.05–0 p-value and distribution, 0–0.05 are considered (b,d): NDVI a negative and temperature and positive correlation significant and correlation, their p-value respectively, distribu- whiletion from1982 <−0.05 and to 2015. >0.05 Notably, are considered −0.05–0 slight and 0–0.05 negative are and considered positive a significant negative and correlations, positive significant respectively. correlation, respec- tively, while <−0.05 and >0.05 are considered slight negative and positive significant correlations, respectively. In contrast, the highest correlation coefficient (i.e., 0.844) is observed in most parts of westThe African correlation countries, coefficient north and rate west between of Central the annual Africa NDVI and and in the the Nile mean delta temperature of Egypt. Inis addition,shown in theFigure evergreen 9b, and forest represents in the a tropical higher climatenegative zone rate, of whic Africah is showed negatively a positive corre- correlationlated at −0.873 between and positively mean NDVI correlated and temperature. at 0.844. A The very significance strong negative test (Figure correlation9d) showed coef- noficient correlation (−0.873) of was significance identified when in the only northern 11.7% part (positive of Southern 5.9% and Africa, negative especially 5.8%) showedaround pNamibia-values betweenand Botswana,−0.05 andin south 0.05. AMadagascar, slight positive west correlation of Ethiopia is and shown the bymiddle the main parts part of bySomalia. 53%, while the slight negative correlation was 35%. Furthermore,In contrast, the the highest mean correlation NDVI illustrates coefficient different (i.e., 0.844) amounts is observed of responses in most to changesparts of inwest seasonal African precipitation countries, north and temperatureand west of Centra (Figurel Africa 10a,b). and Based in the on Nile these delta results, of Egypt. in most In ofaddition, the vegetated the evergreen areas in forest the study in the area, tropical a positive climate correlation zone of Africa between showed the averagea positive NDVI cor- andrelation the accumulatedbetween mean seasonal NDVI and precipitation temperature. is identified, The significance and the test correlation (Figure 9d) coefficients showed variedno correlation according of tosignificance the seasons when and spaceonly 11.7% in the (positive study area. 5.9% On and a seasonal negative scale, 5.8%) the showed area in allp-values seasons between accounted −0.05 for and up 0.05. to 50% A slight of the positive pixels with correlation a positive is correlationshown by the coefficient. main part A seasonalby 53%, while descending the slight order, negative based oncorrelation positive correlation was 35%. coefficients of the simulated pixels, wereFurthermore, observed in springthe mean (SON), NDVI autumn illustrates (MAM), different summer amounts (DJF) of and responses winter to (JJA), changes with 67.21%,in seasonal 63.28%, precipitation 54.82% and and 50.95%, temperature respectively. (Figure In10a,b). winter, Based which on these shows results, a few positivein most correlationof the vegetated coefficients areas in between the study the area, mean a po NDVIsitive andcorrelation precipitation, between negative the average correlation NDVI coefficientsand the accumulated were also seasonal found in precipitation the most vegetated is identified, areas, and especially the correlation in south-east coefficients Africa, includingvaried according Madagascar. to the In seasons contrast, and for space the spring in the season, study area. the negative On a seasonal correlation scale, coefficient the area wasin all most seasons evident accounted in the desertfor up areas.to 50% of the pixels with a positive correlation coefficient. A seasonalThe correlation descending coefficients order, based between on positi the meanve correlation NDVI and coefficients temperature of the in allsimulated seasons exhibitedpixels, were an obviousobserved spatial in spring difference, (SON), mostlyautumn with (MAM), a negative summer correlation, (DJF) and while winter none (JJA), had awith percentage 67.21%, lower63.28%, than 54.82% fifty. and In addition, 50.95%, respectively. the biggest positiveIn winter, correlation which shows coefficient a few pos- rate identifieditive correlation from September coefficients to between November the wasmean accounted NDVI and for, precipitation, from −0.852 negative to 0.811. correla- During the spring season, African vegetation and temperature experienced both strong positive and negative correlations in different parts. It was in the autumn season (MAM) that the Sustainability 2021, 13, 1234 15 of 22

biggest negative correlation (−0.894) was recorded, particularly in the southern parts of Africa. Moreover, occupation of the space counted by pixels with negative correlation, which were the most predominant, was observed in chronological order, as follows: winter Sustainability 2021, 13, x FOR PEER REVIEW(JJA), summer (DJF), spring (SON) and autumn (MAM), as 74.95%, 68.45%, 66.66%16 of and 24

