Open AccessArticle

Scenario Analysis of Shorelines, Coastal Erosion, and Land Use/Land Cover Changes and Their Implication for Climate Migration in East and West Africa

Oye Ideki

and

Osinachi Ajoku

Atmospheric Sciences Program, College of Arts and Science, Howard University, Washington, DC 20059, USA

Author to whom correspondence should be addressed.

J. Mar. Sci. Eng. 2024, 12(7), 1081; https://doi.org/10.3390/jmse12071081

Submission received: 16 May 2024 / Revised: 13 June 2024 / Accepted: 19 June 2024 / Published: 26 June 2024

(This article belongs to the Section Coastal Engineering)

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Figure 1
Map of Africa showing the study area: Senegal, Kenya and Tanzania. "> Figure 2
Erosion along the shoreline in Senegal, Tanzania, and Kenya. "> Figure 3
Accretion along the shoreline in Senegal, Tanzania, and Kenya. "> Figure 4
Net change trend of land loss (sqkm). "> Figure 5
Shoreline changes along Senegal’s coast from 1986 to 2022. "> Figure 6
A section of the coastline of Senegal showing the shorelines of different years. "> Figure 7
Shoreline changes along Kenya’s coast from 1986 to 2022. "> Figure 8
A section of the coastline of Tanzania showing the shorelines of different years. "> Figure 9
Shoreline scenario modeling on land use/land cover in Senegal. "> Figure 10
Land use/land cover change: (a) 1986, (b) 2006, (c) 2016, and (d) 2022 in Senegal. "> Figure 11
Graphical representation of land cover composition and change from 1986 to 2022. "> Figure 12
Land use/land cover changes in Kenya: 1986 (a) 1986, (b) 2006, (c) 2016, and (d) 2022. "> Figure 13
Composition of land use/land cover in Tanzania. "> Figure 14
Land use/land cover change analysis in Tanzania: (a) 1986, (b) 2006, (c) 2016, and (d) 2022. "> Figure 14 Cont.
Land use/land cover change analysis in Tanzania: (a) 1986, (b) 2006, (c) 2016, and (d) 2022. "> Figure 15
Shoreline scenario modeling on land use/land cover. "> Figure 16
Communities and population displacement at 10 m shoreline shift in Senegal. "> Figure 17
Communities and population displacement at 20 m shoreline shift in Senegal. "> Figure 18
Communities and population displacement at 30 m shoreline shift in Senegal. "> Figure 19
Shoreline changes on land use/land cover. "> Figure 20
Scenario analysis of Shoreline shift on land use in Tanzania. "> Figure 21
Effect of shoreline scenarios on population density (sq/km) in Kenya. "> Figure 22
Survey on major drivers of environmental migration. "> Figure 23
Degree of exposure to coastal erosion. "> Figure 24
Degree of impact of natural hazards. "> Figure 25
Gender dimensions of migration. "> Figure 26
Reflectiveness as a coping strategy. "> Figure 27
Changing jobs as a coping strategy. "> Figure 28
Protection of the shoreline/building of sea walls. "> Figure 29
Sharing and bearing as a coping strategy. ">

Versions Notes

Abstract

Climate change-induced sea level rise, shoreline changes, and coastal erosion are projected to drive massive population displacement and mobility in Africa. This study was conducted to examine the pattern of shoreline changes, coastal erosion, land use/land cover dynamics, projections, and their implications on internal migration in Senegal, Kenya, and Tanzania, representing West and East Africa. The digitized shoreline was mapped into erosion, accretion, and trend analysis, which further explains the vulnerability and physical processes that could trigger human displacement within the context of environmental/climate migration. Analysis of land use and land cover dynamics was obtained from Landsat 5 TM of 1986, Landsat 7 ET of 2006, Landsat 8 OLI/TIRS of 2016, and Landsat 9 OLI/TIRS of 2022 and computed using ArcGIS 10.7 for land-use change and percentage change in square kilometers was conducted to examine land use/land cover dynamics and their contributions to the risk of coastal erosion in the study regions. The outcome of the shoreline analysis reveals that 972.03 sqkm of land has been lost to coastal erosion in Senegal from 1986 to 2022 with 2016–2022 described as the period with the highest in terms of land loss. In Kenya, −463.30 sqkm of land has also been lost to coastal erosion and agents of wave processes, with 1986–2006 recording the highest share of −87.74% loss of valuable land, while in Tanzania, −1033.35 sqkm of valuable land has been lost from 1986 to 2022 to coastal erosion, with 2006–2016 alone recording −10.4634% of land loss. The result of the land use/land cover percentage change analysis indicates a massive loss of vegetation cover with a significant increase in settlement representing urbanization. The scenario analysis of the shoreline at 10, 20, and 30 m indicates that 567 persons per sqkm at 10 m, 25,904.6 persons per sqkm at 20 m, and 25,904.5 persons per sqkm will be displaced in Senegal at 30 m. In Kenya, 57,746 persons per sqkm are projected to be displaced at 10 m while 1210.5 persons per sqkm will be displaced at 20 m and 7737.32 persons per sqkm will be displaced at 30 m. In Tanzania, the maximum population density projected to be displaced at 10, 20, and 30 m is 10,260.97 per sqkm. Structured questionnaires were administered to elicit responses from coastal dwellers on their perception of coastal erosion and climate migration as part of ground truthing and the result of the survey affirms that coastal erosion and its exposure are the major drivers of climate migration in the study area.

Keywords:

sea level rise; shoreline changes; coastal erosion; land use/land cover dynamics; population displacement; climate change; climate migration; West and East Africa

1. Introduction

The shoreline often used interchangeably with coastline refers to the boundary line or the interface between the land and water bodies characterized by sandy or rocky beaches [1] The shoreline is also dynamic as it is subject to various changes either gradually or rapidly by seaward or towards the land [2].

Generally, the shorelines in many regions of the world are affected by various coastal processes such as slop, tidal fluctuations, sediment characteristics, wave dynamics, near-shore circulation, and sea level rise [3]. The shoreline is also driven by littoral transport, which accounts for the removal of eroded materials and tidal processes.

Sea level rise has recently emerged as a threat to shoreline and coastline stability globally as it accounts for significant changes observed along the coast over time [4]. It is on record that the mean sea level rose to 11–16 cm during the last 20th century with concomitant impact on shoreline and coastline and further exacerbated coastal erosion along the coast [5].

In Africa, many coastal countries are vulnerable to sea level rise, particularly large growing cities with high population density situated in the coastal zone [6,7]. Climate change-driven sea levels are posing extra risks for coastal ecosystems. Increased frequency and severity of these events including storms and other marine processes are some of the drivers of coastal erosion.

Africa is highly susceptible to sea level rise, and it is expected to experience some of its deleterious impacts. While the global sea level rise averages 3–4 mm, recent sea level rise measurements in Eastern Africa, particularly the Indian Ocean, is now 5 mm per year [8]. The direct impacts of sea level rise include inundation of low-lying areas, shoreline erosion, coastal wetland loss, saltwater intrusion, higher water tables, and higher extreme water levels leading to coastal flooding, which could interact with other factors to trigger human displacement [9].

A recent study conducted by [10] on sea level rise affirmed that sea level rise (SLR) accounts for 66% of the observed rate of erosion in vulnerable regions, thereby resulting in the inundation of low-lying areas, impacting at least 100 million persons globally who live within 1 m of mean sea level [11]. Aside from the physical and natural causes of shoreline changes, human disturbances such as damming, dredging, mining, and urbanization, which are impacting water resources, are responsible for the erosion and accretion rates of shorelines gre [12]. The problem of coastal erosion is common today and results in big economic and environmental losses: for instance, a change in ecosystems occurs due to the sitting of new coastlines [13,14].

