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March 28, 20260 citationsOpen Access

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

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PEP EnecheAAAbdulateef AhmedAEA. N Ekebuike

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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Cite This Study

Eneche et al. (2018) studied this question.

synapsesocial.com/papers/69c772818bbfbc51511e315bhttps://doi.org/10.5281/zenodo.19229028
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