Ramkumar Yaragarla
I Hug Trees
Published: 2025-12-04

Observation Metadata

Satellite Platform Sentinel-2B (Sentinel 2)
Observation Date 2025-12-03
Cloud Cover 28.564194%
Spectral Bands Used B04 (Red), B08 (NIR), B11 (SWIR1), B12 (SWIR2)
Processing Date 2025-12-07
Satellite preview

Sentinel-2 satellite preview of Auckland-region, captured on 2025-12-03 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 Auckland-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 statistics for the Auckland-region reveal a mean NDVI value of 0.249, indicating a moderate level of vegetation cover across the region. The median NDVI value is slightly lower at 0.208, suggesting that the distribution of vegetation is skewed towards areas with lower NDVI values. The standard deviation of 0.199 indicates a significant variation in vegetation cover within the region. Spatially, the NDVI color map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/ndvi_color.png) and greyscale map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/ndvi_greyscale.png) illustrate that while there are pockets of high vegetation cover, much of the region exhibits moderate to low vegetation density. This distribution pattern highlights the need for targeted urban forestry initiatives to enhance green cover in less vegetated areas.

1.2 Built-Up Intensity (NDBI Analysis)

The NDBI analysis for Auckland-region shows a mean NDBI value of -0.089, indicating a relatively low level of built-up intensity across the region. However, the minimum NDBI value of -1.0 and the maximum of 0.459 suggest a wide range of urbanization levels. The standard deviation of 0.165 further emphasizes this variability. The NDBI color map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/ndbi_color.png) and greyscale map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/ndbi_greyscale.png) indicate that while some areas exhibit high built-up intensity, many parts of the region remain less urbanized. This pattern suggests opportunities for strategic urban planning to balance development with green spaces.

NDVI color

NDVI Color Visualization

NDVI greyscale

NDVI Greyscale (Index Values)

NDBI color

NDBI Color Visualization

NDBI greyscale

NDBI Greyscale (Index Values)

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.

EVI color

EVI Color Visualization

EVI greyscale

EVI Greyscale (Index Values)

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.

MNDWI color

MNDWI Color Visualization

MNDWI greyscale

MNDWI Greyscale (Index Values)

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.

NDMI color

NDMI Color Visualization

NDMI greyscale

NDMI Greyscale (Index Values)

2. Vegetation-Development Balance Analysis

2.1 NDVI-NDBI Difference Analysis

The NDVI-NDBI difference analysis for Auckland-region reveals a mean difference of 0.339, indicating a general dominance of vegetation over built-up areas. The difference bins data shows that 36.97% of the region falls into the highest positive difference bin (1.21 to 1.62), suggesting strong vegetation dominance in these areas. Conversely, 9.07% of the region falls into the lowest negative difference bin (-0.43 to -0.02), indicating areas where built-up intensity surpasses vegetation cover. The difference map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/cities_ndvi_ndbi_diff_color.png) and legend (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/cities_ndvi_ndbi_diff_legend.png) visually represent these patterns, with warmer colors indicating vegetation dominance and cooler colors indicating built-up dominance. This analysis highlights the spatial balance between vegetation and built-up areas, informing targeted urban greening strategies.

2.2 Overlay Interpretation

The combined NDVI-NDBI overlay image (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/cities_ndvi_ndbi_overlay.png) reveals distinct patterns when vegetation and built-up indices are viewed together. Areas with strong vegetation cover appear in shades of green, while highly built-up areas are represented in shades of red. This overlay helps identify regions where vegetation and built-up areas coexist, as well as areas where one dominates over the other. Such insights are crucial for urban planners to make informed decisions about land use and green space allocation.

NDVI-NDBI difference

NDVI-NDBI Difference Map (Green = vegetation-dominated; Red = built-up-dominated)

Legend
Combined overlay

Combined NDVI-NDBI Overlay

3. Urban Heat Island Risk & Thermal Vulnerability

3.1 Heat Risk Distribution

The urban heat index bins data for Auckland-region shows a mean heat index value of -0.339, indicating a generally lower risk of urban heat island effects across the region. However, the distribution is skewed, with 45.91% of the region falling into the lowest risk bin (0.02 to 0.43), suggesting moderate to low heat risk in these areas. Conversely, 0.13% of the region falls into the highest risk bin (-1.62 to -1.21), indicating pockets of high heat risk where built-up intensity exceeds vegetation cover. The heat index map (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/cities_urban_heat_index_color.png) and legend (https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/cities_urban_heat_index_legend.png) visually represent these heat risk zones, with warmer colors indicating higher heat risk. These findings underscore the importance of targeted cooling strategies in high-risk areas to mitigate public health impacts.

3.2 Key Findings & Implications

  • Total green cover percentage: 37.73%
  • Healthy vegetation percentage: 17.15%
  • Vegetation health score: 0.475
  • Green infrastructure score: 0.432
Urban heat index

Urban Heat Index Map (Red = high risk zones)

Legend

4. Strategic Recommendations for Green Infrastructure

4.1 What's Working Well

  • The region exhibits a healthy vegetation percentage of 17.15%, indicating successful green space management in certain areas.
  • The green infrastructure score of 0.432 suggests that existing urban forestry initiatives are contributing positively to the region's ecological balance.

4.2 Critical Challenges

  • The total green cover percentage of 37.73% indicates room for improvement in overall vegetation coverage across the region.
  • The vegetation health score of 0.475 suggests that while there are healthy vegetation areas, a significant portion of the green cover may be under stress or less vibrant.

4.3 Evidence-Based Recommendations

  1. Implement targeted urban greening projects in areas with low vegetation cover to increase the total green cover percentage.
  2. Enhance maintenance and care for existing vegetation to improve the vegetation health score, focusing on areas identified as under stress.
  3. Develop strategic cooling corridors in high heat risk zones to mitigate urban heat island effects and protect public health.

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 Auckland-region.

Interactive map: Use mouse/touch to zoom and pan. The overlay shows the satellite image bounds used for NDVI/NDBI calculations.

Note: The analysis boundaries may extend beyond administrative city limits as they represent the satellite image crop captured on 2025-12-03. 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-2B Level-2A (atmospherically corrected)
  • Provider: Microsoft Planetary Computer
  • Observation Date: 2025-12-03
  • Cloud Cover: 28.564194%
  • 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. (2025). Urban Green Cover & Heat Risk Assessment: Auckland-region. I Hug Trees. Retrieved from https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/digest.html

Satellite data: Copernicus Sentinel-2 (ESA), processed via Microsoft Planetary Computer.

BibTeX Entry

@misc{ihugtrees_urban_auckland-region_2025,
  author = {Yaragarla, Ramkumar},
  title = {Urban Green Cover \& Heat Risk Assessment: Auckland-region},
  year = {2025},
  publisher = {I Hug Trees},
  url = {https://ihugtrees.org/data-analytics/sentinel-ndvi/Auckland-region/2025/12/04/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:

Urban Green Cover Assessment: Zurich Region, Switzerland Satellite-based NDVI and NDBI analysis revealing vegetation patterns, built-up intensity, and heat risk zones across Zurich's urban and suburban landscape. Urban Green Cover Assessment: Melbourne Region, Australia Comprehensive vegetation health mapping and urban heat island analysis for Greater Melbourne, with actionable recommendations for green infrastructure planning. Green Weave Diaries: Stories from Urban Forests In-depth articles, case studies, and narratives exploring the intersection of urban development, environmental science, and community-led greening initiatives.