Project Scope

Since the beginning of this year, 2024, Nigeria has experienced extended periods of extreme heat. According to CarbonBrief, some places in Nigeria exceeded temperatures of 40 degrees Celsius throughout February, which caused the Nigerian Meteorological Services to issue heat warnings.

There are many causes for the extreme heat Nigeria is experiencing, but the major driver is human-caused climate change.

In this project, I created a Heat Risk Index of Oyo State to identify areas within Oyo State at risk of extreme heat that should be prioritized when coming up with a localized adaptation plan for extreme heat.

Methodology

Data Used and Sources

• Multispectral Landsat Imagery

• Sentinel 2-10m Land Use/Land Cover Data

• Nigeria Population Data from GRID3

• Nigeria Administrative Boundaries

Variables for Heat Risk Index Analysis

• Average Land Surface Temperature

• Tree Canopy Cover

• Population Density

Data Preparation and Processing

Deriving LST from the Multispectral Landsat Imagery

Land Surface Temperature was derived from the Multispectral Landsat imagery, configuring the processing template to Band 10 Surface Temperature in Celsius, the mosaic operator to Mean, and a definition query to filter the cloud cover to be less than or equal to 5%.

Filtering the LST Data by Location

The LST is filtered by location to focus on my area of interest, Oyo State. The Copy Raster and Extract by Mask tools are used in this step.

Summarizing LST Data

The Zonal Statistics as Table tool was used to summarize all the temperature values within my area of interest to determine the maximum value.

Deriving Lack of Tree Canopy

The land cover data for Oyo State was remapped, assigning the tree cover cells a value of 1 and all other classes a value of 0.

The Zonal Statistics as Table tool was used to count the number of tree cover cells and the total number of cells within each LGA in Oyo State.

The percent tree cover and the percent lacking tree cover for each LGA were calculated.

Calculating Population Density

The population count and area of each LGA in Oyo State were used to calculate their population density.

Data Analysis

Combining input variables into Heat Risk Index

The three input variables (Average Land Surface Temperature, Lack of Tree Canopy Cover, and Population Density) were combined.

The Join Field tool was used to add the Average LST  attribute and the Percent Lacking Tree Cover attribute to the Oyo State feature layer.

Standardizing Fields

The input variables are in different units of measurement, so the Population Densify, Average LST, and Percent Lacking Tree Cover fields were standardized onto the same scale using the Standardize Field geoprocessing tool.  

Results

The HRI value was derived with an Arcade Expression using the Sum function with the standardized inputs.

Conclusion

Using the input variables Land Surface Temperature, Lack of Tree Canopy Cover, and Population Density, I have been able to calculate the Heat Risk Index of Oyo State. 

This analysis can be used to prioritize areas that would benefit the most from mitigation plans of extreme heat, such as tree planting.