Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.

Ex situ adaptation in the form of relocation has become inevitable in some low-lying coastal zones where other adaptation strategies become impractical or uneconomical. Although relocation of coastal low-lying communities is anticipated globally, little is still known about the factors that influence household-level adoption. This study draws on an extended version of Protection Motivation Theory (PMT) to assess the factors influencing the relocation intention of three highly vulnerable coastal rural communities in Ghana. A total of 359 household heads were randomly selected for a questionnaire survey. The study employed binary logistic regression to identify key factors that influence residents’ readiness to relocate. The results indicated that cognitive and compositional factors were more important than contextual factors in explaining the intention to relocate among coastal rural communities in Ghana. However, contextual factors mediated or attenuated the influence of cognitive and compositional factors on relocation intention. Based on the findings, this study advocates for intensive education on the effects of future sea-level rise impacts on communities as well as structural and non-structural measures to improve the socio-economic capacity of rural communities.

Despite water being a basic human need, the residents of Sibi in Ghana’s Nkwanta North District struggle to obtain potable water, which negatively influences their livelihoods. This study aimed to evaluate the impacts on households’ livelihoods due to difficulties in accessing potable water and accordingly give policy recommendations. Data were collected through questionnaire surveys, interviews, geographic information systems (GIS), and remote sensing (RS) techniques. Questionnaire surveys were administered to 314 randomly selected household heads. The results indicated that the water sources available in Sibi were not sufficient; the boreholes and public tabs/standpipes in the communities were not dependable for regular access. As a result, households needed to depend on distant streams and dams for water. The households generally spent more than two hours at the water sources to collect water. Evidently, the Sibi residents did not have sufficient access to potable water, which severely affected their livelihoods. It is recommended that government agencies collaborate with related non-governmental organizations (NGOs) to help expand potable water projects in Sibi, Ghana.

Extensive ecosystem degradation along the coastal areas of Ghana highlights the need for targeted landscape restoration. This study identified areas with high restoration potential by evaluating environmental, climatic, and anthropogenic determinants of mangrove distribution. We tested multiple habitat suitability models, including Random Forest (RF), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), and an ensemble approach. Mangrove occurrence records were compiled from field observations, drone-derived data, and publicly available biodiversity and mapped datasets. The ensemble GAM-RF model emerged as the most robust model, with the highest predictive performance. Hydrological and topographic factors were the strongest predictors of mangrove habitat suitability, particularly proximity to rivers and the coastline, low-elevation terrain (<5 m asl), and temperature-related bioclimatic variables, while precipitation metrics and anthropogenic proxies played secondary roles. Using the ensemble approach, the estimated potentially suitable habitat ranged from 417 to 1313 km², indicating that substantial areas remain available for mangrove restoration. These findings align with ongoing national and international restoration initiatives, including the coastal restoration plan, and have important implications for coastal protection, carbon sequestration, fisheries habitat, and local livelihoods. Our findings demonstrate that multi-model habitat suitability analyses can guide spatially targeted, and evidence-based restoration planning.

Remote sensing is essential for mapping mangrove forest ecosystems, yet accuracy and spatial resolution remain constrained, particularly in Africa, where mangroves are fragmented, reference data and cloud-free images are scarce. The state-of-the-art of mangrove forest mapping relies on native-resolution, single-date imagery and machine learning. This study evaluates deep learning methods using aggregated multi-date satellite imagery to improve mangrove mapping and national-scale area estimation. Semantic segmentation and super-resolution techniques were applied to Sentinel‑2 (S2) and Sentinel‑1 (S1) images, with training data derived from field observations and high-resolution drone and satellite imagery. Coastal regions of Tanzania and Ghana were used as representative study areas for eastern and western Africa, respectively. Results show strong classification performance, with S2 imagery alone achieving an Intersection over Union (IoU) of 85.8%. Adding S1 provided modest gains (86.3%), while super-resolution significantly improved the accuracy (88.5%). The combined super-resolved S2 and S1 dataset yielded the highest performance (89.2%). Wall-to-wall mapping and statistically rigorous validation and area estimation produced accurate results with quantified uncertainty. Tanzania showed high consistency (F1 = 96%; 1207 km², 95% CI [1151.7, 1263]), while Ghana exhibited slightly higher uncertainty (F1 = 92%; 92.6 km², 95% CI [87.5, 97.7]). Integrating deep learning with multi-date, multi-sensor fusion and super-resolution enables accurate mangrove mapping and area estimation for improved coastal management and restoration.