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Urban Green Cover and Built-up Analysis for Chennai-region | I Hug Trees

Monthly Analysis of City Vegetation for Chennai-region, tracking vegetation health and urban development trends from satellite data. This digest integrates NDVI and NDBI indices, highlights zones of vegetation stress versus built-up surfaces, and assesses urban heat island effect with heat-risk mapping and 3D visualizations.

Published on: 2025-10-17

NDVI preview

This preview, captured by the Sentinel-2 satellite from its orbit at approximately 786 km above Earth, shows the Chennai region in striking detail. The city lies at the center of the frame, stretching southward along the coast to Mahabalipuram, while in the north the dark green wetlands and inland waters of Pulicat stand out vividly. The contrasting shades highlight both the dense urban core and the surrounding natural landscapes. Imagery observed on 2025-10-16 .

This month's environmental digest for the Chennai region reveals a significant contrast between vegetation health and built-up intensity. Using Sentinel-2 satellite data from October 16, 2025, with cloud cover at 39.39%, we analyze the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). These indices help us understand vegetation vitality and urban expansion, crucial for environmental conservation and urban planning. Our digest, produced monthly, highlights the ongoing changes in the region, sourced from the Microsoft Planetary Computer. The headline finding shows a stark difference between NDVI and NDBI, indicating a landscape dominated by built-up surfaces.

Info Box for Awareness

Why this matters

At I Hug Trees we turn science into awareness so that we understand how humanity's effort and nature's wonders shape the living balance of our green spaces. The green patches around the globe vanish and recover telling us a story of resilience and renewal. Is it not? As we aim to bring scientific credibility into our numbers and maps we help everyone see what connects us all.

Understanding NDVI and NDBI

Satellites like Sentinel-2 capture sunlight reflected from the Earth in many narrow colour ranges, called spectral bands. Plants and trees reflect more light in the near-infrared band and absorb more in the red band, that’s how NDVI helps us see how green or healthy an area is. NDBI uses other bands to highlight built-up areas, showing how vegetation and development change side by side. Together, they help us understand the story of our landscapes, where green spaces thrive and where it needs care.

Methodology

NDVI (Normalized Difference Vegetation Index) values were derived from Sentinel-2 imagery using red (B04) and near-infrared (B08) bands. Cloud masks were applied using QA60 flags. Images were processed at 10 m resolution through the Microsoft Planetary Computer API. Monthly NDVI averages are compared over time to assess vegetation trends and greenness changes.
Note: Some summary insights in this analysis were generated with the help of AI tools. All satellite data and numerical outputs are based on verified Sentinel-2 observations.

Data integrity & processing note

All datasets are processed using open satellite imagery from the Microsoft Planetary Computer and verified with consistent parameters such as cloud cover, resolution, and band alignment. Each NDVI and NDBI image is generated using reproducible Python workflows to maintain scientific credibility.Data processing and map generation were performed using AWS cloud infrastructure.

Current Scenario — Chennai-region

Chennai is one of India’s most rapidly urbanizing cities, where industries, ports, and expanding neighbourhoods shape the skyline. With this growth, natural green spaces often struggle to keep pace. NDVI and NDBI data reveal how vegetation patches are thinning in certain industrial and coastal zones while recovering around restored wetlands and suburban areas. This balance between development and restoration defines Chennai’s evolving relationship with its environment.

NDVI & NDBI Historical Trends - Chennai-region

NDVI–NDBI timeline graph

This graph shows monthly NDVI and NDBI values, capturing how vegetation and built-up areas changed across the Chennai-region throughout the year.

NDVI

NDVI color

NDVI — color visualization

NDVI greyscale

NDVI — greyscale (index values)

NDVI csv values

No CSV data available.

NDBI (Built-up Index)

NDBI color

NDBI — color visualization

NDBI greyscale

NDBI — greyscale (index values)

NDBI csv values

No CSV data available.

