Is the African Great Green Wall greening? Some positive signs from a 20-years’ time-series of satellite observations

doi:10.21203/rs.3.rs-1831080/v1

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Is the African Great Green Wall greening? Some positive signs from a 20-years’ time-series of satellite observations

https://doi.org/10.21203/rs.3.rs-1831080/v1

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Land degradation has been recognized as a critical issue requiring urgent actions to cope with climate change and biodiversity loss impacts. The Sahel is probably among the most severely affected region in the World by land degradation, impacting many ecosystem services that are critical for wellbeing and livelihoods. In response to this issue, 11 countries have decided, in 2007, to create the Great Green Wall (GGW) initiative, an ambitious land-restoration program to mitigate the effects of land degradation as well as reducing poverty and improving population livelihoods. Despite initial positive results, the project has difficulties to reach its key objectives. Among the primary obstacles, effective and efficient monitoring, reporting, and verification mechanism has been identified. Here, we used a 20-year satellite derived annual time-series of vegetation biomass estimates to investigate the evolution of the entire GGW. Findings suggest that vegetation biomass has increased of about 22% in the region between 2007 and 2020; almost all countries have significative positive trends in above-ground biomass with noticeable differences between countries; however, precipitation patterns have not yet significantly changed. Results indicate that using satellite EO data can be a valid complement to provide consistent, reliable, timely and continuous information on the GGW.

landsat

ndvi

climate change

biodiversity loss

desertification

land degradation

ecosystem ser-vices

Recent reports from both the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the Intergovernmental Panel on Climate Change (IPCC) have stressed how mega-trends (e.g., population growth, urbanization) are creating unprecedented pressures and demands on land^1–3. Currently, it is estimated that land degradation is undermining the well-being of at least 3.2 billion people and costing more than 10 per cent of total Gross Domestic Product (GDP) worldwide^4,5. Therefore, avoiding, reducing, and reversing land degradation and restoring degraded land is an urgent action⁶. Without sustainable land management, the goals of the Paris Agreement or the Global Biodiversity Framework^7–9 will not be reached. Therefore, timely actions are necessary for meeting the objectives of various Multilateral Environmental Agreements (MEAs) and Global Environmental Goals (GEGs) and guide and assess progress towards policy outcomes¹⁰.

The importance of land degradation has been recognized by the Sustainable Development Goals (SDGs) endorsed by the United Nations in 2015¹¹ that contains land-related targets and indicators under 14 out of the 17 SDGs¹⁰. Land is a critical and limited resource, providing many goods and services, essential for climate change adaptation and mitigation, biodiversity loss, increase food and water security, land degradation as well as protecting biodiversity and ecosystem services vital to all forms of life on our planet and to ensure human well-being¹². The drivers of land degradation are predominantly local, so actions to address them should be based on the understanding of the local interplay of various factors and how they affect land degradation. The issue of land degradation is recognized as a global concern, but the approaches to addressing it have been inadequate and fragmented. Despite the agreed goal of halting land degradation, land degradation is projected to increase in the twenty-first century under all development scenarios¹³.

Desertification can be considered as a particular type of land degradation in drylands for which land productivity is decreasing or even lost caused by either natural (e.g., climate change) or anthropogenic (e.g., overexploitation of soil) processes¹⁴. Among the areas under desertification, the Sahel, the biogeographic region in Africa between the Sahara in the north and the Sudanian savanna in the south, is amongst the most severely affected region in the World. Long-term climate trends show increasing temperatures, declining precipitations, and higher frequency of extreme climate events (e.g., droughts)¹⁵. Altogether, these have severely impacted local communities as well as agro-sylvo-pastoral landscapes leading to important environmental imbalances and major land degradation issues ¹⁶. This is a major driver of populations’ vulnerability seriously affecting many ecosystem services that are critical to everyone’ s wellbeing and livelihoods and putting them among the most severely affected people to climate change, bringing rural depopulation and poverty¹⁷.

To reverse this trend, 11 Sahelian countries (Burkina Faso, Chad, Djibouti, Eritrea, Ethiopia, Mali, Mauritania, Niger, Nigeria, Senegal, Sudan) have established in 2007 the Pan-Africa Initiative for the Great Green Wall (GGW) with the objective to establish a land-restoration program to attenuate the effects of desertification and land degradation as well as reducing poverty and improving livelihoods of local population¹⁸. The GGW is a coordinated and integrated effort through tree-based development initiatives to green and reforest a strip of land of 15 km wide and 7100 km long from West (Senegal) to the East (Djibouti)(figure 1) with the ultimate objectives to halt the desert’s advance, regulate the temperature along the GGW path, reduce wind speed and soil erosion as well as increasing the humidity of the local microclimate/small water cycle¹⁹ and addressing policy, financial and institutional barriers to developed effective adaptation strategies to fight climate change and land degradation²⁰.

