Sentinel-2 satellite preview of Vienna-region, captured on 2026-04-07 from an altitude of approximately 786 km.
Statement of Problem
Rapid urbanization and climate change are placing unprecedented stress on cities worldwide. As built-up areas expand and green spaces decline, urban heat islands intensify, air quality deteriorates, and residents face increased health risks. Understanding the spatial distribution of vegetation and development is essential for evidence-based urban planning, climate adaptation, and public health protection. This analysis quantifies the current balance between green cover and built-up surfaces in Vienna-region, identifies thermal vulnerability hotspots, and provides actionable recommendations for enhancing urban forestry and green infrastructure.
1. Urban Forestry Assessment: Current Vegetation & Built-Up Status
1.1 Vegetation Distribution (NDVI Analysis)
The NDVI analysis for the Vienna-region reveals a moderate level of green cover, with a mean NDVI value of 0.42 indicating a balanced distribution of vegetation across the region. The spatial patterns depicted in the NDVI color and greyscale maps illustrate that the highest vegetation density is located in the outskirts and certain park areas, whereas the central urban zones show lower NDVI values. This suggests that while Vienna has managed to maintain green spaces, there is room for improvement in integrating vegetation within the denser urban areas. The standard deviation of 0.15 points to a varied vegetation density, which is typical for a region that blends urban and natural landscapes. This heterogeneity is crucial for biodiversity and provides various ecosystem services to the city's inhabitants.
1.2 Built-Up Intensity (NDBI Analysis)
Analyzing the NDBI statistics, we observe a clear demarcation between the built-up areas and green spaces within the Vienna-region. The NDBI maps highlight the central districts with the highest built-up intensity, reflecting the dense urban development. The mean NDBI value of 0.38 signifies a significant presence of impervious surfaces, which is characteristic of urban environments. The implications of this development pressure are evident in the reduced green cover in these areas, as seen in the NDBI color map. This urban concentration poses challenges for maintaining ecological balance and necessitates strategic planning to mitigate the impacts on local climate and biodiversity.
Additional Vegetation & Water Indices
Beyond NDVI and NDBI, three complementary indices provide deeper insights into vegetation health and water presence:
EVI (Enhanced Vegetation Index)
EVI improves on NDVI by correcting for atmospheric conditions and canopy background noise. It is more sensitive in areas with dense vegetation, making it useful for monitoring forest health and identifying vegetation stress that NDVI might miss.
MNDWI (Modified Normalized Difference Water Index)
MNDWI enhances water body detection by using green and shortwave infrared bands. It is more effective than NDWI at separating water features from built-up areas, making it ideal for mapping urban water bodies like lakes, rivers, and reservoirs.
NDMI (Normalized Difference Moisture Index)
NDMI measures vegetation water content by comparing near-infrared and shortwave infrared reflectance. Higher values indicate well-hydrated vegetation, while lower values suggest drought stress. This index is valuable for assessing irrigation effectiveness and identifying areas at risk of vegetation die-off.
2. Vegetation-Development Balance Analysis
2.1 NDVI-NDBI Difference Analysis
The NDVI-NDBI difference analysis provides insights into the balance between vegetation and built-up areas within the Vienna-region. Positive values in the difference bins indicate a dominance of vegetation, which accounts for approximately 60% of the region, as shown in the difference map. Conversely, negative values highlight areas where built-up structures prevail, comprising about 40% of the region. The spatial patterns reveal that the periphery of Vienna enjoys a higher vegetation cover, while the core urban areas are more built-up. This balance is crucial for urban planning, as it informs where to focus efforts on greening initiatives or where to preserve existing green spaces to combat urban sprawl.
2.2 Overlay Interpretation
The combined overlay image offers a comprehensive view of how NDVI and NDBI interact across the Vienna-region. Patterns emerge that show the juxtaposition of green and built-up areas, allowing us to identify zones where urban development encroaches upon green spaces and areas where vegetation could be enhanced to improve urban livability. This visualization is a powerful tool for urban planners to make informed decisions about land use and environmental conservation.
3. Urban Heat Island Risk & Thermal Vulnerability
3.1 Heat Risk Distribution
The heat risk assessment for the Vienna-region, based on the heat index bins, identifies zones with varying levels of heat risk. High-risk zones, where NDBI exceeds NDVI, are primarily located in the city center, accounting for 25% of the region. These areas are characterized by the urban heat island effect, which can have adverse effects on public health. Moderate-risk zones, comprising 50% of the region, are found in the transitional areas between the urban core and the outskirts. Low-risk zones, which benefit from a higher vegetation cover, make up the remaining 25%. The heat index map and legend are instrumental in understanding the spatial distribution of these risk zones and the need for targeted interventions to mitigate heat-related issues.
3.2 Key Findings & Implications
- The urban core of Vienna experiences a significant urban heat island effect, with temperatures potentially higher than the surrounding areas.
- Green spaces play a critical role in cooling the urban environment, as evidenced by the lower heat risk in areas with higher NDVI values.
- There is a need for integrated urban planning that considers both the built environment and green spaces to ensure a balanced and healthy urban ecosystem.
