I Hug Trees

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

Half yearly 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: 2025-09-30

NDVI preview

This preview, captured by the Sentinel-2 satellite from its orbit at approximately 786 km above Earth, shows the Melbourne region in striking detail. Urban greenness and built-up patterns across Melbourne, from Newport in the west to East Melbourne and the Bayside in the east, spanning the southern suburbs up to the northern CBD. Imagery observed on 2025-09-18 .

The latest environmental digest for the Melbourne region uses Sentinel-2 satellite data to assess vegetation health and built-up intensity via NDVI and NDBI indices. This monthly analysis, curated by I Hug Trees, ensures only images with cloud cover below 50% are used, reflecting meaningful changes over time. The data is sourced from the Microsoft Planetary Computer. This month's findings show a notable contrast between vegetation and built-up areas, highlighting the dynamic urban landscape of Melbourne.

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

Melbourne’s landscape offers a strong contrast to many other cities. It's a mix of planned green corridors, urban parks, and fast-growing suburbs. While the city continues to expand outward, efforts in tree canopy programs and sustainable planning have helped preserve much of its green character. Satellite data shows consistent vegetation cover in its outer zones, but inner districts still face gradual decline due to dense construction. These insights remind us how policy, community action, and climate awareness together shape a city’s living balance.

NDVI

NDVI color

NDVI — color visualization

NDVI greyscale

NDVI — greyscale (index values)

NDVI csv values

minmaxmeanmedianstddev
-0.58533990383148190.73095184564590450.18878000974655150.181798845529556270.145381897687912

NDBI (Built-up Index)

NDBI color

NDBI — color visualization

NDBI greyscale

NDBI — greyscale (index values)

NDBI csv values

minmaxmeanmedianstddev
-0.77281248569488530.70785087347030640.068977013230323790.045402292162179950.1893252432346344

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 -0.585 to 0.731 with a mean of 0.189 and median of 0.182, indicates varied vegetation cover across the region. Areas with higher NDVI values suggest robust vegetation health, while lower values point to sparse or stressed vegetation. The NDBI, with a minimum of -0.773, maximum of 0.708, mean of 0.069, and median of 0.045, reveals the intensity of built-up surfaces. The positive mean and median NDBI values suggest a dominance of built-up areas, though the high standard deviation of 0.189 indicates significant variability. Comparing NDVI and NDBI, the mean difference of 0.069 - 0.189 = -0.120 shows that vegetation predominates over built-up surfaces in the region. The NDVI–NDBI difference map (see ndvi_ndbi_diff_color.png) illustrates this, with areas where NDVI exceeds NDBI likely offering cooling effects, contrasting with heat-risk zones where NDBI is higher than NDVI (see ndvi_ndbi_heatrisk.png).
  • Focus on areas with low NDVI and high NDBI for potential urban heat island mitigation.
  • Enhance green spaces in regions showing a significant NDVI–NDBI difference to promote cooling.
  • Monitor changes in NDVI and NDBI over time to assess the effectiveness of urban planning strategies.

3D Renders (Rayshader & Rayrender)

Rayshader

Rayshader 3D visualization derived from NDVI height-extrusion. Click on image for light-box effect

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 (ndvi_map.html) to zoom in on specific areas, adjust the transparency slider for layer comparison, and validate features against high-resolution basemaps. Note that the analysis is limited by the cloud cover percentage from the metadata, emphasizing the need for field validation and a consistent monitoring cadence to capture accurate changes over time.

Urban heat island effect

The analysis reveals that vegetation predominates in the Melbourne region, offering potential cooling effects across much of the area. However, zones with higher built-up intensity pose increased urban heat risks. Regular monitoring, coupled with strategic green space enhancements, is crucial for mitigating these effects. Community engagement in local tree-planting initiatives can further support urban cooling efforts.

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 znd NDBI analysis.


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}
    }