Urban Growth Monitoring and Prediction Using Remote Sensing Urban Monitoring Indices Approach and Integrating CA-Markov Model: A Case Study of Lagos City, Nigeria

Abstract

As one of the swiftly advancing megacities globally, Lagos faces significant challenges in managing its urban expansion. Mainly, this study focuses on monitoring and predicting urban growth using a comprehensive approach incorporating Global Land 30 (GL30), satellite-based nighttime light observations, and built-up and population density data. The application of remote sensing techniques, combined with utilizing the GL30 dataset, provides an effective means to monitor and predict urban growth trends and patterns. The major patterns occurred from 2000 to 2020, including increased cultivated land; reductions in grasslands, shrublands, and wetlands; and major urbanization. Predictive models indicate that urbanization will continue. Furthermore, employing the Cellular Automata (CA) Markov model in land-use and land-cover (LULC) change prediction. The findings revealed significant changes in LULC over the two decades. Particularly, the percentage of artificial terrain increased from 17.016% to 25.208%, and the area under cultivation increased significantly, rising from 46,771 km2 (1.238%) in 2000 to 75,283 km2 (1.993%) in 2020. Grasslands fell from 7.839% to 1.875%, while forest cover somewhat increased, climbing from 39.319% to 43.081%. Additionally, marshes fell from 9.788% to 5.646%, while shrublands decreased from 4.421% to 2.640%. Surprisingly, bare ground decreased sharply from 0.677% to 0.003%. To forecast future LULC changes, the study also used a Markov Chain Transition Matrix. According to the data, there is a 3.54% chance that agricultural land will become urban, converting it from being used for agriculture to urban development. On the other hand, just 1.05% of forested regions were likely to become municipal areas. This study offers foundations for the upcoming research to enhance urban growth models and sustainability strategies in the face of rising urbanization and environmental concerns in the region, as well as laying the groundwork for informed decision-making in the region.

Smart Aquaponics: An Automated Water Quality Management System for Sustainable Urban Agriculture

“…The mention of obtaining optimum values for each controller (vegetable tank, fish tank, and heater system) indicates a tailored approach to optimization. Earlier systems [19][20][21][22][23][24][25][26] may have employed generic control settings or overlooked the significance of fine-tuning control parameters for specific components within the aquaponic system. By optimizing the parameters for each subsystem, the proposed system is likely to achieve a superior overall performance and efficiency.…”

“…Research for managing water quality in treatment plants has intensified due to its substantial potential to enhance operational efficiency, ensure regulatory compliance, and improve overall water treatment effectiveness [16][17][18]. Simultaneously, research efforts [19][20][21][22][23][24][25][26][27][28][29][30] have concentrated on refining water management control. In the realm of aquaponics, where water plays a pivotal role, the research paper [29] offers a succinct overview of the proposed solutions in the literature.…”

“…In addition, they established a PID control system for optimizing the wastewater treatment process and ensuring system stability. This literature review [24][25][26][27][28] collectively serves as a fundamental foundation for our project, highlighting the importance of effective water management and the incorporation of advanced technologies to meet our goals of optimizing resource utilization and improving the stability of aquaponic systems. By drawing the insights gained from both studies, our goal is to create a holistic approach that integrates observed practices in water treatment into our aquaponics project.…”