The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests.

Little research has focussed on the use of low point density airborne laser scanning (ALS) technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest to derive key stand characteristics for forestry. The objective of this research was to develop models of Aleppo pine structural variables from ALS point clouds with a point density of 1 point m⁻² and field information, in an area located in north-eastern Spain. Field stand metrics were obtained within 45 plots, 30 m in diameter. A multiple regression analysis was performed in order to develop the models for mean height, squared mean diameter, basal area, timber volume, stand density and crown ratio, which led to the following R² values: 0.87, 0.84, 0.89, 0.89, 0.48 and 0.69, respectively. ALS-derived metrics included in the models were maximum height, the 99th, 95th and 50th canopy height percentiles, the skewness, the kurtosis, the standard deviation, the coefficient of variation and the percentages of all and first returns from above 1 m of the ground surface. With the exception of stand density, the final models are appropriate to be used in forest management operations in Mediterranean Aleppo pine forest. This study demonstrates the usefulness of low point density ALS data to accurately estimate key structural parameters of Aleppo pine forest using an area-based approach.