Interactions between large high-severity fires and salvage logging on a short return interval reduce the regrowth of fire-prone serotinous forests

P. 54-63New fire disturbance regimes under accelerating global environmental change can have unprecedented consequences for ecosystem resilience, lessening ecosystem natural regeneration. In the Mediterranean Basin, fire-dependent obligate seeder forests that are prone to increasingly frequent stand-replacing fires and then salvaged logged repeatedly can be vulnerable to additional disturbances for decades. In this study, we investigated, for the first time, the cumulative and interactive effects of two large high-severity fires at a short (<15-year) return interval and the subsequent burned timber harvesting with biomass removal on the post-disturbance recovery of such forests. We further assessed the type and amount of the material legacies (deadwood) that persisted through the different post-disturbance successional trajectories, as well as the influence of these legacies on forest regeneration. The early recovery of the studied forests after two consecutive large fires and post-fire logging was, in the first place, driven by fire repetition, which led to reduced seedling recruitment and enhanced regrowth of resprouter shrubs. Despite no interactive effects between fire and logging were detected after a single large fire event, two repeated fires followed by salvage harvesting had a greater negative impact than two fires alone (synergistic effects) on seedling establishment; while a lower positive impact (subadditive effects) on the recovery of resprouter shrubs. There was also an interaction modification effect in which fire repetition worsened the per-unit impact of salvage logging on forest regeneration. Nonetheless, the residual legacies, i.e., fine and coarse woody debris (unburned needles, downed branches, pieces of deadwood, and burned pine cones) that remained after the manual harvesting of the burned trees, aided seedling re-establishment and hindered the regrowth of the shrubby understorey. These findings indicate that high-intensity salvage logging after two large high-severity fires at a short return interval is inadvisable in fire-prone serotinous pine forests, unless it explicitly retains the key material legacies that help tree natural regeneration and enhance ecosystem resilience to the next disturbance.S

Remote sensing applied to the study of fire regime attributes and their influence on post-fire greenness recovery in pine ecosystems

Producción CientíficaWe aimed to analyze the relationship between fire regime attributes and the post-fire greenness recovery of fire-prone pine ecosystems over the short (2-year) and medium (5-year) term after a large wildfire, using both a single and a combined fire regime attribute approach. We characterized the spatial (fire size), temporal (number of fires, fire recurrence, and return interval), and magnitude (burn severity of the last fire) fire regime attributes throughout a 40-year period with a long-time series of Landsat imagery and ancillary data. The burn severity of the last fire was measured by the dNBR (difference of the Normalized Burn Ratio) spectral index, and classified according to the ground reference values of the CBI (Composite Burn Index). Post-fire greenness recovery was obtained through the difference of the NDVI (Normalized Difference Vegetation Index) between pre- and post-fire Landsat scenes. The relationship between fire regime attributes (single attributes: fire recurrence, fire return interval, and burn severity; combined attributes: fire recurrence-burn severity and fire return interval-burn severity) and post-fire greenness recovery was evaluated using linear models. The results indicated that all the single and combined attributes significantly affected greenness recovery. The single attribute approach showed that high recurrence, short return interval and low severity situations had the highest vegetation greenness recovery. The combined attribute approach allowed us to identify a wider variety of post-fire greenness recovery situations than the single attribute one. Over the short term, high recurrence as well as short return interval scenarios showed the best post-fire greenness recovery independently of burn severity, while over the medium term, high recurrence combined with low severity was the most recovered scenario. This novel combined attribute approach (temporal plus magnitude) could be of great value to forest managers in the development of post-fire restoration strategies to promote vegetation recovery in fire-prone pine ecosystems in the Mediterranean Basin under complex fire regime scenarios.Ministerio de Economía y Competitividad, y el Fondo Europeo de Desarrollo Regional (FEDER), en el marco del GESFIRE (AGL2013-48189-C2-1-R) y proyectos FIRESEVES (AGL2017-86075-C2-1-R)Junta de Castilla y León en el marco de los proyectos FIRECYL (LE033U14) y SEFIRECYL (LE001P17)Ministerio de Educación (FPU14/00636