In its latest assessment of the GGW implementation, the United Nations Convention to Combat Desertification (UNCCD) revealed that nearly 4 million hectares of land have been restored, more than 350,000 jobs have been created and approximately $90 million in revenue has been generated from 2007 to 2018 through GGW activities²¹. The report also indicates that land restoration has had a positive impact on 15 of the 17 SDGs. However, UNCCD was calling to renew financial support and accelerate the GGW implementation to reach ambitious objectives defined for 2030: restauration of 100 million Ha of degraded land; sequestration of 250 million tons of carbon; and create 10 million jobs in rural areas. Despite these initial positive outcomes, the project has struggled to reach its key objectives with less than one-fifth of the designated land are have been restored or rehabilitated²². Moreover, UNCCD reports that among the key identified issues, monitoring, reporting, verification (MRV) is a major bottleneck to the implementation of the GGW initiative.

To tackle this issue, Earth Observations (EO) continuously acquired by satellites can be a reliable mean to enhance quality, coverage, and availability of information on remote areas and can complement in-situ/sensors-acquired information on the ground²³. It can provide the necessary baseline to assess present status/conditions and determine trends through detailed, synoptic, regular, consistent, spatially explicit, multi-spectral observations to track environmental changes from local to global scales²⁴. To our knowledge no comprehensive study on the entire GGW path has been done to monitor and assess the evolution of vegetation using remotely sensed data. Sacande et al.²⁵ used satellite data in some western Sahelian countries (Burkina Faso, Niger, Nigeria, Senegal) over a two years period (2018-2020) to monitor changes in biomass and long-term impact of restoration interventions. Wu et al.²⁶ used also remote sensing data to investigate spatio-temporal changes of vegetation and their driving factors in the Sahelian desert/grassland biome transition zone. The study area partially overlaps in some parts the GGW and the time frame covers the period 1982-2015. Similarly, the work done by Shucknecht et al.²⁷ explored the relationship of satellite-derived precipitation estimates and vegetation index and compute trends on Senegal for the period 1981-2014.

Consequently, the aim of this study is to (1) assess if the project implementation results in a significant vegetation cover increase and (2) investigate the possible effects of the GGW on precipitations patterns. We used a time-series of more than 20 years, over two periods (pre-GGW, 1999-2006; post-GGW 2007-2020) of Landsat imagery²⁸ to evaluate the impact of project interventions on above-ground biomass. We used a biophysical parameter, the Normalized Difference Vegetation Index (NDVI)²⁹, as a proxy for vegetation biomass, to subsequently characterize temporal and spatial variations and trends over time along the entire GGW path, providing a quantitative investigation of changes required to monitor and report on the implementation status of the GGW.

We computed for each year, from 1999 to 2020, the annual mean NDVI (to smooth the inter-annual variations) for each country and then calculated various metrics to assess changes along the entire GGW path. To distinguish vegetation trends induced by project interventions, we separated the pre-GGW period (1999–2006) and the post-GGW period (2007–2020) to possibly characterize changes in vegetation biomass before and after the establishment of the GGW initiative. We also looked at the possible effects of project interventions on general precipitation trends in the region to identify changes as this is one of the expected positive impacts of the GGW^30,31.

Ndvi Regimes - Shifts To Higher Values

In Fig. 2, we compared three years (2007-2014-2020) over the entire period post-GGW. 2007 corresponds to the year of establishment of the GGW initiative; 2014 is in the middle of the considered period; finally, 2020 coincides with the last year of observations. We detected that the number of pixels gradually moved towards greater NDVI values. The lower values (i.e., < 0.125) tend to reduce while the higher values (i.e., ≥ 0.25) become more frequent. We can observe that values of the interval 0.125 to 0.188 are the most abundant over the entire study period. In 2007, the highest number of pixels occurs in the 0.125–0.188 and 0.188–0.250 classes. In 2014, extreme values are decreasing whereas middle classes (i.e., 0.125–0.188 / 0.188–0.250) are increasing. Finally, in 2020, this shift towards higher values continues with a noticeable increase in the number of pixels in the 0.188–0.250 and the 0.250–0.313 classes, to the detriment of the 0.125–0.188 class. Such identified pattern can be interpreted as a visible sign of greening conditions along the GGW path and therefore an increase in vegetation biomass.

Trends In Ndvi - Comparing Pre-ggw (1999–2006) Vs. Post-ggw (2007–2020)

NDVI regime changed significantly during the period 2007 to 2020. However, trends in NDVI show slight variations in each country. We considered Ethiopia as a good example of the general pattern identified in the trends (Fig. 3). All other countries trends are provided as supplementary material (figures S1 to S10). Commonly, each country shows a decline of NDVI values in the pre-GGW period while in the post-GGW period they all show an increase. For each country, the X-axis corresponds to date and the Y-axis represents NDVI values. A liner regression line is superimposed on each produced graph together with the R² value. We can notice that only Djibouti has a linear regression line which increases for the period 1999–2006 (figure S3). For all other countries, during the same period, the average NDVI value tend to decrease. In the post-GGW period, all countries, have a linear regression line that increases.

