Environmental Engineer and Researcher.
Address: Padova, Italy
Here are current academic publications:
Abstract:Rapidly growing coastal cities along the Gulf of Guinea are increasingly exposed to geo- environmental hazards, including coastal erosion, sea-level rise, and land subsidence. One of these increasingly impacted cities is Douala in Cameroon. In Douala, rapid urban expansion has intensified environmental stress, contributing to accelerated land subsidence in vulnerable zones, with InSAR-estimated rates averaging -2.7 mm/year and reaching -17.6 mm/year in some areas between 2018 and 2023. These processes are largely driven by land-use changes, and high rates are mainly attributed to the urbanisation of croplands and wetlands. With Douala’s population expected to double by 2050, ongoing land-use changes, urbanisation, and related increased groundwater extraction are likely to exacerbate land subsidence risks. Despite this, the ability to understand, project, plan, and integrate future land subsidence into urban planning strategy remains limited due to data gaps and underdeveloped modelling approaches. This study attempts to address the knowledge gap by applying Singular Spectrum Analysis (SSA) to InSAR-derived land subsidence time series (2018–2023) to extract dominant trend components. A statistical modelling framework is then used to identify the best-fitting function (linear, polynomial, or exponential) for each measurement point. These models are applied to project cumulative land subsidence across Douala until 2050. The projected cumulative land subsidence is combined with projected sea-level-rise values (SSP1-SSP5) and up-to-date high- resolution elevation assessments to assess the future exposure of the Douala Coastland. Our results provide estimates of relative sea-level rise for Douala up to 2050 and provide valuable insights for urban planning and coastal resilience strategies of the Gulf of Guinea’s sinking cities.
Keywords: Coastal Subsidence, Coastal Vulnerability, Urban Adaptation, Relative Sea-Level Rise.
Abstract:Rapidly growing coastal cities in the Gulf of Guinea, particularly low-lying areas like Douala, in Cameroon, face significant geo-environmental hazards, exposing the population, environment, and infrastructure to the impacts of sea-level rise, coastal erosion, and land subsidence. In Douala, rapid urban expansion has intensified environmental stress, contributing to land subsidence in vulnerable zones, with observed rates averaging -2.7 mm/year and reaching -17.6 mm/year in some areas over the period between 2018 to 2023. These phenomena are largely driven by land-use changes, mainly the urbanisation of croplands and wetlands. Despite this, the mechanisms driving land subsidence remain poorly understood, mainly due to limited data availability. This study aims to address this knowledge gap by applying a Deep Learning model to investigate the relationships between land-use change, shallow soil properties, building features, and subsidence patterns. The model combines land-use change data (1992-2022), geological soil data, urban built-up data from the Global Human Settlement Layer (1975-2020), and subsidence measurements from advanced InSAR. Empirical equations are implemented to quantify land subsidence and identify key spatial and temporal drivers of ground deformation, to support subsoil characterization, and to predict future subsidence patterns. The goals of this work are to develop the first integrated model of land subsidence associated with the urbanization of Douala and provide valuable insights for urban planning and geo- environmental risk mitigation in the region.
Keywords: land subsidence, neural network, urbanisation
Abstract: Douala, a city situated on the coast of Cameroon in the Gulf of Guinea, is characterized by its low elevation above sea level and sedimentary geology, making it particularly susceptible to erosion, subsidence, and sea level rise. Currently, Douala City and its surrounding mangrove forests experience alarming rates of coastal erosion, frequent flooding, complete land loss, and evidence of subsidence from regional and continental research. This raises critical questions and reveals numerous research gaps, such as the need to better understand current coastal dynamics; approaches for monitoring and predicting Douala's low coastland changes; the need to understand the rates, causes, and patterns of subsidence; and lastly, the understanding of the combined impacts of multiple factors on coastal city vulnerability. Therefore, this study aims to fill these knowledge gaps by investigating, understanding, and estimating the causes, consequences, and coastal vulnerability of land subsidence. In this study, remote sensing data, InSAR analysis, spatial analysis, and statistical analysis were used to assess the actual land subsidence rate, determine the factors influencing land subsidence, estimate the influence of land use change on subsidence processes, and establish an integrated vulnerability assessment for the coastal areas of Douala. The findings of this study indicate an average rate of subsidence amounting to 2.9 mm/year, which is indicative of subsidence occurring in all areas of the city. Furthermore, the effects of land use were observed to be dependent on the period and rate of change. These results will be of great importance in gaining a more comprehensive understanding of the dynamics of Cameroon's mangrove landscape and the susceptibility of coastal infrastructure to subsidence, coastal retreat, and potential flooding events. These findings can be utilised to develop sustainable management strategies for the coastal zone of Douala.
Keywords: Land Subsidence, Land use, InSAR, Spatial Analysis, Flooding.