Remote Sensing Applied to the Study of Fire Regime Attributes and Their Influence on Post-Fire Greenness Recovery in Pine Ecosystems

P. 1-18We aimed to analyze the relationship between fire regime attributes and the post-fire greenness recovery of fire-prone pine ecosystems over the short (2-year) and medium (5-year) term after a large wildfire, using both a single and a combined fire regime attribute approach. We characterized the spatial (fire size), temporal (number of fires, fire recurrence, and return interval), and magnitude (burn severity of the last fire) fire regime attributes throughout a 40-year period with a long-time series of Landsat imagery and ancillary data. The burn severity of the last fire was measured by the dNBR (difference of the Normalized Burn Ratio) spectral index, and classified according to the ground reference values of the CBI (Composite Burn Index). Post-fire greenness recovery was obtained through the difference of the NDVI (Normalized Difference Vegetation Index) between pre- and post-fire Landsat scenes. The relationship between fire regime attributes (single attributes: fire recurrence, fire return interval, and burn severity; combined attributes: fire recurrence-burn severity and fire return interval-burn severity) and post-fire greenness recovery was evaluated using linear models. The results indicated that all the single and combined attributes significantly affected greenness recovery. The single attribute approach showed that high recurrence, short return interval and low severity situations had the highest vegetation greenness recovery. The combined attribute approach allowed us to identify a wider variety of post-fire greenness recovery situations than the single attribute one. Over the short term, high recurrence as well as short return interval scenarios showed the best post-fire greenness recovery independently of burn severity, while over the medium term, high recurrence combined with low severity was the most recovered scenario. This novel combined attribute approach (temporal plus magnitude) could be of great value to forest managers in the development of post-fire restoration strategies to promote vegetation recovery in fire-prone pine ecosystems in the Mediterranean Basin under complex fire regime scenariosS

Differentiating between fatal and non-fatal mining accidents using artificial intelligence techniques

Using statistical methods for categorical data analysis, namely multiple correspondence analysis and Artificial Intelligence through Bayesian networks, we analysed a database of occupational mining accidents for Spain for the period 2004–2017 to identify the factors most associated with the occurrence of fatal and non-fatal accidents. The results obtained allow to shed light on the hidden patterns present in different work situations where accidents can have fatal consequences. In addition, this study exemplifies the application of statistical techniques suitable for Big Data and data-driven decision making in the mining sector.Xunta de Galicia | Ref. ED431C 2018/4

Impact of burn severity on soil properties in a Pinus pinaster ecosystem immediately after fire

P. 1-11We analyse the effects of burn severity on individual soil properties and soil quotients in Mediterranean fire-prone pine forests immediately after a wildfire. Burn severity was measured in the field through the substrate stratum of the Composite Burn Index and soil samples were taken 7–9 days after a wildfire occurred in a Pinus pinaster Ait. ecosystem. In each soil sample, we analysed physical (size of soil aggregates), chemical (pH, organic C, total N and available P) and biological (microbial biomass C, b-glucosidase, urease and acid phosphatase activities) properties. Size of aggregates decreased in the areas affected by high burn severity. Additionally, moderate and high severities were associated with increases in pH and available P concentration and with decreases in organic C concentration. Microbial biomass C showed similar patterns to organic C along the burn severity gradient. The enzymatic activities of phosphatase and b-glucosidase showed the highest sensitivity to burn severity, as they strongly decreased from the low-severity scenarios. Among the studied soil quotients, the C : N ratio, microbial quotient and b-glucosidase : microbial biomass C quotient decreased with burn severity. This work provides valuable information on the impact of burn severity on the functioning of sandy siliceous soils in fire-prone pine ecosystems.S

Fire recurrence and emergency post-fire management influence seedling recruitment and growth by altering plant interactions in fire-prone ecosystems

