A remote-sensing assessment of oak forest recovery after postfire restoration

doi:10.21203/rs.3.rs-2613325/v1

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Research Article

A remote-sensing assessment of oak forest recovery after postfire restoration

https://doi.org/10.21203/rs.3.rs-2613325/v1

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published 07 Mar, 2024

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Background:

Mediterranean Europe is witnessing an increase in extreme wildfire events, which has led to increasing socioeconomic and ecological impacts. Postfire restoration emerges as an important tool for impact mitigation and ecosystem recovery. However, there is a large knowledge gap on the ecological effects of such interventions. We used remote sensing to assess the impacts of postfire restoration on the recovery of deciduous oak forests in Portugal, based on a total of 3013 sampling points in areas with and without postfire intervention. We quantified the vegetation index NDVI as a proxy of oak forest recovery for a period of 4 years after the fire, for fire events that occurred in 2016 and 2017. Temporal changes in NDVI were modelled with a Generalized Additive Mixed Model (GAMM), as a function of postfire restoration, fire characteristics (burn severity, number of fires, and time since fire), topography (aspect, elevation, and slope) and postfire drought events (PDSI).

Results:

The fitted model accounted for 26.4% of NDVI variance. The model showed a significant positive effect of postfire restoration on NDVI recovery over time, although with a minor effect; it also showed a negative effect of severe drought and a positive effect of severe wetness on NDVI recovery. Regarding fire characteristics, low and high burn severities showed a positive effect on recovery, while moderate burn severity levels showed a null effect. Number of fires showed an increasingly negative effect on recovery, until a maximum of 6 fires.

Conclusions:

Oak forest recovery was positively affected by postfire restoration, although this effect was small and likely limited by lack of available data. On the contrary, oak forest recovery was negatively affected by increased drought severity but its relation with fire characteristics varied with thresholds of burn severity levels and number of fires. Our study highlights the importance of monitoring the effects of postfire restoration on forest recovery, in order to improve knowledge on postfire forest management and species adaptation. This becomes even more relevant under forecasted scenarios of increased wildfire frequency and severity interacting with other climate-driven disturbances, which will further impact the capacity of forest recovery in the future.

Postfire restoration

remote sensing

oak forests

vegetation recovery

wildfires

time series

vegetation indices

deciduous oaks

In the last decades, global changes have led to a change in wildfire regimes (e.g., frequency and severity) across the world, with an increase of extreme wildfire events (Rogers et al. 2020; Pausas and Keeley 2021; Harvey and Enright 2022). In Mediterranean Europe in particular, and despite the decreasing trend in annual burned area in the last years, there has also been an increment in extreme wildfire events, which has led to increasing socioeconomic and ecological impacts (Tedim et al. 2015; Doerr and Santín 2016; Turco et al. 2016; Fernandez-Anez et al. 2021). For example, changes in wildfire regimes have impacted Mediterranean forests in different ways, by transforming forests into shrublands and ceasing natural succession in a self-reinforcing feedback loop, by creating more connected and homogeneous vegetation patches that increase wildfire risk, and by forcing adaptation of forest management to the new wildfire regime, such as species selection for afforestation (e.g., species with higher fire resilience or managed with short-rotation silviculture), among other impacts (Moreira et al. 2011; Vallejo et al. 2012; Mateus and Fernandes 2014; Navarro and Pereira 2015).

Wildfires affect plant survival, regeneration and growth (Bright et al. 2019; Pausas and Keeley 2019; Castro Rego et al. 2021) and therefore modify the structure, composition, and dynamics of ecosystems. Thus, fire impacts can limit or delay vegetation recovery (McLauchlan et al. 2020). Fire characteristics that may influence vegetation recovery include fire frequency, time since fire and burn severity (Moreira et al. 2012; Tepley et al. 2018; Castro Rego et al. 2021). For example, increased wildfire frequency and severity in recent years in Mediterranean Europe have halted postfire forest recovery due to changes in vegetation dynamics (Francos and Lemus Canovas 2021), changes in vegetation cover (Tessler et al. 2016) or decreased ecosystem resilience (Tepley et al. 2018; McLauchlan et al. 2020). The rate of postfire vegetation recovery is also influenced by other factors such as postfire climate and topography. Postfire weather conditions can be determinant for vegetation response (Pausas 1999a; Torres et al. 2018; McLauchlan et al. 2020), especially precipitation and temperature (Díaz-Delgado et al. 2002; Torres et al. 2018), facilitating or hindering juvenile recruitment (Littlefield 2019). Topography has the potential to alter microclimatic conditions (Correia et al. 2015), thus affecting vegetation growth and its resilience to disturbances (Roder et al. 2008; Viana-Soto et al. 2017). Additionally, site topography (e.g., slope) directly affects fire spread, which will indirectly affects fire impacts (burn severity) (Hawbaker et al. 2013; Fernandes et al. 2016). The trajectories of vegetation recovery after a wildfire may also be conditioned by the regeneration capacity of the dominant vegetation. Resprouting individuals can persist after fires, resulting in long-lived specimens and more stable populations (Pausas 1999a; Lamont et al. 2011). Additionally, resprouter species regenerate vegetatively after fire and can therefore accomplish faster postfire ground cover, in comparison to seeders (Pausas 1999a; Pausas 1999b; Maia 2014).

