Abstract

BackgroundLagos is the largest megacity in Africa, with numerous lagoons and wetlands. Due to anthropogenic activities, Lagos’ wetlands have undergone significant alterations in the last few decades. The UN's Sustainable Development Goals (SDGs) and the campaign programs for carbon neutrality in the city offer a plan of action that recognizes the necessity of managing water-related habitats, such as wetlands. Wetlands are essential to accomplishing the SDGs because they provide a solution to many global issues like climate change, food insecurity, and water scarcity. In some ways, each of the SDGs is related to wetlands, but SDGs 6 and 13 are particularly significant to this study. The application of remote sensing and Geographic Information Systems (GIS) is a crucial tactic for monitoring the achievement of these SDGs. ObjectivesThe study aims to map and simulate the spatial-temporal changes in the Lagos wetland from 2000 to 2050 for the achievement of a carbon-neutral city. MethodsIn this study, Landsat ETM+ (2000, 2010) and OLI 2020 were used to monitor the spatial-temporal changes in Lagos wetlands. The maximum likelihood classification method was adopted for the supervised classification. The simulation analysis was done with the cellular automata-Markov model. ResultsThe findings show that the water body increased throughout the study, rising from 658.76 km2 in 2000 to 681.60 km2 in 2010, and to 712.0 km2 in 2020. Marshy areas have decreased progressively from 39.41 km2 in 2000 to 14.48 km2 in 2010 and 0.078 km2 in 2020. Swamps and mangrove wetland classes had changed from 448.24 km2 and 406.29 km2 in 2000 to 317.74 km2 and 538.87 km2 respectively in 2010, and to 435.29 km2 and 405.33 km2 respectively in 2020. The simulated results show that water bodies, swamps, marshy areas, and mangroves will respectively change to 711.99 km2 and 747.11 km2, 405.26 km2 and 400.16 km2, 0.15 km2 and 0.13 km2, as well as 455.29 km2 and 455.30 km2 in the simulated years 2030–2050. ConclusionThe study shows that Lagos has witnessed spatial-temporal changes in its wetland classes within the last two decades. The results prove that remote sensing and GIS are effective tools for studying water-related habitats such as wetlands.

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