Early Response of Post-Fire Forest Treatments Across Four Iberian Ecoregions: Indicators to Maximize Its Effectiveness by Remote Sensing

Abstract

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery.

1. Introduction

Restoration and rehabilitation techniques are essential actions to support quick recovery in forest ecosystems affected by severe wildfires [1]. These post-fire measures are applied to limit the effects of runoff and erosion in severely burned areas [2]. There are many post-fire management techniques [3], and they often use vegetal residues (e.g., straw or woodchips), burned and felled trees, and other natural materials in the areas that are most prone to erosion. Besides their aim at reducing post-fire hydrological risk, these measures can also influence vegetation recovery. Previous studies have shown contrasting effectiveness of post-fire management techniques on vegetation recovery in wildfire-affected forests, from non-significant effects reported by some authors [4,5,6] to higher recovery rates compared to non-treated areas of other investigations [5,6,7,8,9].

Monitoring post-fire vegetation recovery is an essential task for forest managers and land planners to verify the effectiveness of the different actions after post-fire management on the forest ecosystem and to elaborate criteria for locating future post-fire emergency works in a specific ecoregion.

However, monitoring post-fire vegetation recovery in the field is delicate since it is generally expensive and time-consuming. Furthermore, the results of the monitoring studies are not comparable due to the use of different and non-standardised methods for data collection and processing [10,11]. Therefore, drawing useful indications from systematic data across different ecoregions is difficult. In contrast, the dynamics of vegetation recovery after forest fires can be efficiently and easily studied using remote sensing (RS) techniques and satellite imagery, such as Landsat images [12]. Spectral trajectories are a reliable proxy of vegetation recovery on variable temporal and spatial scales thanks to meaningful vegetation indices [13]. Detailed reviews [13,14] are available about sensors, methods, indices, and metrics. This has provided clear evidence of the potential usefulness and power of satellite imagery.

Several factors have been identified as crucial issues of RS for post-fire monitoring [13]. One concern is the temporal mismatch between ecological processes and RS information. Consistent connections might require variable time scales for different ecoregions [15], which suggests minimizing this mismatch. Other issues are the applicability of the spectral indices (SIs) proposed for monitoring post-fire vegetation [14,16]; heterogeneous plant communities with similar ground cover or Leaf Area Index (LAI) values can yield different values of the same index [17]. Several SIs have been proposed for monitoring post-fire vegetation recovery [13,18], such as NDVI-related indices, indices that include the short-wave infrared spectral (SWIR) domain, Normalized Burn Ratio (NBR), and Green Chlorophyll Index (GCI). However, the consistency of the spectral responses, even in the same index, can be negatively influenced by interferences between vegetation and other ground cover, according to fire severity, vegetation diversity and phenology, and topography [19]. For instance, communities of different vegetation may be interspersed with black-burned areas covered by charcoal and ashes [20,21]. This means that SIs detecting changes in vegetation cover after the fire and post-fire management techniques must show proper sensitivity in the treated areas. Despite their widespread use in a variety of climatic and geomorphological conditions, the results of many applications of RS techniques and SIs are often contradictory. Their applicability is also debated and therefore remains unclear [22,23]. This indicates a pressing need for more research to identify the most suitable SI for monitoring the effectiveness of post-fire management works for vegetation recovery in fire-affected forests. As of now, no meta-analysis at a regional scale has coped with the inherent differences among sites, fires, and treatments, which would give indications to support post-fire forest management.

To fill this gap, the present study aims to compare the effectiveness of post-wildfire emergency works on early vegetation recovery between treated and untreated soils in four ecoregions in Spain using a meta-analysis to identify key moderators and conditions for increasing earlier vegetation recovery. To achieve this general objective, different goals were pursued, including answering the following questions: Which SIs are more sensitive to detect the changes associated with vegetation recovery after the emergency works are performed? What types of moderators of different nature (ecoregion, soil properties, physiography, climate, and time elapsed from fire and treatment) influence the response of those SIs and what is their importance on the recovery process? Management implications stem from the four case studies of real-world projects carried out by different regional forestry administrations with a contrasting technical background.

2. Materials and Methods

2.1. Study Area

The contrasting Iberian “ecoregions” included in this study are Iberian sclerophyllous and semi-deciduous forests, Iberian conifer forests, Atlantic mixed forests, and Northeastern Spanish Mediterranean forests [24]. In each ecoregion, four fire events were monitored and post-fire management actions were undertaken as emergency works to protect soil from erosion without disturbing natural vegetation recovery: (i) in 2005, conifer forests, Cazorla (Jaén, Andalusia); (ii) in 2012, sclerophyllous forests, Hellín (Albacete, Castilla-La Mancha); (iii) in 2016, Atlantic forests, Arbo (Pontevedra, Galicia); and (iv) in 2018, Mediterranean forests, Lluxent (Valencia, Comunidad Valenciana) (Figure 1).

