Thermal sensitivity of western Canadian ecosystems: a remote-sensing based vulnerability analysis

Methods

We applied the approach of Ermida et al. (2020) to quantify land surface temperature (LST) using high-resolution satellite products (LANDSAT 5, 7, 8, 30-m resolution). We then calculated the historical LST trend during summer periods from 1985 to 2020 (beta coefficient associated with the year effect). To calculate the thermal sensitivity of a given location, we combined high-resolution LST with historical air temperature from ERA5 (Muñoz Sabater, 2019). Thermal sensitivity was calculated as the beta coefficient in a linear model relating daily ERA5 air temperature with LST over time.

We then fit a series of random forest models (Breiman, 2001) to identify the drivers of (1) LST trend and (2) thermal sensitivity using a collection of remotely sensed topographic, climatic, soil and land cover variables in Google Earth Engine (Gorelick et al. 2017). Model development was conducted using randomforest package (Breiman et al., 2018) in R version 4.4.1 (R Core Team 2024). Final models were used to create predictive maps of thermal refugia across western Canada

Technical info

This dataset consists of two GeoTIFF rasters representing thermal sensitivity predictions for western Canada (including British Columbia and Alberta). Both rasters have ~1-km resolution (0.0089 degree), using EPSG:4326 as the coordinate system. The original thermal sensitivity prediction layers were 100m-resolution. To reduce the output size, we resampled these rasters with nearest neighbor and exported 1-km resolution files for sharing purposes.

Here, we provide two raster outputs:

1. ‘lm_avgLST_trend.tif’: summer LST trend in 1984-2023. Raster values represent average summer LST (June to August) annual increment. The larger the value, the larger the observed summer LST warming in recent decades.
2. ‘lm_slope_LST_Tmax.tif’: thermal sensitivity, defined as the decoupling between air temperature (exposed environment) and land surface temperature (LST). Thermal sensitivity values greater than 1 indicate that LST increases faster than air temperature does, while values below 1 indicate that surface ecosystems are buffered from warming (i.e., thermal refugia).