65.64%, respectively. Further, a negative correlation between the mean NDVI and the temperature occurrence in winter was observed in most parts of strongly vegetated African countriestion coefficients along thewere Sahel also regionfound in and the Southern most vegetated Africa, areas, while especially a positive in correlation south-east wasAf- observedrica, including in the Madagascar. high mountains In contrast, of eastern for countries the spring and season, eastern-northern the negative partscorrelation of the DRco- Congo,efficient and was tropical most evident west Africa. in the desert areas.

(a) (b)

FigureFigure 10.10. (a) Spatial distribution distribution of of the the correlation correlation coefficients coefficients between between the the mean mean NDVI NDVI and and seasonal seasonal precipitation; precipitation; (b) (bSpatial) Spatial distribution distribution of ofthe the correlation correlation coefficients coefficients between between the the mean mean NDVI NDVI and and seasonal seasonal temperature. temperature.

TheThe summercorrelation was coefficients the second between highest the in me termsan NDVI of pixel and occupancy; temperature the in temperature all seasons stronglyexhibited devastated an obvious the spatial NDVI difference, in east Africa, mostly and with southern a negative Africa correlation, within the while Kalahari none Desert.had a percentage During the lower spring than season, fifty. In the addition vegetation, the of biggest east Africa positive and correlation southern Africacoefficient and almostrate identified the entire from of Madagascar,September to showNovember a strongly was accounted negative correlationfor, from −0.852 with to temperature, 0.811. Dur- whileing the it showedspring season, positive African correlations vegetation in most and regions temperature of central experienced and southern both west strong Africa posi- at hightive and correlation. negative In correlations autumn, the in correlation different parts. coefficient It was is in significantly the autumn negative season (MAM) in southern that Africa,the biggest in the negative southern correlation part of Madagascar (−0.894) was and recorded, in most particularly regions of in east the Africa. southern Positive parts correlationof Africa. Moreover, coefficients occupation were observed of the inspace central counted Africa by and pixels northeast with negative Madagascar. correlation, whichMoreover, were the Spearmanmost predominant, correlation was analysis observed was in performed,chronological where order, a veryas follows: strong win- cor- relationter (JJA), between summer (DJF), NDVI spring and precipitation (SON) and autumn is considered (MAM), toas be74.95%, 0.98% 68.45%, (0.5 positive 66.66% and 0.4865.64%, negative); respectively. strong Further, correlation a negative at 3.13% co (1.64%rrelation of between positive andthe mean 1.48% NDVI of negative); and the mod-tem- erateperature correlation occurrence at 13.77% in winter (7.08% was ofobserved positive in and most 6.69% parts of of negative); strongly vegetated weak correlation African atcountries 32.77% (16.46%along the positive Sahel region and 16.11% and Southern of negative) Africa, and while very weaka positive correlation correlation at 49.55% was observed in the high mountains of eastern countries and eastern-northern parts of the DR Congo, and tropical west Africa. The summer was the second highest in terms of pixel occupancy; the temperature strongly devastated the NDVI in east Africa, and southern Africa within the Kalahari De- sert. During the spring season, the vegetation of east Africa and southern Africa and al- most the entire of Madagascar, show a strongly negative correlation with temperature, Sustainability 2021, 13, x FOR PEER REVIEW 17 of 24

