Picturing where wildlands and people meet at a global scale UWMadison nature
). Also, we only considered pixels with an estimated building density of more than 20%, thereby removing areas with very low building density for which data accuracy can be limited. We defined all pixels with an aggregated building density greater than 0.5% in their surrounding as candidate WUI pixels. Compared to the commonly used definition of 6.17 buildings per km, this threshold is usually slightly higher .
We identified candidate hotspot countries as the top ten countries in their respective world region with the highest WUI area share, that had more than 20% of their wildfire area within the WUI and were more than 10,000 kmin size. Among these, we selected the two countries with the most people affected by wildfire in the WUI. If their borders were within 200 km, we replaced the second-ranked country with the third-ranked .
For population data, we analysed the Global Human Settlement Population dataset that represents population per grid cell, with 100 m resolution. This dataset is based on the building density dataset we used to map the WUI but excludes non-residential buildings. It was created by disaggregating census data to grid cells using building density as weight. We computed area-weighted summaries of population data.
For biomass, we analysed global maps of aboveground biomass carbon density for 2010 , with 300 m resolution. We converted biomass carbon density to mass and applied a factor of two to convert carbon equivalent mass to dry matter biomassFor wildfire data, we analysed the MODIS Collection 6.1 Active Fire dataset and extracted grid cell-based fire frequency data from 2003 to 2020 .
Where applicable, we used a pixel-based area-correction factor when computing area statistics to adjust skewed area statistics caused by our projection system (
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