4. How to Calculate NDVI from Landsat Satellite Images in Python | Remote Sensing & GIS Tutorial скачать в хорошем качестве

4. How to Calculate NDVI from Landsat Satellite Images in Python | Remote Sensing & GIS Tutorial

Ready to analyze vegetation with remote sensing? In this beginner-friendly tutorial, I’ll show you how to calculate NDVI (Normalized Difference Vegetation Index) from a Landsat satellite image using Python. We’ll go step-by-step, from finding your bands to calculating and visualizing NDVI—perfect for GIS, environmental science, or data science projects! What you’ll learn: What NDVI is and why it matters for vegetation analysis How to find and import Landsat bands in Python Step-by-step NDVI calculation using NumPy How to display your NDVI map with Matplotlib How to interpret NDVI results for real landscapes Tools used: Python (JupyterLab, but works in Jupyter Notebook, Google Colab, or VS Code) Libraries: numpy, rasterio, matplotlib Landsat data downloaded from USGS Earth Explorer Useful Links: USGS Earth Explorer: https://earthexplorer.usgs.gov/ JupyterLab: https://jupyter.org/ rasterio: https://rasterio.readthedocs.io/en/la... Subscribe for more tutorials on remote sensing, GIS, and Python data analysis every week! If you found this helpful, give it a like, drop a comment, and don’t forget to subscribe for more! #RemoteSensing #Python #NDVI #Landsat #SatelliteImagery