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

Monthly Analysis of City Vegetation for Melbourne-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 .

Welcome to the latest environmental digest for the Melbourne region, powered by Sentinel-2 satellite data. This month's analysis focuses on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) to assess vegetation health and built-up intensity. Only images with cloud cover below 50% are considered, ensuring reliable data. This digest is produced monthly to capture meaningful changes, sourced from the Microsoft Planetary Computer. Today's headline: vegetation shows a slight edge over built-up areas, hinting at potential cooling effects across the region.

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 — Melbourne-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 - Melbourne-region

NDVI–NDBI timeline graph

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

NDVI

NDVI color

NDVI — color visualization

NDVI greyscale

NDVI — greyscale (index values)

NDVI csv values

minmaxmeanmedianstddev
-0.4629593491554260.74170249700546260.191077545285224910.188478112220764160.13983795046806335

NDBI (Built-up Index)

NDBI color

NDBI — color visualization

NDBI greyscale

NDBI — greyscale (index values)

NDBI csv values

minmaxmeanmedianstddev
-0.72134959697723390.63560611009597780.072844326496124270.0546580366790294650.16923962533473969

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, ranging from -0.463 to 0.742 with a mean of 0.191 and median of 0.188, indicates a varied landscape with pockets of dense vegetation alongside less vegetated areas. The standard deviation of 0.140 suggests significant variability in vegetation cover across the region.

NDBI values, spanning from -0.721 to 0.636 with a mean of 0.073 and median of 0.055, point to a landscape where built-up surfaces are present but not overwhelmingly dominant. The standard deviation of 0.169 reflects a mix of urban and natural areas, with some built-up hotspots.

Comparing NDVI and NDBI, the mean difference of 0.073 - 0.191 = -0.118 indicates that vegetation predominates over built-up surfaces in the Melbourne region. This suggests a landscape where green areas likely offer cooling effects, especially where NDVI exceeds NDBI (see ndvi_ndbi_diff_color.png).

The NDVI–NDBI difference map highlights areas where vegetation or built-up surfaces dominate. Zones where NDBI is greater than NDVI are flagged as higher heat-risk areas, while areas where NDVI is greater than NDBI are likely cooling or vegetated zones (see ndvi_ndbi_heatrisk.png).

  • Identify and monitor vegetation hotspots for potential cooling corridors.
  • Target built-up areas with high NDBI values for green space integration.
  • Consider the variability in vegetation cover for urban planning and conservation efforts.

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.

Explore the interactive overlay (ndvi_map.html) to zoom in on specific areas, adjust the transparency slider for layer comparison, and validate features against high-resolution basemaps. Remember, the analysis is based on images with a cloud cover of 8.59%, which may affect certain areas. For the most accurate assessment, consider field validation and establish a repeat monitoring cadence to track changes over time.

Urban heat island effect

In summary, the Melbourne region exhibits a landscape where vegetation slightly predominates over built-up surfaces, offering potential cooling benefits. However, areas with higher built-up intensity are at increased urban heat risk. Regular monitoring, targeted green space restoration, and community engagement in urban planning are recommended to mitigate heat island effects and enhance urban livability. Disclaimer: this analysis refers to the satellite crop / geo-bounds stored under the 'Melbourne-region' folder (may include extended suburbs) and does not represent the full administrative limits of Melbourne.

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

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}
    }