Fuel Modelling Characterisation Using Low-Density LiDAR in the Mediterranean: An Application to a Natural Protected Area

Abstract

Fuel structure and characteristics are important to better understand and predict wildfire behaviour. The aim of the present study was to develop a methodology for characterising fuel models using low-density and free LiDAR data that facilitate the work of managers of protected territories. Field inventories were carried out in order to understand the characteristics of the stand and the variables that fuel models must include. This information, together with the use of the intensity and structure provided by LiDAR, was used to perform statistical analyses. The linear regressions obtained to characterise the stand of the mixed Quercus spp.–Pinus ssp.-dominated stand had an R2 value ranging from 0.4393 to 0.66. While working with low-density LiDAR data (which has more difficulties crossing the canopy), in addition to the obtained results, we performed the statistical analysis of the dominant stand to obtain models with R2 values ranging from 0.8201 to 0.8677. The results of this research show that low-density LiDAR data are significant; however, in mixed stands, it is necessary to only use the dominant stratum because other components generate noise, which reduces the predictive capacity of the models. Additionally, by using the decision tree developed in combination, it is possible to update the mapping of fuel models in inaccessible areas, thereby significantly reducing costs.

Operational fuel model map for Atlantic landscapes using ALS and Sentinel-2 images

“…When forest fuels of vast areas are to be mapped, the execution of field work may compromise affordability and time efficiency. For example, the methods presented by Ferrer Palomino and Rodríguez y Silva (2021) or Heisig et al (2022) might be difficult to apply on a broader scale since they are designed for pilot areas or for local conditions and therefore the models developed cannot easily be extrapolated to other conditions (Huesca et al 2019;Ferrer Palomino and Rodríguez y Silva, 2021;Heisig et al 2022).…”