Monitoring land use and land cover change using ensemble machine learning classifiers

SUBHRA SWETANISHA; AMIYA RANJAN PANDA; DAYAL KUMAR BEHERA; SHREELA DASH

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Monitoring land use and land cover change using ensemble machine learning classifiers

Author(s)

SUBHRA SWETANISHA School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

AMIYA RANJAN PANDA School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

DAYAL KUMAR BEHERA Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India

SHREELA DASH Department of Computer Science and Engineering, Centurion University of Technology and Management, Bhubaneswar, India

Abstract

Land Use and Land Cover (LULC) classification is a better tool for change detection and other remote sensing applications. Changes in LULC have become a vital aspect of conventional strategies for land cover monitoring. The aim of this work is to monitor the changes using classified images of the machine learning models. A change detection exercise was conducted in the research region, namely Kendrapara District, Odisha. Landsat7/8 imagery was used in 1999 and 2020 to track potential changes, particularly in agricultural land and urban or built-up land, to detect urban growth. This work uses three machine learning models to determine the best classifier from SVM, XGBoost and Ensemble Model of SVM and XGBoost. The ensemble classifier outperforms the other two machine learning models. Then the best model is used to analyze the change. This change detection may enable the government to implement legal measures & norms and develop the city.

Publication Details

Page(s) 5179-5188

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 18, Year: 2022

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Keywords

SVM, change detection, Land Use and Land Cover, East Odisha, XGBoost

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