Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

Abstract

In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km2) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.

Determine the Land-Use Land-Cover Changes, Urban Expansion and Their Driving Factors for Sustainable Development in Gazipur Bangladesh

“…Fast remote-sensing-based LULC mapping is now available and offers high accuracy at a reduced price [13]. Remote sensing is a dynamic tool that provides valuable information series covering both time and space in detail [14]. A global positioning system (GPS) is an important device used to gather field data as reference data to calibrate a classification or in a Geographic Information System for mapping [15].…”

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

“…The said grid size is set with the minimum spacing range in mind where features of one point can significantly influence LST. The LST and LULC data from previous steps are utilized for the training of a Neural Network in Terrset in order to predict LST (Vinayak et al, 2021). Additionally, we input the information on the latitudes and longitude of the defined samples to improve the model efficiency considering the notion; the more the input parameters, the better the network model's efficiency.…”

Urban modelling and forecasting of landuse using SLEUTH model

“…The researchers used models that included the multitemporal LULC information along with geographical and socio-economical urban growth driving variables such as elevation, slope, distance from roads, population and other geographic components. In the recent past, some popular static and dynamic models such as Multi-Layer perceptron (MLP) using Land Change Modular (LCM) (Megahed et al 2015;Vinayak et al 2021), CA-Markov (Nath et al 2020;Khwarahm et al 2021a, b), ANN-based prediction (Rahman et al 2017;Anand and Oinam 2020), SLEUTH urban growth model (Ilyassova et al 2021;Alay et al 2021) and ANN-Markov chain-based model (Al Rifat and Liu 2022) were used by different researchers globally. These models are compared in Table 1.…”