Such time-series of NDVI allow to monitor the evolution of vegetation biomass and possibly verify the impacts of restoration interventions on the ground. Positive significant NDVI trends may reveal potential improvement in land conditions whereas negative significant trends may indicate potential degradation. We therefore computed a significance test (Table 1) for each country and both periods. In the pre-GGW, only Sudan and Djibouti show significant negative trends while for the post-GGW period all countries except Senegal and Mauritania show significant positive trends. Significative negative trends may indicate degrading land conditions whereas significative positive trends indicate improving vegetation conditions.

Table 1

Significance of the pre- and post-GGW NDVI trends per country. Significant values are in bold.
Countries	GGW 1999–2006	GGW 2007–2020
Burkina Faso	0.3752	0.0059
Chad	0.2029	2.877x10^− 5
Djibouti	0.0310	0.0044
Eritrea	0.4589	7.174x10^− 7
Ethiopia	0.1441	0.0005
Mali	0.3891	0.0015
Mauritania	0.7265	0.1146
Niger	0.5386	3.516x10^− 13
Nigeria	0.1233	9.288x10^− 9
Senegal	0.2656	0.2564
Soudan	0.0404	2.2x10^− 16

Ndvi Differences

To identify countries that have revealed major changes in vegetation biomass, we computed the difference of NDVI between the last year of the pre-GGW period (i.e., 2006) and the last year of the post-GGW period (i.e., 2020) such as the example for Senegal (Fig. 4). We can observe substantial differences across the country with some hotspots showing a positive vegetation development (i.e., gain coded light to dark green) as well as negative (i.e., light to dark orange) experiencing a loss of vegetation. Even if the GGW doesn’t cross the greenest regions of the country, it shows some light changes (i.e., light green).

Mean values along the GGW path for the years 2006 and 2020 have been calculated for each country and then differences have been determined and expressed in percentage. We can notice that all countries except Mali have a positive difference meaning that vegetation biomass is increasing. Soudan, Chad, Ethiopia, and Djibouti are countries that undergo major positive changes. This observation is in line with what is reported by UNCCD²¹. The differences between 2006 and 2020 are significative with

a Wilcoxon signed rank test whose value W, 2, is lower than the value, 13, of the table of critical values of the Wilcoxon signed rank test for an alpha value of 0.05.

Table 2

NDVI differences 2006–2020 per country
Countries	NDVI mean − 2006	NDVI mean − 2020	Difference	Difference [%]
Burkina Faso	0.17	0.21	0.04	23.5
Chad	0.17	0.22	0.05	29.4
Djibouti	0.11	0.14	0.03	27.3
Eritrea	0.20	0.24	0.04	20.0
Ethiopia	0.23	0.29	0.06	26.0
Mali	0.18	0.15	-0.03	-16.7
Mauritania	0.19	0.24	0.05	26.3
Niger	0.17	0.21	0.04	23.5
Nigeria	0.22	0.27	0.05	22.7
Senegal	0.20	0.23	0.03	15.0
Soudan	0.16	0.22	0.06	37.5
Mean	0.18	0.22	0.04	22.2

Trends In Precipitation

One of the expected effects of the increasing vegetation biomass, to halt the desert’s advance, is the increase of precipitation³². We used time-series of precipitation data to characterize precipitation trends in the region over the same two periods along the GGW path (figures S11 to S21, supplementary material). The results indicate, for all countries, that in the pre-GGW period precipitation trends were almost stable or exhibiting a small decrease though precipitations seem to increase in the post-GGW period. This pattern agrees with other analyses of climate data on the study area^27,33.

However, no significant precipitation trend could be detected with all countries, except Djibouti, showing non-significant p-values over the two periods (Table 3).

Table 3

Significance of the pre- and post-GGW precipitation trends per country. Significant values are in bold.
Countries	GGW 1999–2006	GGW 2007–2020
Burkina Faso	0.5457	0.09264
Chad	0.8622	0.06267
Djibouti	0.2392	1.589x10-⁰⁶
Eritrea	0.888	0.2755
Ethiopia	0.9405	0.3624
Mali	0.7783	0.4838
Mauritania	0.3386	0.5371
Niger	0.7366	0.0687
Nigeria	0.6218	0.08394
Senegal	0.9836	0.6402
Soudan	0.8603	0.06088

Like the NDVI trends, we also computed for each country mean precipitation values along the GGW path for the years 2006 and 2020 (Table 4). Then differences have been determined and expressed in percentage. We can observe that all countries have a positive difference meaning that precipitation are increasing. Nevertheless, the differences are considerably different from one country to another. This reflects the fact that rainfall is a complex meteorological phenomenon with high spatio-temporal variability. Mauritania, Senegal, Burkina Faso, and Djibouti are countries that undergo major positive changes. The mean increase for the GGW is approximately 27% which agrees with what is reported by recent climate models^20,34. Differences between 2006 and 2020 are significative with a Wilcoxon signed rank test value W, 5, is lower than the value, 13, of the table of critical values of the Wilcoxon signed rank test for an alpha value of 0.05.

