# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 1 Introduction  
The evolution and transformation of land use land cover (LULC) has emerged as one of the drivers of global environmental change [1]. The LULC changes are mainly driven by urbanization thrusts, population growth, depletion of natural resources and climate change impacts [2]. Remote sensing (RS) technology is a powerful tool and offers a more convenient and efficient approach to obtaining information about the Earth's surface [1]. The fusion of Geographic Information System (GIS) and RS has gained widespread prominence in the context of LULC mapping and monitoring, effectively supporting sustainable development programs in different geographic contexts [3]. The focus on the Himalayan region has beena breeding ground for studies using GIS and RS techniques to examine LULC dynamics and provide valuable insights for sustainable planning and management efforts [4] and supported policy and decision-making processes in different regions, e.g., [5]. Undoubtedly, the LULC maps are among the most important documents providing information for various applications in land use policy development, agricultural monitoring, urban planning, ecosystem services, nature conservation, and dynamic assessment [6]. Furthermore, the application of artificial intelligence (AI) has been notable in the areas of earth monitoring and earth system sciences [7]. Machine learning [8; 9] and deep learning techniques [10; 11; 12] have emerged, amplifying the effectiveness of GIS and RS technologies. In addition, GIS-based image classification methods have made significant progress in LULC studies [13; 14], including the assessment and identification of changes in dynamics [15; 16]. In particular, image classification for geospatial mapping has received significant attention given the availability of medium to high-resolution multispectral satellite imagery [17], while the global products available are very broadly suited to local planning and management. Sentinel-2 satellite imagery has been widely used for LULC studies [18; 19; 20; 21]. As of 2015, the Sentinel-2A Multispectral Imager (MSI) offers 13 optical bands: four bands at 10 m spatial resolution, six bands at 20 m spatial resolution, and three bands at 60 m spatial resolution and the 10 m spatial resolution can be used to improve the spatial resolution of other 20 or 60 m bands [22; 23]. Furthermore, the spatial and temporal resolution of Sentinel-2 imagery offers high potential for the generation of accurate datasets [24], including new opportunities for specific applications and investigations into lithological classification [25], mapping nitrogen-phosphorus ratio in feed [26], crop type mapping and identification [27; 28], identification of ancient artifacts [29] among others. In particular, the combination of Sentinel-2 satellite imagery and an appropriate classification method has been shown to provide better results, especially for the heterogeneous environments of the Himalayas [17]. Similarly, Landsat datasets are also widely used for RS studies as they provide spatially resolved decadal time series over 30 years, despite offering 30 m resolution and a 15 m panchromatic band. Other sources of data for LULC studies are Satellite Pour I'Observation de la Terre (SPOT), Synthetic Aperture Radar (SAR), and Moderate Resolution Imaging Spectroradiometer (MODIS). The SCP in QGIS provides a set of tools for geospatial analysis. The SCP is a Python tool for downloading and processing remote sensing data [30], which was developed in the QGIS environment [31]. The SCP plugin works semi-automatically and provides a platform to download several RS products, facilitates the processing of satellite imagery, and automatically implements unsupervised and supervised classification by setting some input parameters. The SCP plugin implements the traditional pixel-based approach to image classification.