P. 63-75Projections of future wildfire regimes forecast an increased frequency of large high-severity fires that create very harsh environmental conditions and constitute a challenge to post-fire ecosystem regeneration. Under these new circumstances, better knowledge of the plant interaction mechanisms underlying post-fire seedling establishment success would aid restoration management to achieve the intended targets. We evaluated the combined effect of recurrent large stand-replacing fires and conventional post-fire restoration activities (salvage logging after a single large fire, and direct seeding and linear subsoiling plus seedling planting after two subsequent large fires) on tree seedling recruitment and performance (development, annual growth, and biomass) in the early stages of succession in fire-prone maritime pine (Pinus pinaster Ait.) ecosystems. We quantified plant facilitative/competitive interactions between naturally recruited pine seedlings, neighbouring seedlings and potential nurse shrubs with different post-fire regeneration strategies (obligate seeders vs resprouters), by computing the relative interaction index (RII). The results evidenced that fire recurrence altered plant species composition and conditioned initial pine seedling recruitment and establishment, prevailing over the expected negative impact of salvage logging and positive impact of seeding. Seedling recruitment was sufficient to ensure natural tree regeneration after a single fire event and undermined by repeated fires. Both delaying burned timber removal during salvage logging operations and retaining immature dead trees without commercial value onsite in subsoiled stands enhanced seedling recruitment via facilitative interactions. Higher seedling growth and height under shrubs than in open ground resulted in lower aerial and root biomass, indicating elongation in response to shade, and net competition for resources. Inter-specific competition between naturally regenerated seedlings and shrubs was aggravated by intra-specific competition with neighbouring seedlings and by mechanical site preparation in subsoiled stands. All in all, post-burn increased soil fertility most likely counterbalanced the environmental stress created by fire, shifting the net outcome of plant interactions from positive (facilitation) to negative (competition). We recommend alternative post-fire management actions that decrease plant competition and take advantage of facilitation by residual burned wood, to ultimately accelerate ecosystem recovery after large stand-replacing fires.S

Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: a case study on a large fire in NW Spain

P. 512-520This study analyses the suitability of remote sensing data from different sources (Landsat 7 ETM+, MODIS and Meteosat) in evaluating the effect of fuel conditions on fire severity, using a megafire (11 891 ha) that occurred in a Mediterranean pine forest ecosystem (NW Spain) between 19 and 22 August 2012. Fire severity was measured via the delta Normalized Burn Ratio index. Fuel conditions were evaluated through biophysical variables of: (i) the Visible Atmospherically Resistant Index and mean actual evapotranspiration, as proxies of potential live fuel amount; and (ii) Land Surface Temperature and water deficit, as proxies of fuel moisture content. Relationships between fuel conditions and fire severity were evaluated using Random Forest models. Biophysical variables explained 40% of the variance. The Visible Atmospherically Resistant Index was the most important predictor, being positively associated with fire severity. Evapotranspiration also positively influenced severity, although its importance was conditioned by the data source. Live fuel amount, rather than fuel moisture content, primarily affected fire severity. Nevertheless, an increase in water deficit and land surface temperature was generally associated with greater fire severity. This study highlights that fuel conditions largely determine fire severity, providing useful information for defining pre-fire actions aimed at reducing fire effects

Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems

P. 24-32The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.S

Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems

P. 137-144The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6-22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, mono-temporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices.S

Using Unmanned Aerial Vehicles (UAV) for forest damage monitoring in south-western Europe

P. 1-8Prescribed burns are being considered as a management tool for the prevention of forest fires in many countries that have important firefighting problems. Knowledge of fire intensity and eliminated vegetation fuel are of great interest to evaluate their effectiveness. Both parameters are directly related to burn severity, so their evaluation is fundamental to predict the post-fire evolution of burned area. In this study we evaluated two prescribed burnings carried out in North of Spain during October 2017 by using multispectral data from an Unmanned Aerial Vehicle (UAV). In particular, four surface reflectance images were obtained in green (550 nm), red (660 nm), red-edge (735 nm) and near infrared (790 nm) at very high spatial resolution (GSD 20 cm) from which different spectral indexes were computed. Additionally, vegetation and soil burn severity was measured in 153 field plots and an analysis of variance (ANOVA) between each spectral index and burn severity (both in vegetation and soil) was performed. A Fisher’s least significant difference test determined that three vegetation burn severity levels and two soil burn severity levels could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that multispectral data from UAVs may be considered as a valuable indicator of burn severity for prescribed burnings.S