Table 4

Precipitation differences 2006–2020 per country
Countries	Precip. mean – 2006 [mm]	Precip. mean – 2020 [mm]	Difference [mm]	Difference [%]
Burkina Faso	5.91	8.69	2.78	47.0
Chad	4.90	5.56	0.66	13.5
Djibouti	2.69	3.78	1.09	40.5
Eritrea	6.99	7.32	0.33	4.7
Ethiopia	8.78	8.81	0.03	0.3
Mali	6.07	8.11	2.04	33.6
Mauritania	3.75	7.77	4.02	107.2
Niger	6.35	8.72	2.37	37.3
Nigeria	7.92	9.26	1.34	16.9
Senegal	4.86	7.30	2.44	50.2
Soudan	5.29	5.49	0.2	3.8
Mean	5.77	7.35	1.58	27.4

Overall, these results can be considered as a first comprehensive overview about vegetation changes and possible influence on precipitation patterns over the entire GGW course using more than 20 years of consistent calibrated satellite observations. While large-scale restoration interventions certainly explain part of the observed positive trends of NDVI, it is likely that changes in micro- and meso-climates also contributes to this increase of vegetation biomass. Additional analysis would be required to further assess local changes and possible drivers^35,36. Our results are conforming with the one-fifth ratio determined by UNCCD on designated land restored or rehabilitated. They also confirm recent studies that detected similar patterns but limited to specific countries of the GGW^25,27 or in other parts of the Sahel²⁶. Such positive pattern can be an inspiring example of ecosystem restoration in action, especially with the recent launch of the United Nations decade on Ecosystem Restoration (https://www.decadeonrestoration.org). Our results suggest that along most of the GGW, vegetation is growing that can possibly then help ecosystems to become more healthier, improving livelihood and well-being³³, food security³⁷, ending poverty³², combating climate change and biodiversity loss, and planting the seeds of peace.

The GGW is greening. Our results indicate that over the entire GGW a mean increase of approximately 22% of vegetation biomass has been observed over a 13-year period. The observed shift toward higher NDVI values gives a good indication of the general trend over the study area. Moreover, the two most frequent classes (i.e., 0.125–0.188 and 0.188–0.250) provide relevant information on the plausible types of vegetations. Low values of NDVI in (semi-)arid environments indicate the presence of grasslands or shrubs, and more generally corresponds to an absence of canopy cover³⁸. This type of vegetation is typical of the Sahelian region. The increase over the years in the number of pixels in the higher values may indicate a certain densification of the vegetation that can be attributed to either to a spatial increase of grassland or shrubs or a change in vegetation type^29,35. To distinguish between these two-options, field data are necessary. Indeed, it is important to understand if this observed increase in vegetation biomass can be attributed the restauration interventions of the GGW initiative or if this might be caused by other reasons such as changes in climatic conditions. The information obtained with NDVI trends likely reveal that the establishment of the GGW had a significant positive impact on the greening of most of this zone. It would also be interesting to know if the greening is due to an increase in agricultural activity in the region rather than other types of restauration interventions. However, according to the article by Martin Brandt, in Senegal, it seems that it is rather a development of the forest cover³⁹.These observations are also in line with a recent study reported an unexpectedly large number of isolated trees in West Sahara and Sahel⁴⁰.

Noticeable differences between countries. Even though at regional scale, the GGW is greening, relatively substantial differences between countries can be observed. The time-series indicate that all countries, except Senegal and Mauritania, have significant positive NDVI trends. However, this observed greening is evolving at different rates in countries. The slopes of computed trends are low (e.g., Mali) while for other is greater (e.g., Nigeria). Overall, these significant positive trends are encouraging when compared with those from the period pre-GGW that evidenced a decrease of mean NDVI values for all countries, except Djibouti. Despite these differences, it seems that all national authorities have adopted restauration measures to halt desertification. This also tends to demonstrate the necessity to act locally to mitigate regional effects of land degradation. This disparity between countries can be also observed with the differences of mean NDVI values between years 2006 and 2020. Except Mali, all countries have a mean NDVI value that is higher than 2006. This corroborates the previous findings from trends and histograms. However, when we look at the time series of Mali, we see that its growth curve does indeed increase. This can be explained by the fact that there may be annual fluctuations. The fact that Mali has its part of the GGW with a lower value in 2020 than in 2006 may be due to a bad weather year for example, although this hypothesis is unlikely given the significant values of Mauritania which found nearby. We can also notice that, globally, the western countries of the GGW (Senegal, Mauritania, Mali, Burkina Faso, Niger) have lower than average 2006–2020 differences while the eastern countries of the GGW (Ethiopia, Sudan, Chad, ...) have greater differences. Only Djibouti has values close to those of Western countries. It would be interesting to see if this tendency for the East to have larger difference values than the West persists over time and what is the reason for this. We can imagine, for example, that this East-West difference may be due to milder weather in the East than in the West for this year, or that the Eastern countries are investing more in the re-greening of the GGW than the western countries, but it would be necessary to analyze the finances of the GGW project to answer this. There are also all the social, political, and geopolitical factors that should be considered. This could be the subject of future research to determine which factors interact with the GGW project and how.