Lastly, vegetation recovery following a wildfire may be further conditioned by postfire restoration interventions (Moreira et al. 2012), which aim to facilitate or accelerate vegetation recovery across three phases: (i) emergency stabilization (applied in the short-term after fire extinction), (ii) the intermediate stage (up to 2 years after the fire), and (iii) the recovery stage (long-term) (Moreira et al. 2014). Nevertheless, certain postfire restoration practices such as salvage logging or site preparation for sowing or plantation can damage surface roots or natural regeneration (postfire resprouts or seedlings) and cause additional stress to the surviving trees, potentially provoking their death (Catry et al. 2010; Moreira et al. 2012; Cutino et al. 2018).

Portugal is the southern European country most affected by wildfires, with large burned areas resulting from a small number of fire events, with extraordinary negative environmental, social, and economic impacts (Pereira et al. 2006; Mateus and Fernandes 2014; Tonini et al. 2017). In Portugal, wildfires occur predominantly in the northern and central regions (Pereira et al. 2006; Tonini et al. 2017) where the native forest was dominated by deciduous oaks (reaching a maximum between ca. 9000 BP and ca. 5000 BP). Deciduous oak forests in Portugal have decreased gradually over time, mostly due to anthropogenic disturbances (Fletcher et al. 2007; Reboredo and Pais 2014). In the present, only some original traces of those forests remain, across small fragmented patches (Silva 2007; ICNF 2019). Oak species are resprouters, with the ability to regenerate after major disturbances such as wildfires, even at young ages (Alves et al. 2018; Marañón et al. 2020). Such postfire regeneration capacity, in association with wetter environments and subsequent fire resistant fuels commonly found in mature oak forests (Dimitrakopoulos and Papaioannou 2001; Fernandes 2009), turn these forests into fire-resilient landscapes (Díaz-Delgado et al. 2002; Botequim et al. 2017). Nevertheless, high fire frequency and/or severity can lead to oak forest degradation, difficulties in oak natural regeneration and replacement of oak trees by shrublands (Tarrega and Luis-Calabuig 1987; Álvarez et al. 2009; Clarke et al. 2013; Acácio et al. 2017; Monteiro-Henriques and Fernandes 2018).

In Portugal, postfire restoration has been increasingly integrated into national policies, especially since the beginning of the twenty-first century, being implemented mainly through public funding (Ribeiro et al. 2018). However, postfire restoration is often ineffective (Lopes et al. 2022) and the lack of systematic monitoring constrains treatments optimization (Observatório Técnico Independente 2019; Ribeiro et al. 2020). For example, monitoring of vegetation recovery after postfire restoration is seldom done, not only in Portugal, but also in Europe (DGRF 2015; Ribeiro et al. 2020). As a consequence, there is a knowledge gap on the ecological effects of postfire restoration in Europe, especially in the long-term (Robichaud et al. 2009), with very few studies available (e.g. (Bontrager et al. 2019)). In particular, available research on postfire restoration effects on the regeneration of Mediterranean deciduous oaks is still scarce (Espelta et al. 2012).

Several studies have shown the use of remote sensing techniques and indices to assess vegetation recovery in time (Bastos et al. 2011; Gitas et al. 2012; Viana-Soto et al. 2017; Hislop et al. 2018; Gouveia et al. 2018; Torres et al. 2018; Meneses 2021; Pérez-Cabello et al. 2021), including assessments under different types of restoration (Chen et al. 2014) and related with the implementation of postfire restoration treatments (Vo and Kinoshita 2020; Carrari et al. 2022). Remote sensing provides biophysical measurements in a time-efficient and less costly manner, in comparison to fieldwork (Szpakowski and Jensen 2019; Pérez-Cabello et al. 2021). In addition, the recent developments in remote sensing are leading to a transition from bitemporal to continuous approaches, allowing analysis of spectral trajectories across regular time series (Woodcock et al. 2020; Pérez-Cabello et al. 2021). By supplying frequent medium to high resolution (< 100 m) observations, it is possible to monitor short-term postfire dynamics (Szpakowski and Jensen 2019; Pérez-Cabello et al. 2021).

