Extrapolating forest canopy cover by combining airborne LiDAR and Landsat data: The case of the Yeste fire (Spain)

Wildfires play a key role on forest composition and structure in the Mediterranean biomes. Hence, Mediterranean species are adapted to fire, developing ecological strategies to naturally recover. Nevertheless, climate change impacts and land use changes are expected to increase the frequency and intensity of extreme wildfire events, endangering forest resilience to fire. Combining LiDAR and Landsat data provides a valuable opportunity to temporally extend detailed information on the forest structure. This study attempts to evaluate the feasibility of extrapolating LiDAR-derived canopy cover variables, as indicators of vegetation recovery, to Landsat time-series using Support Vector Regression (SVR) in a large forest fire. Canopy Cover (CC) and Canopy Cover above 2 m (CC2m) were derived from LiDAR data acquired in 2009 and 2016 from the National Plan for Aerial Orthophotography of Spain (PNOA) and time-series of annual Landsat composites for the period 1990-2020 were generated through the Google Earth Engine platform. We calibrated a SVR model from a stratified random sample using a 60% of the sample from 2016 for calibrating and the remaining 40% from both 2016 and 2009 for spatial and temporal validation, respectively. The two canopy cover variables yielded highly acceptable accuracy, with an R2 of 0.78 (CC) and 0.64 (CC2m), and an RMSE around 12.5-15% for the spatial validation, and with an R2 of 0.74 (CC) and 0.51 (CC2m), and an RMSE around 14-16.5% for the temporal validation. These results ensure the applicability of the extrapolation of the LiDAR-derived canopy cover variables to Landsat timeseries.