Precipitation trends seems to increase but not significantly. The observed precipitation trends seem to increase in all countries but none of them can be considered as a significant change. Indeed, to differentiate between natural short-term fluctuations and long-term changes, it is recommended to have time-series of at least 25 years⁴¹. The GGW initiative is only 15 years old and consequently, it is not yet possible to determine the long-term effects on local climate. However, these positive trends can possibly be interpreted as an early sign of positive impacts that need to be monitored carefully in the coming years. The computed differences between the last year of the pre- and post-GGW show a mean increase of nearly 27% but showing important differences between countries. This is explained by the complexity and high spatio-temporal variability of precipitations⁴². It is expected that once completed, the GGW could double rainfall within the Sahel and would also decrease average summer temperatures throughout much of northern Africa, according to recent climate simulations³⁴. It is even expected that it can have profound effects on the West African Monsoon changing the strength and position of the monsoon ^43,44. These authors highlighted the importance to take into consideration the effects of plant cover and dustiness to better model the changes in monsoon. With the observed increase in vegetation biomass along the GGW, this can help creating a local pool of moisture amplifying the amount of water cycling from soil to atmosphere as well as reducing the amount of dust in the air. Finally, one possible effect suggested by these simulations is that the changes of a greener Sahara can extend beyond the region and may shift atmospheric circulation patterns westward resulting in influences on climate patterns such as El Niño Southern Oscillation³⁴.

Satellite EO data is a valuable mean to monitor the evolution of GGW. Our results demonstrate that consistent assessment of restoration interventions in (semi-)arid landscaped on above-ground biomass can be performed, using medium-resolution satellite images. The proposed methodology to monitor vegetation biomass has the advantage to be fully replicable in all countries, and ideally should be complemented with field data. As emphasized by UNCCD, effective monitoring, reporting, and verification mechanisms of long-term impact of restoration measures are crucial to support continuous accounting and payments of interventions²¹. Such approach is a good complementary source of information to usual data sources (e.g., in-situ measurements, interviews of local population). The increasing availability of high-resolution satellite data (e.g., Copernicus Sentinels) combined with recent advancements and emergence of efficient analytical tools are increasing the potential of monitoring methods based on EO data and stimulating countries to adopt them to track progresses towards national and international development agendas ⁴⁵. In that sense, the development of Digital Earth Africa (DEA - https://www.digitalearthafrica.org) may represent a valuable opportunity for support the monitoring and reporting on the GGW. DEA is a continental-scale EO data cube initiative that is aiming to provide access to petabyte of satellite imagery to deliver decision-ready satellite-based information products to help African countries decision-makers to address sustainable development challenges. Therefore, with DEA, methods used in this study, can be easily applied, improved, and operationalized to support the monitoring and reporting mandate of each country concerned by the GGW initiative. Finally, due to recognized importance of land degradation issues in the SDG¹⁵, and the specific methods proposed by UNCCD to monitor the related indicator SDG15.3.1 using satellite EO data^46,47, DEA can also be a valid resource to report on this specific indicator^11,48 along the GGW path potentially improving the knowledge⁴⁹ on environmental restoration in this severely affected region of the World.

Satellite data. The satellite data time-series covers a period from 1999 to 2020 and comes from Landsat 7 and 8 that are sun-synchronous satellites jointly operated by the United States Geological Survey (USGS) and National Aeronautics and Space Administration (NASA). They are essentially designed for land applications. Landsat 7 carried the Enhanced Thematic Mapper (ETM+) sensor where Landsat 8 has the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). A detailed description of these satellites can be found on the Committee on Earth Observation Satellites (CEOS) EO Handbook Database: http://database.eohandbook.com

Since 2008, Landsat data are provided under a free and open access license. This has enabled dense time-series analysis and improving environmental changes monitoring capabilities. Data are downloaded as Collection 2/Tier 2 – Level 2 Surface Reflectance encompassing a surface of approximately 185 km by 180 km. Collection 2/Tier 2 scenes are data with the highest available data quality (e.g., geometric and radiometric corrections) and considered suitable for time-series analysis (https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-2). Level 2 corresponds to surface reflectance (i.e., the estimate based on Landsat sensor observations of the fraction of incoming solar radiation reflected from Earth’s surface). These data are corrected for atmospheric perturbations (e.g., aerosol scattering, thin clouds) enabling direct comparison between multiple images and dates. This corresponds to the CEOS Analysis Ready Data (ARD) level. Two different models are applied to generate these ARD products. Landsat 7 are corrected with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm whereas Landsat 8 OLI applies the Land Surface Reflectance Code (LaSRC) algorithm. Both models use auxiliary climate data from Moderate Resolution Imaging Spectroradiometer (MODIS) and a radiative transfer model to evaluate atmospheric conditions over a given scene (https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance).

Landsat data are provided by USGS with Quality Assessment (QA) information to help users to determine their suitability for specific applications. An 8-bit LandsatLook Quality Image and 16-bit Quality Assessment Band (pixel_qa) are also included. Values are bit-packed and provide information pertaining to a pixel condition of fill, clear, water, cloud shadow, snow, cloud (yes/no), cloud confidence and cirrus cloud confidence (Landsat 8 only). Details on each file are described at: https://www.usgs.gov/landsat-missions/landsat-collection-2-quality-assessment-bands.