In this study, we used remote sensing to assess the impacts of postfire restoration on the recovery of deciduous oak forests in Portugal. We analysed oak forest recovery for a period of 4 years post fire, for fire events that occurred in 2016 and 2017, by comparing recovery between areas with and without postfire intervention. We asked the questions: i) Did postfire restoration have a positive impact on the recovery of oak forests? and ii) How did fire characteristics, topography and postfire drought events affected oak forest recovery? Assessment of the impacts of postfire restoration on forest recovery over time can provide valuable information to improve understanding of restoration success and effectiveness of implemented practices, ecosystem resilience and landscape dynamics, contributing also to improve forest management (Pérez-Cabello et al. 2021).

Study area

Mainland Portugal (latitude 37º-42ºN, longitude 6º-10ºW) is positioned in the extreme southwest of continental Europe, on the Iberian Peninsula. The Portuguese climate is Mediterranean, with a north-south gradient (Mora and Vieira 2020). There are considerable physiographic differences between northern and southern regions, with the northern half having a more rugged topography, a denser river network, and the majority of forests and semi-natural areas (Tonini et al. 2017). In 2015, Portugal was predominantly covered by forests (36%), shrublands and pastures (31%), and agricultural land (23%) (ICNF 2019). The main forest species in the country are blue gum eucalypt (Eucalyptus globulus), cork oak (Quercus suber) and maritime pine (Pinus pinaster), with 26.2%, 22.3% and 22.1% of total forest area, respectively. Deciduous oaks account for only 2.5% of total forest area (ICNF 2019).

Selection of spatial sampling units

We collected all postfire restoration projects related with the fires of 2016 and 2017 and approved for public funding by the Portuguese Rural Development Program. According to this funding program, projects must be submitted a few months after fire occurrence and can include emergency stabilization measures (e.g. mulching and erosion barriers) and intermediate-stage interventions (e.g. salvage logging, sowing/planting), with considerable variability regarding time of implementation, as seen in (Lopes et al. 2022). Each project may include one or several parcels, and each parcel is identified by a unique number. Project data and parcel identification numbers were provided by the Financing Institute for Agriculture and Fisheries (IFAP) and parcel spatial location was obtained from IFAP online services, which show the parcel coordinates based on its identification (ICNF 2021).

Spatial data on oak land cover was extracted from the 2015 National Land Use and Land Cover (LULC) Map (hereafter COS2015, Cartografia de Ocupação do Solo 2015, Direção-Geral do Território 2022). We selected “oak forests” land cover class from COS2015, which includes three main species, namely, Pyrenean oak (Quercus pyrenaica (Willd.)), Portuguese oak (Quercus faginea (Lam.)), and Pedunculate oak (Quercus robur (L.)), since map categories do not distinguish the deciduous oak species. This class is dominated by deciduous oaks, including pure stands or mixed stands (with other broadleaves).

Burned areas between 2016 and 2017 were collected from the national database of burned areas, which contains this information in vector format since 1975 (ICNF 2021).

We intersected the three spatial layers (in vector format), namely, (i) polygons (parcels) with postfire restoration projects, (ii) polygons dominated by deciduous oak forests and (iii) burned areas, in order to obtain the final polygons for analysis, which corresponded to areas dominated by oak forest that burned in 2016–2017 where postfire restoration was subsequently implemented. Polygons with an area lower than 1000 m² were excluded from further analysis.

We created a buffer area of 500 meters around each final polygon to produce control polygons without postfire restoration interventions. Control polygons were also dominated by deciduous oak forests that burned in 2016–2017, and with at least 1000 m². Contiguous buffers were aggregated to constitute unique spatial units (with a single identification number), in a total of 60 final unique spatial units.

Lastly, we created random points within the final polygons (differentiated between intervention and control points, if located within polygons with postfire restoration or within control polygons, respectively), with a minimum distance of 100 m between points. In total, 3013 random points were originated, of which 43.6% (n = 1314) were located within polygons with postfire restoration projects, and 56.4% (n = 1699) within control polygons (Fig. 1). Spatial analysis was performed in ArcGIS Pro (Esri Inc. 2022). Random points were used as the sampling units for data collection and analysis.

Data Collection

For each sampling point, we collected remote sensing data using the Google Earth Engine (GEE) platform (Gorelick et al. 2017) and used GIS to obtain complementary spatial data on variables related with fire characteristics and topography (Table 1).

