Modelling Land Use and Land Cover Changes Detection of Sokoto State, Nigeria Using Cellular Automaton and Markov Algorithm.

Abstract

Sokoto state has experienced extensive land degradation, leading to significant ecological and socio-economic impact on its population. This division on land has disrupted local ecosystem and affected the livelihoods of the people, creating challenges for both environmental sustainability and economic development in the State. Currently, desert features cover approximately 580,841 km² of Nigeria's landmass, accounting for 63.8% of the country's total area and affecting around 30 million people, or 17% of the national population. Sokoto State ranks second highest in desertification severity within Nigeria, leading to reduced land productivity, biodiversity loss, and ecological imbalance, which threaten the livelihoods of agrarian communities. This project aims to combat desertification in Sokoto State using advanced remote sensing and Geographic Information System (GIS) methodologies. The study will assess the extent and severity of desertification, identify contributing factors, and propose sustainable land management strategies. The research timeframe spans from 2000 to 2020, with data analysed at five-year intervals. Using multi-temporal satellite imagery from Landsat and integrating spatial data layers, including population and rainfall data from the Nigerian Meteorological Agency (NIMET), the project will conduct land use/land cover analysis, forecasting, and modelling. The findings aim to provide critical insights for local communities, policymakers, and stakeholders, enabling informed decisions and proactive measures to preserve and rehabilitate Sokoto State's fragile ecosystem.