Mapping Urban Heat: A Data-Driven Approach to Climate Resilience

Why Urban Heat and Tree Canopy Mapping Matter for Councils

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.

Geoneon’s Urban Heat and Canopy Solution

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:

• Heat Susceptibility Index: where land gets hottest, based on satellite-derived land surface temperature (LST).
• Heat Exposure Index: how much heat surrounds residential buildings, reflecting local conditions.
• Social Vulnerability Index: where people are more sensitive to heat, based on age and socioeconomic disadvantage.
• Residential Heat Risk Index: a composite of exposure and vulnerability that highlights the areas where residents are most at risk.

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.

Methodology

Heat Susceptibility Index

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:

• Correcting for atmospheric effects and surface emissivity using NDVI and vegetation proportion.
• Calculating median LST across multiple cloud-free summer dates to capture a "typical hot day" while avoiding outliers.
• Classifying the final image into deciles, creating a 1–10 index of susceptibility.

Limitations:

• Data is captured at fix time during the day, so results reflect relative heat at the time of capture.
• Highly reflective roofs and steep topography can influence temperature estimates.

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.

Heat Exposure Index

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.

Social Vulnerability Index

Vulnerability is calculated at the SA1 level using:

• Demographics: People under 5 and over 64 years old.
• Socioeconomic Index (IRSAD): Reversed so higher values indicate greater disadvantage.

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.

Residential Heat Risk Index

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.

Tree Canopy Monitoring

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:

• City-wide and by suburb
• By land use (e.g. residential, industrial, bushland)
• By parcel type (e.g. public authority land, private land, road corridors)
• Around schools, hospitals, bus stops, and green streets

Figure 4 Tree canopy height estimation layer is a layer derived from Geoneon’s artificial intelligence models and very-high-resolution satellite imagery.

Prioritising Tree Planting for Climate Equity

We also create a Prioritisation Index to help councils decide where to plant trees for maximum community benefit.

Steps include:

1. Define a 400 m buffer around all residential buildings to create a walkable residential catchment.
2. Select only government-managed parcels within that area, excluding private property.
3. For each parcel, calculate:
   • Average heat susceptibility (normalised 0–1)
   • Average social vulnerability (normalised 0–1)
   • Canopy cover deficit (inverse of canopy percentage, normalised 0–1)
4. Add the three scores and classify into five levels:
   • Very Low Priority
   • Low Priority
   • Medium Priority
   • High Priority
   • Very High Priority

This approach ensures tree planting supports vulnerable communities and aligns with heat adaptation goals.

Designed for Local Government

Our solution is designed with council workflows in mind:

• Integrates with GIS systems
• Based on open and high-resolution data
• Transparent methodology, ready for audit or review
• Customisable for different land uses or policy objectives
  
  

Interested in Bringing This to Your City?

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.