2.1.1. Cazorla (J_Ca)

This wildfire started after a lightning storm and was active from 7 to 11 August 2005, burning 5889 ha. The wildfire burned an Iberian conifer forest ecoregion in the surroundings of Tranco de Beas reservoir located in Sierras de Cazorla, Segura and Las Villas Natural Park (796 to 1532 m above sea level). The climate is Csa to Csb according to the Köppen–Geiger classification [25]. Summer is the warmest and driest season with heatwaves of over 40 °C (which favors wildfire occurrence) with an almost total absence of rain. In the other seasons, rainfall is unevenly distributed with an average annual value of 882 mm. The area is characterized by karstic processes and the soils are developed over limestones and dolomites. According to the Spanish map of potential vegetation (Rivas Martínez, 1987) [26], this area is covered by a supra-meso-Mediterranean series of Quercus faginea Lambert and Q. ilex L. The pre-fire cover was composed of Spanish black pine (Pinus nigra Arnold subsp. salzmannii (Dunal) Franco) at the higher elevations (>1000 m) and patches of pine reforestations—mainly with Aleppo pine (Pinus halepensis Mill.) and sometimes maritime pine (Pinus pinaster Ait.)—at lower altitudes (<1000 m above sea level). with native vegetation patches of Quercus ilex L. at both elevations. Understory vegetation consisted of shrub species, such as Quercus coccifera L., Juniperus oxycedrus L., Daphne gnidium L., Ulex parviflorus Pourr., Berberis hispanica Boiss. & Reut., Pistacia lentiscus L., Pistacia terebinthus L., and Salvia rosmarinus Spenn, among others.

2.1.2. Hellín (Al_He)

This fire took place between 1 and 6 July 2012, burning 6500 ha with a high rate of spread due to weather conditions and the high fuel load (90% of the area was burnt in the first seven hours after ignition). The burned area in the sclerophyllous forest ecoregion is in the Sierra Seca and Los Donceles in the municipality of Hellín (314 m and 808 m above sea level), although the fire also spread 1370 ha in the Sierra de Pajares and Cubillas (Moratalla, Region of Murcia). The climate is typical of cold semi-arid climate or steppe (BSk, according to Köppen–Geiger) with an average annual temperature of 15.9 °C and scarce annual precipitation (326 mm). July is the warmest (26.3 °C) and the driest month (6 mm). Limestone materials, almost dolomitized with marls and clays, yielded soils with loamy–clay texture. Hillslopes with variable steepness form a landscape of plains and hills, where fields of crops and natural vegetation alternate. The pre-fire vegetation showed a strong anthropogenic influence, with Aleppo pine forests (Pinus halepensis Mill.) either of natural origin or from reforestation. The thickets, either as the main vegetation or as accompanying the Aleppo pine, correspond to tussock grass, rosemary and thyme, with the presence of thermophilic species (Helictotrichon filifolium (Lag.) Henrard, Stipa tenacissima L., Rosmarinus officinalis L., Anthyllis cytisoides L., Cistus clusii Dunal, etc.).

2.1.3. Arbo (Po_Ar)

Two wildfires were lit in this location, one started on 2 August 2016, burning 112 ha. The second wildfire occurred on 10 August 2016, and after three days almost completely burned 1992 ha. Both wildfires together burned a total area of 2106 ha in the municipality of Arbo (Pontevedra) (300 and 515 m above sea level, respectively). The fire was blown towards all directions due to strong and changing winds, and was favored by the complex orography. The climate is temperate oceanic with mild summers (Csb, according to Köppen–Geiger) with noticeable precipitation (1593 mm/year), even in the driest month (July, 41 mm). The average annual temperature is 14 °C, and August is the hottest month (18.9 °C). Soils are developed on granite with a sandy loam texture. Topography is characterized by moderate to steep slopes. Pre-fire vegetation of thickets and stands of Maritime pine (Pinus pinaster Ait.) and eucalyptus (Eucalyptus globulus L.) of non-commercial size. The dominant shrub species are Ulex europaeus L., Erica cinerea L., and Pterospartum trdidentatum (L.) Willk.

