What question did this study set out to answer?

The aim is to analyze and predict the dynamics of land use and cover in Nnewi Metropolis over time.

March 28, 2026Open Access

Application of VIS and Land Change Modelers in Characterizing and Predicting the Urban Land Use/Cover Dynamics of Nnewi Metropolis, Nigeria

Key Points

The aim is to analyze and predict the dynamics of land use and cover in Nnewi Metropolis over time.
Used Landsat satellite images from 1986, 2001, and 2016.
Applied Ridd's VIS-W model and linear spectral mixture analysis to categorize land use types.
Employed cellular automata Markov chain and the Land Change Modeler to predict future land use for 2031.
Utilized ArcGIS and other statistical software for analysis.
Classification accuracy achieved a Kappa coefficient greater than 0.85.
Vegetation decreased significantly, while impervious surfaces increased.
The prediction for 2031 indicates a loss of 36.85 sq.km of vegetation and a gain of 14.86 sq.km of impervious surfaces.
Statistical analysis confirmed significant changes in land use categories for 2031.

Abstract

Based on a sub-pixel approach, this study analysed the Land Use/Cover (LU/C) dynamics of Nnewi Metropolis in Anambra State, Nigeria. Landsat TM/ETM+ satellite imageries of 1986, 2001 and 2016 were characterized into different LU/Cs using Ridd’s Vegetation, Impervious Surface, Soil and Water (VIS-W) model via Linear Spectral Mixture Analysis (LSMA). LU/C endmember fractions obtained were hardened to produce the final LU/C maps of the study area, per epoch. Cellular Automata Markov (Ca-Markov) chain and the Land Change Modeler (LCM) were used to predict future LU/C for the year 2031 and the transition of each LU/C categories between 2016 and 2031, respectively. ArcGIS 10.5, Idrisi Selva and Statistical Package for Social Science (SPSS 22) were used to perform the analyses. The result of the classification yielded a high level of accuracy (Kappa coefficient >0.85) and revealed that vegetation reduced over the years; impervious surface increased exposed soil fractions reduced while water cover fluctuated throughout the study epoch. Change detection analyses showed that the magnitude of change for each LU/C category were highest in recent period (2001 – 2016), while the result of the Markov chain analysis revealed continued change, especially in the reduction of vegetated areas (-36.85sq.km) and water surfaces (-0.65sq.km) as well as in the proliferation of impervious surfaces (14.86sq.km) and soil fractions (22.63sq.km) in the year 2031. Furthermore, the application of LCM revealed all the LU/C transition categories, earmarking about 29sq.km of vegetal cover to be converted to impervious surfaces. The result of the Chi-square analysis however revealed that the predicted changes in LU/C categories for 2031 were statistically significant (α= 0.01). Thus, this study is to serve as an objective base for environmental managers and planners to trace, model and simulate the implication of the rapid dynamics of urban landscapes from a continuous perspective, particularly when other geographical phenomena are to be better understood. It is also recommended that government and environmental managers are to roll-out phased programmes to track and manage the changing landscape or to revamp the current LU/C change scenario in Nnewi by making use of the LU/C transition map presented in this study.

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