We quantified two vegetation spectral indices: the Normalized Difference Vegetation Index (NDVI) and the Modified Soil Adjusted Vegetation Index 2 (MSAVI2). NDVI is widely used to analyse the state of recovery of burnt vegetation (Paci et al. 2017; Torres et al. 2018; Pérez-Cabello et al. 2021), and is the most robust vegetation index for vegetation recovery assessments (Veraverbeke et al. 2012; Szpakowski and Jensen 2019), showing a strong relationship with aboveground biomass for a wide range of ecosystems (Gitas et al. 2012). Since all vegetation indices have advantages and disadvantages, it is common to work simultaneously with more than one index (Wegmann et al. 2016). Therefore, we selected MSAVI2 since it minimizes the effect of bare soil of the Soil Adjusted Vegetation Index (SAVI), which is in turn used to rectify the effect of soil brightness in areas where vegetative cover is low (Qi et al. 1994). Both vegetation indices range from − 1 to 1, with negative values indicating clouds and water, positive values near zero indicating bare soil, and higher positive values indicating denser vegetation (the closer to 1, the denser the vegetation). Postfire vegetation indices (NDVI and MSAVI2) were used as proxies of oak forest recovery and were calculated as monthly median values for the 48 months after the fire, for each random point. Monthly medians were computed to diminish the leverage effect of extreme values.

We also quantified the differenced Normalized Burn Index (dNBR) for each random point. The index dNBR is important to discriminate different degrees of severity on surfaces affected by fire (Keeley 2009) and is the standard index for burn severity assessment (Szpakowski and Jensen 2019). To obtain the dNBR, we calculated the difference between the Normalized Burn Index (NBR) for prefire and postfire (NBR prefire - NBR postfire) and used the time interval of 21 days before the fire start date and 21 days after the fire end date. Burn severity levels are based on USGS (Key and Benson 2006). Due to the possible existence of unburned islands inside the burned areas (Tedim and et. al. 2018), 11.2% of sampling points (n = 338) presented dNBR values that corresponded to the levels of “Unburned” and “Regrowth” (dNBR < 100, Key & Benson (2006)). Therefore, those sampling points were excluded from subsequent analysis, with a total of 2675 points remaining.

We collected additional variables for each random point, namely: (i) Number of fires (fires occurred between 1975 and the analysed fire event, 2016 or 2017) and time (number of years) since the last fire (before 2016 or 2017), both collected from the national database of burned areas, which starts in 1975 (ICNF 2021); (ii) postfire monthly values of the Palmer Drought Severity Index (PDSI), which uses temperature and precipitation records to estimate relative dryness (values between − 1 and − 4 correspond to different drought levels) from TerraClimate (Abatzoglou et al. 2018); and (iii) topographic information (aspect, elevation, and slope) from the NASA DEM Digital Elevation Model (NASA JPL 2020).

Data collected to characterize the sampling points are shown in Table 1.

Table 1

– Data collected, respective time periods, resolution, and data sources. *Depending on the fire year (2016 or 2017).
Group	Variable	Period*	Temporal resolution	Spatial resolution	Data source
Fire variables	Number of fires	1975–2015 1975–2016	Minimum detectable area variable over time. Between 1975–1983 = 35ha; 1984–2004 = 5 ha; since 2005 it is possible to detect smaller fires		National database of burned areas (ICNF 2021)
	Time since the last fire	1975–2015 1975–2016			National database of burned areas (ICNF 2021)
	Burn severity (dNBR)	2016–2020 2017–2021	monthly	10 m	Sentinel-2 (Copernicus Sentinel 2022)
Postfire vegetation indices	NDVI		monthly
Postfire vegetation indices	MSAVI2		monthly
Postfire drought	PDSI		monthly	4638 m	TerraClimate (Abatzoglou et al. 2018)
Topography	Aspect	-	-	30 m	NASA SRTM Digital Elevation (NASA JPL 2020)
	Elevation
	Slope

Spatial analysis was performed in ArcGIS Pro (Esri Inc. 2022).

Data analysis

To evaluate the impact of postfire restoration projects, fire variables, topography and postfire drought events on oak forest recovery, we modelled changes in time of two vegetation indices, NDVI and MSAVI2, as a function of burn severity, time since the last fire, number of fires, PDSI, aspect, elevation, and slope (see Table 1), using a Generalized Additive Mixed Model (GAMM) from the R package mgcv (Wood 2022). The effect of postfire restoration was added to the model as a factorial variable called “Intervention” which takes the value 1 for restored areas and 0 for control areas. We also added the variable Time that indicates how much time has passed since the fire. Since NDVI/MSAVI2 values present temporal correlation, we added a correlation structure of order 1 (corAR1) to the model’s residuals. We used the identification of each buffer (buffer id) as a random effect to account for discrete spatial heterogeneity.