2.1.4. Lluxent (V_Ll)

This wildfire occurred on 6 August 2018, burning the Serpis Basin across seven municipalities close to Lluxent (200 and 750 m above sea level). The total burned area was 3270 ha, mostly forest (60%) and agricultural–forest interface. The area, at high erosion risk due to high rainfall intensity, shows a typical Mediterranean climate (Csa, Kö-ppen-Geiger) with an average annual temperature of 16.4 °C and rainfall of approximately 604 mm/year, with July being the driest and warmest month. Soils, mostly developed on limestones and dolomites with a very permeable lithology (forming part of an active karst system), are very stony and shallow due to a superficial skeleton that deepens in the fissures. In valleys, the soil is generally with eluvial and illuvial horizons. Topography is variable, showing steep slopes (>45%) and a dense drainage network of narrow and deep ravines. Following [26], the phytosociological formations correspond to thermo-meso-mediterranean belts and series of holm and cork oaks. Pre-fire vegetation consisted of shrublands and tree forests, alternating patches of thickets of oak-pine formations (Q. coccifera L., Q. ilex L., P. pinaster Ait. and P. halepensis Mill.), pines (P. pinaster and P. halepensis), and hardwood stands (Q. suber L. and Q. ilex L.). The burned area was mostly (70%) covered with scrub (0.3–1.2 m in height), grassland-scrub (20%, <1.2 m), and scrub under the canopy of Cork oak, Maritime, and Aleppo pines [27].

2.2. Description of Post-Fire Management Techniques

In the first months after the fire emergency, the public services of the four regions implemented post-fire management actions in the areas burned at the highest erosion risk. In more detail, in Cazorla (J_Ca), Hellín (Al_He), and Lluxent (V_Ll), logging of burned and felled trees and construction of contour-felled log debris (CFD) barriers for slope stabilization were carried out. These barriers were built along the contour lines using branches and woody residues left by fires. Thus, contour-felled log debris consists of collecting branches and felling small burned trees and laying them on the ground along the slope contour. The mean density of CFD was between 10 and 20 CFD/ha with a mean length of 25–50 meters/CFD. In Arbo (Po_Ar); the selection of the areas to be treated was based on the analysis of fire severity on vegetation and soil simultaneously [28]. The treatment areas presented high values of fire severity in the soil and high values of fire severity in vegetation and soil. The untreated areas included areas of high fire severity in vegetation but not in soil so the erosion risk was considered low and did not need treatment due to the high potential for regeneration of natural vegetation in the area (Fernández and Vega, 2016) [4]. To reduce the risk of post-fire soil erosion, helimulching with cereal straw was carried out. A helicopter applied cereal straw mulching at a rate of 2.5–3.0 Mg ha⁻¹. The initial mulch depth was 3–5 cm. Other management actions such as salvage logging were not carried out in any case, except for the trees used in the construction of CFD.

In the four sites, treated and untreated areas were analyzed, the latter being considered as a control, with both having been ecologically similar pre-fire and therefore comparable. Although two types of treatments were applied, they were not analyzed separately but studied as a whole, since each one represents a widely used practice in its respective ecoregion. Hence, the term treatment is used here as synonymous of emergency works. This study does not aim to compare the effectiveness between treatments, but rather to evaluate their effect in relation to the absence of intervention (control).

2.3. Classification of the Burned Areas

Google Earth Engine (GEE) platform was used to quickly and easily classify burned areas and minimize systematic processing errors. GEE also allows the replicability and incorporation of new data using the same methodology.

2.3.1. Classification of Fire Severity Based on dNBR Values

Images from Landsat missions 5, 7, and 8 covering the time span of fires (2005, 2012, 2016, and 2018) were generated and georeferenced. A set of cloud-free images was filtered for each study area (Hellín, Cazorla, Arbo, and Lluxent) and then split by date into two sub-sets of images before and after each fire, respectively. Each dataset was integrated to cover two months before and after the fire by appending three to four images of the affected area.

The dNBR (differenced Normalized Burn Ratio) index was calculated from NBR (Normalized Burn Ratio) related to the pre- and post-fire images in order to detect the change in greenness in the study areas. The pixels with a value over 0.1 indicate that the surface changes due to the fire and post-fire disturbances. Finally, the dNBR values were reclassified into three categories of ground damage due to fire severity: (i) low (0.1–0.27); (ii) medium (0.27–0.6); and (iii) high (>0.6) severity (Figure 2c), joining in the medium class moderate–low and moderate–high severity as a simplification of [29]. This reclassification was automatically and similarly carried out for each fire using the BADE tool [30].

2.3.2. Classification of Physiographic Variables Using DEM

The SRTM Digital Elevation Data V.4 Digital Elevation Model (DEM) produced by NASA (30 m resolution) was used after georeferencing to calculate and classify the slope and aspect in each burned area, adopting the following classes (Figure 2):

-

Low (<15%), Medium (15 to 30%), and High (>30%) for the slope according to FAO slope categories, related sensitivity to soil erosion [31], and the review of effect of slope by Çellek [32].

-

South (90° to 270°) and North (<90° and >270°) for the aspect due to radiation demand is a key factor in these regions by water limitations [33].

