Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests

• Published in: International journal of applied earth observation and geoinformation 2022-04, Vol.108, p.102754, Article 102754
• Main Authors: Viana-Soto, Alba, García, Mariano, Aguado, Inmaculada, Salas, Javier
• Format: Article
• Language: English
• Subjects: Canopy cover; Height; Landsat; LiDAR; Post-fire recovery; support vector regression (SVR)
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2022.102754

•Short to long-term recovery evaluated attending to cover, height and heterogeneity.•Vegetation cover recovered to a pre-fire state but mean height did not.•Less than 50% of burned pixels recovered to a pre-fire structure 26 years post-fire.•LiDAR-derived forest variables extrapolated to Landsat with moderate accuracy. Understanding post-fire recovery dynamics is critical for effective management that enhance forest resilience to fire. Mediterranean pine forests have been largely affected by wildfires, but the impacts of both changes in land use and climate endanger their capacity to naturally recover. Multispectral imagery is commonly used to estimate post-fire recovery, yet changes in forest structure must be considered for a comprehensive evaluation of forest recovery. In this research, we combine Light Detection And Ranging (LiDAR) with Landsat imagery to extrapolate forest structure variables over a 30-year period (1990–2020) to provide insights on how forest structure has recovered after fire in Mediterranean pine forests. Forest recovery was evaluated attending to vegetation cover (VC), tree cover (TC), mean height (MH) and heterogeneity (CVH). Structure variables were derived from two LiDAR acquisitions from 2016 and 2009, for calibration and independent spatial and temporal validation. A Support Vector Regression model (SVR) was calibrated to extrapolate LiDAR-derived variables using a series of Landsat imagery, achieving an R2 of 0.78, 0.64, 0.70 and 0.63, and a relative RMSE of 24.4%, 30.2%, 36.5% and 27.4% for VC, TC, MH and CVH, respectively. Models showed to be consistent in the temporal validation, although a wider variability was observed, with R2 ranging from 0.51 to 0.74. A different response to fire was revealed attending to forest cover and height since vegetation cover recovered to a pre-fire state but mean height did not 26-years after fire. Less than 50% of the area completely recovered to the pre-fire structure within 26 years, and the area subjected to fire recurrence showed signs of greater difficulty in initiating the recovery. Our results provide valuable information on forest structure recovery, which can support the implementation of mitigation and adaptation strategies that enhance fire resilience.