Two GAMMs have been attempted, one for each vegetation index (MSAVI2 and NDVI). As preliminary analysis, we evaluated the mathematical pairwise relation between the variables through XY graphs, in order to assess the association of each predictor variable with each vegetation index. Due to the absence of well-defined associations with MSAVI2, we considered that only NDVI provided a valid model, corroborated through model checking tools. The final predictor variables selected for the NDVI model were: postfire drought (PDSI), burn severity, number of fires, Time (that accounts for temporal correlation), and the factorial variable Intervention. Due to negative values, the variable PDSI was first transformed (addition of 6) and then logarithmized. Statistical analyses were carried out with R (4.0.3) statistical environment (R Core Team 2020). Data were handled with the support of Dplyr and Tidyr packages (Wickham 2020; Wickham et al. 2020). Except when specified, statistical analyses were performed with base R functions.

R scripts with data analysis are shown as Supporting Information (Annex I).

Characterization of sampling points: fire variables, postfire drought and topography

Descriptive statistics for the data collected are shown in Table 2. The altitude of random points locations ranged from 22 m to about 1300 m and the majority of the sampling points was exposed to ESE (East-South-East, between 90º and 135º) or WNW (West-North-West, between 270º and 315º) aspects, with highly variable slopes. Number of fires ranged from 0 to a maximum of 10 fire events in 1975–2016/2017, from which 27% of sampling points (815) never burned, 43.5% (1312 points) burned once or twice, and 56.5% (1701 points) burned 3 or more times. Time since last fire was also highly variable, ranging from 1 year until a maximum of 42 years (for points that never burned), in accordance with the variability of number of fires. Burn severity, as measured by dNBR, ranged from − 191 to 1171.

Table 2

*Descriptive statistics of potential explanatory variables.*¹The values are limited by the temporal resolution, which only allows a maximum value of 42 years (1975–2017). In addition, sampling points with no fire events during this period were not considered for the descriptive statistics. The different levels of burn severity showed a similar distribution between sampling points with and without (control) postfire interventions, with the moderate-high burn severity level being the most frequent (33.64%), followed by high (26.13%), and moderate-low levels (24.07%) (Table 3). Note that all levels of burn severity were included in Table 2, but the points with values equivalent to “Unburned” and “Regrowth” (dNBR < 100) were removed from the model analysis, as already mentioned in the methods section.
Group	Variable	Min	Mean	Max	SD
Fire characteristics	Number of fires	0	2	10	1.7
	Time since last fire ¹ (years)	1	16	42	10
	Burn severity (dNBR)	-191	457	1171	256
Postfire drought	PDSI	-5.5	-1.6	3.4	2
Topography	Aspect (º)	0	186	356	100
	Elevation (m)	22	618	1326	197
	Slope (º)	0	12	47	8

Table 3

Contingency table of burn severity and intervention
Burn severity levels	Regrowth	Unburned	Low	Moderate-low	Moderate-high	High
dNBR values	-250 ≥ & < -100	-100 ≥ & < 100	100 ≥ & < 270	270 ≥ & < 440	440 ≥ & < 660	≥ 660	Total
Points with postfire Intervention	7 (0.2%)	140 (4.6%)	223 (7.4%)	287 (9.5%)	371 (12.3%)	286 (9.5%)	1314 (43.6%)
Control Points	2 (0.1%)	189 (6.3%)	209 (6.9%)	357 (11.8%)	529 (17.6%)	413 (13.7%)	1699 (56.4%)
Total	9 (0.3%)	432 (10.9%)	432 (14.3%)	644 (21.4%)	900 (29.9%)	699 (23.2%)	3013 (100%)

PDSI monthly values showed similar seasonal patterns across sampling points in the analysed periods (2016/2017 or 2020/2021, depending on the fire event). For the majority of sampling points, severe to extreme drought levels (PDSI< -3) were registered in the first 3 to 5 months after fire, followed by mild to moderate drought levels (-1 < PDSI<-3). Normal to moderate wet periods (3 < PDSI < 0) were mostly concentrated between the 5th and the 15th months after the start of both 2016 and 2017-fires. In contrast, the sampling points located in fires that started in September 2017 (n = 14) showed an unexpected and opposed pattern; these points belong to two spatially close buffers (1.7 Km - distance), forming a cluster situated on a south-east facing hillside near a village, which may be subjected to specific microclimatic conditions.

Effects of postfire restoration, fire characteristics and postfire drought events on oak forests recovery

Overall, the model explained 26.4% of the variance of NDVI, with model checking revealing a well-fitted model (see SM – Annex I). Results show that the effect of postfire restoration intervention was weakly positive but statistically significant (β = 0.011, p < 0.001). This result means that the NDVI in restored areas tended to be 1% higher than in control areas.