while it showed positive correlations in most regions of central and southern west Africa at high correlation. In autumn, the correlation coefficient is significantly negative in south- ern Africa, in the southern part of Madagascar and in most regions of east Africa. Positive correlation coefficients were observed in central Africa and northeast Madagascar. Moreover, Spearman correlation analysis was performed, where a very strong corre- lation between NDVI and precipitation is considered to be 0.98% (0.5 positive and 0.48 Sustainability 2021, 13,negative); 1234 strong correlation at 3.13% (1.64% of positive and 1.48% of negative); moderate 16 of 22 correlation at 13.77% (7.08% of positive and 6.69% of negative); weak correlation at 32.77% (16.46% positive and 16.11% of negative) and very weak correlation at 49.55% (24.65% positive and (24.65%24.915 of positive negative). and For 24.915 NDVI of an negative).d temperature, For NDVI a very and strong temperature, correlation a very is strong cor- 1.43% (0.69%relation of positive is 1.43% and 0.74% (0.69% of ofnegative); positive strong and 0.74% correlation of negative); is 6.46% strong(3.02% correlationof pos- is 6.46% itive and 3.46%(3.02% of negative); of positive moderate and 3.46% correlati of negative);on is 18.14% moderate (8.78% correlation of positive is 18.14% and 9.36% (8.78% of positive of negative); andweak 9.36% correlation of negative); is 32.01% weak (15.71% correlation of positive is 32.01% and 16.30% (15.71% of ofnegative) positive and and 16.30% of very weak correlationnegative) andis 41.94% very weak(20.73% correlation of positive is 41.94% and 21.20% (20.73% of negative) of positive (Figure and 21.20% A2). of negative) The NDVI–precipitation(Figure A2). occupi The NDVI–precipitationed a larger size, between occupied −1 and a larger −0.9525, size, from between −0.2941− 1to and −0.9525, 0.325 and fromfrom 0.9525−0.2941 to 1 with to 0.325 62.3%, and 53.5% from 0.9525and 63.91%, to 1 with respectively, 62.3%, 53.5% while and the 63.91%, NDVI– respectively, temperature whileoccupied the NDVI–temperaturea larger size on the occupiedcorrelation a largerbetween size −0.9529 on the and correlation −0.2941, between and −0.9529 from 0.3255 toand 0.9529,−0.2941, with and 57.7% from and 0.3255 55.8%, to respectively 0.9529, with (Figure 57.7% and A1). 55.8%, respectively (Figure A1).

3.5. Residual Analysis3.5. Residual Analysis Residual-basedResidual-based NDVI-temperature NDVI-temperature linear regression linear analysis regression was used analysis to remove was usedthe to remove limitation of thetemporal limitation autocorrelation of temporal and autocorrelation to show the normal and to distribution show the normal between distribution NDVI between and climate factors.NDVI and Figure climate 11 showed factors. a Figureslight increase 11 showed in the a slight trend increase of the NDVI–temper- in the trend of the NDVI– − ature relationshiptemperature in tropical relationship Africa, particul in tropicalarly Africa, in tropical particularly rainforests in tropical up to 0.056 rainforests y−1 and up to 0.056 y 1 a slight decreaseand ain slight arid and decrease semi-arid in arid regions and semi-aridof the Sahara regions and ofKalahari the Sahara Deserts and − Kalahari0.54 Deserts −1 y−1. In the tropical−0.54 savanna y . In the and tropical temperate savanna zones, and especially temperate in zones,northeastern especially southern in northeastern Af- south- rica, eastern ernAfrica Africa, and eastern southwestern Africa and Centra southwesternl Africa, NDVI–precipitation Central Africa, NDVI–precipitation residuals in- residuals −1 creased slightly,increased to 0.74 slightly, y−1. In tropical to 0.74 y rainforests,. In tropical tropical rainforests, monsoons, tropical arid monsoons, and semi-arid arid and semi-arid −1 zones, it declinedzones, slightly it declined to −1.2 slightly y−1. These to − 1.2findings y . Thesesuggest findings that the suggest average that patterns the average in patterns NDVI–temperaturein NDVI–temperature residuals and NDVI–precipitation residuals and NDVI–precipitation have strong geographic have strong character- geographic charac- istics. The outcometeristics. revealed The outcome that positive revealed NDVI–precipitation that positive NDVI–precipitation residuals in areas residuals with in- in areas with creasing vegetationincreasing trends vegetation are spatially trends aggregated, are spatially and aggregated, are mainly andlocated are along mainly the located Sa- along the hel belt, in theSahel eastern belt, semi-arid in the eastern zone, semi-aridin some parts zone, of in western some parts Africa of and western southeast Africa Af- and southeast rica. RegardingAfrica. NDVI–temperature, Regarding NDVI–temperature, it is obvious that it is the obvious main thatincreases the main are increasesseen in the are seen in the slight increaseslight in vegetation increase in in vegetation Madagascar in Madagascarand in some andtropical in some regions. tropical regions.

