Land use and land cover classification using machine learning algorithms in google earth engine

Natural resources are under tremendous amounts of threat as a result of the expanding human population, which over time intensifies changes in Land use and Land cover (LULC). Understanding how various machine learning classifiers function is crucial as the demand for an accurate estimate of LULC from satellite images. The purpose of this research was to classify the LULC in the entire Karnataka state, using three distinct methods on the Google Earth Engine (GEE) namely RF (Random Forest), SVM (Support Vector Machine) and CART (Classification Regression Trees), are examples of machine learning techniques. The LULC is classified by the training sets using supervised classification. The NDVI (Normalized difference vegetation index) was assessed and used to increase classification accuracy. The LULC classification for the years 2015 to 2021 utilizes multi-temporal images like Sentinel-2, Landsat-8, and MODIS data with spatial resolution of 10 m, 30 m, and 250 m. Agricultural land, Built-up land, Forest land, Fallow land, wasteland, water body and others, are major LULC classes, it lies on a level I classification. According to the findings, the change % of agricultural land is high from 2015 (64.03%) to 2021 (67.81%), this roughly increased about 3.78% during the study year. While water bodies increased by 5.25 to 6.3%. Based on the results, the largest LULC group is agricultural land (122,789.4 km(2) or 64.03%), followed by forest land (37,678.56 km(2) or 19.65%). Increased built-up land in the studied area indicates extraordinarily rapid urban growth in major cities of the state. This research offers a reliable approach for comprehensive, automated, and LULC classification in Karnataka State.