Transferability of vegetation recovery models based on remote sensing across different fire regimes

• Published in: Applied vegetation science 2020-07, Vol.23 (3), p.441-451
• Main Authors: Fernández‐Guisuraga, José Manuel, Suárez‐Seoane, Susana, Calvo, Leonor, Feilhauer, Hannes
• Format: Article
• Language: English
• Subjects: Community composition; Community structure; Composition; Coniferous forests; Decision making; Decision trees; Forest fires; image texture; Land management; megafire; model transferability; random forest regression; Recovery; Regression analysis; Remote sensing; Root-mean-square errors; Satellite imagery; Satellites; Sentinel‐2; Spatial data; Spatial discrimination; Spatial resolution; Surface layers; Vegetation; Vegetation cover; Wildfires; WorldView‐2
• ISSN: 1402-2001; 1654-109X
• DOI: 10.1111/avsc.12500

Aim To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location The northwest and east of the Iberian Peninsula. Methods In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability. We evaluate transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data. The best transferability results were obtained between areas with more homogeneous community composition arising from the species regenerative traits. The use of fine‐grained satellite imagery for model calibration exhibited the lowest transferability error.