Vegetation Typification Integrated with Time Series Analyzed Machine Learning Algorithimic Model
G. JayaLakshmi, Kannuri Sai Chandra Rohith, T. Pranoom, Valluru Hari Vamsi · 2024
Using the Google Earth Engine platform, we surveyed all vegetation types using information on remote areas. Specifically, we use Landsat images and Sentinel2A data for conversion. Our aim is to improve the quality of vegetation by typification of vegetation using the various capabilities of the Landsat images. We use state-of-art image processing and machine learning algorithms to accurately classify different plant species in selected study areas. We also track temporal changes in vegetation using Sentinel2A imagery, making it possible to analyze land cover changes over time. Our approach to facility monitoring and change detection is broad because it combines the unique and rich nature of Sentinel2A. change the world. Where we used the starch based mechanism for the plant based classification scientifically and then make them to classified (ndvi) index ranges