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... Anthropogenic activities, including deforestation, pollution, and unsustainable land use practices, are pivotal in driving land cover changes within the Dar es Salaam Metropolitan City. The significance of these activities is well-documented across multiple studies (Igulu & Mshiu, 2020;Kibassa & Shemdoe, 2016;Manyama et al., 2019;Masao, 2020;Mkalawa, 2016;Mkalawa & Haixiao, 2014;Mnyali & Materu, 2021;Msuya et al., 2021). Specifically, deforestation disrupts the natural equilibrium, resulting in adverse environmental consequences and long-term health challenges for the city's residents. ...
... For example, the withdrawal of agricultural subsidies would make SES vulnerable because of the continuous impact of the human-environment relationship towards poverty and maintaining sustainable agriculture, as stated in SDGs. More recently, (Manyama et al., 2019) analyzed and mapped the impact of the built environment on socio-ecological services along the Dar es Salaam metropolitan coastline. Panel regression revealed that the relationship between the built environment and vegetation cover is inverse. ...
Stress on ecological resources affects the sustainability of the socio-ecological system (SES). Interconnections within SES are involved. Therefore, this study considered indicators that are composite of the interconnections to estimate SES efficiency. We employed the non-parametric benchmarking order-α model, from Data Envelopment Analysis (DEA), to estimate SES efficiency and alleviate possible intricacies. We evaluated twenty-four Sub-Saharan African (SSA) nations observed from 2000 to 2014. More than half of them were inefficient. An increase in food production and environmental performance is essential for SES efficiency improvement. Quantile regression found that human development (through the lifespan, education, and standard of living) is related to the SES efficiency improvement. The SES efficiency is likely negatively associated with higher values of both female proletariat and carbon emissions. Policymakers should increase the concerted efforts of empowering human capacity and minimize the gender gap within SSA countries to become efficient and fulfill sustainable development goals.
Comprehending the interactions between humans and their environment necessitates modeling human–environment interactions. This study employs time-series satellite imagery from Landsat Thematic Mapper (1995 and 2009) and Landsat 8 Operational Land Imager (2022) to examine land use/land cover (LULC) change in Dar es Salaam, Tanzania, after image pre-processing with the Google Earth engine code editor, while random forest machine learning in R classified LULC. Geographically weighted regression (GWR) correlates LULC changes to socioeconomic factors spatially. Analysis reveals a dynamic LULC transformation between 1995 and 2022, with a 14.9% increase in built-up areas and a 14.6% decline in bushland. Out of the total LULC, 65.8% experienced gains and losses, while 34.2% remained stable. The GWR model, surpassing the ordinary least squares (OLS) model, achieves an R2 value of 0.73, indicating a strong relationship between LULC changes and socioeconomic factors, explaining 73% of the variation. The influences of these factors exhibit variations across different LULC change types. Population density and proximity to the city center significantly contribute to LULC changes, while the impacts of gross domestic product and distance to roads are comparatively less significant. Poverty does not drive LULC changes significantly. The findings indicate that urbanization and urban sprawl, influenced by population density and distance from the city center, significantly impact land use and cover changes. Effective urban planning strategies should be prioritized to address this, considering factors such as population density and distance from the city center to mitigate the considerable effects on land use and cover changes in the study area.
Monitoring shoreline movements is essential for understanding the impact of anthropogenic activities and climate change on the coastal zone dynamics. The use of remote sensing allows for large-scale spatial and temporal studies to better comprehend current trends. This study used Landsat 5 (TM), Landsat 8 (OLI), and Sentinel-2 (MSI) remote sensing images, together with the Otsu algorithm, marching squares algorithm, and tidal correction algorithm, to extract and correct the coastline positions of the east coast of Laizhou Bay in China from 1984 to 2022. The results indicate that 89.63% of the extracted shoreline segments have an error less than 30 m compared to the manually drawn coastline. The total length of the coastline increased from 166.90 km to 364.20 km, throughout the observation period, with a length change intensity (LCI) of 3.11% due to the development of coastal protection and engineering structures for human activities. The anthropization led to a decrease in the natural coastline from 83.33% to 13.89% and a continuous increase in the diversity and human use of the coastline. In particular, the index of coastline diversity (ICTD) and the index of coastline utilization degree (ICUD) increased from 0.39 to 0.79, and from 153.30 to 390.37, respectively. Over 70% of the sandy beaches experienced erosional processes. The shoreline erosion calculated using the end point rate (EPR) and the linear regression rate (LRR) is 79.54% and 85.58%, respectively. The fractal dimension of the coastline shows an increasing trend and is positively correlated with human activities. Coastline changes are primarily attributed to interventions such as land reclamation, aquaculture development, and port construction resulting in the creation of 10,000.20 hectares of new coastal areas. Finally, the use of Kalman filtering for the first time made it possible to predict that approximately 84.58% of the sandy coastline will be eroded to varying degrees by 2032. The research results can provide valuable reference for the scientific planning and rational utilization of resources on the eastern coast of Laizhou Bay.