The smooth terms of covariables were all significant (p < 0.001). Partial effects plots (Fig. 3) show the effect of each variable on NDVI recovery rate (model response), namely: i) a negative effect of severe drought events and a positive effect of severe wetness; ii) a positive effect of low burn severities (dNBR < 270), a null effect of moderate-low (270 > dNBR > 440) and moderate-high burn severities (440 > dNBR > 660), and an increasingly positive effect with higher burn severities (dNBR > 660); iii) an increasingly negative effect of number of fires (one or more fires), which appears to achieve its maximum with a probability of 5 or 6 fires. Above 6 fires, there is a decrease of the negative effect, associated with wider confidence intervals. Lastly, the variable time shows a well-adjusted undulating trend (small confidence intervals) associated with yearly seasons, but with a clear recovery over time as shown by the maximum annual values.

Effects of postfire restoration interventions on oak forest recovery

We found a tendency for an increase in NDVI over time, which indicates a generalized postfire oak forest recovery in the study areas. NDVI maximum annual values increased during the spring meteorological season, which corresponds to the period between the start of the Mediterranean growing season (March) and the maximum of photosynthetic activity (June) (Piedallu et al. 2019). We also observed a shift in annual maximum NDVI values amid the second and the third year (based on partial effects model with values below 0.03 in the first year and over 0.1 after the second year). Such trends agree with other studies that show a gradual recovery of oak forest communities, dominated by the recovery of herbaceous species during the first 2 years, with the recovery of woody species (shrubs and trees) becoming stronger from the third year onwards (Calvo et al. 2003; Capitanio and Carcaillet 2008).

Postfire restoration interventions showed a significant positive impact on oak forest recovery, however with a small model effect. The small effect observed may be the result of the diversity of postfire restoration interventions (e.g., emergency or medium/long-term interventions) and corresponding execution periods included in the selected projects. Indeed, the type of postfire restoration treatment and its moment of implementation is extremely important since distinct interventions and moments of implementation may promote opposite impacts. For example, emergency interventions such as protection of the burned soil to avoid erosion (such as mulching or barriers) should be implemented a few months after the fire to promote an effective restoration and help to prevent fire negative impacts (Vega et al. 2013; Fernández and Vega 2014; Bontrager et al. 2019; Fernández et al. 2019; Fernández 2021; Girona-García et al. 2021), hence facilitating the recovery of burned vegetation (Calvo et al. 2003). However, the execution of interventions outside the optimal timeframe is common in projects with public funding in Portugal (Lopes et al. 2022), and can have a negative impact on vegetation, including inhibition of vegetation recovery (Bautista et al. 1996; Alloza et al. 2016).

Another example is salvage logging, which is a common postfire restoration technique implemented in the medium and long-term in the Mediterranean Basin, in order to remove burnt trees for subsequent reforestation and to generate income (Leverkus et al. 2012). Nevertheless, this intervention creates disturbances in the vegetation, and may lead to changes in the ecosystem structure and functions (Bautista et al. 2010; Morgan et al. 2015; García-Orenes et al. 2017). The magnitude of the effects depends on the type, intensity, frequency and spatial pattern of logging, combined with the characteristics of the fire and post-fire weather conditions (Lindenmayer et al. 2008), with possible negative impacts on vegetation regeneration, loss of organic matter or increased soil erosion and compaction (Bautista et al. 2010). Nevertheless, in some cases there has been no negative impact on vegetation recovery (Fernández and Vega 2016). Particularly in Portugal, 40% of managed oak stands are clear-cut after the fire, of which 24% are cut within the first 3 months and 57% between 3 and 6 months after fire (Sousa 2011). Sowing or planting are also interventions executed in the medium-long term after fire (some months or years after the fire) but may delay vegetation recovery since rates of survival and growth of artificially sowed/planted trees are lower than survival and growth of trees originated from natural regeneration, which starts a few weeks after the fire in the case of broadleaved species (Moreira et al. 2009b; Catry et al. 2010a).

