# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Image Classification Methods  
#### 2.3.1 The SCP and Random Forest Classifier  
The SCP is a powerful QGIS plugin and offers a range of tools for geospatial analysis. The SCP is a Python graphical user interface (GUI) tool developed by [30] in the QGIS environment for downloading and processing remote sensing data. Typically, SCP currently supports Landsat, Sentinel, MODIS, and GOES satellite data and can process any size of corresponding RS data and essentially does not require a high-end computer. The SCP plugin works on most major operating systems as QGIS is a cross-platform open-source software compatible with Windows, macOS, and Linux. The SCP 7.10.11 was used in the current study. The SCP plugin provides a semi-automatic workflow to download multiple RS products, facilitates the processing of satellite imagery, and implements unsupervised and supervised classification. The SCP plugin implements the traditional pixel-based approach to image classification. The SCP is also integrated with the Sentinel Application  
Figure 2: Implementation of the methodology. Bands B2 to B8A and B11-B12 were used for image classification (LULC) in SCP (a total of 10 optical bands with 10 m and 20 m resolution are included in the image processing. B1, B11, and B12 with a resolution of 60 m are excluded). For NDVI, only the two optical bands B4 and B8 are used for analysis. Platform (SNAP) application tools, enabling SCP to implement the Random Forest Classifier, a supervised machine learning algorithm. Efficient classifier selection is required for successful image classification to classify the spectral signature features. RF classifier is widely applied as it offers efficient and high performance resulting in high accuracy of the classification model [46]. In machine learning, the typical workflow involves dividing the dataset into two parts: the training set and the testing set. The training set is used to train the machine learning model, while the testing set is used to evaluate its performance and generalization ability on unseen data that executes an ensemble learning method based on a decision tree, combined with massive ensemble regression and classification trees [8]. However, in the context of the QGIS-based SCP, the focus is on classification tasks for the ROI classes using RF. The vegetation, water, built-up, and barren land ROIs were created with polygons using composites grouped under each class with similar spectral signatures and tested for preview. These ROIs can be reviewed until the classification reaches sufficient accuracy. In addition, higher thresholds such as the number of training samples and decision trees to run the RF classifier are set to achieve higher performance of the classification model. SCP uses the training data iteratively to improve the classification instead of using a separate testing set. The workflow is highlighted below. * User-defined training areas: The user manually selects representative samples from the image, called ROI or training polygons. These areas should represent the different land cover classes present in the image. * Feature extraction: SCP extracts a set of features (attributes) from the image data within the training areas. These features could include spectral values from different bands, texture measures, indices, or other relevant information. * RF training: SCP utilizes the ROIs and the associated extracted features to train an RF classifier. RF is an ensemble machine learning algorithm that constructs a multitude of decision trees and combines their predictions for classification. * Classification: Once the RF model is trained, SCP applies it to the entire image to classify the remaining pixels. The model uses the extracted features from the image to assign each pixel to a specific land cover class. * Iterative improvement: SCP assesses the initial classification results visually (preview).