2.3.3. Zoning Areas by Physiography and Fire Severity

The images classified by physiography and fire severity as above were overlapped, and 18 potential combinations of classes (2 aspects × 3 slopes × 3 fire severities) were achieved (Figure 3). The overlapped images of some sites did not contain all the potential classes, while some classes may be found in control and treated areas for some sites.

2.3.4. Vectorization of Burned Areas

The different combinations of physiographic and fire severity classes in each study area (both for treated, T, and control, C, sites) were georeferenced and then vectorized to generate different geometric features (patches) for the subsequent calculation of SIs. The patches included in this process had an area over 900 m² (Landsat pixel) to calculate the correct mean of each element. Moreover, the digital spatial information about the areas treated by the different forest services overlapped on those vectorial images after their quality check. Figure 4 depicts the distribution of patches in C and T sites by area in each of the 18 combinations of classes and shows large areas in C sites compared to T. Only the areas being simultaneously in T and C patches classes were considered for comparisons.

2.4. Spectral Indices of Vegetation

Vegetation recovery in the short term after the fires was evaluated using GEE based on SIs that detect the greenness and water content of plants. First, six SIs (i.e., NBR, NDVI, NDII, MSI, GCI, EVI2, see below for the acronym meaning) (

Table 1. Spectral indices with relevant equations to evaluate post-fire vegetation recovery in this study.

Index	Equation	References
Normalized Burn Ratio (NBR)	$NBR={\displaystyle \frac{(NIR-{SWIR}_2)}{(NIR+{SWIR}_2)}}$	[37]
Normalized Difference Vegetation Index (NDVI)	$NDVI={\displaystyle \frac{(NIR-RED)}{(NIR+RED)}}$	[38,39]
Normalized Difference Infrared Index (NDII)	$NDII={\displaystyle \frac{(NIR-{SWIR}_1)}{(NIR+{SWIR}_1)}}$	[40]
Moisture Stress Index (MSI)	$MSI={\displaystyle \frac{{SWIR}_1}{NIR}}$	[41,42]
Green Chlorophyll Index (GCI)	$GCI={\displaystyle \frac{NIR}{GREEN}}-1$	[43]
Enhanced Vegetation Index 2 (EVI2)	$EVI2=2.5\ast {\displaystyle \frac{(NIR-RED)}{(NIR+2.4\ast RED+1)}}$	[44]

Notes: NIR: near Infrared; SWIR: short-wave infrared.

) were calculated for the whole burned area in the study areas for vectorized patches of both C and T sites. Each SI was calculated at three dates after the fire: (i) 2nd to 4th month (hereafter indicated as “3 months”); (ii) 5th to 7th month (“6 months”); and (iii) 8th to 10th month (“9 months”). The duration of these time windows yielded representative information in all the pixels (avoiding clouds and gaps of each image to prevent noise by using Landsat’s proprietary “CFMask” algorithm [34]) by time averages over the study areas, which consider the different phenological according to the seasons [35]. More specifically, NBR, equal to the ratio between NIR (near infrared) and SWIR₂ (ShortWave InfraRed 2), is often used to identify burned areas and burn severity. Normalized Difference Vegetation Index (NDVI), i.e., the ratio between the red (R) and near-infrared (NIR) values, quantifies the vegetation greenness and thus vegetation density and changes in plant health. Normalized Difference Infrared Index (NDII), which uses a normalized difference instead of a simple ratio and increases with the water content of the plant, measures the reflectance and is sensitive to changes in the water content of plant canopies. Also, the Moisture Stress Index (MSI) measures the reflectance but is sensitive to the water content of canopy leaves and decreases with lower water stress and higher water content. Green Chlorophyll Index (GCI), a chlorophyll index, measures the total chlorophyll content of leaves and is consistent across most species. Enhanced Vegetation Index 2 (EVI2) is sensitive to areas with high biomass while minimizing soil and atmosphere influences (Table 1). All the SIs used are widely applied to assess vegetation recovery after a fire [36].

2.5. Environmental Covariates

In addition to the specific ecoregion and time elapsed from fire, other physiographic, soil, and climatic factors were considered as environmental covariates or moderators to capture the biogeoclimatic differences among sites due to natural drivers. Eleven factors were adopted and derived from open-access and globally distributed databases in the GEE platform to allow method replicability and extension for future work:

-

physiographic factors (elevation, soil slope, and aspect), derived from DEMs (see Section 2.3.2);

-

pedologic factors (texture type, sand, and clay contents, pH, volumetric water content, and bulk density), derived from 250 m resolution maps;

-

climatic factors (monthly precipitation and average monthly diurnal temperature, period 2000–2017, 1000 m resolution).

