Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model

Abstract

Abstract:Spatial-temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial-temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA-Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA-Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations.

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

“…All Landsat scenes were obtained from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov) ( Table 1). Landsat SR data underwent post-production processing entailing geometric rectification, atmospheric correction and other processing (for more details, http://landsat.usgs.gov/CDR_LSR.php), rendering data suitable for scientific analysis [29,40,46,47] The SR data product includes quality assessment (QA) bands used here to identify and omit pixels with snow, clouds and cloud shadows. Geometric accuracy was verified for all satellite images, which were projected to the UTM projection (datum WGS 1984).…”

“…Nonetheless, we employed the ML classifier here due to its broad familiarity [18,[50][51][52], All Landsat scenes were mosaicked, temporally 'stacked', subset by district boundary, and analyzed using ENVI for 19 out of 20 districts comprising the Tarai study area (Figure 1). The LULC data for the 20th district (Jhapa) was derived from a previous study [29]. District boundaries were buffered by five kilometers when sub-setting a scene for classification.…”

“…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.…”

A Synthesis of Studies on Land Use and Land Cover Dynamics during 1930–2015 in Bangladesh

“…Multiple driving factors are responsible for the LUCC and urban expansion [119]. Since 1980s, the country's population has grown rapidly (87 to 144 million between 1981-2011) [120].…”

Land-Cover Change Analysis and Simulation in Conakry (Guinea), Using Hybrid Cellular-Automata and Markov Model

“…This model has been widely used in many land-cover change simulations, for example, a case study in the Ashanti region (Ghana) based on the CA-Markov model showed an upsurge in built up area, and a decline in agricultural and forest land-cover [47]. In Abuja city (Nigeria), future land-cover simulation has revealed a growing trend in settlement that might take over allotted spaces for green areas and agricultural land [48]. CA-Markov model as rule-based land-cover simulation model, imitates process and often addresses the interaction of components forming a system, with the great capability to handle temporal dynamic [49].…”