Figure 11. Overall trends of the NDVI residuals in a regression of (a) the mean NDVI–temperature and (b) NDVI–precipitation.

4. Discussions 4.1. Analysis of Vegetation Trend Dynamics One of many ways in which change in vegetation can be revealed and quantified is by analyzing the fluctuating time series of consecutive years and providing the direction of change through slope analysis [36]. Trends with zero slope values are stable vegetation, while the positive and negative trends represent an increase and decrease in vegetation, Sustainability 2021, 13, 1234 17 of 22

respectively [37]. For this study, the results revealed that over 79.16% of Africa showed improved vegetation density. This is consistent with the results of Ndayisaba, Guo [11], which reported that over 82.1% of vegetation was improving. This resulted from the increase in precipitation, and reforestation. The highlight is that, in 2011, there was a significant increase in vegetation, although it is not clear whether there were any special precipitation anomalies. Our results are consistent with those of Davis-Reddy [21], which reported a sharp increase in NDVI in 2011 and Brandt, Rasmussen [38], and, equally, testified to the increase in vegetation cover between 1992 and 2011, resulting in a slight increase in the population, driven by the increase in CO2 and rainfall. Although the increase in NDVI has been observed from 1992, a strong increase in all climate zones was noticed from 2011 to 2015. Significant degradation occurred in Central and Eastern Africa, bordering the countries in Southern Africa, from Angola, Zambia and Mozambique to Madagascar (Figures4a and5a ). This degradation occurs strongly during summer season, the months from December to February (Figures8 and 10a) which may have been influenced by the ENSO events that were reported to be the strongest during this season [39]. Further, from 1982 to 1984, especially in arid areas, there has been a dramatic decline in NDVI, possibly due to drought conditions from 1982 to 1985 in northern tropical Africa [40]. In 1993, there was a sharp decline in NDVI in tropical, boreal and polar climate zones, and in 1992, there was a sharp decline for arid and warm temperate zones (Figure2), which may have resulted from an obvious drop in rainfall occurrence from 1990. This finding is consistent with the results of Davis-Reddy [21], indicating that NDVI decreased in Southern Africa in 1992/1993 and resulted from the warm ENSO phase and low rainfall event. However, NDVI shows limitations in identifying and attributing changes in vegetation cover: (1) In arid regions, where seasonal variations in the content of atmospheric aerosol, such as dust and soil background, can cause significant changes in NDVI that are not accompanied by vegetation change [41,42], and in heterogeneous savanna regions, NDVI may have been affected by mixed land cover effects. These areas were masked and the NDVI value < 0.1 was removed to achieve a robust assessment. (2) The NDVI often tends to saturate in densely vegetated areas like tropical rainforest; however, these areas were not excluded from the analysis, to consider the abrupt change and shift which may happen.

4.2. Climate Variability Trends It is obvious that precipitation is increasing significantly from the Saharan region to the Horn of Africa, in most of Madagascar, in the northern part of southern Africa and northern Morocco and Tunisia. It is declining markedly in central Africa, in a small southern part of west Africa and even in southern Africa. The temperature is notably decreasing in western and southern Africa, and increasing rapidly in northeastern Africa and the northwestern part of Central Africa (Figure7). Furthermore, the report highlighted wetter rains in Tanzania and Kenya from March to May. This is consistent with our results, which show an increase in the precipitation rate in this region (Figure8). Looking at a small-scale map, the results of increasing and decreasing precipitation rates are consistent with those of Ndayisaba, Guo [11], which showed that precipitation decreases in the west and increases in the east of Rwanda. The temperature increased at a remarkable rate of 0.0734 ◦C y−1, with a decrease of −0.0472 ◦C y−1. This shows that the temperature has risen to 0.86 ◦C in the last 34 years (1982–2015). The results are consistent with those of the IPCC 2014, which reported an increase of 0.85 ◦C for the period of 1880–2012 [43]. Indeed, the IPCC reported that the African land surface temperature increased by 0.5–2 ◦C over the last century [44]. Sustainability 2021, 13, 1234 18 of 22