The causative mechanism of coastal erosion includes strong wave action, upstream discharge, river bathymetry, global warming, and sea level rise. Furthermore, soil properties such as texture and structure govern pore size distribution and exert an enormous influence on soil loss due to erosion. Studies suggest that soil moisture has a substantial influence on water runoff and soil erosion [15,16].

In addressing the planning challenges associated with coastal regions for sustainable development, land use/land cover studies have emerged as an index for proper quantification of different types of land loss due to coastal erosion, which is crucial for a better understanding of the impacts of shoreline change [17]. Land cover change has also been described as the most significant regional anthropogenic disturbance to the environment. In essence, both land use and land cover changes are products of prevailing interacting natural and anthropogenic processes by human activities [18]. Land use and cover change and land degradation are therefore driven by the same sets of proximate and underlying factors.

In West Africa, coastal areas are home to 31% of the population and provide 56% of the sub-change, and land degradation is therefore driven by the same sets of proximate and underlying factors [19]. Land use and cover change are therefore central to the understanding of environmental processes [20]. It is particularly important for proper planning and implementation of appropriate environmental practices. Land use and land cover analysis using satellite imagery is a cost-effective way to monitor long-term changes. Many different methods are used to classify land use and land cover. The maximum likelihood, random forest, support vector machine, and artificial neural network methods were found to be the most applied methods for LULC analysis [21,22,23]. It is evident that in recent decades, human activity has significantly influenced land use and land cover globally [24]. As a result, it is expected that the coastal ecosystem will experience changes due to the huge number of human footprints in the coastal regions. Given the menace of loss or gain of land resources due to erosion/accretion processes that characterized the coasts of Senegal, Kenya, and Tanzania, it has become imperative to map existing land use and land cover (LULC) and to integrate resulting data products with information on future shoreline change.

Coastal land loss due to sea level rise, shoreline changes, and coastal erosion has the potential to influence the migration decisions of the people living in coastal African regions in search of prosperous livelihoods [25]. According to the review report of [26], climate migration refers to people forced to leave their homes due to sudden environmental/climate changes internally or abroad. Climate-induced migration has increased despite COVID-19 lockdowns, with 14.6 million people displaced due to slow-onset events. Migration and climate change are now at the front burner of international discourse given the intensification of slow-onset climate events. The nexus between climate change and migration is complex. Migration can also be described as a highly personal decision that people arrive at after consideration of a wide range of factors such as economic, environmental, demographic, social, and political factors. Migration itself represents a critical resilience approach against the deleterious impact of climate change. However, most studies have focused on migration from the perspective of a failure to adapt to climate change, while others viewed it as an adaptation strategy itself [27].

In a recent publication released by [28], migration driven by climate- and weather-related hazards was revealed to have reached unprecedented levels, with 24.5 million persons internally displaced attributed to climate-related disasters. The report further added that the intensity and frequency of hazards that trigger human displacement are on the increase, thereby threatening increasing livelihoods and ecosystems, exacerbating existing vulnerabilities, and undermining resilience. Relatedly, another study projects that by 2050, West Africa alone could record 32 million people, while the total number of environmental migrants from the rest of the continent could reach 86 million if no concrete and proactive action is taken [29]. In terms of countries with the highest projected number of internal migrations from climate-related hazards, Senegal ranks the highest in West Africa with 1 million, followed by Niger and Cape Verde with 30.26% and 6.02%, respectively. Others are São Tomé and Príncipe (0.09 percent) and Guinea (0.27 percent), respectively. The contributory factors to Senegal’s vulnerability to environmental migration emanate mainly from increased exposure to sea level rise, high rate of coastal erosion along the coast, and climate extremes such as drought and floods [30].

In Kenya, the coastal city of Mombasa is particularly exposed to coastal erosion and sea level rise, with an estimated area of 4–6 km² at the risk of being submerged with a rise in sea level of only 0.3 m. Furthermore, tourism, shipping, and the agricultural sectors, which constitute 95% of the coastal economy, have continued to experience a decline in revenue projection because of coastal erosion [31,32]. In Tanzania, climate change and rising sea level projection of 1.5 and 3 feet by 2050 could see over 5 million residents of the city and millions of people in other parts of Tanzania residing in the coastal areas at risk of being displaced if the government fails to address the twin problem [33].

Refs. [34,35] report that coastal erosion and flooding are the main drivers of environmental degradation and account for more than 60% of total economic loss in West Africa. A separate study by [36] on the state of the world’s beaches reported that 24% of the world’s sandy beaches are eroding at rates exceeding 0.5 m/year while 28% are accreting and 48% are eroding at high rates and concluded that the majority of the shorelines in coastal areas are eroding, thereby raising serious concerns. There is every reason to see the menace of shoreline changes, coastal erosion, land use/land cover changes, and the propensity for people to migrate as a problem in Senegal, Kenya, and Tanzania that needs to be accorded research attention. Taking into consideration the identified gaps in the literature and the resultant failure of previous studies, to comprehensively investigate the links among shoreline changes, coastal erosion, land use, and land cover dynamics across regional lines, which are the major focus of this study, as the trio has been recognized as physical drivers of human mobility in East and West Africa sub-regions. This research is therefore novel and presents a bold attempt to examine drivers of coastal processes, such as shoreline changes and coastal erosion, and the impact of future projection on population displacement using different scenarios with perspectives from East and West Africa in a single study using a geo-information technique. No researcher has been able to critically analyze the impact of shoreline changes in different scenarios in East and West Africa in a single study. To fill the existing gap, our study has focused on analysis, mapping, and capturing the coastal changes along the coastlines of Senegal, Kenya, and Tanzania, as well as assessing land use/land cover changes in the study area. In addition, we also investigated shoreline and land use/land cover changes which is critical in understanding the impact of their future projection on human displacement. Thus, this study examines the adaptation and coping mechanisms of residents who are at risk of population movement due to environmental changes and this is the relevance of the present study. Consequently, this study is anchored in the following objectives.

Analyzing, mapping, and capturing the coastal changes along the coastlines of Senegal, Kenya, and Tanzania.
Assessing land use/load cover changes in the study area.
Conduct shoreline and land use/land cover change analysis and the impact of their future projection on human displacement.
Examine the adaptation and coping mechanisms of residents who are at risk of population movement because of environmental change.

2. Materials and Methods

2.1. The Study Area

Senegal is a coastal West African country located at latitude 14° N of the equator and 14° of the prime meridian, bordering Mauritania to the North, Mali to the East, Guinea and Guinea Bissau to the South, and the Atlantic Ocean to the West, as shown in Figure 1. Senegal occupies a surface area of 197.712 km², out of which 19,200 km is land and 4190 km is water, where the Gambia forms an enclave within Senegal [37]. Tanzania is located on latitude 6° S and 35° N and has a long stretch of coastline of about 1424 km of mainland coastline and several islets including the islands of Zanzibar to the northeast and Mafia to the South [38]. Kenya on the other hand, is located on latitude 100° N and 38° E in East Africa, bordering South Sudan to the Northwest, Somalia to the East, Tanzania to the South, and Ethiopia to the North, as shown in Figure 1 below [39].

2.2. Data Collection and Analysis

The data set used for this study was obtained from secondary sources. The shorelines were directly digitized from the Google search engine through the historical settings of 1986, 2006, 2016, and 2022. The years were chosen because of the availability of the appropriate information needed for the study. The land use and land cover maps were obtained from the Landsat imageries downloaded from the Earth Explorer of the United States Geological Survey (https://earthexplorer.usgs.gov. The Gridded Population of the World of 2020 was downloaded from https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11 (accessed on 29 July 2022) for the population density (number of persons per square kilometer). This was projected for 2022 in ArcGIS 10.7. The shorelines of different periods of this study were captured from the Google search engine through the historical settings of 1986, 2006, 2016, and the current year of 2022. The communities and population density displacement were captured with the use of the buffering method, whereby the radii of 10 m, 20 m, and 30 m were generated along the coast to the hinterland. Each radius was used to clip the population density imagery to provide the minimum and maximum population density being affected in each radius.