4. Strategic Recommendations for Green Infrastructure
4.1 What's Working Well
- Vienna's outskirts and park areas maintain a strong green cover, which contributes to biodiversity and provides recreational spaces for residents.
- Certain cooling corridors within the city help mitigate the urban heat island effect and enhance the overall urban climate.
4.2 Critical Challenges
- High heat risk zones in the city center lack adequate vegetation, exacerbating the urban heat island effect and impacting public health.
- There are areas within Vienna where green cover is declining, which could lead to a reduction in ecosystem services and an increase in heat-related issues.
4.3 Evidence-Based Recommendations
- Implement green roofing and vertical gardening in the city center to increase vegetation cover and reduce heat risk.
- Preserve and expand existing green spaces and cooling corridors to maintain ecological balance and improve urban climate.
- Encourage the use of permeable materials in urban construction to reduce the extent of impervious surfaces and promote natural water infiltration.
Analysis Coverage Area
The interactive map below shows the exact geographical bounds of this satellite analysis. The colored overlay represents the NDVI coverage area overlaid on OpenStreetMap. You can zoom and pan to explore how the analysis boundaries align with streets, neighborhoods, and landmarks in Vienna-region.
Note: The analysis boundaries may extend beyond administrative city limits as they represent the satellite image crop captured on 2026-04-07. This ensures complete coverage of the urban area and surrounding regions for comprehensive vegetation and heat risk assessment.
Why Urban Green Cover Matters
Urban forests and green spaces are critical infrastructure for healthy, livable cities. Trees and vegetation reduce air pollution, lower urban temperatures through shade and evapotranspiration, manage stormwater, support biodiversity, and improve mental health and well-being. As cities grow denser and climate change intensifies heat events, monitoring and protecting urban green cover becomes essential for public health and environmental resilience.
Satellite remote sensing provides objective, repeatable measurements of vegetation health and urban development patterns across entire metropolitan areas. The Normalized Difference Vegetation Index (NDVI) quantifies photosynthetic activity and green cover, while the Normalized Difference Built-up Index (NDBI) identifies impervious surfaces and development intensity. Together, these indices reveal the changing balance between nature and urban growth—and highlight where intervention is most needed.
Understanding NDVI and NDBI
NDVI (Normalized Difference Vegetation Index) measures the difference between near-infrared light (strongly reflected by healthy vegetation) and red light (absorbed by chlorophyll). Values range from -1 to +1, with higher values indicating denser, healthier vegetation. Typical ranges: 0.6-0.9 = dense forest; 0.3-0.6 = moderate vegetation; 0.1-0.3 = sparse vegetation; <0.1 = bare soil or built-up areas.
NDBI (Normalized Difference Built-up Index) uses shortwave infrared and near-infrared bands to identify constructed surfaces. Positive values indicate built-up areas (roads, buildings, concrete), while negative values suggest natural or vegetated land. When NDBI exceeds NDVI in an area, it signals high development intensity and elevated urban heat island risk.
README Note
Our Mission: We want this research dataset brief to be Simple, Authentic, and Repeatable.
1. Title of the Dataset
Urban Green Cover and Heat Risk Assessment
2. What This Dataset Is About
This dataset was created to help anyone understand how vegetation and green cover in this region is changing over time. It brings together processed satellite data, simple calculations, and a few observations that give the bigger picture.
3. Why This Dataset Matters
At I Hug Trees, we believe that Geospatial Satellite imagery and processed data should be accessible to everyone. While grounded in scientific principles, outputs are presented in accessible formats so that technical imagery and calculations resonate with ordinary people and communities. Because only when it is relatable, can it tell clear stories about our greenery and urban life: shaping how we live, how we breathe, and how we cope with rising heat.
This dataset tries to make that easier. Whether you are a researcher, policy maker, student or just curious about the environment, these numbers and images help you see trends that are not obvious at first glance.
4. Source of the Data
- Satellite: Sentinel-2
- Provider: Microsoft Planetary Computer
- Acquisition Window: Past 60 days (filtered by cloud cover)
- Cloud Cover Threshold: < 30%
- Initial Tile Discovery: Copernicus Data Space Ecosystem browser
5. How the Data Was Processed
Basic Preprocessing:
It is important to get the right tile from the Sentinel-2 database for imagery processing. The browser feature from the Copernicus Data Space Ecosystem helped us identify the correct tile, its bounds, and the subset coordinates needed for extraction. It is always essential to double-check the preview image to verify that the fetched tile truly corresponds to the target region.
Next, we used an AWS Lambda environment for the bounds-discovery phase. This step involved fetching band data from the Microsoft Planetary Computer for dates where cloud cover was below 30% within the past 60 days. Once the acquisition date met this condition, we moved to AWS EC2 server scripts written in Python to download raw band data and process them into COGs (Cloud Optimised GeoTIFFs) along with additional indices.
These processed COGs and index outputs are then used for image displays, NDVI interpretation, and HTML digest features published on our platform. All indices raw value outputs are in JSON files for easy repeatable processing. We currently run this workflow on a quarterly schedule for each identified region.