Landsat data are selected to cover two distinct periods that correspond to the before/after implementation of the GGW initiative. The first period ranges from 01.01.1999 to 31.12.2006 while the second covers the 01.01.2007 to 31.12.2020 timeframe. Only images located on the GGW path and with less than 30 percent cloud cover were selected. The selected threshold for cloud cover is a trade-off between having enough data and having usable (i.e., cloud-free) data. If clouds are still present on the image, we filtered them using the “pixel_qa” band to remove all pixels that are considered as clouds or clouds shadows (i.e., bits 3,5,6,7). This ultimately to ensure that all images can be considered as clean before the computation of NDVI and will not negatively influence the generated outputs.

Precipitation data. Climate data on precipitation are provided by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) that is a 40 years (1981–2022) global dataset on rainfall. CHIRPS combines 0.05° resolution satellite imagery with in-situ station measurements to generate a gridded rainfall time-series used for trend analysis. Detailed information on this dataset can be found in a Nature Scientific Data paper⁵⁰.

Country borders and GGW path. In absence of an official data repository to obtain the entire GGW path, we digitalized the course of the Great Green Wall using a shapefile format file given by the National Agency of the Great Green Wall of Senegal (ANGMVS). For the rest of the GGW, it was carried out according to the delimitation presented in Cheikh Mbow's article¹⁶ while keeping the same width as the ANGMVS shapefile. The path can be seen on Fig. 1.

The country borders are obtained from the Large Scale International Boundary (LSIB) dataset (https://geonode.state.gov/layers/catalog:geonode:LSIB) produced by the Office of the Geographer and Global Issues at the U.S. Department of State and directly available in the Google Earth Engine.

Data processing. Data are accessed and processed on the Google Earth Engine (GEE) (https://earthengine.google.com), a cloud-based processing platform that allows easy access to high-performance computing resources for processing large volumes of EO data⁵¹. The analysis is performed using the JavaScript Application Programming Interface (API) through a web-sabed graphical user interface on: https://code.earthengine.google.com. Data are access methods are described at: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T2_L2 for Landsat 7; https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 for Landsat 8; https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD for CHIRPS data; and https://developers.google.com/earth-engine/datasets/catalog/USDOS_LSIB_2017 for LSIB data. The GGW shapefile has been directly uploaded.

NDVI computation. The Normalized Difference Vegetation Index (NDVI) is a well-accepted and commonly used index for spatial vegetation monitoring at different scales using remote sensing data due to its simplicity to perform rapid assessments on seasonal pattern of vegetation activity or evaluate vegetation dynamics and health at different scales. It is designed to assess the spectral contribution of green plants to multispectral observations and is directly related to photosynthesis informing on vegetation conditions. The NDVI is calculated from Near InfraRed (NIR) and Red (R) spectral reflectance measurements as follows:

NDVI = (NIR – RED)/(NIR + RED) (1)

where NIR corresponds to Band 4 for Landsat 7 and Band 5 for Landsat 8 and RED corresponds to Band 3 respectively 4. NDVI is a measure of the state of vegetation health based on how the vegetation reflects light in the red (i.e., strongly absorbed) and near-infrared (NIR) (i.e., strongly reflected) bands. The index measures values between − 1.0 to 1.0 representing roughly vegetation for positive values; clouds-water-snow for negative values; and bare soils and rocks for values close to zero. When vegetation is affected by dehydratation or becomes sick usually the colors turn from green to brown. This change is particularly significant in NIR light that is increasingly absorbed when vegetation deteriorates. Therefore, NDVI can provide accurate information on the presence of chlorophyll and can be correlated with plant health.

NDVI regimes are determined by generating annual and stacked histograms using computed NDVI annual means for 1999 to 2020 for each of the 11 countries along the GGW path. Then the values are summed up to obtain yearly histograms per country to have a first overview of the evolution of this index over time. We also make a stacked histogram of the years 2007-2014-2020 to show the evolution of the NDVI values on the entire GGW path over time. This graph is plotted in Excel with the values of the histograms of 2007,2014 and 2020 obtained in GEE (Fig. 2).

Trends in NDVI are generated using the produced annual means NDVI time-series to investigate how vegetation has evolved in each country of the GGW path over the 1999–2020 period. The purpose of this analysis is to compare the 11 countries and possibly identify the differences of interventions and their effects of vegetation assuming that NDVI changes/greening can be a valuable proxy for biomass increase. Then, results are separated into two groups of time series. One which goes from 01.01.1999 to 31.12.2006, and the second from 01.01.2007 to 31.12.2020. If we separated these two periods it is to see the influence of the creation of the GGW on the Sahel and on the region crossed. As the GGW project started in 2007, it makes sense to have a linear regression line of the mean NDVI value before the start of the GGW (1999–2006) and a linear regression line after the start of this project (2007–2020). Unfortunately, we were not able to take the data before 1999. Indeed, there was too much missing data on this region of Africa, which did not allow for good coverage of satellite images, with Landsat 5 or Landsat 7. Linear models are then created in the R software based on the time series to find, or not, the significance of the results. To make these linear models, it was necessary to download the results of the time series in Excel, then “layout” these data to be able to exploit them in R. All the linear models are in the form of: lm(Date ~ NDVIvalue), or the linear model of the time variable (in X) as a function of the NDVI variable (in Y). The software returns a p-value associated with the F-test performed. If the p-value is lower than the threshold 0.05, then we reject the null hypothesis and we admit that the result of the time series is significant.

