Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

Priyanka Gupta; Sharda Haryani; V. B. Gupta

doi:10.52756/ijerr.2024.v38.014

Authors

Priyanka Gupta School of Data Science & Forecasting, Devi Ahilya University, Indore, M.P., India; D. Y. Patil College of Engineering, Akurdi, Pune, M.H., India https://orcid.org/0009-0003-1670-7561
Sharda Haryani Prestige Institute of Management & Research, Indore, M.P., India https://orcid.org/0000-0001-8720-7095
V. B. Gupta School of Data Science & Forecasting, Devi Ahilya University, Indore, M.P., India https://orcid.org/0009-0005-7385-9523

DOI:

https://doi.org/10.52756/ijerr.2024.v38.014

Keywords:

Arc GIS, Land Use Land Cover (LULC), Machine Learning, Indore

Abstract

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

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