# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Data Collection and Processing  
To assess spatiotemporal LULC and NDVI and perform change detection analysis for Gelephu, the author conducted a study using satellite imagery data. The availability of Sentinel-2 satellite data between 2016 and 2023 was examined and downloaded from the USGS website [[https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/)]([https://ers.cr.usgs.gov/](https://ers.cr.usgs.gov/)) (accessed on 2 February 2022 and 3 June 2023, respectively). The Sentinel-2 images used in the current study for 2016 and 2023 correspond to 24 October 2016, and 21 January 2023, respectively. The selection of satellite images took into account a cloud coverage range of 0-5%, specifically targeting the drier months to minimize cloud interference. This approach was chosen to ensure data availability and to improve the quality of the image classification used for the analysis. The Sentinel-2 provides 13 spectral bands which consist of 10 m, 20 m, and 60 m spatial resolution products and the three red-edge bands that capture the strong reflectance of the vegetation in the near-infrared region of the electromagnetic spectrum. The Sentinel-2 multispectral imagery is used as it provides better accuracy for LULC as the 10 m RGB band sets and the B8 near-infrared band provide much higher spatial resolution compared to other satellite products. Sentinel-2A images are classified as medium-resolution multispectral images. The most popular open-access remote sensing data for image classification are the satellite products Landsat and Sentinel-2. In the current study, Sentinel-2 imagery is used because it offers a relatively high spatial resolution of 10 to 20 m. A high spatial resolution was required to enable accurate image classification in the heterogeneous state of the study area.  
GIS is utilized in the current project to determine the coordinate reference system (CRS) of the study area using EPSG:32646-WGS 84/UTM-Zone 46N. The UTM Zone 46N includes parts of countries such as India, Bangladesh, Bhutan, Myanmar, and China. This CRS is based on the World Geodetic System 1984 (WGS 84) ellipsoid, a popular reference ellipsoid for worldwide positioning and mapping applications. Within the designated UTM zone, this coordinate reference system provides a localized and accurate basis for a range of areas for GIS operations such as mapping, spatial analysis, and data integration.  
The methodological flow chart implemented for the image classification workflow is shown in Figure 2, which shows the data download, pre-processing, and post-processing of LULC and NDVI including the change detection and statistical evaluation. Two sets of Sentinel-2A satellite data from 2016 and 2023 are archived. Each data set is processed in the SCP in QGIS to first convert the Sentinel-2 band set raster from calibrated digital numbers (DN) to reflectance. Based on this degree of reflection, the colour composites required for image classification are then generated. The colour composites are essentially helpful in creating Regions of Interest (ROI) that helped formulate training data when implementing the RF algorithm during the image classification process [17]. In particular, the colour composites such as band sets 4-3-2 (true colour), 5-6-2 (healthy vegetation), 5-4-3 (infrared colour, vegetation), and 7-6-4 (false colour, urban) were analysed and used as shown in Figure 3.  
Figure 1: Study area. (**a**) Geographical location of Bhutan; (**b**) Digital elevation model of the study area.