4.3. Correlation between Temperature, Precipitation and NDVI Although a large part of Africa did not show a significant correlation, the annual correlation between NDVI and climate data in Figure9 demonstrates a strong positive correlation between NDVI and precipitation in the semi-arid zone up to 0.930. In winter (JJA), precipitation in north Africa above the Sahel region decreases; however, NDVI increases along Morocco and the Nile delta in Egypt. This results from the fact that, in this season, the temperature is too cold and the decrease in precipitation often triggers colder weather across the region, which can increase the greenery of the vegetation. Furthermore, this can result from the use of irrigation in agricultural activities. Moreover, from December to February, precipitation decreases, resulting in a decrease in vegetation in south-central Africa, especially in Zambia, Zimbabwe, Mozambique and Botswana. This may be caused by the drier than normal weather caused by ENSO, as reported by [45]. The annual temperature is positively correlated with NDVI in West Africa and in the western and northern parts of central Africa (Figures9b and 10a). This correlation is also observed from September to November (Figure 10b). The evergreen forest is a humid tropical zone, which is the main cause of the positive correlation. Furthermore, this correlation also may be resulted from aerodynamic effects, explained by Sanderson and Santini [46], as the forest drags and slows the wind speeds, resulting in an enhanced transfer of heat and moisture between forest and air. The seasonal correlation between NDVI and climate data showed a strong negative correlation between temperature and NDVI in south and east Africa during almost all four seasons (Figure 10), and along southern Sahel in winter. However, it identified a slight positive correlation in other regions, including deserts. The strong seasonal negative correlation between NDVI and temperature across the Sahel, in JJA, for a small area in MAM, and in the west and east of DJF, may be due to reforestation, which decreased warm days in the region [47]. The seasonal NDVI and precipitation were generally correlated in vegetated areas, while they were negatively correlated, particularly in desert areas. The forest of western Madagascar showed a positive correlation with temperature, while it showed a negative correlation with precipitation over all seasons. Results of non-significant correlation, and annual changes in precipitation and temperature, were not able to explain the variation in vegetation, thus there are other factors, like human activities, which may mainly affect the vegetation. Moreover, there are the other key factors (climate and non-climate) that are responsible for vegetation cover changes at the country level, e.g., for climate factors, Igbawua, Zhang [48] reported that the dynamics of vegetation have also affected demog- raphy and land use, and are responsive to the humidity. The other non-climate factors which affect vegetation changes in Africa are numerous. For example, recent develop- mental progress in Africa controls tropical rainforest growths and revegetation due to stages in urbanization processes experienced at country-levels, including oil spillages in the mangroves of west Africa [49].

5. Conclusions Throughout Africa, during the 34-year period (1982 to 2015), the spatial–temporal veg- etation dynamics were assessed through the NDVI3g time series from the GIMMS analysis. To overcome data constraints from different regions (arid, rainforest), the datasets are treated with caution. To identify and analyze the main factors, CHIRPS precipitation and ERA-Interim temperature datasets were used in this study. The various statistical matrices, including linear regression and spearman analyses, were applied to help identify the rate of increases and decreases in vegetation changes in Africa. The results indicate that vegetation has a positive annual rate of 0.013 y−1 over a statistical pixel area greater than 79.16% of the total area. The precipitation exhibited a historic increase of 21.17 mm y−1 and the temperature showed an increase rate of 0.0734 ◦C y−1. It was noted that the rate of decrease (−0.014 y−1) for vegetation occupied a Sustainability 2021, 13, 1234 19 of 22