2.6. Data Analysis

A meta-analysis was adopted to explore the complex interactions among the aforementioned factors on large spatial patterns. Meta-analysis is commonly used to evaluate the effects of forest treatments [45].

This analysis was carried out according to the following steps: (i) overlapping the covariates in each site; (ii) calculation of the effect size between pairs of T vs. C sites; (iii) meta-analysis estimating the true effect size and its heterogeneity between T and C areas in burned areas in effect sizes; and (iv) identification of causes of heterogeneity due to covariates.

In more detail, in the first step, for each combination of the physiography severity class, site, SI, and date, the mean value, standard deviation, and sample size (number of patches) of both T and C were calculated. For example, in the Hellín site, we calculated 15 classes × 3 dates × 6 indices × 2 conditions (T and C), totaling 540 cases, 90 per index, or 45 C vs. T comparisons per index that are the basis for analyzing the effect size of the post-fire treatments.

In the second step, the effect size was calculated. This variable provides information on the direction and magnitude of the standardized and unbiased effect of post-fire management works, whilst its sampling variance expresses the precision of the estimation.

In the third step, the Metafor package (version 4.8-0) in R software [45,46] was used for meta-analysis applied to the effect size using random and mixed-effects models. The estimators for the true effects were obtained via weighted least squares and the Wald-type test and confidence intervals were obtained. The effect size metric selected was the response mean difference (MD) i.e., the difference between the Mean of Control and the Mean of Treatment sites, as frequently used in ecological meta-analysis [45]. A negative number means a larger value for the treatment and positive number means a larger value for the control. If the 95% confidence intervals (Cis) of the effect size do not cover zero, the responses of Sis to post-fire treatments are considered statistically significant.

The amount of residual heterogeneity (the τ² statistic) among the true effects of MD was estimated with the restricted maximum-likelihood estimator (REML) which is known for producing unbiased estimates of between-study variance under a random-effects framework. The null hypothesis (Ho: τ² = 0) was tested using Cochran’s Q-test. The I² and H² statistics [47] were used to further interpret the estimated amount of heterogeneity. I² estimates the percentage of the total variability in the effect size estimates (which consist of heterogeneity and sampling variability) that can be attributed to heterogeneity among the true effects, with thresholds often Interpreted as low (25%), moderate (50%), and high (75%) heterogeneity. H² represents the ratio of the total amount of variability in the observed outcomes to the amount of sampling variability. It provides an alternative way to quantify excess variability when τ² = 0 (H² = 1), indicating that all variability can be explained by within-study error alone. Part of the heterogeneity in the true effects may be caused by the influence of moderators, which was assessed by fitting mixed-effect models that included either categorical or interval-defined moderators (

Table 2. Covariates or moderators selected to evaluate the effect size of post-fire vegetation regeneration in the four case studies Jaén-Cazorla (J_Ca), Albacete-Hellín (Al_He), Pontevedra-Arbó (Po_Ar), and Valencia-Lluxent (V_Ll). The numbers are the mean values; the range is in square brackets.

Covariates or Moderators	Study Areas
	Al_He	J_Ca	Po_Ar	V_Ll
Elevation (m above sea level)	531.74 [453.72–596.98]	1181.68 [1131.48–1246.33]	412.83 [367.07–519.21]	480.4 [461.55–503.93]
Aspect (°)	170.55 [133.93–207.88]	146.11 [118.21–194.81]	150.75 [72.99–179.94]	185.64 [179.62–194.68]
Slope (%)	19.29 [7.76–35.64]	23.85 [10.57–36.84]	20.94 [12.11–32.05]	15.21 [9.36–21.22]
Texture *	4.77 [4.23–5.34]	6.36 [6.28–6.46]	8.78 [8.6–9]	5.54 [5.21–5.89]
pH (-)	7.72 [7.62–7.82]	7.16 [7.09–7.23]	5.43 [5.29–5.59]	7.05 [6.99–7.11]
SWC (%)	27.79 [25.66–30.04]	35.89 [35.21–37.3]	32.15 [30.77–32.72]	36.21 [35.45–36.85]
Clay (%)	28.64 [27.62–29.3]	26.35 [26.2–26.65]	13.89 [13.16–14.99]	27.57 [26.98–28.13]
Sand (%)	32.59 [31.91–34.01]	32.68 [31.6–33.53]	58.74 [56.65–61]	35.29 [35.02–35.53]
Bulk Density (kg/m3)	13.98 [13.42–14.65]	12.10 [11.87–12.26]	12.94 [12.72–13.14]	13.26 [13.02–13.43]
Precipitation (mm)	331.03 [323.63–338.18]	548.71 [533.23–560.26]	1452.45 [1430.52–1479.87]	518.51 [514.81–523.93]
Temperature (°C)	25.39 [25.13–25.63]	20.43 [20.12–20.66]	20.01 [19.91–20.09]	21.92 [21.86–22.01]
Slope Class	Low: slope < 15%, Medium: slope 15%–30% and High: slope > 30%
Aspect Class	North: aspect (90–270°) and South: aspect (<90° and >270°)
Fire severity	Low: (0.1–0.25 dNBR), Medium: (0.25–0.5 dNBR) and High: (>0.5 dNBR)
Classification	1: North, Low, Severity1; 2: North, Low, Severity2; 3: North, Low, Severity3; 4: North, Medium, Severity1; 5: North, Medium, Severity2; 6: North, Medium, Severity3; 7: North, High, Severity1; 8: North, High, Severity2;
	9: North, High, Severity3; 10: South, Low, Severity1; 11: South, Low, Severity2; 12: South, Low, Severity3; 13: South, Medium, Severity1; 14: South, Medium, Severity2; 15: South, Medium, Severity3; 16: South, High, Severity1; 17: South, High, Severity2; 18: South, High, Severity3
Ecoregion	Al_He: Hellín, Schlerophyl. forests, J_Ca: Cazorla, conifer forests Po_Ar: Arbo, Atlantic forests,
	V_Ll: LLuxent, Medit. Forests
Time elapse from fire	3 months, 6 months and 9 months post-fire

