Wetland change prediction of Ogun-River Basin, Nigeria: Application of cellular automata Markov and remote sensing techniques

Abstract

Wetland changes are very serious ecosystem problems, which stem from unrestrained human and environmental actions. Despite significant attention in environmental management research, the means to tackle wetland changes are still gaining momentum in science and research within developing countries. With the increasing availability of remote sensing data and flexible solutions, finding the right solution to wetland changes becomes a key research and policy agenda. Herein, we assessed the wetland change prediction of Ogun River (OR) basin, Nigeria, during 1999, 2009, and 2019 by analyzing land cover change (LCC) images using cellular automata-Markov (CA-Markov) chain and remote sensing (RS) techniques. The acquired shuttle radar topographic mapper and Landsat remotely sensed data were applied to create thematic-maps of the normalized difference vegetation index (NDVI), normalized difference salinity index (NDSI), and digital elevation model (DEM) of the study area. The CA-Markov required the supervised classification of the land cover maps of 1999, 2009, and 2019. The results of the elevation, NDVI, and NDSI revealed the qualitative differences in the study area. The LCC analysis indicated that farmlands and built-up areas increased by 54.56 and 33.21 %, respectively. However, waterbodies, wetlands, and vegetation decreased by 0.42, 3.53, and 8.28 % respectively. These findings agree with the CA-Markov with an accuracy exceeding 70 %, and, thus predict the wetland changes for the year 2030. The major attributions to wetland cover variations in the study area are built-up encroachment with extensive, spontaneous, and uncontrolled agricultural activities. This study, through its findings, provides relevant guidelines and information on wetland changes required by stakeholders for environmental policy, planning, and sustainability.