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Monitoring the Land Use, Land Cover Changes of Roorkee Region (Uttarakhand, India) Using Machine Learning Techniques
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Author(s): Ashish Kumar (Department of Civil Engineering, Indian Institute of Technology, Roorkee, India), Rahul Dev Garg (Department of Civil Engineering, Indian Institute of Technology, Roorkee, India), Prabhishek Singh (School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India), Achyut Shankar (University of Warwick, Coventry, United Kingdom & Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India), Soumya Ranjan Nayak (School of Computer Engineering, KIIT (Deemed), Bhubaneswar, India)and Manoj Diwakar (Graphic Era (Deemed), Dehradun, India)
Copyright: 2023
Volume: 14
Issue: 1
Pages: 16
Source title:
International Journal of Social Ecology and Sustainable Development (IJSESD)
Editor(s)-in-Chief: Elias G. Carayannis (George Washington University, USA)
DOI: 10.4018/IJSESD.316883
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Abstract
Satellite images play an important role for capturing Earth's surface. Using satellite images land cover monitoring could be done through which the modification or changes on land surface could be identified. Comparison can be made on the basis of past satellite image analysis, which helps to identify the changes that are occurring or have already occurred. Although there exist many techniques for land cover monitoring, proper land cover identification and detection of changes on the land cover is still a challenge. In the recent years, machine learning techniques have been utilized in distinct areas of image analysis and resulted in positive outcomes. Hence, in this paper, four supervised machine learning algorithms (i.e., support vector machine [SVM]), neural network [NN], maximum likelihood [MLC], and parallelepiped [PP] algorithms) have been utilized for land cover identification and detecting the amount of changes that have occurred in the individual land cover classes.
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