Wildfires represent one of the major agent of change as far as forest ecosystems are concerned. These natural hazards are monitored and studied at different stages, exploiting many and innovative tools. For example, post-fire studies are mainly targeted in quantify the impact of wildfire events on forests and monitor the recovery of natural environments. The study of post-fire vegetation restoration is of great importance for decision makers and landscape planners, as it can provide useful information to update landscape vulnerability maps, monitor forest recovery processes and identify forest repopulation areas. In this contest, satellite remote sensing represents a time- and cost-effective alternative to the traditional methods used for post-fire vegetation dynamics monitoring, especially over large areas. In particular, the Sentinel-2 MSI sensors, thanks to free data access with unprecedented trade-off in spatial-temporal resolutions (10-60 m pixel size and 5-days revisit time), represents a great occasion of improvement on such topic.
The objective of this contribution is to identify post-fire vegetation restoration dynamics for the study area surrounding Naples (Italy). This study case was in fact interested by severe wildfire events during summer 2017. Specific objectives are: (i) identify representative trajectories of vegetation restoration and (ii) evaluate how land use vulnerability (e.g. forest resilience, landslide susceptibility) can be better evaluate from identified dynamics.
A database of 218 Sentinel-2 A and B acquisitions was processed in order to produce smoothed temporal series of Leaf Area Index (LAI) values for the period 2016-2018. LAI time series were smoothed using a Whittaker approach, to avoid the residual noise rate affecting time series due to cloud contamination, and masked using a reference burned area map. During the time series analyses phase, each burned area was considered as a single Region of Interest (ROI) and a representative LAI time signature was extracted in the pre-fire, fire and post-fire years. Successively, a set of temporal features were extracted from time series and a clustering procedure was implemented with the aim to isolate clusters of post-fire LAI response behavior. In the last methodological step, clusters were compared against already available topographic and ancillary information, trying to qualitatively investigate which environmental drivers could be related to specific fire vegetation restoration processes.
Results lead to identify different vegetation recover responses and to associate them to the main environmental drivers. Future perspectives of this research study lies in the development of automatic approaches to analyze Sentinel-2 time series data and operatively map vegetation restoration typologies over burn-affected areas.