2.3. Image Geo-Processing for Land Use Change and Percentage Change

This study made use of multi-spectral satellite images from Landsat 5 TM of 1986, Landsat 7 ETM of 2006, Landsat 8 OLI/TIRS of 2016, and Landsat 9 OLI/TIRS of 2022 for the land use/land cover analysis. All the imageries were of 30 m spatial resolution and cloud-free. Other information on the imageries can be found in Table 1. The imagery was enhanced by combining the bands 2, 4, 5, and 6, which are the bands useful for land use/land cover classification. The supervised classification using maximum likelihood algorithm classifiers [40] was used to classify similar spectral signatures into various classes, which included vegetation cover, water bodies, and built-up areas The maximum likelihood classifier was chosen because it is the most widely adopted parametric classification algorithm [41]. The area of each land use class was computed in ArcGIS 10.7, which was used to compute the land use change and percentage change in squared kilometers. Thereafter, the stacked layers were mosaicked to cover the entire study area. The land use/land cover classification in Senegal, Kenya, and Tanzania was guided by the information obtained from West Africa: Land Use and Land Cover Dynamics are available at https://eros.usgs.gov/westafrica/data-downloads (accessed on 28 July 2022).

The boundary shapefile of Senegal, Kenya, and Tanzania was used to clip the mosaicked imagery. Supervised classification was used to classify the imagery into major land use as guided by different agencies. The land use/land cover classification in Senegal was guided by the information obtained from West Africa: Land Use and Land Cover Dynamics and is available at https://eros.usgs.gov/westafrica/data-downloads (accessed on 20 July 2022). The land use/land cover classification in Kenya was guided by the information from Kenya Sentinel2 Land Use Land Cover 2016, which was available at https://servirglobal.net/data-maps/GeoPortalMetaDataViewer (accessed on 29 July 2022). The land use/land cover classification of Tanzania was guided by the information obtained from the Regional Centre for Mapping of Resource for Development available at https://rcmrd.africageoportal.com/maps/rcmrd::tanzania-land-use-land-cover-map-2000–2010-map/about (accessed on 21 May 2022).

The area of each land use class was computed in ArcGIS 10.7, which was used to compute the land use change and percentage change in squared kilometers. The percentage change was computed using Equation (1) as given in Enaruvbe and Atedhor (2015).

\frac{(\frac{d}{t 1}) * 100}{y 2 - y 1}

(1)

where

d is the difference in the value of the area covered by a land cover category at the initial time point and final time point;

t₁ is the value of the area covered by a land cover category in the initial time point;

y1 and y2 are the base and final years, respectively.

2.4. Shoreline Analysis

The digitized shoreline was analyzed into both erosion and accretion. The trend was arithmetically obtained by subtracting the erosion value from the accretion value. The percentage change of the trend was also calculated.

2.5. Method of Data Analysis

This study employed the use of descriptive statistics involving the use of spatial extent in terms of land use/land cover analysis, minimum, maximum, and mean in terms of climate data and population density data, and spatial coverage in terms of shoreline changes. Zonal statistics in a GIS environment were adequately employed to extract the minimum, maximum, and mean values of total precipitation, air temperature, and population density data, which were used for further statistical analysis. The results of the analysis were presented in tables and graphs.

2.6. Questionnaire Analysis

In order to achieve objective 4 of this research, which is to examine the adaptation and coping strategies of residents vulnerable to environmental migration, a structured questionnaire was prepared and administered electronically in Kenya and Tanzania, while that of Senegal was carried out physically.

The target population of this study comprises residents or people exposed to the risk of coastal erosion in the three countries. For Senegal, St. Louis was chosen, while Mombasa and Dar es Salaam were picked for Kenya and Tanzania. The choice for these three regions was based on the historical record as one of the regions exposed to coastal erosion in the countries. The population of St Louis, Mombasa, and Dar es Salaam are shown in Table 2:

The population of the three major cities considered for the administration of the structured questionnaires used for this study is shown in Table 2 above. The individual population was used to determine the sample size for the distribution of the questionnaires.

2.7. Sampling Techniques

Considering the large sample size, the research employed the Taro Yamene (Yamene, 1973) formula to reduce the sample size to a manageable size.

The formula is given below:

\frac{n = N}{N + 1 (e)^{2}}

(2)

where

n = sample size;

N = number of elements in the population;

e = allowable error (%);

Substituting the total formula;

n = 10,833,992.

1 + 10,883,992 (0.05)²

(3)

n = 400.

A total of 400 questionnaires were administered equally to Senegal, Kenya, and Tanzania, out of which 295 were successfully returned, representing more than 50% response rate using a purposive sampling technique in which the target population was already determined. Thereafter, the returned questionnaires were analyzed using simple percentages and presented in tables and charts.

3. Results

3.1. Shoreline Changes along the Coasts of Senegal, Kenya, and Tanzania from 1986 to 2022

Table 3 and Figure 2, Figure 3 and Figure 4 reveal erosion, accretion, and net change rates in terms of the trend of land loss and percentage of land loss in square kilometers, respectively, in Senegal, Kenya, and Tanzania. The result indicates that from 1986 to 2006, the shoreline witnessed the highest erosion rate as 919.17 sq km of land was lost due to coastal erosion, while the accretion rate was only 4.86 sq km along the Senegal coast. Similarly, 914.31 sq km represents a net change of land loss, while the percentage of land loss during the period was 99.47 sq km. Furthermore, 2016 to 2022 can be described as the best period along Senegal’s coast, whereby 128.91 sq km of land was gained. On the Kenyan coast, it is found that the areal coverage of erosion was higher during the period between 1986 and 2006 and between 2006 and 2016, confirming 99.74% and 87.74% loss of land, respectively, during this period. Accretion of about 29.91 sq km (34.05%) was recorded between 2016 and 2022. In a related development, it is found that along the Tanzania coastline, apart from the erosion recorded between 2006 and 2016, accretion of 439.06 sq km (124.81%) was experienced during the period between 1986 and 2006 and 593.24 sq km (468.85%) between 2016 and 2022. From the analysis, it is common to the coastlines of the three countries that accretion was experienced in the later year (2016–2022). Overall, from 1986 to 2022, erosion of different magnitudes was recorded along the coastline of the three countries under study. Thus, Senegal recorded a 99.74% loss, Kenya recorded 75.67%, and Tanzania recorded 78.60%. However, the maps showing the shoreline changes are displayed in Figure 5 and Figure 6 for Senegal’s coastline, Figure 7 for the Kenyan Coastline and Figure 8 for the Tanzanian coastline.

3.2. Land Use/Land Cover Analysis

In line with the research objectives enumerated in section one above, the results of the land use/land cover dynamics in Senegal, Kenya, and Tanzania are presented as follows.

The composition and percentage change in land use/land cover from 1986 to 2022 is shown in Table 4. The percentage of swamp forests in 1986 was 37.10%; however, in 2016, it reduced drastically to only 6.98% and stands at 7.23% in 2022. Similarly, waterbodies which were 3.34% in 1986 reduced to 1.49% in 2022. Changes were also observed in the composition of wetlands as it declined from 5.44% in 1986 to 2.52% in 2022. Settlements along the shoreline increased from 3.02% in 1986 to 2022, while cropland increased from 51.11% in 1986 to 62.04% in 2022. This implies enormous changes in the land use/land cover composition during the period of the study.

