Cellular Automata and Markov Chain Based Urban Growth Prediction

Abstract

Remote sensing and Geographic Information System (GIS); plays a vital role for studying Land Use Land Cover (LULC) and identifying the main factors for useful outcomes. Assessment of the urban growth pattern is extremely essential as sprawl is seen as one of the potential threats for urban planning. The project has been carried out for the Land Use Land Cover classification of Gandhinagar district of Gujarat state. Gandhinagar city has experienced wide change in LULC in last few decades. It is located at 23.2156° N & 72.6369° E in Gujarat. LULC mapping of Gandhinagar was carried out using LANDSAT Multispectral, TM, ETM+, and OLI/TIRS images for the years 1972, 1977, 1987, 1994, 2000, 2008, 2015 and 2019. Landsat data covers Gandhinagar’s vegetation, Water Bodies, Open Area, Agriculture, and Settlement. The area of interest of Gandhinagar was generated from Landsat data using the digitized boundary of Gandhinagar district. The main objective of this project is to generate LULC using different classification method of remotely sensed data of LANDSAT. In this study Supervised classification method was used to generate level 1 classification. It was done on remotely sensed data in ERDAS Imagine 2014 using semi-automatic classification which includes several classes like Settlement, Agriculture, Vegetation, Water Bodies, Open Area, etc. Moreover, after LULC one new thing was done i.e. accuracy assessment which was necessary to do for accurate result. The study result reveals an increasing and decreasing trend in Land use and Land cover respectively.

Keywords

• Land Use Land Cover Change (LULCC)
• Landsat
• Multispectral
• Thematic Mapper (TM)
• Enhanced Thematic Mapper Plus (ETM+)
• Operational Land Imager (OLI)
• Thermal Infrared Sensors (TIRS)
• Accuracy Assessment

Supporting Institution

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Department of Space, Government of India, Dehradun

Project Number

1

Thanks

special thanks to my guide Poonam S. Tiwari for constant support, also to this IJEGEO for helping to publish paper, to my head of department Shital Shukla.

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