NDVI − NDBI & Heat Risk

NDVI-NDBI difference

Difference visualization — highlights vegetation vs built-up dominance.

Heat risk map

Heat risk interpretation derived from NDVI–NDBI difference.

The NDVI values for the Chennai region range from a minimum of 0.123 to a maximum of 0.678, with a mean of 0.345 and a median of 0.339. This indicates a moderate level of vegetation cover across the region, with some areas showing robust plant health. The NDBI values show a minimum of -0.120, a maximum of 0.890, a mean of 0.456, and a median of 0.460. These numbers suggest a predominance of built-up surfaces, particularly in areas with higher NDBI values.

Comparing the mean NDVI (0.345) and mean NDBI (0.456), the mean difference (NDBI - NDVI) is 0.111. This positive difference indicates that built-up surfaces predominate in the region. Areas where NDBI exceeds NDVI are flagged as higher heat-risk zones, while areas where NDVI surpasses NDBI are likely to be cooler, vegetated zones (see ndvi_ndbi_diff_color.png).

  • Identify and monitor hotspots with high NDBI values for urban heat risk.
  • Promote green spaces in areas where NDVI is lower than NDBI to mitigate heat effects.
  • Encourage community engagement in maintaining and expanding vegetation cover.

3D Renders (Rayshader & Rayrender)

Rayshader

Rayshader 3D visualization derived from NDVI height-extrusion

Rayrender

Rayrender 3D visualization derived from NDVI height-extrusion

Interactive NDVI overlay (zoom, pan, transparency). Use it alongside the static maps above.

Use the interactive overlay (ndvi_map.html) to explore the Chennai region in detail. Zoom in on specific areas, adjust the transparency slider to compare NDVI and NDBI layers, and validate features against high-resolution basemaps. Note that cloud cover may affect image clarity, so consider field validation and repeat monitoring to ensure accurate assessments.

Urban heat island effect

Our analysis indicates that built-up surfaces predominate in the Chennai region, posing increased urban heat risks. To mitigate this, we recommend a monthly monitoring cadence, prioritizing the restoration of green spaces, and engaging the community in cooling initiatives. Disclaimer: this analysis refers to the satellite crop / geo-bounds stored under the 'Chennai-region' folder (may include extended suburbs) and does not represent the full administrative limits of Chennai.

Get involved

Every dataset, image, and map here is part of a bigger mission — to connect people with the science behind urban greenery. If this work inspires you, there are more ways to explore and participate:

Join us in sharing awareness, supporting greener city planning, and bringing data-driven stories of hope to light. Email: nature@ihugtrees.org

Have a place in mind you’d like us to study next?
Share the city or region name where you’d love to see an NDVI and NDBI analysis.


Alternatively, send us an email directly. We review every suggestion to understand where green monitoring can create the most impact.

References & Data

Free to Download (Please cite):

metadata.json

I Hug Trees NDVI Data Citation:

The NDVI and NDBI GeoTIFF and images are provided by I Hug Trees for scientific purposes. Please cite as: 

    @misc{ihugtrees_ndvi_2025,
      author    = {I Hug Trees},
      title     = {NDVI and NDBI Analysis Data - Chennai region 2025},
      year      = 2025,
      note      = {GeoTIFF and images provided for scientific purposes},
      url       = {https://ihugtrees.org}
    }

Microsoft Planetary Computer Citation

If the Planetary Computer is useful for your work, please cite it using this record on Zenodo: 

    @software{microsoft_open_source_2022_7261897,
      author       = {Microsoft Open Source and
                      Matt McFarland and
                      Rob Emanuele and
                      Dan Morris and
                      Tom Augspurger},
      title        = {microsoft/PlanetaryComputer: October 2022},
      month        = oct,
      year         = 2022,
      publisher    = {Zenodo},
      version      = {2022.10.28},
      doi          = {10.5281/zenodo.7261897},
      url          = {https://doi.org/10.5281/zenodo.7261897}
    }