statistical pixel area of 20.84% of the total area. This rate is higher than the rate of increase, at the rate of 0.001 y−1, explaining the implicit strong degradation of the vegetation. While for temperature and precipitation, the increases are greater than the decreases, which are −0.0472 ◦C y−1 and −14.11 mm y−1, respectively. Particularly in central Africa, the findings showed that there was a significant increase in temperature and a significant decrease in rainfall. Thus, the tropical vegetation has undergone destructive changes, and therefore gradually perishes different plant species, especially tree cover, broadleaved, deciduous, closed to open (>15%) and shrubland. These can be attributed to urbanization processes at the country level, which are currently spreading throughout most parts of tropical Africa. However, our analysis did not consider urbanization effects, which is a limitation of the study. Because the simple linear regression analysis is commonly used to establish relationships between variables, but not their causation, a relationship between two variables does not mean one causes the other to change. However, our study still shows the influence of climate variability, given the positive correlation between vegetation and precipitation throughout Africa, except in deserts, and a strong negative correlation between vegetation and temperature in almost all the vegetated areas, except the tropical rainforest zone (Figure1). Additionally, our study shows a large, stable and a continuous decrease in vegetation, mostly in the central and eastern parts of Africa. Furthermore, the increase in temperature caused by global warming remains a serious problem in Africa. Therefore, it is suggested to ensure the reduction in all sources of temperature rise. We can conclude that monitoring the level of climate change is essential to identify the onset of depression, regardless of geographical influences. Future studies should consider a more detailed investigation of the influence of current urbanization processes on African vegetation and endangered plant species.

Author Contributions: Conceptualization, S.U., L.J., Y.D., L.N., T.Y. and T.W. and G.L.; investigation and supervision, A.B. and W.X.; methodology and writing—original draft, V.N.; software L.J. and T.Y. All authors contributed to the proper reading and improvement of the ideas in the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Program on Key Basic Research Project of China, Grant number 2018YFE0106000; Science and Technology Partnership Program, Ministry of Science and Technology of China, Grant number KY 201702010. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: This article used five datasets and the five datasets related to this article are NDVI3g, https://ecocast.arc.nasa.gov/data/pub/gimms/[ 26], CHIRPS, ftp://ftp.chg. ucsb.edu/pub/org/chg/products/CHIRPS/[29], ERA-interim, https://apps.ecmwf.int/datasets/ data/interim-full-moda/levtype%3Dsfc/[ 31], land cover https://www.esa-landcover-cci.org and climate zone, downloaded from www.gloh2o.org/koppen[25]. Acknowledgments: The authors are deeply grateful to the European Centre for the Medium Range Weather Forecast (ECMWF), ESA CCI Land Cover project, United States Geological survey and the NASA team for the provision of data. Moreover, the authors would like to acknowledge the Chinese government, Xinjiang Institute of Ecology and Geography, (UCAS), and University of Chinese Academy of Sciences (UCAS), for their financial support and lab facilities during this study. Conflicts of Interest: The authors declare that there is no conflict of interest. Sustainability 2021, 13, 1234 20 of 22 SustainabilitySustainability 2021, 13, x 2021 FOR, 13PEER, x FOR REVIEW PEER REVIEW 22 of 24 22 of 24

AppendixAppendixAppendix A. The ANDVI-PrecipiA. TheThe NDVI-PrecipitationNDVI-Precipitation andtation NDVI-Temperature andand NDVI-Temperature Spearman SpearmanSpearman Correlations CorrelationsCorrelations

Figure A1. Spearman correlation between NDVI and precipitation, NDVI and temperature from Figure A1. Spearman correlation between NDVI and precipitation, NDVI and temperature from Figure A1.1982 Spearman to 2015. correlation between NDVI and precipitation, NDVI and temperature from 1982 to 2015.1982 to 2015.