NDVI differences are produced to explore the differences in NDVI value between 2006, before the launch of the GMV, and 2020, the most recent year, to highlight the effect of the GGW. In addition, maps were produced to indicate in which areas there were changes, both positive and negative, in the average NDVI value per year between these two years, by country. NDVI values for each pixel of 2006 are subtracted to those of 2020; negative values indicate a decrease of NDVI while positive value indicates an increase of vegetation biomass. Zonal statistics are then computed to obtain the difference (expressed in percentage) only the GGW path of a given country (Table 2). A Shapiro-Wilk test and a quantile-quantile plot are used to determine if the data is normally distributed. Finally, a Paired Samples t-Test is conducted to ensure that the values are significantly different between the two years considered.

Trends in precipitation. The same methodology used to determine trends in NDVI has been applied to CHIRPS data over the two selected periods.

Acknowledgments

We would like to thank the University of Geneva and the Amadou Mahtar Mbow for supporting us in this research. We also would like to thank the Google Earth Engine team that gave us the opportunity to efficiently process large amount of satellite data.

The views expressed in the paper are those of the authors and do not necessarily reflect the views of the institutions they belong to.

Authors contribution

L.D. carried out the analyses with contributions from G.G. and B.S. G.G, L.D. and B.S. wrote the article. All authors contributed ideas for analyses, comments, and critiques on drafts.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Code availability

The code is freely available on GitHub: https://github.com/ggiuliani/ggw under an Apache 2.0 license.

Competing interests

The authors declare no competing interests.

