Wildfires are one of the most widespread disturbances of forest ecosystems. Countries of the Mediterranean basin registered the largest number of fires and burned area in the last decade. Optical remote sensing, particularly Landsat images, has been commonly used to characterise forest disturbance and subsequent recovery for long time series. Time series techniques such as temporal segmentation algorithms have been developed to facilitate the understanding of postfire vegetation recovery dynamics. This study aims to extract the main types of natural recovery trajectories from a Large Forest Fire occurred in 1994 from a thermophilous pine forests (Pinus Halepensis and Pinus Pinaster) in the long-term (1994-2018). We built annual composites from Landsat Surface Reflectance images and calculated Tasseled-cap components, which are sensitive to canopy moisture and structure (Wetness - TCW) and percent vegetation cover (Angle – TCA). We evaluated fire severity and fire recovery relationship. The differenced Normalised Burn Ratio (dNBR) was used as a fire severity proxy, whereas recovery processes were assessed from spectral profiles using LandTrendr temporal segmentation algorithm. TCW and TCA were used as inputs to LandTrendr and the outputs of fitting were subsequently used to classify recovery types based on a k-means classification with the optimum number of clusters based on the Elbow Method. Groups of continuous positive recovery, non-continuous recovery and continuous recovery with slope changes were identified. The proposed method could be an approach to model the long-term recovery for the Mediterranean areas and help decisionmakers in determining which areas could not recover naturally.