* Texture was coded numerically as follows: 12: sand, 11: loamy sand, 10: silt, 9: sandy loam, 8: silt loam, 7: loam, 6: sandy clay loam, 5: silty clay loam, 4: clay loam, 3: sandy clay, 2: silty clay, 1: clay.

). When the model shows a significant omnibus test (Q_M), it suggests that at least one moderator contributes significantly to explaining variability in effect sizes, calculating the coefficients of the moderators estimated as mentioned above.

In the fourth and final step, the analysis of reasons for variation in the effect size due to covariates or moderators was carried out, this being the common goal of meta-analyses. The list of selected moderators to test post hoc analysis is shown in Table 2.

3. Results

3.1. Effects of Post-Fire Treatments on Vegetation Recovery from Spectral Indices

The spectral indices showed a response to treatment with the exception of EVI2 (

Table 3. Results of the statistical analysis by random effects models applied to multispectral vegetation index in the four study areas.

Spectral Index	Mean		Estimate MD	Std. Error	z-Value	CI-lb	CI-ub
	Control	Treatment
EVI2	0.217 (0.42)	0.218 (0.38)	0.0015	0.0023	0.6452	−0.0031	0.0061
GCI	1.361 (1.88)	1.475 (1.55)	−0.146	0.0243	−6.0054 ***	−0.1936	−0.0983
MSI	1.296 (1.00)	1.216 (1.04)	0.084	0.0155	5.4179 ***	0.0536	0.1144
NBR	0.026 (0.24)	0.073 (0.20)	−0.0497	0.0099	−5.0286 ***	−0.0691	−0.0303
NDII	−0.099 (0.02)	−0.077 (−0.01)	−0.0245	0.006	−4.0659 ***	−0.0363	−0.0127
NDVI	0.285 (0.42)	0.319 (0.38)	−0.0355	0.0045	−7.916 ***	−0.0442	−0.0267

Notes: MD is the mean effect size between C and T sites; CI-lb and CI-ub are the lower and upper boundaries of the confidence intervals, respectively; *** significant mean at p-value < 0.0001; the numbers between brackets are the index averaged over three years before the fire.

), which was excluded by the following analysis. After comparing the six indices to identify the most sensitive indicator of contrasts between C and T sites, we found that the effect sizes were higher for GCI, MSI, NBR, NDVI, and NDII in this order, indicating a significant vegetation recovery in the treated patches. As expected, MSI showed an opposite pattern to the other SIs.

The descriptive statistics reported in Table 1 show that the values of NBR and NDII indices are very similar, which suggests the use of NBR (preferably used for studies about forest fires) rather than both NBR and NDII. It is also worth mentioning the different recovery rates of the SIs before and after the fires, shown by the comparison of mean values (Table 3). The post-fire GCI index, for instance, is 70% of the pre-fire value in the control (1.36/1.88), and 95% in the treated sites (1.47/1.55), while NBR post-fire recovery is between 11% in the control (0.026/0.24) and 36% in the treated site (0.07/0.2) relative to pre-fire baseline (averaged over 3 years before).

All SIs are higher in the treated sites compared to those without any post-fire treatment, except MSI (since this index, higher values are more stressful). Moreover, those indexes increased over time in both sites, and this increase was higher for GCI and lower for NBR in the last 3-month period compared to the time window immediately after the fire. In contrast, MSI, lower in T sites compared to the control, always decreases over time (Figure 5a).

