Journal of AgriSearch, 9 (3): 265-269 An Open Access International Peer Reviewed Quarterly ISSN : 2348-8808 (Print), 2348-8867 (Online) https://doi.org/10.21921/jas.v9i03.11013 Decadal Land Use Land Cover Change Analysis using Remote Sensing and GIS in Nagpur city of Maharashtra, India ARTI KUMARI *, ASHUTOSH UPADHYAYA , S S NAGARKAR , NAGIREDDY M REDDY , 5 RAJKISHORE KUMAR AND ANIL K SINGH 1 2 3 4 6 ABSTRACT INTRODUCTION 265 An attempt has been made to analyze the LULC change pattern of Nagpur over the past decade (2010-2020) using remote sensing and GIS. In this study, the LULC map for selected years was prepared by supervised classification using a maximum likelihood algorithm from Landsat data, and accuracy assessment by confusion matrix. The results showed that there were major changes in built-up areas (17.37% expansion) and barren land (19.32% deduction). However, water bodies and forest cover decreased slightly by 0.17% and 0.76%, respectively. Overall, the acreage used for agriculture increased by 2.88% and seems to have been replaced by barren / forest areas. Overall, the LULC change detection algorithms used for classification was very effective with an overall accuracy of 78.88 and 73.30% and a kappa coefficient of 0.74 and 0.67, respectively for 2010 and 2020, considered substantial. Overall, Nagpur's land cover changes constantly due to overcrowding; water and forest bodies are adversely affected by rapid urbanization. The study concludes that previous 10 years of Nagpur LULC trend analysis will help to understand land use change pattern by line departments and take necessary actions to reduce the negative impact of land use and land cover change, as well as proper land use planning and management of the Nagpur city. Land use and land cover; remote sensing and GIS; maximum likelihood; confusion matrix Keywords: Land use change assessments are very important in understanding the relationship between humans and nature (Halimi et al., 2017; Pasha et al., 2016). Significant changes and technological advances at the regional level have led researchers to collect more information. Over the last three decades, the combination of remote sensing and GIS tools has made it easier to monitor changes in land use land cover (LULC) from the past to the present, as surface changes are more rapid and widespread (Reid et al., 2000; Hossain et al., 2020). This technology has changed locally and globally and has brought great benefits to the scientific community. Now, LULC is changing rapidly due to rapid urban settlements and overpopulation (López et al ., 2001; Sikarwar and Chattopadhyay, 2016; Riggio et al., 2017). Urbanization is a rapid land use change process that results in a variety of spatial patterns throughout the landscape. Natural resources are also depleted due to corresponding population growth and inadequate land management. However, these changes may be due to several factors also that depend on the socio- economic, political and climatic conditions of each region (Kafi et al., 2014). Despite of it, climate change brings a number of challenges, particularly in terms of soil and water quality, quantity, and long-term sustainability, all of which necessitate judicious management (Abhilash et al. 2020). In case of land management, optimal land use requires not only information on existing land use / land cover, but also the ability to monitor the dynamics of land use changes. Although, land use management necessitates the creation of a comprehensive land cover (LC) database and expert systems that serve as a foundation for natural resource management and land use planning (Abhilash et al. 2021). Therefore, up-to-date information on the speed and nature of change is essential for proper land use planning and management of land resources for productive use. Throughout the history of remote sensing, various change detection algorithms have been used for detection and new technologies are still underway. Data from remote sensing satellites is the primary source of information that provides the potential to obtain information about changes in LULC over the last few decades, using a wide variety of algorithms depending on research needs. Generally, satellite images were used to prepare LULC map using various algorithms like support vector machine, maximum likelihood, artificial neural network etc. Among these algorithms, the support vector machine, maximum likelihood, neural network, and Mahalanobis distance excelled in high accuracy where as Minimum distance, spectral angle mapper, and spectral information divergence achieve moderate accuracy while parallel piped achieves low accuracy (Esmail et al. 2016; Zewdie and Csaplovies 2017; Chughtai et al. 2021). Keeping in view, an attempt has been made to study land use/land cove changing pattern over decades (2010-2020) in Nagpur city using supervised classification with Maximum Likelihood algorithm. Such type of analysis gives LULC changing pattern that is beneficial for future replanning of natural resources utilization within Nagpur city. 1, 2, 6 Div. of Land and Water Management, ICAR Research Complex for Eastern Region, Patna, Bihar, India 3 Deptt. of Agricultural Engg., College of Agriculture, Dr. BSKKV, Dapoli, Maharashtra, India 4 Deptt. of Civil Engineering, NIT Trichy, Kerala, India 5 Deptt. of Soil Science and Agricultural Chemistry, Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India *Corresponding Author E-mail: kumariarti995@gmail.com Received on Accepted Published online : : : 18/07/2022 12/09/2022 30/09/2022