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Research Paper | Geoinformatics | India | Volume 10 Issue 3, March 2021
Land Use Land Cover Change Modeling using Multi-Layer Perceptron-Markov Chain; A case Study of Ahmedabad City
Rawal D. | Gupta V.
Abstract: Geospatial technology is now widely used to capture, analyze and manage rapid urbanization in our cities. Consideration and wise use of land are essential guidelines for the advancement of human culture. Land use studies are critical to the progress of human culture. From this point of view, the evolution of land use and land cover (LULC) is a very important subject to consider. The amount of land used for each purpose is constantly evolving. Land use is affected by a number of factors such as physical, economic and social factors. Therefore, land use and land cover information (LULC) is critical for all types of natural resource management and action plans. Ahmedabad shows remarkable activities in terms of urbanization and industrialization over the past few years. It is essential to study the trends and magnitude of changes in LULC as well as the population change in the towns and villages in the Ahmedabad City for better policy and development planning. This study aims to produce a land use/land cover map of Ahmedabad City to detect the changes that have taken place over a given period and to predict the future scenario using the change detection model. In this study, remote sensing data from the Linear Imaging Self-Scanning Sensor (LISS) IV were used to detect LULC changes in the town of Ahmedabad. Imagery from 2007, 2011, 2015, 2017 and 2020 was obtained for this study. The supervised classification is carried out, five main classes have been selected for Level 1 classification and have been divided into eleven more detailed sub-classes for Level 2 classification. Analysis of changes over the period (2007-2020) revealed very dynamic exchanges across land cover classes. Predict the future LULC scenario for Ahmedabad by using Multilayer Perceptron - Markov Chain Model (MLP-MC) for the year 2033
Keywords: LULC Change Modelling, Image Classification, Change Detection, MLP-MC Model, Prediction
Edition: Volume 10 Issue 3, March 2021,
Pages: 641 - 652