Past and Future Trajectories of Farmland Loss Due to Rapid Urbanization Using Landsat Imagery and the Markov-CA Model: A Case Study of Delhi, India

Abstract

This study integrated multi-temporal Landsat images, the Markov-Cellular Automation (CA) model, and socioeconomic factors to analyze the historical and future farmland loss in the Delhi metropolitan area, one of the most rapidly urbanized areas in the world. Accordingly, the major objectives of this study were: (1) to classify the land use and land cover (LULC) map using multi-temporal Landsat images from 1994 to 2014; (2) to develop and calibrate the Markov-CA model based on the Markov transition probabilities of LULC classes, the CA diffusion factor, and other ancillary factors; and (3) to analyze and compare the past loss of farmland and predict the future loss of farmland in relation to rapid urban expansion from the year 1995 to 2030. The predicted results indicated the high accuracy of the Markov-CA model, with an overall accuracy of 0.75 and Kappa value of 0.59. The predicted results showed that urban expansion is likely to continue to the year of 2030, though the rate of increase will slow down from the year 2020. The area of farmland has decreased and will continue to decrease at a relatively stable rate. The Markov-CA model provided a better understanding of the past, current, and future trends of LULC change, with farmland loss being a typical change in this region. The predicted result will help planners to develop suitable government policies to guide sustainable urban development in Delhi, India.

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

“…Measuring and, above all, modeling urban expansion and its impacts requires the exploitation of both (historical) remote sensing data and data on actual socioeconomic attributes [2,8,13]. Urban modeling thus enables policymakers and officials to understand and foresee the future expansion and helps in designing LUC policies better geared towards sustainable development [18,31,40].…”

“…The neighborhood cells are the adjacent pixels: the cells that are the immediate neighboring cells of a particular cell in the grid. Finally, the transition rules spell out the transformation of cells into another state due to the change in neighborhood cells' status [2,22,48].…”

“…Urbanization and urban sprawl have become every-day realities across the world [1][2][3]. The world's cities are now home to more than half of the total world population [4], even though "urbanization" consists of variegated processes that unfold unevenly in time and space.…”

Spatiotemporal Changes and the Driving Forces of Sloping Farmland Areas in the Sichuan Region

“…The influence of agricultural intensification factors, such as the multiple cropping index and sown areas of crops, was relatively weak. Given the decrease in the overall slope cultivated area and the increased portion of moderately steep slope land (10-25 • ) in the cultivated area, special attention should be paid to the scientific conservation of sloping farmland during rapid social and economic development.Sustainability 2019, 11, 906 2 of 16 has significantly decreased because of degradation and the conversion of farmland into building land [17,18], where the increasing speeds of construction and forest land are faster than other land use types [19].Slope cropland is an ecosystem involving the interaction of both natural and social factors [20][21][22]. The evolution of the sloping farmland system has not only been influenced by natural conditions, but also by social and economic factors [23,24].…”

“…Slope cropland is an ecosystem involving the interaction of both natural and social factors [20][21][22]. The evolution of the sloping farmland system has not only been influenced by natural conditions, but also by social and economic factors [23,24].…”