IPBES. Summary for policymakers of the thematic assessment of land degradation and restoration. https://www.ipbes.net (2018).
IPCC. Climate Change and Land - Summary for Policy Makers. 43 (2019).
Gaub, F. Global trends to 2030: challenges and choices for Europe. (European Strategy and Policy Analysis System, 2019).
Metternicht, G., Akhtar-Schuster, M. & Castillo, V. Implementing land degradation neutrality: From policy challenges to policy opportunities for national sustainable development. Environmental Science & Policy 100, 189–191 (2019).
Chasek, P. et al. Land degradation neutrality: The science-policy interface from the UNCCD to national implementation. Environmental Science & Policy 92, 182–190 (2019).
Sims, N. C., Barger, N. N., Metternicht, G. I. & England, J. R. A land degradation interpretation matrix for reporting on UN SDG indicator 15.3.1 and land degradation neutrality. Environmental Science & Policy 114, 1–6 (2020).
Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat Ecol Evol 5, 411–418 (2021).
Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat Sustain 2, 1083–1093 (2019).
Nicholson, E. et al. Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework. Nat Ecol Evol 5, 1338–1349 (2021).
Sims, N. C. et al. Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals. Environmental Science & Policy 92, 349–355 (2019).
Gonzalez-Roglich, M. et al. Synergizing global tools to monitor progress towards land degradation neutrality: Trends.Earth and the World Overview of Conservation Approaches and Technologies sustainable land management database. Environmental Science & Policy 93, 34–42 (2019).
Johnson, J. A. et al. Mapping Ecosystem Services to Human Well-being: a toolkit to support integrated landscape management for the SDGs. Ecological Applications 0, e01985 (2019).
UNCCD. Global Land Outlook: Land Restoration for Recovery and Resilience. 204 (2022).
Cherlet, M. et al. World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management. (Publications Office of the European Union, 2018).
Goffner, D., Sinare, H. & Gordon, L. J. The Great Green Wall for the Sahara and the Sahel Initiative as an opportunity to enhance resilience in Sahelian landscapes and livelihoods. Reg Environ Change 19, 1417–1428 (2019).
Mbow, C. The Great Green Wall in the Sahel. Oxford Research Encyclopedia of Climate Science https://oxfordre.com/climatescience/view/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-559 (2017) doi:10.1093/acrefore/9780190228620.013.559.
Barbier, E. B. & Hochard, J. P. Land degradation and poverty. Nat Sustain 1, 623–631 (2018).
Gadzama, N. M. Attenuation of the effects of desertification through sustainable development of Great Green Wall in the Sahel of Africa. World Journal of Science, Technology and Sustainable Development 14, 279–289 (2017).
O’Connor, D. & Ford, J. Increasing the Effectiveness of the “Great Green Wall” as an Adaptation to the Effects of Climate Change and Desertification in the Sahel. Sustainability 6, 7142–7154 (2014).
Jones, K. & Rosenstock, T. Can the Great Green Wall deliver more than trees? A rapid review of the potential multidimensional impacts of trees in the Sahel. (2021).
UNCCD. The Great Green Wall Implementation Status and Way Ahead to 2030. 68 (2020).
Get Africa’s Great Green Wall back on track. Nature 587, 8–8 (2020).
McKenna, P. B., Lechner, A. M., Hernandez Santin, L., Phinn, S. & Erskine, P. D. Measuring and monitoring restored ecosystems: can remote sensing be applied to the ecological recovery wheel to inform restoration success? Restoration Ecology <bvertical-align:super;>n</bvertical-align:super;><bvertical-align:super;>/</bvertical-align:super;><bvertical-align:super;>a</bvertical-align:super;>, e13724.
Zhu, Z. et al. Benefits of the free and open Landsat data policy. Remote Sensing of Environment 224, 382–385 (2019).
Sacande, M., Martucci, A. & Vollrath, A. Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel. Remote Sensing 13, 3767 (2021).
Wu, S., Gao, X., Lei, J., Zhou, N. & Wang, Y. Spatial and Temporal Changes in the Normalized Difference Vegetation Index and Their Driving Factors in the Desert/Grassland Biome Transition Zone of the Sahel Region of Africa. Remote Sensing 12, 4119 (2020).
Schucknecht, A., Meroni, M. & Rembold, F. Monitoring project impact on biomass increase in the context of the Great Green Wall for the Sahara and Sahel Initiative in Senegal. European Commission Joint Research Centre, Ispra, Italy (2016).
Wulder, M. A. et al. Current status of Landsat program, science, and applications. Remote Sensing of Environment 225, 127–147 (2019).
Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution 20, 503–510 (2005).
Nzabarinda, V. et al. A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa. ISPRS International Journal of Geo-Information 10, 129 (2021).
Odoulami, R. C., Abiodun, B. J. & Ajayi, A. E. Modelling the potential impacts of afforestation on extreme precipitation over West Africa. Clim Dyn 52, 2185–2198 (2019).
Turner, M. D. et al. Environmental rehabilitation and the vulnerability of the poor: The case of the Great Green Wall. Land Use Policy 111, 105750 (2021).
Sacande, M. et al. Socio-economic impacts derived from large scale restoration in three Great Green Wall countries. Journal of Rural Studies 87, 160–168 (2021).
Pausata, F. S. R. et al. The Greening of the Sahara: Past Changes and Future Implications. One Earth 2, 235–250 (2020).
Yang, Y. et al. Factors Affecting Long-Term Trends in Global NDVI. Forests 10, 372 (2019).
Huang, S., Tang, L., Hupy, J. P., Wang, Y. & Shao, G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. (2020) doi:10.1007/s11676-020-01155-1.
Mirzabaev, A., Sacande, M., Motlagh, F., Shyrokaya, A. & Martucci, A. Economic efficiency and targeting of the African Great Green Wall. Nat Sustain 5, 17–25 (2022).
Mayaux, P., Bartholomé, E., Fritz, S. & Belward, A. A new land-cover map of Africa for the year 2000. Journal of Biogeography 31, 861–877 (2004).
Brandt, M. et al. Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Global Change Biology 21, 1610–1620 (2015).
Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).
Caloiero, T., Caloiero, P. & Frustaci, F. Long-term precipitation trend analysis in Europe and in the Mediterranean basin. Water and Environment Journal 32, 433–445 (2018).
Lausier, A. M. & Jain, S. Overlooked Trends in Observed Global Annual Precipitation Reveal Underestimated Risks. Sci Rep 8, 16746 (2018).
On the potential impact of the Great Green Wall on the West African Monsoon https://meetingorganizer.copernicus.org/EGU22/EGU22-2891.html (2022) doi:10.5194/egusphere-egu22-2891.
Niang, C. et al. Transport pathways across the West African Monsoon as revealed by Lagrangian Coherent Structures. Sci Rep 10, 12543 (2020).
Dhu, T. et al. National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices. Data 4, 144 (2019).
UNCCD. Good Practice Guidance - SDG Indicator 15.3.1. 115 https://www.unccd.int/sites/default/files/relevant-links/2017-10/Good%20Practice%20Guidance_SDG%20Indicator%2015.3.1_Version%201.0.pdf (2017).
Markos, A., Sims, N. & Giuliani, G. Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production. Big Earth Data 0, 1–22 (2022).
Giuliani, G. et al. Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube. Big Earth Data 4, 1–20 (2020).
Giuliani, G. et al. Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation. International Journal of Applied Earth Observation and Geoinformation 88, 102068 (2020).
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2, 150066 (2015).
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18–27 (2017).

No competing interests reported.

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Is the African Great Green Wall greening? Some positive signs from a 20-years’ time-series of satellite observations

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Abstract

Figures

Introduction

Results

Ndvi Regimes - Shifts To Higher Values

Trends In Ndvi - Comparing Pre-ggw (1999–2006) Vs. Post-ggw (2007–2020)

Ndvi Differences

Trends In Precipitation

Discussion

Methods

Declarations

References

Additional Declarations

Supplementary Files

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