Figure 9 provides a graphical representation of shoreline scenario analysis of different land uses. The models reveal that most of the impact is expected to occur in 2022 with settlements and cropland the key sectors that are projected to be adversely affected.

The spatial plots of the land use and land cover changes in Senegal between 1986 and 2022 are shown in Figure 10. The geospatial analysis indicates the presence of rich vegetation cover and wetlands with limited settlement growth in 1986, as shown in Figure 10a. The vegetation appears undisturbed given the low rate of urbanization. However, as captured in Figure 10b,c, the land use/land cover pattern experienced some changes due to increased urban activities, leading to the depletion of the natural vegetation.

The land use and cover change detection analysis further revealed that by 2022 as shown in Figure 10d, urban growth and settlements have increased significantly leading to loss of vegetation cover and other ecological resources. The pattern of land use and land cover change demonstrated in 2022 has implications for the hydrology and local climatic conditions of the study area.

The composition of the various land use and land cover in Kenya is presented in Table 5. The table further displays the spatial extent and percentage contribution of each of the different classes of land use/land cover. A close examination of the analysis reveals that the percentage of cropland/agriculture in 1986 was 46.05%, followed by 12.60% for settlement, while shrubland and sparse vegetation had 12.06% and 0.01%, respectively. Thick vegetation and water bodies have 28.22% and 0.21% and, lastly, wetlands have 0.85%.

In 2022, settlement experienced an almost double increase in composition (26.87%) signifying massive urban development along the coast, while thick vegetation dropped from 18.58% in 2016 to 6.63% in 2022. The reduction in vegetal cover is further evidence of massive deforestation ongoing in Kenya.

The bar chart shown in Figure 11 gives a graphical illustration of Table 5 above. It highlights the spatial variability of the different land use types from 1986 to 2022.

The geospatial analysis of the various land cover types in Kenya from 1986 to 2022 is shown in Figure 12. The geospatial analysis indicates the presence of rich vegetation cover including shrubs, sparse vegetation, and thick vegetation, while water bodies and wetlands are also shown together with major towns and cities along the coast in Figure 12a–c. However, in 2022, as demonstrated in Figure 12d, the geospatial output revealed the presence of increased settlement representing urban growth and loss of veneration, which increases the risk of coastal infrastructure to shoreline changes and coastal erosion in the study area.

The spatial extent and percentage composition of different land use in Tanzania were computed and analyzed as shown in Table 6. The result indicates a massive reduction and clearance of the natural vegetation as forestland, which was 72.% in 1986 and reduced to 63.81% in 2022. Again, grassland with 84.69% contribution in 1986 only had 73.87% coverage in 2022. Wetland composition fell from 0.85% in 1986 to 0.69% in 2022. However, while the natural vegetation was on a downward trend, settlement representing urbanization increased significantly from 2.74% in 1986 to 17.23% in 2022.

A graphical illustration of the massive growth in urban development and the associated reduction in the natural vegetation in Tanzania is shown in Figure 13. This has enormous implications, as it exposes the shoreline to attacks from waves, coastal erosion, and other marine process. The spatial plots of the land use/land cover change analysis are presented below.

The outcome of the geospatial analysis of land use and land cover changes in Tanzania is shown in Figure 14. The study further indicates the presence of rich vegetation covers, including forestland, grassland, cropland, wetland, settlement, and other land uses. As presented in Figure 14a–c, the lurch vegetation represents evidence of reduced urban activities.

Furthermore, the result of the land use/land cover change detection analysis, as shown in Figure 14d, indicates that the presence of settlement encroaching on the coastline has increased. This contrasts with the pattern observed in 1986 and 2006 Figure 14a,b, which shows remarkable changes in the overall land use/land cover pattern of the study area.

3.3. Scenario Modeling of Shoreline Land Use/Land Cover Changes and Their Implications on Climate Migration

This section focuses on shoreline and sea level rise, land use/land cover modeling, and its implications on population and internal migration. The results are discussed as follows:

The projected impact of shoreline changes across different scenarios on land use/land cover in Senegal is shown in Table 7. At a 10 m shift in shoreline, 4.28% of the swamp forest/mangrove will be affected, while 23.93% of water bodies are expected to be at risk. The projection for 30 m indicates that 4.43% of the remaining forest is expected to be at risk due to changes in the shoreline, while water bodies are also projected to be affected. Wetland resources, which constitute 40.18% of the biodiversity, are also projected to experience varying degrees of destruction, while 10.19% of settlements are projected to be at risk of inundation. There is also a projected loss of 23.63% of crops and other agricultural produce.

Graphical presentation of shoreline scenario on various land use types in Senegal, as captured in Figure 15.

Table 8 indicates population densities per square kilometer that will be affected by shoreline changes in different scenarios. For a shift in the shoreline at 10 m, the maximum population that will be affected is 567.06 sqkm, while 25,904.6 sqkm of the population is projected to be impacted at the 20 m shift in the shoreline. The projection at 30 m shift will see 25,904.5 sqkm of the population at risk of displacement. Given the scale of the data provided in this analysis, there is a need for proactive and concerted action to avoid the projected catastrophes.

The plot in Figure 16 displays major cities and towns in Senegal that will be affected by a 10 m change in the shoreline. Prominent towns and cities such as Dakar and Almadies in Senegal are projected to be heavily impacted given the huge population and concentration of other economic activities in those cities. This could result in the economy plunging and reverse development gains, as most of the revenue-generating sectors are in the affected cities.

The population density and communities that will be impacted by the 20 m shoreline shift are shown in Figure 17. It is obvious from the result of the geospatial analysis that sea level rise and shoreline changes at 20 m will result in a displacement of 25,904.6 sqkm of the population density, while the lowest is projected at 25.77 sqkm.

The outcome of the scenario analysis as shown in Figure 18 indicates that shoreline changes at 30 m are projected to result in a population displacement of 25,904.5 sqkm, while the lowest to be impacted is 19.66 sqkm. The impact is considered less under this scenario due to factors such as distance from the sea and the nature of the underlying rocks.

The projected impact of shoreline changes across different scenarios on land use/land cover in Kenya is shown in Table 9. At a 10 m shift in shoreline, 9.04% of cropland/agricultural production will be affected, while 46.09% of settlements are expected to be at risk. Similarly, 42.31% of shrubs and 15.54% of spare vegetation will be affected by 10 m changes in the shoreline. Again, the impact on thick vegetation is expected to be 23.52%, while 0.0407% of wetlands will be lost should the shoreline change by 10 m. The projection for 20 m changes in the shoreline indicates that 9.76% of cropland and agriculture will be affected, while 44.88% of settlements along the coastline will be at risk. In total, 43.04% of shrubs, 15.58% of sparse vegetation, and 23.31% of thick vegetation are expected to be destroyed. The graphical representation of the result is shown in Figure 19 below.

As shown in Figure 19, most of the impact on the land use/land cover of the study area is projected to manifest more in the 20 m scenario. This implies that there will be a huge loss of arable land and vegetation should the shoreline shift further at 20 m.

Table 10, reveals that shoreline changes at 10 m will result in a displacement of 577.46 sqkm of the population, while 1210.55 sqkm is the maximum population projected to be at risk at 20 m shift in the shoreline. The highest impact is projected to occur at 30 m, where 7737.32 sqkm of people are expected to be affected in Kenya.

