Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994–2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally.

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.

Wildfires cause disturbances in ecosystems and generate environmental, economic, and social costs. Studies focused on vegetation regeneration in burned areas acquire interest because of the need to understand the species dynamics and to apply an adequate restoration policy. In this work we intend to study the variables that condition short-term regeneration (5 years) of three species of the genus Pinus in the Mediterranean region of the Iberian Peninsula. Regeneration modelling has been performed through multiple regressions, using Ordinary Least Squares (OLS) and Geographic Weight Regression (GWR). The variables used were fire severity, measured through the Composite Burn Index (CBI), and a set of environmental variables (topography, post-fire climate, vegetation type, and state after fire). The regeneration dynamics were measured through the Normalized Difference Vegetation Index (NDVI) obtained from Landsat images. The relationship between fire severity and regeneration dynamics showed consistent results. Short-term regeneration was slowed down when severity was higher. The models generated by GWR showed better results in comparison with OLS (adjusted R² = 0.77 for Pinus nigra and Pinus pinaster; adjusted R² = 0.80 for Pinus halepensis). Further studies should focus on obtaining more precise variables and considering new factors which help to better explain post-fire vegetation recovery.

Forests in Europe are undergoing complex changes that require a comprehensive monitoring of disturbance occurrence. Here, we present the European Forest Disturbance Atlas (EFDA), a Landsat-based approach for mapping annual forest disturbances across continental Europe from 1985 onwards. We built a consistent Landsat data cube of summer composites and compiled reference data on forest land use and forest disturbances. A classification-based approach was developed to detect forest disturbances annually, therefore accounting for multiple disturbance events per time series. The EFDA contains annual layers on disturbance occurrence, severity, and agent, as well as aggregated layers on the number of disturbances and the latest and greatest disturbance year. Based on the annual disturbance estimates (1985–2023), we quantified a total forest disturbed area of 439 000 km², which increases to 610 000 km² when accounting for multiple overlapping disturbance events. Map accuracies of the disturbance classification showed an overall F1 score of 0.89, with very low errors (<1 %) for the undisturbed class and with commission and omission errors for the disturbed class of 17.3 % and 22.5 %, respectively. Further, temporal validation revealed that errors decreased over time, with commission errors decreasing substantially to 10.6 % after the year 2000. We designed and implemented our workflow to create annual forest disturbance maps for easy updating when new data arrive and in an open-access framework to facilitate reproducibility, thus paving the road for an operational forest disturbance system in Europe. EFDA products are available at https://doi.org/10.5281/zenodo.13333034 (Viana-Soto and Senf, 2024).

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.