Help – User Guide for THERMATO
THERMATO is a QGIS plugin developed to support heat exposure risk analysis and mapping based on remote sensing data. This plugin is designed to help users determine the impact of heat exposure on socioeconomic data, calculate measurable heat exposure impact risk indices, and automatically generate standardized heat risk classification maps.
Overview
THERMATO integrates multiple remote sensing indices — LST, NDBI, and NDVI — together with population density data to compute an Urban Heat Risk index for a given area. The plugin automates the full analysis pipeline: from raster input ingestion and classification, through user-defined component weighting, to final heat risk raster and vector outputs along with a detailed report log.
Parameters
The following table lists all input parameters accepted by the THERMATO plugin.
Parameter| Label | Name | Type | Description |
|---|---|---|---|
| LST Layer | Input | Raster | Satellite images that cover/display Earth's surface temperature data. These images can be obtained from open-source satellites such as Landsat 8 and MODIS, etc. |
| LST Classification Table optional | Vector Table | Table | Classification of LST data into several values that will be operated on later in the Urban Heat Risk Weighting Expression. |
| NDBI Layer | Input | Raster | Spatial index specifically designed to automatically detect, extract, and highlight built-up areas or man-made infrastructure. |
| NDBI Classification Table optional | Vector Table | Table | Classification of NDBI data into several values that will be operated on later in the Urban Heat Risk Weighting Expression. |
| NDVI Layer | Input | Raster | Index for measuring the greenness, density, and health of vegetation in an area. |
| NDVI Classification Table optional | Vector Table | Table | Classification of NDVI data into several values that will be operated on later in the Urban Heat Risk Weighting Expression. |
| Population Density Layer (people/km²) | Input | Raster | A raster dataset representing the spatial distribution of population density, expressed in people per square kilometer (people/km²). Each pixel contains the estimated number of people per unit area, which is used to assess human exposure and vulnerability in urban heat risk analysis. This data can be obtained using WorldPop. |
| Population Density Classification Table optional | Vector Table | Table | Population density data in shapefile format, obtainable from local government statistical agencies or other sources. This data will be overlaid with LST, NDVI, and NDBI data. |
| Component Weights | Table | Table | Manual weighting assigned by the user for each input component. The total weight across all components must equal 100%. |
| Manual Classification Breaks | Table | Table | Classification of the final results by rounding continuous data into discrete values (e.g., 1.5 becomes 2). |
| Output Folder | Folder | Folder | Select the destination folder where all final result files will be saved. |
Outputs
THERMATO produces three output types upon successful execution.
Output| Label | Name | Type | Description |
|---|---|---|---|
| Heat Risk Raster Output | Output | Raster | Heat risk calculated by overlaying raster-formatted input layers. |
| Heat Risk Vector Output | Output | Vector | Heat risk calculated by overlaying vector-formatted input layers. |
| Report Log | Output | Txt |
A text log folder containing the following output files:classification_rules.txthigh_risk_coords.csvreport.htmlresampling_log.txtsummary.txt
|
Index Descriptions
THERMATO relies on four key spatial indices. Below is a detailed explanation of each.
LST – Land Surface Temperature
LST refers to satellite images that cover and display Earth's surface temperature data. It is a critical indicator of urban heat island effects and thermal environments. LST data can be obtained from open-source satellite missions such as Landsat 8 and MODIS.
NDBI – Normalized Difference Built-up Index
NDBI is a spatial index specifically designed to automatically detect, extract, and highlight built-up areas or human-made infrastructure. This index operates by leveraging the physical characteristics of hard construction materials such as concrete and asphalt, which reflect Short-Wave Infrared (SWIR) waves much more strongly than Near-Infrared (NIR) waves.
// Range: -1.0 to +1.0
• Negative values → presence of water bodies or vegetation cover
• Positive values (→ 1.0) → built-up areas or impervious surfaces; higher values represent denser and more massive urban density
NDVI – Normalized Difference Vegetation Index
NDVI is an index used to measure the greenness, density, and health of vegetation in a given area. It operates by comparing the reflectance of red light waves — which are absorbed by plant chlorophyll for photosynthesis — with Near-Infrared (NIR) waves, which are strongly reflected by healthy leaf structures.
// Range: -1.0 to +1.0
• Negative values → non-vegetated elements such as water bodies or clouds
• Values close to zero → bare soil or asphalt roads
• Positive values (→ 1.0) → increasingly dense and healthy vegetation cover, such as forest canopies
Population Density
Population density is a demographic and spatial measure that quantifies the number of people living per unit of land area (typically expressed as people per square kilometer or square mile). In spatial analysis and urban planning, this metric is crucial for assessing human settlement patterns, resource distribution, and infrastructure demand.
When integrated with remote sensing data like NDVI and NDBI, population density helps evaluate the relationship between human overcrowding and environmental impacts — such as the Urban Heat Island (UHI) effect, where areas with high built-up index values and high population density generally experience higher thermal risks.