I Hug Trees

Published on: 2025-11-21

NDVI preview

This preview, captured by the Sentinel-2 satellite from its orbit at approximately 786 km above Earth, shows the Sydney-region Imagery observed on 2025-11-19.

Welcome to the latest environmental digest for the Sydney region, powered by Sentinel-2 satellite data from November 19, 2025. 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 for our monthly updates. Sourced from the Microsoft Planetary Computer and curated by I Hug Trees, this digest reveals a region where vegetation slightly edges out built-up surfaces, offering a glimpse into the dynamic balance between nature and urbanization.

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.

NDVI & NDBI Historical Trends — Sydney-region

NDVI–NDBI timeline graph

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

NDVI

NDVI color

NDVI — color visualization

NDVI greyscale

NDVI — greyscale (index values)

NDVI CSV Values

minmaxmeanmedianstddev
-0.57836151123046880.74991792440414430.25736206769943240.288915336132049560.1786777228116989

NDBI (Built-up Index)

NDBI color

NDBI — color visualization

NDBI greyscale

NDBI — greyscale (index values)

NDBI CSV Values

minmaxmeanmedianstddev
-0.87702977657318120.8672407865524292-0.0015591159462928772-0.032373838126659390.22941246628761292

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 readings for the Sydney region show a minimum value of -0.578, indicating areas with little to no vegetation, and a maximum of 0.750, suggesting lush, healthy vegetation in certain spots. The mean NDVI of 0.257 and median of 0.289 point to a generally moderate level of vegetation cover across the region. The standard deviation of 0.179 reflects variability in vegetation density.

In contrast, the NDBI values range from a minimum of -0.877 to a maximum of 0.867, with a mean of -0.002 and a median of -0.032. These figures suggest that, on average, vegetation and open areas slightly outweigh built-up surfaces. However, the high maximum NDBI value indicates the presence of significant built-up hotspots within the region.

Comparing the mean NDVI (0.257) and mean NDBI (-0.002), we find a mean difference of -0.259, indicating that vegetation predominates over built-up surfaces in the Sydney region. This predominance of vegetation is likely contributing to cooling effects across the area. The NDVI–NDBI difference map (see ndvi_ndbi_diff_color.png) highlights zones where vegetation (NDVI > NDBI) offers cooling benefits and areas where built-up surfaces (NDBI > NDVI) may pose higher heat risks (see ndvi_ndbi_heatrisk.png).

  • Identify and monitor vegetation hotspots for potential conservation efforts.
  • Target built-up areas with high NDBI values for urban cooling initiatives.
  • Explore corridors with high NDVI values for green infrastructure development.

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 (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 limited by the cloud cover percentage noted in the metadata. We recommend field validation and suggest a monthly monitoring cadence to capture seasonal changes and ongoing trends.

Urban heat island effect

In summary, the Sydney region exhibits a slight dominance of vegetation over built-up surfaces, as indicated by the mean difference of -0.259 (NDBI - NDVI). This vegetation predominance likely contributes to cooling effects across the area. We recommend a monthly monitoring cadence to track changes, prioritize restoration efforts in low-vegetation areas, and engage the community in urban greening initiatives. Disclaimer: this analysis refers to the satellite crop / geo-bounds stored under the 'Sydney-region' folder (may include extended suburbs) and does not represent the full administrative limits of Sydney.

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_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}
    }