The projected impact of shoreline changes across different scenarios on land use/landcover in Tanzania is shown in Table 10 above. At a 10 m shift in the shoreline, 43.23% of the forested lands will be affected while 38.13% of grassland are expected to be at risk. Similarly, 3.57% of shrubs, and 24.76% of the wetlands is further projected to be affected. In the same vein, 11.72% of settlements along the shoreline are projected to be at risk of inundation and finally, 21.83% of other land uses will also be impacted. The projection for 20 m changes in the shoreline indicates that 42.53% of the forestland will be affected while 39.78% of grassland along the coastline will be at risk. 3.79% of cropland and 23.34% of the wetlands are projected to be at risk of being eroded. Similarly, 11.82% of the settlements located around the shoreline and other land use types will be severely affected.

The graphical representation of shoreline scenario modeling on different land uses is shown in Figure 20. The models reveal that most of the impact is expected to occur at 30m where settlements are projected to be adversely affected.

The impact of shoreline changes in 2022 on population density is shown in Figure 21, while Table 11 below provides insight into the minimum and maximum population density based on the three scenarios used in the study. The maximum population density that shoreline changes would affect as of 2022 is 33,646.2 sq km. The various towns and cities that are projected to be displaced are indicated on the spatial plot.

The impact of shoreline changes at different scenarios on the population density of the study area indicates that at a 10, 20, and 30 m shift in shoreline, 10,260.97 sqkm of the population would be displaced, as shown in Table 12. This represents increased vulnerability and climate emergencies for Tanzania, as population displacement due to climate and environmental drivers could create social and economic distortions.

3.4. Analysis of Adaptation and Coping Strategies for Population Vulnerability to Shoreline and Coastal Erosion

This section examines adaptation and coping strategies of popupations exposed to the risk of coastal erosion in the study area. This was achieved through the administration of a structured questionnaire to elicit responses from the population of the study. A total number of 400 questionnaires were administered, out of which 295 were returned, representing a more than 50% response rate. The results are presented as follows.

3.5. Migration Indicators

Respondents were asked to comment on the major driver of environmental migration in their respective regions and the result of the analysis as presented in Table 13 and Figure 22 indicate that coastal erosion and drought account for 40.6% and 27% of the causes that trigger environmental migration. Other factors that drive environmental migration are natural disasters such as floods (13.5%) and land degradation (10.2%). This implies that increasing rates of coastal erosion and sea level rise can force people to migrate for safety.

Questions were asked for respondents to comment on the degree of exposure of people to the risk of coastal erosion, and the result is presented in Table 14 and Figure 23. The results indicate that 35.9% of respondents live near the coast and therefore increased vulnerability to the risk of coastal erosion, while 27.1% are into farming, 28.8% are predominantly fishermen, and 8.1% are into tourism. This implies that there is a high propensity by those who are acutely exposed to coastal erosion to migrate to other areas as a way of escape.

As shown in Table 15 and Figure 24, respondents remarked that 30.2% of people living in the affected regions are affected by droughts, while 33.9% of people’s livelihoods are affected by coastal erosion. A total of 20.3% of respondents believe floods affect their homes and livelihoods given the intensity and frequency of rainfall in their region. An amount of 10.2% believe that they suffer from crop losses, while 5.4% of respondents mentioned landslides as an environmental problem affecting them. This implies that coastal erosion and other environmental factors can compel people to migrate if the situation continues to deteriorate.

To ascertain the gender dimensions of migration, respondents were asked about the gender that is frequently involved in migration. The result of the analysis, as shown in Table 16 and Figure 25, indicated that 54.2% of people who were involved in environmental migration were men, followed by youths at 33.8%. A total of 11.8% of those who responded affirmed that women are not actively involved in migration.

3.6. Coping Strategies of Migrants

Attempts were made to examine the coping strategies of those exposed to coastal erosion and climate change hazards and the result from the questionnaire survey, as demonstrated in Figure 26 and Table 17, which indicated that 33.8% strongly agreed that reflectiveness is one of the strategies they employed, while 25.4% agreed with the use of reflectiveness. However, 16.9% disagreed with the use of reflectiveness, while 15.2% only disagreed, and 8.5% were indifferent.

The result of the analysis carried out on changing jobs as a coping strategy, as shown in Table 18 and Figure 27, revealed that 50.8% strongly agreed with changing jobs as a coping strategy in the face of threats from coastal erosion and environmental migration, while 27.1% only agreed to the question. Those that strongly disagreed and only disagreed were 15.2% and 6.8%. Globally, environmental migrants take up new jobs when they arrive in their new location as a coping strategy. Similarly, those who decide to stay in their homes also change occupations in the face of continuous threats from coastal erosion and other climate-induced hazards.

The efficacy of protecting the shoreline/building a sea wall as a measure of mitigating the effect of coastal erosion was put to test, as shown in Figure 28 and Table 19, with 56.3% of respondents in their submission strongly agreeing that the shoreline should be protected and for the building of a sea wall to reduce the impact of coastal erosion. The result shows that 25.7% of the respondents simply agreed to the question, while 13.5% objected strongly to the measure. Only 4.4% of respondents disagreed with the question.

Sharing and bearing is an internationally known coping strategy often deployed by both internal and international migrants. This study, as captured in Table 20 and Figure 29, indicates that 33.9% strongly agree with using the sharing and bearing coping strategy, while 31.5% agree with the question. A total of 16.9% responded strongly disagree, while 13.6% disagreed with the use of sharing and bearing the pains arising from being forced to leave their ancestral homes. However, 4.1% were indifferent.

4. Discussion

The preceding sections of this study have substantially addressed the nexus between shoreline and land use/land use changes and internal migration tendency. Findings from the geospatial analysis employed in this study revealed increasing rates of erosion leading to tremendous shifts in the shoreline along the coast. Findings implicated coastal erosion and other marine processes as sea level rise as responsible for the morphology of the shoreline. Erosion rates were particularly higher in 1986, 2006, and 2016 in all three countries studied with a falling trend observed in 2022. This study also established increasing rates of accretion, indicating that erosion, which is on a falling trend, has seen accretion increase in recent times. The impact of shoreline changes on coastal resources, livelihoods, and migration is enormous as it has the potential to influence people’s decision to migrate for safety reasons. The report of the Internal Displacement Monitoring Centre [42] stated that, in 2019, 2 million people were displaced across sub-Saharan Africa because of natural disasters, including coastal and shoreline hazards, which further lends credence to the validity of this research.

The result of the land use/land cover dynamics conducted in this study area shows significant changes in the land use and land cover systems of Senegal, Kenya, and Tanzania between 1986 and 2022. The agricultural-related activities, which were the dominant land use type in 1986, witnessed a drastic reduction in 2022, implying increased conversation of farmland and other forested lands into settlements and urban development. The increase in urban sprawl around the coastline as observed in the geospatial outputs in the three countries considered is an indication of elevated risk of coastal infrastructures to shoreline changes. The progressive increase in settlements representing urban growth could impact the hydrology and land surface temperature of the coastal regions. The findings reported in this study are consistent with earlier studies conducted by [43] in which they reported increased human pressure on agriculture, leading to significant land changes in land use and land cover between 1992 and 2015 in the Patako region of Senegal. Similarly, ref. [17] in their study on prediction of land use and land cover change in two watersheds in the Senegal River Basin noted an increase in urban expansion representing settlements and increased population growth. Furthermore, the outcome of our analysis of land use and land cover changes presented in our study is in agreement with previous research work conducted by [44] on the implication of Biomass Burning Aerosols (BBA), and land use changes on the hydrological cycle in sub-Saharan Africa where the authors reported that regions associated with increased BBA and the conversion of crops and forest land correlates with reduced precipitation and soil moisture and loss of vegetation greenness. Their studies agree with the findings established in this study, thus validating the methodology and the analysis carried out in our study. Similarly, previous studies by [39] on coastal erosion in Dar es Salem, Tanzania, reported a 5 m seaward shift leading to coastal erosion and loss of valuable properties and other resources in the city. The result of our study further affirms that 972.03 sqkm of land has been lost to coastal erosion in Senegal from 1986 to 2022 with 2016–2022 described as the period with the highest in terms of land loss. In Kenya, −463.30 sqkm of land has also been lost to coastal erosion and agents of wave processes, with 1986–2006 recording the highest share of −87.74% loss of valuable land, while in Tanzania, 1033.35 sqkm of valuable land has been lost from 1986 to 2022 to coastal erosion with 2006–2016 alone recording −10.4634% of land loss. Land is a valuable natural capital with huge economic potential and inestimable value, with the increasing loss of this natural resource to erosion, the impact on livelihoods and migration is significant, as people will be stripped of valuable economic resources with increased vulnerability and risk. Similar studies by [45] affirm that coastal communities that depend on agriculture or coastal livelihoods face a disproportionately higher risk of adverse consequences and are mostly likely to face multiple compounding threats across food, water, and energy systems. Shoreline changes due to coastal erosion and land loss are well pronounced along Africa’s coastal belt and have also been reported in Ghana in a study conducted by [3] on shoreline changes and land use/land cover along the Keta coast in Ghana revealed that the Keta coastline has experienced more erosion than accretion from 2000 to 2021, thus supporting the findings reported in this study.