Effects of postfire drought events and fire characteristics on oak forest recovery

The drought index PDSI was the variable with the higher effect in the postfire recovery of NDVI. During the analysed period, PDSI monthly values were mostly negative (reflecting mild to extreme drought levels), with severe drought months observed in the first months after the fire. In fact, the hydrological year 2016–2017 was the 9th driest year since 1931 and in 2017–2018 most of mainland Portugal was under severe to extreme drought. In addition, this drought event was distinct from former drought years since drought severity was aggravated in the autumn of 2017 (IPMA 2020). Our results showed that postfire recovery of NDVI was negatively affected by severe drought levels and positively affected by increased wetness, in accordance with available literature. Indeed, the initial stages of the oak life cycle are especially vulnerable to the reduction of precipitation (Montagnoli et al. 2016; Marañón et al. 2020), and severe droughts will likely affect postfire regeneration capacity of oak forests (Acácio et al. 2017; Marañón et al. 2020). A recent study also showed that precipitation deficits were associated with changes from deciduous oak forests to other land cover types in Portugal (Acácio et al. 2017). In general, post-fire climatic conditions have been pointed out as one of the most important predictive factors for postfire vegetation recovery (Pausas et al. 1999; Torres et al. 2018; Nolan et al. 2021).

Regarding burn severity, our results showed that NDVI responded positively to both low and to high burn severities, while intermediate severities showed a reduced effect on NDVI recovery rate. Although both extreme categories appear to have a similar outcome, the NDVI response can be justified by different reasons. On one hand, low burn-severity fires will, in general, provide beneficial consequences, since trees will not be top-killed, surface litter will be partially consumed, and the soil organic layer will remain largely intact (Keeley 2009; Fernandes et al. 2010). This will result in availability of soil nutrients and habitat rejuvenating, favouring certain plants and life forms (Castro Rego et al. 2021), since plant mortality caused by low-moderate severity fires is highly selective and dependent of plant species and individual size (Marzano et al. 2012).

On the other hand, higher burn severity levels (registered by more than 50% of analysed points) will cause higher impacts on the soil and vegetation when compared to lower severity fires (Viana-Soto et al. 2017; Castro Rego et al. 2021). High-severity fires (often crown fires) originate near total mortality of the above-ground vegetation, including a significant amount of post-fire stem mortality in the deciduous oak trees (Catry et al. 2010b; Catry et al. 2013), and total consumption of the forest floor (Keeley 2009; Tepley et al. 2018), and also lead to changes in ecosystem recovery dynamics, decreasing seed dispersal, viability, and success rate (Marzano et al. 2012). Therefore, high burn severities likely imply that vegetation will require more time to recover (Keeley 2009; Vega et al. 2013; Tepley et al. 2018). As such, NDVI increase under high burn severity is partially explained by the recovery of the understorey vegetation that dominates the burned area (Castro Rego et al. 2021) and also by the greater canopy severity (top kill) that stimulates basal budding of trees (Moreira et al. 2009a; Frelich et al. 2015). Such differences in the recovery of vegetation cannot be detected by NDVI measurements, which show vegetation greenness and do not guarantee that the same type of pre-fire vegetation is being regenerated (Gouveia et al. 2010; Meneses 2021). In agreement with our results, postfire NDVI values in pine forests mixed with oaks and other woody species under moderate/high burn severities increased quickly over time, achieving those of the lightly burned areas two years after the fire (Lee and Chow 2015).

Lastly, number of fires showed a small negative effect on NDVI recovery rate, which decreased with more than 6 fires. Indeed, recurrent fire events may lead to a decrease in oak dominance in favour of other tree species, with the magnitude of this effect being dependent on the number of fires (Burton et al. 2010; Frelich et al. 2015). Furthermore, higher fire frequency may lead to a diminishing capacity of oaks to resprout (Frelich et al. 2015). Until a certain frequency of fires, its effect is increasingly negative, by reducing sapling density, with Pedunculate oak and Pyrenean oak saplings being especially sensitive to fire frequency (Monteiro-Henriques and Fernandes 2018), and by altering the stand structure (Burton et al. 2010). For example, repeated burning can transform an uneven-aged oak stand into an even-aged stand (Knapp et al. 2017). On the contrary, and as our results indicate, when fire frequency is above a certain threshold, it leads to lower fuel loading and lower burn severity, and consequently lower impacts in oak forest recovery (Burton et al. 2010; Steel et al. 2015; Knapp et al. 2017).

Future research needs

Spectral indices are increasingly used in fire ecology (Szpakowski and Jensen 2019) and the majority of the authors considers NDVI as an extremely valuable tool, applicable without additional field validation (Gitas et al. 2012). Spectral indices also show relatively high accuracies of estimation. For example, the index dNBR showed a global accuracy of 81% to estimate burn severity with Sentinel-2 satellite imagery, in comparison with in situ measurements (Sobrino et al. 2019). Nevertheless remote sensing data are approximations of reality (Szpakowski and Jensen 2019). There is a wide range of possible indices available, each with its own strengths and weaknesses which will have an impact on the results (Fernández-Guisuraga et al. 2018; João et al. 2018; Szpakowski and Jensen 2019). In addition, remote sensing data are generally limited by atmospheric effects, saturation, and sensor characteristics, and as such, a careful interpretation of results is recommended (Huang et al. 2021). Nevertheless, newer techniques are approaching maturity (e.g. hyperspectral imagery or Unmanned Aerial Vehicles to obtain ultra-high resolution imagery), which will enable more accurate collection of data (Fernández-Guisuraga et al. 2018; Veraverbeke et al. 2018; van Gerrevink and Veraverbeke 2021), and hence better models.

