Land use land cover classification and tow decaed change detection using remote sensing and GIS of Mungeli district

Introduction

Land use/land cover (LULC) changes are critical indicators of global environmental change. Satellite-based remote sensing data, with their repetitive and synoptic coverage, have proven highly effective in mapping LULC patterns and changes over time. Geographic Information System (GIS) techniques allow for the quantification of such changes, even when spatial datasets differ in resolution or scale. These techniques have enhanced our understanding of the dynamics of human activities across space and time. Land use typically refers to the human utilization of land-such as for agriculture, settlements, or industrial activities-while land cover denotes the physical material on the Earth's surface, including natural vegetation, water bodies, soils, and built-up features (NRSA, 1989). Land cover reflects the biotic and abiotic features present on the Earth's surface, whereas land use describes the ways humans modify or manage this cover. Land cover categories may include forests, grasslands, water bodies, and barren land, while land use includes cropland, built-up areas, and recreational spaces. Over the years, remote sensing and GIS have played a crucial role in LULC mapping across various regions of India. These technologies allow the assessment of land cover transformations with greater accuracy, lower cost, and less time. They also serve as vital tools for sustainable land management and environmental planning. In the present study, an investigation has been carried out in the Mungeli district of Chhattisgarh to detect LULC changes over a 20-year period from 2000 to 2020. This region, which has seen notable changes in agricultural practices and rural development in recent decades, presents an important case for evaluating human-induced impacts on the environment. This study aims to identify and analyze LULC changes and to contribute to sustainable land resource management in the region.

Methodology 2.1 Study area

Aim and Objectives Aim

The aim of this study is to produce a land use/land cover (LULC) map of Mungeli district, Chhattisgarh, in order to detect changes that have occurred over a 20-year period (2000-2020) using change detection techniques.

Objectives 1. To classify and analyze different land use and land cover

types in the study area using remote sensing and GIS techniques. 2. To generate two decade LULC map. 3. To assess spatial and temporal changes in LULC over two decade.

Land Use and Land Cover

Temporal satellite data from USGS Landsat archives was used for the analysis of the study area, covering two reference years: Landsat 7 ETM+ Collection 2 Level-2 data for 2000, and Landsat 8-9 OLI/TIRS Collection 2 Level-2 data for 2020. Satellite images for both pre-monsoon and post-monsoon seasons were selected to capture seasonal variations. Prior to analysis, the images were co-registered, radiometrically normalized, and subset to the study area for further classification and change detection. Land use/land cover (LULC) classification was carried out using the Maximum Likelihood Classifier (MLC) within ArcMap, following a supervised classification methodology. The choice of MLC was guided by extensive literature review, including studies by Rao & Narendra (2006) [5] , Remi et al. (2007) [6] , Chaudhary et al. (2008) [4] , who all found MLC to be a reliable and well-established technique for remote sensing-based LULC mapping. Its robustness and ease of implementation have been well documented by Lillesand & Kiefer (2000). For Level I classification, a total of seven LULC classes were identified: Agricultural Land, Water Body, Forest, Plant, Barren Land and Built-Up Area. These categories were selected to meet the objectives of the study and to effectively map features relevant to land management and environmental assessment.

Classification at this generalized level allows for consistent interpretation across both time periods with an expected accuracy of 85-90%, as supported by Anderson et al. (2001) [1] . This level of classification is particularly suitable for regionalscale studies such as district-wide assessments. The area statistics for each land use/land cover (LULC) category were calculated in square kilometres using the attribute table.

The LULC classes include Agricultural Land, Barren Land, Built-up Land, Forest Land, and Waterbodies. These categories were identified through visual interpretation of satellite imagery, corroborated with field verification. The digitized features were analysed to obtain land use/land cover statistics for each class for both the years under study.

Results and Discussion

The general land use of an area depicts an idea of overall areal utilization of resources, whether natural or cultural. In this paper, changes in the land use and land cover of Mungeli district are evaluated from the differences between a 20-year period (2000-2020), as shown in figureThe satellite datasets used in the current study yielded the following comparisons with respect to land use/land cover change detection: from the current study, it is evident that there has been a considerable decrease in agricultural and forest areas and a significant increase in built-up and waterbody areas over the period of 2000 to 2020. Mungeli district, which is primarily agricultural, is experiencing growing urbanization and land conversion pressures. It is necessary to closely monitor the land use/land cover changes to maintain a sustainable environment and ensure proper regional development.

Conclusion

The present study successfully demonstrates the utility of remote sensing and Geographic Information System (GIS) technologies in detecting, analysing, and interpreting land use and land cover (LULC) changes over a 20-year period (2000-2020) in Mungeli district, Chhattisgarh, India. Through supervised classification of multi-temporal Landsat data, significant patterns of land transformation have been identified, with key findings indicating a decline in agricultural (-4.56%) and forest areas (-2.61%), and a pronounced increase in built-up land (+186.6%) and waterbodies (+255.5%). These changes point toward a shift in land utilization driven by urbanization, population growth, and evolving agricultural practices. Such trends have direct implications for local ecosystems, agricultural productivity, and long-term environmental sustainability. The study underscores the importance of continuous LULC monitoring using high-resolution satellite imagery and GISbased analytics for evidence-based planning and policy formulation. Effective land management strategies are required to mitigate environmental degradation, balance development with conservation, and guide infrastructure expansion in a sustainable manner. Moving forward, integration of socioeconomic data with LULC trends would enhance the scope of such research, supporting holistic land governance frameworks not only for Mungeli district but also for other rapidly transforming regions in India.

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