This study investigates shoreline and sea level rise modeling and its impact on population and the impact of land use/land cover on migration in Senegal, Kenya, and Tanzania. Findings from the study revealed that at 10, 20, and 30 m shifts in the shoreline of the three countries analyzed, there would be massive loss of the vegetation, forest, and other critical components of the biodiversity, while progressive growth in settlements and other urban activities were observed from the results of the analysis. The projection of shoreline shift in different scenarios and their associated impact on population displacement in Senegal, Kenya, and Tanzania indicates the vulnerability of the coastal population to climate-induced migration. The findings established in this study corroborate previous studies by [46] from 2018 that natural hazards and environmental-related factors could trigger migration, as 2.87% of migrants in three deltas, namely the Ganges–Brahmaputra–Meghna Delta in India and Bangladesh, the Mahandi Delta in India, and the Volta Delta in Ghana, cited environmental reasons for moving out of their ancestral homes. In contrast, 62.26% cited economic reasons.

A similar study by [47] on migration in Ghana further supports the findings of this research in that West Africa is experiencing a substantial flow of migrants from the interior savanna to the forest and coastal zones and previous studies on the climate migration nexus have often underrated environmental degradation in the Brong Ahafo Region as migration drivers.

The above narrative aptly captures the enormity of the impact coastal erosion and changes in the shoreline could have on the environment, population, settlements, and livelihoods, which could increase internal migration and population displacement. The finding established in this study agrees with earlier studies conducted by studies by Lincke and Hinkel (2020) that land loss due to inundation from shoreline changes, coastal erosion, and climate extremes estimated at 60,000 km² to 415,000 km² resulted in migration of up to 17–72 million people or 0.23–0.97% of the global population in 2015. The [48] projects a significant increase in the number of people who will be exposed to the negative impact of climate change such as sea level rise and other climate extremes will further compel people living in vulnerable regions to migrate as an adaptation strategy. This view was also shared by [49] who stated that migration is a response to the consequences of climate change.

Findings from the section of this study on adaptation and coping strategies of residents vulnerable to shoreline changes reveal that coastal erosion is the main driver of environmental migration in the study area, followed by drought and other natural disasters. Respondents affirm that those residing along the coast are the most exposed and bear most of the risk. The result of the questionnaire analysis also established that coastal erosion and sea level rise are on the increase, and this is likely to influence the propensity to migrate internally. In terms of adaptation and coping strategies, this study affirms that adaptation and coping mechanisms remain low, and this, therefore, needs to be scaled up. This position has been corroborated by Mbiyozo [27], who stated that migration offers options for individuals, families, and communities facing climate threats, thus relieving pressure on limited resources. Furthermore, people whose livelihoods depend on climate and environmentally sensitive resources use migration as a critical resilience strategy against climate change impacts. Respondents assert that migrants received no assistance from any source in their new location and appealed for intervention, while most of the respondents support shoreline protection and the building of sea walls to mitigate the impact of coastal erosion and other agents of sea encroachment. Finally, the findings revealed that men and youths are more likely to migrate than women, which shows the gender dimensions of environmental migration in the three countries covered.

5. Conclusions and Recommendations

This study has shed more light on the effectiveness of deploying GIS and remote sensing in modeling shoreline changes, erosion rates, land use/land cover changes, projections, and their implication on population and internal migration The result of the study indicates massive land loss from 1986 to 2022 attributed to coastal erosion and shoreline changes with significant impact on population, settlements, and the natural vegetation.

The results highlight the worrying dimension of changes in the coastline of East and West Africa between 1986 and 2022 with a reference to shoreline retreat and modifications with significant impact on population, settlements, and natural vegetation. The outcome of the research indicates the increasing rate of deforestation as 75% of the natural vegetation has been converted to urban land use and urbanization along the coastline with huge implications on the hydrology of the basin and an increase in Land surface temperature. Finally, the study identified low adaptation and coping strategies among coastal dwellers as the socio-economic factors intensifying the vulnerability of the regions to climate change and internal migration. The research brings to light the increasing scale of deforestation and increase in urban growth along the coastline that contributes to the risks of coastal infrastructures.

Furthermore, the outcome of the study has also shown that coastal erosion contributes to climate-induced migration as revealed by the responses elicited from the questionnaire. Hence, the recommendation of the study is the creation of special development organizations in favor of low-elevation coastal zones of each of the countries considered in the study. The agency must be specifically made responsible for developing those strategies and action plans, tailored to the wide protection of shoreline resources and rigorous coastal zone monitoring. In addition, migration ideas should be understood and integrated into the fashioning of policies and action plans for the protection of coastal resources and shoreline monitoring. Multidimensionality of climate change-induced migration issues can be investigated and grasped better through mainstreaming hence improving capacity to respond to climate change-induced human displacements. The study provides evidence that stagnant actions towards erosion, deforestation, and climate change migration issues can be dangerous. The course of action advocated can serve to lay the foundation for resilience and sustainable development in regions prone not only to sea level rise and coastal erosion but which at the same time face multiple issues of environmental degradation and human mobility concerns.

Author Contributions

O.I. conceived and developed the research plan including scenario analysis, O.A. reviewed the draft manuscript including data visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines approved by the United National Economic Commission for Africa UNECA Addis Ababa.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data used for this study can be accessed from this link https://earthexplorer.usgs.gov.

Acknowledgments

The authors acknowledge the significant contribution of the United Nations Economic Commission for Africa (UNECA) and the African Institute for Development and Economic Planning (IDEP) based in Dakar, Senegal, for their support of the first author during his research fellowship.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of Africa showing the study area: Senegal, Kenya and Tanzania.

Figure 2. Erosion along the shoreline in Senegal, Tanzania, and Kenya.

Figure 3. Accretion along the shoreline in Senegal, Tanzania, and Kenya.

Figure 4. Net change trend of land loss (sqkm).

Figure 5. Shoreline changes along Senegal’s coast from 1986 to 2022.

Figure 6. A section of the coastline of Senegal showing the shorelines of different years.

Figure 7. Shoreline changes along Kenya’s coast from 1986 to 2022.

Figure 8. A section of the coastline of Tanzania showing the shorelines of different years.

Figure 9. Shoreline scenario modeling on land use/land cover in Senegal.

Figure 11. Graphical representation of land cover composition and change from 1986 to 2022.