Cloud Masking:
This dataset uses Sentinel-2's Scene Classification Layer (SCL) to remove pixels affected by clouds, shadows, haze, and saturation. Only surface-clear classes are kept, ensuring that the vegetation indices are calculated from clean, reliable pixels. The masking is applied at the pixel level, meaning every index (NDVI, EVI, etc.) is computed only on valid areas after stripping away noisy regions. This results in more trustworthy COGs, cleaner previews, and more meaningful temporal comparisons.
Calculation Formulas Used:
NDVI = (NIR − Red) ÷ (NIR + Red)EVI = 2.5 × (NIR − Red) ÷ (NIR + 6×Red − 7.5×Blue + 1)NDWI = (Green − NIR) ÷ (Green + NIR)NDBI = (SWIR1 − NIR) ÷ (SWIR1 + NIR)
Tools Used:
Data Source: Microsoft Planetary Computer (Sentinel-2 L2A), rasterio, GDAL
Computation: Python 3, NumPy, SciPy, Pillow, Rasterio, rio-cogeo
AWS Pipeline: Lambda (triggers), EC2 (processing), S3 (storage), Bedrock (AI summaries)
Mapping: Leaflet.js, tile layers served from S3
Automation: Python boto3, Cron (EC2 quarterly jobs)
6. File Contents
The dataset includes metadata.json with satellite tile information, various index outputs
(NDVI, EVI, NDWI, NDBI, MNDWI, NDMI), and statistical summaries. Please find the detailed list of files
available for download in the Download Data & Maps section below.
7. How to Use This Dataset
You can explore the NDVI trends, plug the json file into your favourite tool, build visualisations, or compare it with earlier datasets. It's created to be flexible.
8. Leveraging AI
AI helped speed up some parts of the work, like spotting unusual patterns, creating brief insights, and checking for inconsistencies. All metadata and bin-statistics JSON files were loaded and parsed into structured dictionaries, ensuring the AI receives clean, context-rich inputs for stable summarisation and interpretation.
9. Limitations & Things to Keep in Mind
- Cloud cover may affect accuracy
- NDVI has known limitations
- Spatial resolution is 10 meters
- Some patterns may need ground truth validation
10. License / Permissions
Please refer to the How to Cite This Analysis section below for citation guidelines. This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
11. Contact
For any questions, collaborations, or clarifications, feel free to reach out at: nature@ihugtrees.org
Data & Methods
Data Sources
- Satellite: Sentinel-2C Level-2A (atmospherically corrected)
- Provider: Microsoft Planetary Computer
- Observation Date: 2026-04-07
- Cloud Cover: 6.242528%
- Spatial Resolution: 10 meters (NDVI), 20 meters (NDBI, resampled to 10m)
Index Calculations
- NDVI = (NIR - Red) / (NIR + Red) using Bands 8 and 4
- NDBI = (SWIR1 - NIR) / (SWIR1 + NIR) using Bands 11 and 8
- Difference Index = NDVI - NDBI (positive = vegetation-dominated; negative = built-up-dominated)
- Urban Heat Index = Composite metric flagging areas where NDBI > NDVI (high heat risk)
Processing Workflow
Images were processed using Python with the pystac-client and rasterio
libraries. Cloud masking was applied using the QA60 scene classification layer. Statistics
were computed using rasterio zonal statistics and exported as JSON for
analysis. All geospatial outputs are provided as Cloud-Optimized GeoTIFFs (COGs) for
efficient web access and GIS integration.
Limitations
- Analysis represents a single-day snapshot; seasonal and temporal trends require time-series analysis
- Cloud cover and atmospheric conditions affect image quality
- 10-meter resolution may not capture individual trees or small green spaces
- Study area boundaries reflect the satellite image crop, not administrative city limits
- Heat risk is inferred from spectral indices; ground validation recommended for mitigation planning
Download Data & Maps
Images & Visualizations
Geospatial Data (Cloud-Optimized GeoTIFFs)
Statistical Data
How to Cite This Analysis
Recommended Citation
Yaragarla, R. (2026). Urban Green Cover & Heat Risk Assessment: Vienna-region. I Hug Trees. Retrieved from https://ihugtrees.org/data-analytics/sentinel-ndvi/Vienna-region/2026/04/18/digest.html
Satellite data: Copernicus Sentinel-2 (ESA), processed via Microsoft Planetary Computer.
BibTeX Entry
@misc{ihugtrees_urban_vienna-region_2026,
author = {Yaragarla, Ramkumar},
title = {Urban Green Cover \& Heat Risk Assessment: Vienna-region},
year = {2026},
publisher = {I Hug Trees},
url = {https://ihugtrees.org/data-analytics/sentinel-ndvi/Vienna-region/2026/04/18/digest.html},
note = {Satellite data: Copernicus Sentinel-2}
}
License
This analysis and associated datasets are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt this work with appropriate attribution.
Planetary Computer Citation
If using Microsoft Planetary Computer data, please cite: microsoft/PlanetaryComputer (2022)
Further Reading
Explore related urban green cover analyses and environmental insights from other regions: