Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain

Abstract

This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven time-periods (1988-1992, 1992-1996, 1996-2000, 2000-2004, 2004-2008, 2008-2013, and 2013-2016). The classification was conducted using the support vector machines (SVM) technique. A hybrid simulation model that combined the Markov-Chain and Cellular Automata (MC-CA) was used to predict the future urban sprawl existing by 2024 and 2032. Research analysis explored the significant expansion in urban cover which was manifested at the cost of cultivated land. The urban area totaled 40.53 km 2 in 1988, which increased to 144.35 km 2 in 2016 with an average annual growth rate of 9.15%, an overall increase of 346.85%. Cultivated land was the most affected land-use from this expansion. A total of 91% to 98% of the expanded urban area was sourced from cultivated land alone. Future urban sprawl is likely to continue, which will be outweighed by the loss of cultivated land as in the previous decades. The urban area will be expanded to 200 km 2 and 238 km 2 and cultivated land will decline to 587 km 2 and 555 km 2 by 2024 and 2032. Currently, urban expansion is occurring towards the west and south directions; however, future urban growth is expected to rise in the southern and eastern part of the study area, dismantling the equilibrium of environmental and anthropogenic avenues. Since the study area is a cultural landscape and UNESCO heritage site, balance must be found not only in developing a city, but also in preserving the natural environment and maintaining cultural artifacts.

Urban Expansion Occurred at the Expense of Agricultural Lands in the Tarai Region of Nepal from 1989 to 2016

“…Notwithstanding limited localized studies of urban sprawl around individual Nepalese urban centers [18,22,29,[34][35][36][37][38][39], there have been no large scales analyses of Nepalese urban expansion and its implications for land-cover change and agricultural production. In response, this article observes long-term trends in urbanization in the Tarai region and highlights associated underlying socio-economic factors.…”

Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy

“…Urban growth leads to land development, industrialization, and urbanization, but creates problems related to population, traffic, and environmental degradation [4].These challenging factors have effects on past, current, and future urban land in terms of urbanization and economic activities. It is therefore important to use historical satellite data using remote sensing techniques and an authentic model for future forecasting of LULC [5,6]. The major premise of change detection through remote sensing is that it can identify the aberrant and normal transitions in land cover between two or more dates [7].…”

Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management

“…They also ignore differences among the cells in terms of their temporal evolution characteristics at different urbanization stages". They offered a gradient cellular automaton for solving such problems and conclude that "simulation pattern derived from the gradient CA can better reflect the local disparity and temporal characteristics of urban dynamics" [32]; • determining linkages between educational infrastructure and shifts in the pattern of spatial allocation of land use change; the authors used cellular automata (parameterized on the Bayesian weights of evidence method) and analyzed the impact of educational infrastructure on urban land use change in a selected peri-urban area [33]; • modeling urban land expansion and land use/cover change (LUCC) [34] or modeling urban landscape evolution [35] by integrating cellular automata and Markov chains; • simulating tourism growth; the authors used cellular automata to analyze the impact of the spatio-temporal growth of the city on tourism growth [36];…”