Figure A2. Spearman correlation between (a)NDVI and precipitation, (b) NDVI and temperature Figure A2.Figure Spearman A2. Spearman correlation correlation between between (a)NDVI (left and) NDVIprecipitation, and precipitation, (b) NDVI and (right temperature) NDVI and tempera- from 1982 to 2015, with assigned classification of 0–0.19: very weak, 0.2–0.39: weak, 0.4–0.59: mod- from 1982ture to from2015, 1982with toassigned 2015, with classification assigned classificationof 0–0.19: very of weak, 0–0.19: 0.2–0.39: very weak, weak, 0.2–0.39: 0.4–0.59: weak, mod- 0.4–0.59: erate, 0.6–0.79: strong, 0.8–1 very strong correlation, in the positive and negative direction. erate, 0.6–0.79:moderate, strong, 0.6–0.79: 0.8–1 strong,very strong 0.8–1 correla very strongtion, in correlation, the positive in an thed positivenegative and direction. negative direction. ReferencesReference Reference 1.1. Sellers,Sellers, P.J.;P.J.; Dickinson, R.E.; R.E.; Randall, Randall, D.A.; D.A.; Betts, Betts, A.K.; A.K.; Hall, Hall, F.G.; F.G.; Berry, Berry, J.A.; J.A.;Collatz, Collatz, G.J.; Denning, G.J.; Denning, A.S.; Mooney, A.S.; Mooney, H.A.; Nobre, H.A.; 1. Sellers, P.J.; Dickinson, R.E.; Randall, D.A.; Betts, A.K.; Hall, F.G.; Berry, J.A.; Collatz, G.J.; Denning, A.S.; Mooney, H.A.; Nobre, Nobre,C.A.; et C.A.; al. Modeling et al. Modeling the exchanges the exchanges of energy, of energy, water, water, and carbon and carbon between between continents continents and the and atmosphere. the atmosphere. ScienceScience 1997, 1997275,, C.A.; et al. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 1997, 275, 275502–509., 502–509. [CrossRef][PubMed] 502–509. 2.2. McPhaden,McPhaden, M.J. M.J. Genesis Genesis andand EvolutionEvolution ofof thethe 1997-981997-98 ElEl Nino.Nino. ScienceScience 19991999,, 283283,, 950–954.950–954, [doi:10.1126/science.283.5404.950.CrossRef][PubMed] 2. McPhaden, M.J. Genesis and Evolution of the 1997-98 El Nino. Science 1999, 283, 950–954, doi:10.1126/science.283.5404.950. 3.3. Mason,Mason, S.J. S.J. El El Niño, Niño, climate climate change,change, andand SouthernSouthern AfricanAfrican climate.climate. EnvironmetricsEnvironmetrics 20012001,, 1212,, 327–345.327–345, [doi:10.1002/env.476.CrossRef] 3. Mason, S.J. El Niño, climate change, and Southern African climate. Environmetrics 2001, 12, 327–345, doi:10.1002/env.476. 4.4. Anyamba,Anyamba, A.;A.; Tucker, C.; Eastman, Eastman, J.J. NDVI NDVI anomaly anomaly patterns patterns over over Africa Africa during during the 1997/98 the 1997/98 ENSO ENSO warm warm event. event.Int. J. RemoteInt. J. 4. Anyamba, A.; Tucker, C.; Eastman, J. NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. Int. J. Remote RemoteSens. 2001 Sens., 222001, 1847–1860., 22, 1847–1860. Sens. 2001, 22, 1847–1860. 5.5. Engelbrecht,Engelbrecht, F.; F.; Adegoke, Adegoke, J.; Bopape, J.; Bopape, M.-J.; M.-J.; Naidoo, Naidoo, M.; Garland, M.; Garlan R.M.;d, Thatcher, R.M.; Thatcher, M.; McGregor, M.; McGregor, J.; Katzfey, J.; J.; Werner, Katzfey, M.; J.; Ichoku, Werner, C.; etM.; al. 5. Engelbrecht, F.; Adegoke, J.; Bopape, M.-J.; Naidoo, M.; Garland, R.M.; Thatcher, M.; McGregor, J.; Katzfey, J.; Werner, M.; ProjectionsIchoku, C.; of et rapidly al. Projections rising surface of rapidly temperatures rising surface over Africa temperatures under low over mitigation. Africa underEnviron. low Res. mitigation. Lett. 2015 Environ., 10, 085004. Res. [Lett.CrossRef 2015,] 10, Ichoku, C.;085004, et al. Projectionsdoi:10.1088/1748-9326/10/8/085004. of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 2015, 10, 085004, doi:10.1088/1748-9326/10/8/085004. Sustainability 2021, 13, 1234 21 of 22

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