Analyzing Vegetation Health in Salzburg Using Sentinel-2 and Google Earth Engine


Overview π
In this post, I demonstrate how to use Google Earth Engine (GEE) and the geemap Python library to assess vegetation health in Salzburg, Austria. By calculating the Normalized Difference Vegetation Index (NDVI) for February 2022, we can understand how active or dormant the vegetation was during the winter season.
Why Salzburg? π
Salzburg is located in a region characterized by a mix of urban areas, forests, and agricultural lands. Its seasonal changes make it a great case study for NDVI-based vegetation monitoring.
For this analysis, I used a 10 km buffer zone centered on the coordinates:
Longitude: 13.0432
Latitude: 47.8095
Time Period π
I selected February 1 to February 28, 2022 - a time of year typically associated with low vegetation activity in temperate climates due to snow cover and dormant plant life.
Data Source & Tools π°οΈ
Satellite: Sentinel-2, Surface Reflectance (SR_HARMONIZED)
Index: NDVI (Normalized Difference Vegetation Index)
Libraries:
earthe
ngine-api
andgeemap
Resolution: 100 meters per pixel
What Is NDVI?
NDVI is calculated using the near-infrared (NIR) and red bands of multispectral satellite imagery:
$$NDVI= \frac{\text{NIR} - \text{Red}}{\text{NIR} + \text{Red}}$$
NDVI values range from -1 to 1:
π < 0 - water, snow, or clouds
πΎ 0.2 β 0.5: moderate vegetation
π³ \> 0.5 - dense, healthy vegetation
Method Summary
Using Python, I:
Created a point geometry centered on Salzburg.
Defined a 10 km radius buffer around it.
Fetched Sentinel-2 imagery for Feb 2022.
Calculated the NDVI.
Computed the mean NDVI for the buffer region.
Visualized it on an interactive map.
Result: Mean NDVI in Salzburg π
Mean NDVI: 0.2947
Interpretation
An NDVI value of approximately 0.29 in February is quite moderate for a European winter. This suggests:
Presence of evergreen forests, which retain moderate NDVI levels year-round.
Possibly mild winter conditions or early vegetation regrowth.
Limited snow cover in the imagery, allowing more soil and vegetation reflectance to be captured.
While we generally expect lower NDVI values in February, the result shows that even in winter, natural ecosystems in and around Salzburg retain some photosynthetic activity.
Visual Output πΊοΈ
I also generated an NDVI map centered on Salzburg, color-coded from red (low vegetation) to green (high vegetation), which provides visual context for the analysis area.
NDVI at 0%
NDVI at 35%
NDVI at 100%
Charting the Course
To better understand seasonal vegetation patterns, I plan to:
Compare NDVI values across multiple months (e.g., July vs. February)
Run the same analysis over forested vs. agricultural zones
Export NDVI maps for visualization and sharing
Final Thoughts π¬
NDVI is a powerful tool for monitoring ecological health using freely available satellite data. With tools like Google Earth Engine and geemap, we can easily visualize and analyze changes in the Earth's surface, even down to specific regions like Salzburg.
Stay tuned for a follow-up post comparing summer vs. winter NDVI across different parts of Austria!
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