Figure 13. Composition of land use/land cover in Tanzania.

Figure 15. Shoreline scenario modeling on land use/land cover.

Figure 16. Communities and population displacement at 10 m shoreline shift in Senegal.

Figure 17. Communities and population displacement at 20 m shoreline shift in Senegal.

Figure 18. Communities and population displacement at 30 m shoreline shift in Senegal.

Figure 19. Shoreline changes on land use/land cover.

Figure 20. Scenario analysis of Shoreline shift on land use in Tanzania.

Figure 21. Effect of shoreline scenarios on population density (sq/km) in Kenya.

Figure 22. Survey on major drivers of environmental migration.

Figure 23. Degree of exposure to coastal erosion.

Figure 24. Degree of impact of natural hazards.

Figure 25. Gender dimensions of migration.

Figure 26. Reflectiveness as a coping strategy.

Figure 27. Changing jobs as a coping strategy.

Figure 28. Protection of the shoreline/building of sea walls.

Figure 29. Sharing and bearing as a coping strategy.

Table 1. Image acquisition timelines.

Country	Year	Spacecraft/Landsat Sensor	Resolution (m)	Date of Acquisition	Path and Row	Output Format
Senegal	1986	Landsat 5 TM	30 × 30	23/10/1986	205/049	GeoTiff
		Landsat 5 TM	30 × 30	25/02/1986	205/050	GeoTiff
		Landsat 5 TM	30 × 30	09/02/1986	205/051	GeoTiff
	2006	Landsat 7 ETM	30 × 30	01/11/2006	205/049	GeoTiff
		Landsat 7 ETM	30 × 30	19/12/2006	205/050	GeoTiff
		Landsat 7 ETM	30 × 30	20/12/2006	205/051	GeoTiff
	2016	Landsat 8 OLI/TIRS	30 × 30	20/12/2006	205/049	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	01/11/2016	205/050	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	31/03/2016	205/051	GeoTiff
	2022	Landsat 9 OLI/TIRS	30 × 30	20/02/2022	205/049	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	19/01/2022	205/050	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	03/01/2022	205/051	GeoTiff
Tanzania	1986	Landsat 5 TM	30 × 30	09/12/1986	167/063	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	167/064	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	166/063	GeoTiff
		Landsat 5 TM	30 × 30	12/12/1986	166/064	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	167/065	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	166/065	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	165/066	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	166/067	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	165/067	GeoTiff
		Landsat 5 TM	30 × 30	09/12/1986	165/068	GeoTiff
	2006	Landsat 7 ETM	30 × 30	23/12/2006	167/063	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	167/064	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/063	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/064	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	167/065	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/065	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	165/066	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/067	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	165/067	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	165/068	GeoTiff
	2016	Landsat 8 OLI/TIRS	30 × 30	23/12/2006	167/063	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	13/09/2016	167/064	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	27/12/2016	166/063	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	27/12/2016	166/064	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	24/05/2016	167/065	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	20/07/2016	166/065	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	20/12/2016	165/066	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	08/10/2016	166/067	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	10/05/2016	165/067	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	10/05/2016	165/068	GeoTiff
	2022	Landsat 9 OLI/TIRS	30 × 30	26/02/2022	167/063	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	18/02/2022	167/064	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	19/02/2022	166/063	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	03/06/2022	166/064	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	18/02/2022	167/065	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	03/06/2022	166/065	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	13/07/2022	165/066	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	13/07/2022	166/067	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	07/08/2022	165/067	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	20/06/2022	165/068	GeoTiff
Kenya	1986	Landsat 5 TM	30 × 30	01/02/1986	165/061	GeoTiff
		Landsat 5 TM	30 × 30	01/23/1986	166/061	GeoTiff
		Landsat 5 TM	30 × 30	01/02/1986	165/062	GeoTiff
		Landsat 5 TM	30 × 30	23/01/1986	166/062	GeoTiff
		Landsat 5 TM	30 × 30	23/01/1986	166/063	GeoTiff
		Landsat 5 TM	30 × 30	23/01/1986	167/063	GeoTiff
	2006	Landsat 7 ETM	30 × 30	07/12/2006	165/061	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/061	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	165/062	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/062	GeoTiff
		Landsat 7 ETM	30 × 30	07/12/2006	166/063	GeoTiff
		Landsat 7 ETM	30 × 30	23/12/2006	167/063	GeoTiff
	2016	Landsat 8 OLI/TIRS	30 × 30	11/02/2016	165/061	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	11/04/2016	166/061	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	14/03/2016	165/062	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	14/03/2016	166/062	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	30/03/2016	166/063	GeoTiff
		Landsat 8 OLI/TIRS	30 × 30	18/12/2016	167/063	GeoTiff
	2022	Landsat 9 OLI/TIRS	30 × 30	19/01/2016	165/061	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	13/02/2022	166/061	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	16/04/2022	165/062	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	10/01/2022	166/062	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	30/03/2022	166/063	GeoTiff
		Landsat 9 OLI/TIRS	30 × 30	17/12/2022	167/063	GeoTiff

Table 2. Population of selected coastal regions/cities used for the questionnaire.

Regions/Countries	Population
St Louis, Senegal	258,592
Mombasa, Kenya	352,840
Dar es Salaam, Tanzania	7,047,000
Total	7,658,432

Source: population of selected cities based on 2021 population estimates.

Table 3. Shoreline changes and trends of change in Senegal.

Country	Period	Erosion (sq km)	Accretion (sq km)	Net Change Trend of Land Loss (sq km)	Percentage of Land Loss (sq km)
Senegal	1986–2006	919.17	4.86	−914.31	−99.47
	2006–2016	30.38	22.96	−7.42	−24.42
	2016–2022	20.93	47.91	26.98	128.91
	1986–2022	974.59	2.56	−972.03	−99.74
Kenya	1986–2006	368.39	45.17	−323.22	−87.74
	2006–2016	103.41	90.04	−13.37	−12.93
	2016–2022	87.85	117.76	29.91	34.05
	1986–2022	612.26	148.96	−463.30	−75.67
Tanzania	1986–2006	351.78	790.84	439.06	124.811
	2006–2016	208.25	186.46	−21.79	−10.4634
	2016–2022	126.53	719.77	593.24	468.8532
	1986–2022	1314.76	281.41	−1033.35	−78.5961

Table 4. Land use/Land cover dynamics (Senegal).

Land Use/Land Cover	1986		2006		2016		2022
Land Use/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Swampy Forest/Mangrove	12,465.8	37.10	12,503.33	37.21	2344.39	6.98	2430.41	7.23
Waterbodies	1120.66	3.34	1218.9	3.63	562.48	1.67	499.79	1.49
Wetland	1826.29	5.44	2165.63	6.45	1048.37	3.12	847.54	2.52
Settlement	1015.18	3.02	1087.08	3.24	10,919.41	32.50	8976.4	26.72
Cropland/Agriculture	17,171.72	51.11	16,624.71	49.48	18,725	55.73	20,845.51	62.04
Total	33,599.65	100.00	33,599.65	100.00	33,599.65	100.00	33,599.65	100.00

Table 5. Land use/land cover changes in Kenya.

Land Use/Land Cover	1986		2006		2016		2022
Land Use/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Cropland/Agriculture	23,991.29	46.05	27,090.5	52.00	27,096.95	52.01	27,103.27	52.03
Settlement	6562.12	12.60	6560.68	12.59	7777.94	14.93	13,996.07	26.87
Shrubland	6283.58	12.06	6267.41	12.03	6263.99	12.02	6265.85	12.03
Sparse Vegetation	4.94	0.01	749.65	1.44	748.5	1.44	744.21	1.43
Thick Vegetation	14,702.66	28.22	10,899	20.92	9677.72	18.58	3455.32	6.63
Waterbodies	109.4	0.21	86.55	0.17	86.55	0.17	86.7	0.17
Wetland	441.07	0.85	441.22	0.85	443.28	0.85	443.72	0.85
Total	52,095.06	100.00	52,095.01	100.00	52,094.93	100.00	52,095.14	100.00

Table 6. Land use and land cover in Tanzania.