Our study was also limited by lack of available data. We used data from postfire restoration projects submitted for public funding, which do not specify the interventions implemented or the period of execution (Lopes et al. 2022). In addition, projects data was only available for the most recent years; a longer period of analysis may have led to clearer results. Furthermore, available spatial data on oak land cover for the analysed period aggregates deciduous oak species into a single class, which can also include other broadleaved species, without detailing the dominant oak species or the level of canopy cover. As already discussed, distinct species composition or dominance may alter the overall rate of vegetation recovery (Botequim et al. 2017), and impact the results.

Future changes in fire regimes are expected worldwide as a result of climate change, in particular for southern Europe (e.g., higher frequency and severity) (Dupuy et al. 2020). Although resprouting species such as oaks are likely the most resilient to changing fire regimes, postfire oak recruitment and forest recovery may be hindered by overlapping climate-driven stressors such as pest outbreaks, droughts and heatwaves, which may significantly limit the capacity of vegetation to recover in the future (Nolan et al. 2021). It is therefore necessary to continue studying the impacts of postfire restoration interventions on forest recovery and integrate the possible ecological consequences of restoration into the decision-making process (Robichaud et al. 2009), and accept that, for example, non-interventions can be the most adequate postfire management strategy for the Mediterranean broadleaved forests (Carrari et al. 2022). Ultimately, a better understanding of postfire vegetation dynamics will lead to better decisions in the future regarding postfire forest management.

Our study aimed to assess the effects of postfire restoration on the recovery of deciduous oak forests in Portugal and also to evaluate how other variables might have affected recovery, such as fire characteristics, topography and postfire climatic conditions. Although we observed a positive effect of postfire restoration on oak forest recovery, this effect was small and therefore our findings do not support strong recommendations towards restoration interventions on recently burned oak forests. We also found that increased drought severity limited postfire oak forest recovery and that the relation between fire characteristics and forest recovery varied with thresholds of burn severity levels and number of fires. Nevertheless, lack of available data likely limited obtaining clearer results regarding postfire restoration impacts and more research is needed.

Our study highlights the importance of monitoring the effects of postfire restoration on forest recovery, in order to improve knowledge on postfire forest management, forest dynamics and species adaptation. This becomes even more relevant under forecasted climate change scenarios of increased wildfire frequency and severity interacting with other climate-driven disturbances, which will further impact the capacity of forest recovery in the future.

Availability of data and material

The dataset generated in the present study is available and can be found here: https://doi.org/10.6084/m9.figshare.22015970. More information can be found in Supplementary Material - Annex I “R code for reproducing the analysis”.

Competing interests

The authors declare that they have no competing interests.

Funding

L.F.L. is a PhD fellow funded by the FCT (PT/BD/142963/2018), under the SUSFOR—Sustainable Forests and Products Doctoral Program (SUSFOR PD.00157.2012).

F.S.D was financed by FEDER Funds through the Operational Competitiveness Factors Program - COMPETE and by National Funds through Fundação para a Ciência e a Tecnologia (FCT) through project PTDC/BIA-ECO/28729/2017 - POCI-01-0145-FEDER-028729

P.M.F. was supported by the National Funds by FCT—Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.

V.A. was funded by research contract DL 57/2016/CP1382/CT0002, FEDER funds through the Operational Programme for Competitiveness Factors—COMPETE, and by Portuguese National Funds through FCT—Foundation for Science and Technology under the UID/BIA/50027/2013 and POCI-01-0145-FEDER-006821.

Authors' contributions

LFL, VA conceived and designed the analysis; LFL performed data collection; data analysis and interpretation were performed by LFL in collaboration with FSD, PMF, VA; all authors participated in writing/edition of the manuscript.

Acknowledgements

The authors would like to thank J. Pinho, ICNF, IFAP, and AG-PDR for kindly providing information, J. M. Fernández-Alonso, Lourizan Forestry Research Centre, and FIRELinks CostAction for initial training on remote sensing analysis applied to post-fire topics, and J. Marcelino for support on Google Earth Engine related issues.

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A remote-sensing assessment of oak forest recovery after postfire restoration

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