Abstract
In this study an attempt to generate the LULC maps and investigate change detection analysis over a period of 22 years using Landsat satellite images of 1994, 2000, and 2016 and to predict the LULCC for the year 2016-2032 using CA Markov model in Udham Singh Nagar district, Uttarkhand. Satellite images of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI sensor of nominal spatial resolution 30m were used. Supervised image classifications with the help of parallel pipe algorithm were used in this study. The validity of the Cellular Automata Markov model were used to predict future (16 years) LULC of 2032. The estimation includes two modules to predict the future land use pattern of the study area such as MARKOV and CA-MARKOV model/modules. Commonly, the accuracy of the classification results is assessed by the error matrix calculation. The result of overall change detection indicates agriculture, forest, water body and fallow land are decreased by 121.75 Km2 (14%), 44.70 Km2 (5%), 38.91 Km2 (4.5%) and 230.71 (26.5%); settlement and river sand are increased by 379.89 Km2 (44%) and 56.18 Km2 (6%). The study has an overall classification accuracy 76.84%, and standard kappa coefficient value (K) of 0.722. The model predicts the future change detection in agriculture 32%, forest 38%, fallow land 5%, settlement 20%, water body 3%, and river sand is 2%. This study is very effective for future LULC prediction that is helpful in urban development planning and the field of management of natural resources.
DETECTING WATER BODIES IN LANDSAT8 OLI IMAGE USING DEEP LEARNING
