Classification of Vegetation using Sentinal-2 Images of Jodhpur Region

Plants are the Foundation of Ecosystem. It is necessary to know the Crop Diversity in an Area. This project suggests classification of vegetation using image analysis from remote sensing. We categorize the image into different classes using a variety of algorithms like SVM, CART, RandomForest on Sentinal-2 data. It has spatial resolutions of 10m for Jodhpur, Rajasthan in Google Earth Engine. Finally, we use graphs to visualize this effort. This technique allows us to determine the amount of cropland and Forest that is present there. The current study's goal was to categorize vegetation on the Google Earth Engine platform using three different machine learning algorithms—specifically, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and compare the efficiency of each algorithm using accuracy evaluations. Through supervised categorization, the vegetation of the study area is classified. This study will help researchers in Agriculture sector and scientists, in the department of forestry.