A separate analysis of the SIs site by site shows that GCI, NBR, and NDVI are higher for all treated sites. While MSI is only lower in Al_He, V_Ll and J_Ca are similar and the Po_Ar treated area is slightly higher (Figure 5b).

3.2. Influence of Physiography and Fire Severity on the Spectral Response of Post-Fire Treatments

As shown by the I² statistic, Q test, and p-value, the true effects of the mean difference between the treated and control sites (MD) in the SIs were noticeable and significant when analyzed using random-effects models (

Table 4. Statistical analysis results by random-effects models (without moderators) and mixed-effects models (with moderators related to physiography, fire severity, ecoregion, and time elapsed from fire) applied to the spectral indices in the four study sites.

Spectral Indices	Covariates/Moderators	τ2	I2 (%)	H2	Q	df	p-Value	QM	df	p-Value
NBR	No moderators	0.0122 (SE = 0.0016)	99.2	129.8	16,333	131	***
	Slope class	0.0124 (SE = 0.0016)	99.2	128.6	15,972	129	***	25.7	3	***
	Aspect class	0.0123 (SE = 0.0016)	99.2	128.3	16,207	130	***	25.1	2	***
	Fire severity	0.0121 (SE = 0.0016)	99.2	124.2	15,974	129	***	29	3	***
	Ecoregion	0.0029 (SE = 0.0004)	96.9	31.8	6175	128	***	493.8	4	***
	Time elapsed from fire	0.0122 (SE = 0.0016)	99.2	124.2	13,240	129	***	28	3	***
MSI	No moderators	0.0297 (SE = 0.0039)	98.9	91.2	12,025	131	***
	Slope class	0.0300 (SE = 0.0039)	98.9	90.1	11,860	130	***	29.1	2	***
	Aspect class	0.0301 (SE = 0.0040)	98.9	90.7	11,817	129	***	29.2	3	***
	Fire severity	0.0293 (SE = 0.0039)	98.9	87.3	11,856	129	***	34	3	***
	Ecoregion	0.0088 (SE = 0.0013)	96.4	27.8	5312	128	***	384.6	4	***
	Time elapsed from fire	0.0290 (SE = 0.0038)	98.8	86.3	9213	129	***	34.6	3	***
GCI	No moderators	0.069 (SE = 0.0095)	98.6	69	4750	131	***
	Slope class	0.0688 (SE = 0.0095)	98.5	67.6	4749	130	***	38.1	2	***
	Aspect class	0.0688 (SE = 0.0095)	98.5	67.4	4520	129	***	42.2	3	***
	Fire severity	0.0682 (SE = 0.0095)	98.5	65.1	4594	129	***	41.5	3	***
	Ecoregion	0.0124 (SE = 0.0021)	92.5	13.3	1937	128	***	609.6	4	***
	Time elapsed from fire	0.0687 (SE = 0.0095)	98.5	66.4	4110	129	***	38.6	3	***
NDVI	No moderators	0.0024 (SE = 0.0003)	98.1	51.2	7018	131	***
	Slope class	0.0024 (SE = 0.0003)	98	50.6	7017	130	***	63.8	2	***
	Aspect class	0.0024 (SE = 0.0003)	98	50.6	6360	129	***	63.6	3	***
	Fire severity	0.0024 (SE = 0.0003)	98	50	6797	129	***	62.9	3	***
	Ecoregion	0.0007 (SE = 0.0001)	93.8	16.2	2480	128	***	441.6	4	***
	Time elapsed from fire	0.0023 (SE = 0.0003)	1	48.9	6629	129	***	68.7	3	***

Notes: τ² = amount of heterogeneity; I² and H² = statistics of the amount of heterogeneity; Q = test for heterogeneity; Q_M = omnibus test of moderators; df = degrees of freedom (130 and 1 for Q and Q_M tests, respectively); *** = significant at p-value < 0.0001; SE = standard error; no mod. = no modifications for discretization.

). The inclusions of covariates/moderators by mixed-effects models resulted in a reduction in the total heterogeneity, shown by a significant omnibus test (Q_M) either for categorical or interval-defined moderators (Table 4).

In the mixed-effects models applied to the factors related to physiography, fire severity, ecoregion, and time elapsed from fire, the total amount of heterogeneity (τ²) did not substantially change compared to single random-effects models, basically due to their underlying role in the definition of the cases in the total sample.

