I Hug Trees

Urban Green Cover & Built-up Analysis — Chennai-region

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: 2025-10-06

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 2025-09-08 .

Our latest environmental digest for the Chennai region, observed on September 8, 2025, using Sentinel-2A satellite data, reveals significant insights into vegetation health and urban intensity. We utilize the Normalized Difference Vegetation Index (NDVI) to gauge vegetation health and the Normalized Difference Built-up Index (NDBI) to assess built-up areas. Only images with cloud cover below 50% are considered, ensuring data integrity. This monthly digest, curated by I Hug Trees and sourced from the Microsoft Planetary Computer, highlights a region where vegetation slightly edges out built-up surfaces, offering a nuanced view of urban versus green spaces.

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

NDVI color

NDVI — color visualization

NDVI greyscale

NDVI — greyscale (index values)

NDVI csv values

minmaxmeanmedianstddev
-0.44859218597412110.87507313489913940.215477392077445980.1864541769027710.20326893031597137

NDBI (Built-up Index)

NDBI color

NDBI — color visualization

NDBI greyscale

NDBI — greyscale (index values)

NDBI csv values

minmaxmeanmedianstddev
-0.85643482208251950.845417320728302-0.03905818983912468-0.04766651242971420.3333911895751953

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 -0.449 to a maximum of 0.875, with a mean of 0.215 and a median of 0.186. This indicates a varied landscape with pockets of dense vegetation alongside areas with sparse or no vegetation. The standard deviation of 0.203 suggests significant variability across the region. In contrast, the NDBI values show a minimum of -0.856 and a maximum of 0.845, with a mean of -0.039 and a median of -0.048. This suggests that, on average, vegetation and open areas are more extensive than built-up surfaces, though there are notable built-up hotspots. Comparing NDVI and NDBI, the mean difference (NDBI - NDVI) is -0.254, indicating that vegetation predominates over built-up surfaces in the region. This is visualized in the NDVI–NDBI difference map, where areas with NDVI greater than NDBI are likely cooling zones, and areas where NDBI exceeds NDVI are flagged as higher heat-risk zones (see [ndvi_ndbi_diff_color.png](https://ihugtrees.org/data-analytics/sentinel-ndvi/Chennai-region/2025/10/06/ndvi_ndbi_diff_color.png)).
  • Identify and monitor vegetation hotspots for potential cooling corridors.
  • Target built-up areas with high NDBI values for urban greening initiatives.
  • Use the heat-risk map to prioritize areas for heat mitigation strategies.

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.

To explore the data further, use the interactive overlay available at [ndvi_map.html](https://ihugtrees.org/data-analytics/sentinel-ndvi/Chennai-region/2025/10/06/ndvi_map.html). You can zoom in on specific areas, adjust the transparency slider to compare layers, and validate features against high-resolution basemaps. Note that the cloud cover percentage, as indicated in the metadata, may affect the clarity of some areas. We recommend field validation and suggest a repeat monitoring cadence to track changes over time.

Urban heat island effect

Our analysis indicates that vegetation slightly predominates over built-up surfaces in the Chennai region, with a mean difference of -0.254. This suggests that, while there are areas of significant urban development, the region overall benefits from a higher proportion of green spaces, which likely contribute to cooling effects. We recommend a monthly monitoring cadence to track changes, prioritize urban greening in high NDBI areas, and engage the community in heat mitigation efforts.

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

References & Data

Free to Download (Please cite):

metadata.json

ndvi_color.png

ndvi_greyscale.png

ndbi_color.png

ndbi_greyscale.png

ndvi_ndbi_diff_color.png

ndvi_ndbi_heatrisk.png

ndvi_rayshader.png

ndvi_rayrender.png

ndvi_georef.tif

ndbi_georef.tif

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}
    }