Here is an extensive English summary of the presentation **"Raster Data & Remotely Sensed Images – Practical"** by Thomas Bauer and Franz Suppan from the University of Natural Resources and Life Sciences, Vienna.

This is Part 2.4 of the Geodata Management Course.

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### **Overview**

The presentation provides a comprehensive practical introduction to handling raster data and satellite imagery, focusing on data acquisition, preprocessing, visualization, and analysis using tools like QGIS.

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### **Main Topics Covered**

#### **1. Data Acquisition**

* **Satellite Data Catalogues:**

  * *ESA Copernicus Hub:* [scihub.copernicus.eu](http://scihub.copernicus.eu)
  * *RemotePixel:* [remotepixel.ca](http://remotepixel.ca)
  * *BOKU EODC Portal:* [s2.boku.eodc.eu](https://s2.boku.eodc.eu)
  * *USGS Platforms:* [glovis.usgs.gov](http://glovis.usgs.gov), [earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)
  * *NASA Reverb:* [reverb.echo.nasa.gov](http://reverb.echo.nasa.gov/reverb)
  * *GLCF:* [landcover.org](http://www.landcover.org)
* **Commercial Sources:**

  * Apollo Mapping, Digital Globe, and E-GEOS provide high-resolution data such as WorldView, GeoEye, QuickBird, etc.
* Most platforms require a free account for data access and download.

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#### **2. Satellite Image Products**

* **Sentinel-2 Levels:**

  * *Level-1C:* TOA (Top of Atmosphere) reflectance, 100x100 km² tiles, UTM/WGS84 projection
  * *Level-2A:* BOA (Bottom of Atmosphere) reflectance, same tiling and projection
* **File Format:** JPEG2000 (.JP2)
* **Spectral Bands:** 12 bands (B01–B12) covering visible, NIR, and SWIR regions

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#### **3. Image Preprocessing in QGIS**

* **Creating Multi-Band Images:**

  * Use the GDAL tool *Raster Miscellaneous → Merge* to stack layers.
* **Band Composition & Contrast Enhancement:**

  * Custom RGB compositions (e.g., 7,3,2 for Sentinel-2)
  * Adjust symbology via Layer Properties
* **Clipping:**

  * *Raster → Extraction → Clip Raster by Mask Layer* to focus on specific areas
* **Raster Calculator:**

  * Perform operations like NDVI calculation

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#### **4. Vegetation Indices – NDVI**

* **NDVI (Normalized Difference Vegetation Index):**

  * NDVI = (NIR - Red) / (NIR + Red)
  * For Sentinel-2: NIR = B08, Red = B04
  * Used to assess vegetation health, photosynthetic activity, and biomass
* **Color Rendering & Visualization:**

  * Customize NDVI layer symbology to highlight values
* **Time Series Analysis:**

  * View average NDVI trends over the year, e.g., in Vienna forest
  * Example dataset: `NDVI_field_example.zip` (available via BOKUlearn)

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#### **5. Plugins for Enhanced Analysis**

* **Value Tool Plugin:**

  * Hover the mouse over a raster image to inspect pixel values
* **EO Time Series Viewer Plugin:**

  * Load multiple NDVI images
  * Automatically extracts dates
  * Enables profile visualization and style customization

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### **Conclusion**

This practical guide enables students and practitioners to work effectively with satellite imagery using open-source tools, focusing on Sentinel-2 data. The workflow includes data acquisition, band composition, visualization, NDVI calculation, and time series analysis, forming a solid foundation for applications in vegetation monitoring and land cover analysis.

If you need this summary as a PDF or structured for a report or presentation, just let me know!
