🚀 Project Name: Satellite Analysis
👩🏻💻 Author: Nisa Ünnü
🗓️ Verison: v1.0 (latest version)
The purpose of the satellite analysis project is to process, visualize and perform various analyzes of multispectral data obtained from satellite images. These analyzes include 3D visualization of vegetation health, temperature index, moisture index, roughness index and elevation data.
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Data Reading and Visualization: Libraries such as geopandas, pathlib, glob, earthpy, and rasterio are used to manipulate file paths, read raster data, and manipulate geospatial data. numpy and matplotlib libraries are used to manipulate and visualize data. -
Band Visualization: A loop is used to visualize different bands of Sentinel satellite data. -
Histograms: Histograms are drawn for each band. Histograms show the distribution of pixel values. -
Determining the Position of the Satellite Image on the Map: The pixel coordinates in the middle of the satellite images are converted into geographical coordinates and displayed on the map. -
NDVI (Normalized Difference Vegetation Index) Analysis: NDVI is calculated and visualized from near infrared (NIR) and red (Red) bands. -
Temperature Index Analysis: The temperature index is calculated and visualized from Infrared (IR) and Thermal Infrared (TIR) bands. -
Humidity Index Analysis: Humidity index is calculated and visualized from SWIR (Short Wave Infrared), NIR (Infrared) and another SWIR band. -
Rough Surface Detection: Roughness index is calculated and visualized from different bands. -
Visualizing Elevation Data as a 3D Surface Graph: Elevation data obtained from the Sentinel-2 image is visualized as a 3D surface graph.
Your thoughts and feedback are appreciated to help the Satellite Analysis project improve. For your contributions or questions, please contact Nisa Ünnü.
Thank you for exploring Satellite Analysis!
Happy learning and coding!