Land Use/Land Cover	1986		2006		2016		2022
Land Use/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Forestland	45,499.12	72.48	44,745.23	71.28	42,059.49	67.00	40,059.49	63.81
Grassland	53,162.28	84.69	50,207.7	79.98	49,373.99	78.65	46,373.99	73.87
Cropland	7160.67	11.41	7202.11	11.47	6690.96	10.66	4504.7	7.18
Wetland	531.1	0.85	491.06	0.78	473.48	0.75	433.51	0.69
Settlement	1718.65	2.74	4471.13	7.12	5649.36	9.00	10,814.59	17.23
Other land use	202.1	0.32	402.8	0.64	587.02	0.94	648.02	1.03
Total	62,774.8	100.00	62,774.8	100.00	62,774.81	100.00	62,774.81	100.00

Table 7. Impact of shoreline changes at different scenarios on land use and land cover—Senegal.

Land Use/Land Cover	10 m		20 m		30 m		2022
Land Use/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Swampy Forest/Mangrove	0.29	4.28	0.29	4.24	0.3	4.43	2430.41	7.23
Waterbodies	1.62	23.93	1.62	23.68	1.46	21.57	499.79	1.49
Wetland	2.72	40.18	2.72	39.77	2.72	40.18	847.54	2.52
Settlement	0.63	9.31	0.66	9.65	0.69	10.19	8976.4	26.72
Cropland/Agriculture	1.51	22.30	1.55	22.66	1.6	23.63	20,845.51	62.04
Total	6.77	100	6.84	100.00	6.77	100.00	33,599.65	100.00

Table 8. Effect of shoreline scenarios on population density (sq/km) in Senegal.

Shoreline Scenarios (m)	Minimum	Maximum	Mean
10	27.43	567.06	125.80
20	25.77	25,904.55	2999.78
30	19.66	25,904.5	3125.58

Table 9. Impact of shoreline changes at different scenarios on land use and land cover—Kenya.

Land Use/Land Cover	10 m		20 m		30 m		2022
Land Use/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Cropland/Agriculture	0.598	9.04	1.929	9.76	1.24	9.38	27,103.27	52.03
Settlement	3.049	46.09	8.873	44.88	6.019	45.53	13,996.07	26.87
Shrubland	2.799	42.31	8.509	43.04	5.637	42.64	6265.85	12.03
Sparse Vegetation	1.028	15.54	3.08	15.58	2.057	15.56	744.21	1.43
Thick Vegetation	1.556	23.52	4.609	23.31	3.097	23.43	3455.32	6.63
Waterbodies	0.826	12.48	2.383	12.05	1.624	12.28	86.7	0.17
Wetland	0.407	6.15	1.189	6.01	0.805	6.09	443.72	0.85
Total	6.616	100.00	19.77	100.00	13.22	100.00	52,095.14	100.00

Table 10. Impact of shoreline changes at different scenarios on land use and land cover—Tanzania.

Landuse/Land Cover	10 m		20 m		30 m		2022
Landuse/Land Cover	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage	Spatial Extent	Percentage
Forestland	10.23	43.44	19.97	42.53	29.15	41.69	40,059.49	63.81
Grassland	8.98	38.13	18.68	39.78	28.94	41.39	46,373.99	73.87
Cropland	0.84	3.57	1.78	3.79	2.8	4.00	4504.7	7.18
Wetland	5.83	24.76	10.96	23.34	15.35	21.95	433.51	0.69
Settlement	2.76	11.72	5.55	11.82	8.33	11.91	10,814.59	17.23
Otherland	5.14	21.83	9.99	21.27	14.5	20.74	648.02	1.03
Total	23.55	100.00	46.96	100.00	69.92	100.00	62,774.81	100.00

Table 11. Effect of shoreline scenarios on population density (sq/km) in Kenya.

Shoreline Scenario (m)	Minimum	Maximum	Mean
10	0.293	577.46	143.28
20	0.293	1210.25	151.74
30	0.293	7737.32	274.72

Table 12. Effect of shoreline scenarios on population density (sq/km) in Tanzania.

Shoreline Scenarios (m)	Maximum	Mean
10	10,260.97	728.80
20	10,260.97	483.38
30	10,260.97	405.24

Table 13. Comment on the major driver of environmental migration in your community.

Drivers	Frequency	Percentage Distribution (%)
Drought	80	27
Coastal Erosion	120	40.6
Land Degradation	30	10.2
Natural Disasters	40	13.5
Conflicts	25	0.7
		8.5
Total	295	100

Table 14. Degree of exposure.

Degree of Exposure	Frequency	Percentage Distribution (%)
Do you live near the coast	106	35.9
Are you into farming	80	27.1
Are you into fishing	85	28.8
Are you into tourism	24	8.1
Total	295	100

Table 15. Is your household affected negatively by any of the following events?

Events	Frequency	Percentage Distribution (%)
Droughts	89	30.2
Coastal erosion	100	33.9
Floods	60	20.3
Crop diseases	30	10.2
Landslides	16	5.4
Total	295	100

Table 16. Which set of people migrate more often in your community?

Sex	Frequency	Percentage Distribution (%)
Men	160	54.2
Youths	100	33.8
Women	35	11.8
Total	295	100

Table 17. Reflectiveness as a coping strategy.

Scale of Impact	Frequency	Percentage Distribution (%)
Strongly Agree	100	33.8
Agree	75	25.4
Strongly Disagree	50	16.9
Disagree	45	15.2
Indifferent	25	8.5
Total	295	100

Table 18. Changing jobs as a coping strategy.

Scale of Impact	Frequency	Percentage Distribution (%)
Strongly Agree	150	50.8
Agree	80	27.1
Strongly Disagree	45	15.2
Disagree	20	6.8
Total	295	100

Table 19. Protection of shoreline/sea walls.

Scale of Impact	Frequency	Percentage Distribution (%)
Strongly Agree	166	56.3
Agree	76	25.7
Strongly Disagree	40	13.5
Disagree	13	4.4
Total	295	100

Table 20. Sharing and bearing as a coping strategy.

Scale of Impact	Frequency	Percentage Distribution (%)
Strongly Agree	100	33.9
Agree	93	31.5
Strongly Disagree	50	16.9
Disagree	40	13.6
Indifferent	12	4.1
Total	295	100

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MDPI and ACS Style

Ideki, O.; Ajoku, O. Scenario Analysis of Shorelines, Coastal Erosion, and Land Use/Land Cover Changes and Their Implication for Climate Migration in East and West Africa. J. Mar. Sci. Eng. 2024, 12, 1081. https://doi.org/10.3390/jmse12071081

AMA Style

Ideki O, Ajoku O. Scenario Analysis of Shorelines, Coastal Erosion, and Land Use/Land Cover Changes and Their Implication for Climate Migration in East and West Africa. Journal of Marine Science and Engineering. 2024; 12(7):1081. https://doi.org/10.3390/jmse12071081

Chicago/Turabian Style

Ideki, Oye, and Osinachi Ajoku. 2024. "Scenario Analysis of Shorelines, Coastal Erosion, and Land Use/Land Cover Changes and Their Implication for Climate Migration in East and West Africa" Journal of Marine Science and Engineering 12, no. 7: 1081. https://doi.org/10.3390/jmse12071081

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