Urban areas are heating faster than their rural surroundings. This phenomenon, known as the urban heat island effect, results from a mix of heat-retaining materials, reduced vegetation, and human activity. For local councils, rising urban temperatures mean higher health risks, greater energy demand, and worsening inequalities.
Mapping urban heat and tree canopy cover together provides a powerful framework for climate adaptation. While heat mapping identifies where communities are most at risk, tree canopy analysis highlights where natural cooling solutions already exist — and where they are needed most. Together, they form a comprehensive evidence base for greening strategies, shade planning, and health-focused climate resilience.
At Geoneon, we have developed an integrated assessment framework that combines Earth Observation, AI, and demographic data. Our approach is already helping cities prioritise climate resilience strategies.
We generate four core heat-related layers:
These are complemented by high-resolution tree canopy mapping, which is analysed across multiple years to detect changes in shade, evaluate canopy equity, and inform greening strategies.
We use summer satellite data from Landsat 8 and 9, which includes a thermal infrared band. This allows us to calculate LST for local government areas (LGAs).
The method includes:
Limitations:
Figure 1 The Heat Susceptibility Index shows areas that tend to get the hottest based on satellite measurements of land surface temperature. It uses summer temperature data to highlight heat-prone locations, with higher values indicating hotter areas.
We measure the average heat susceptibility within a 400 m radius around every residential building — roughly a 5-minute walk. This creates a localised view of heat conditions where people live and move daily.
Residential buildings are selected from authoritative building datasets and include categories such as homes, hospitals, care facilities, and accommodation.
Vulnerability is calculated at the SA1 level using:
Each building is linked to its SA1 value. We average the two vulnerability scores (demographics and IRSAD), and classify the result into 10 levels.
Figure 2 The Residential Heat Risk Index combines heat exposure and social vulnerability, showing where both high temperatures and vulnerable populations overlap. Areas with higher values are at greater risk during extreme heat events.
This final index combines the Heat Exposure and Social Vulnerability Indices. Scores are averaged and classified from 1 (lowest risk) to 10 (highest risk).
Because it is linked to residential building footprints, this index gives councils a fine-grained view of who is most affected and where.
Figure 3 Metrics are extracted from the geospatial data to support data-driven decisions. This graph presents the number of residential buildings in urban suburbs classified in two high heat risk categories.
To support heat mitigation efforts, we monitor canopy cover at sub-metre resolution using Maxar satellite imagery. Our AI model classifies tree canopy extent and estimates canopy height.
We track canopy cover:
Figure 4 Tree canopy height estimation layer is a layer derived from Geoneon’s artificial intelligence models and very-high-resolution satellite imagery.
We also create a Prioritisation Index to help councils decide where to plant trees for maximum community benefit.
Steps include:
This approach ensures tree planting supports vulnerable communities and aligns with heat adaptation goals.
Our solution is designed with council workflows in mind:
Geoneon’s urban heat and canopy analysis is available for councils across Australia and internationally. We work closely with local teams to ensure the outputs are practical, timely, and aligned with existing strategies like urban forest plans or climate adaptation frameworks.
Get in touch if you would like to explore heat and canopy mapping for your community.
Geoneon is a climate tech company using satellite data, AI, and geospatial analysis to map and reduce climate risks.