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Land use and land cover changes have greatly contributed to global warming and increased urbanization during the last two decades. It is crucial to track alterations of the globe's surface's usage of land as well as cover in order to obtain accurate information about the study region for all development planning scenarios. Land cover categorization is critical for identifying changes, managing development, and tracking and assessing the ground's surface area. Along with RS and GIS methodologies, machine learning algorithms have recently gained popularity in land use and land cover change detection studies. This present study was conducted to detect land use and land cover (LULC) in a heterogeneous landscape of Pauri Garhwal district of Uttarakhand, India by using different machine learning classifiers, i.e. (i) Random forest. (ii) SmileCart (iii) SmileGradientTreeBoost and (iv) SVM. Overall accuracy and kappa coefficient found for RF, SVM, CART and GTB were (0.98, 0.96), (0.97, 0.95), (0.80, 0.75) and (0.89, 0.80) respectively. Results show that Random Forest (RF) provided highest classification accuracy among all four classifier. [ABSTRACT FROM AUTHOR]

The relationship between humans and nature must be taken into account as LULC changes in order to map and monitor the natural resources accessible on earth's surface. The amalgamation of remote sensing and GIS advancements in technology has made it easier to track variations in land use and cover from times gone by to the today. To properly represent the city, decision makers and policy makers frequently need accurate and updated LULC data. By utilizing remote sensing and GIS methodologies, this study piece illustrates the changes to the LULC status and driving factors associated to it in study area from 2010 to 2020. Machine learning Random Forest (RF) classifier was used to categorize LANDSAT photos in Google Earth Engine. The results of this analysis show that the Pauri Garhwal district in the state of Uttarakhand has experienced a rapid LULC changes from 2010 to 2020 using GIS and remote sensing methods. The fundamental reason for these changes in the study area is the conversion of forest, agricultural land, and arid land into urban areas. Therefore, it is crucial to control the factors causing LULC changes for sustainable ecosystem; otherwise, these LULC classes lose their ability to provide essential services for a healthy ecosystem. [ABSTRACT FROM AUTHOR]