The results emphasize the higher or lower significance of the effect size according to the different categories, and highlight how, for most indices, the post-fire management works were more effective for lower fire severities (Firesev: Low in all indices, Figure 6) and on south-facing slopes (GCI, MSI, and NDVI, Figure 6). The improvement in site conditions due to the soil stabilization treatments, indicated by the mean difference between treatment (T) and control (C) (Figure 6 and Table 3) for Severity 1 (low), was between approx. 10% for MSI and 300% for NBR, while GCI and NDVI were close to the lower difference (about 15% for both). However, vegetation recovery was significantly improved compared to the control for all classes.

3.3. Influence of Ecoregion and Time Elapsed from Fire on the Spectral Response of Post-Fire Treatments

The amount of residual heterogeneity among the true effects of effect size was considerably reduced when the ecoregion was included as a moderator (between 70% and 82%, depending on the index), as revealed by the τ² value for random and mixed models (Table 4). This indicates a considerable effect of the ecoregion on the vegetation recovery. In more detail, the sclerophyllous forest (Al_He site) and the Atlantic forest ecoregions (Po_Ar) showed contrasting and significant effect sizes regardless of the SI, with a higher vegetation recovery in the control areas of the latter case study (Figure 7). The other two ecoregions showed intermediate behavior, with the Lluxent fire (V_Ll, Mediterranean forests) closer to Po_Ar (NBR, Figure 7) and the Cazorla fire (J_Ca, conifer forests) to Al_He (GCI and NDVI, Figure 7).

Regarding the time elapsed from fire, the treatments were more effective in the short term (up to six months, Figure 7), despite the low difference in τ² statistic between the mixed (including this moderator) and the random models (Table 4). This indicates the need for a prompt implementation of post-fire management works immediately after the fire occurrence.

3.4. Influence of Climate and Soil Properties on the Spectral Response of Post-Fire

The significant omnibus test (Q_M) and R² values for the mixed-effects models demonstrated a noticeable influence of soil and climatic covariates on the total amount of heterogeneity (

Table 5. Results of statistical analysis by random-effects models (without moderators) and mixed-effects models (with moderators related to soil properties and climate) applied to the spectral indices in the four study sites.

Spectral Indices	Covariates/Moderators	τ2	I2 (%)	H2	Q	df	p-Value	QM	p-Value
NBR	Soil texture	0.0058 (SE = 0.0008)	98.4	62.1	52.4	7794	***	135.6	***
	pH	0.0063 (SE = 0.0009)	98.5	67.2	48.4	8510	***	117	***
	Soil water content	0.0059 (SE = 0.0008)	98.4	62.5	52.1	9313	***	131.5	***
	Clay	0.0084 (SE = 0.0011)	98.9	89.1	31.4	10,268	***	58.8	***
	Sand	0.0095 (SE = 0.0013)	99	100.8	22.3	11,306	***	37.9	***
	Precipitation	0.0076 (SE = 0.0010)	98.8	81.3	37.5	9546	***	76.1	***
	Temperature	0.004 (SE = 0.0006)	97.7	43.1	67.2	6879	***	245.4	***
MSI	Soil texture	0.0161 (SE = 0.0022)	98	49.7	45.7	6262	***	103	***
	pH	0.0180 (SE = 0.0024)	98.2	55.2	39.6	6886	***	80.7	***
	SWC	0.0142 (SE = 0.0020)	97.7	43.9	52.3	7405	***	132.9	***
	Clay	0.0224 (SE = 0.0030)	98.5	68.6	24.7	7793	***	41.5	***
	Sand	0.0249 (SE = 0.0033)	98.7	76.4	16.1	8371	***	24.6	***
	Precipitation	0.0208 (SE = 0.0028)	98.4	63.9	29.9	7472	***	53.4	***
	Temperature	0.0104 (SE = 0.0015)	96.9	32.4	65	5552	***	219.9	***
GCI	Soil texture	0.0249 (SE = 0.0038)	96.1	25.5	63.9	2762	***	171.3	***
	pH	0.0148 (SE = 0.0024)	93.6	15.5	78.6	2158	***	305.4	***
	Soil Water Content	0.0450 (SE = 0.0064)	97.8	45.1	34.8	2707	***	38.9	***
	Clay	0.0254 (SE = 0.0038)	96.2	26.1	63.2	2983	***	173.5	***
	Sand	0.0296 (SE = 0.0044)	96.7	30.3	57.1	3551	***	147.1	***
	Precipitation	0.0203 (SE = 0.0031)	95.2	21	70.6	2446	***	220.7	***
	Temperature	0.0351 (SE = 0.0051)	97.2	35.5	49.1	2641	***	85.9	***
NDVI	Soil texture	0.0013 (SE = 0.0002)	96.4	27.6	46.7	3564	***	99.7	***
	pH	0.0013 (SE = 0.0002)	96.5	28.7	44.6	3949	***	94.4	***
	SWC	0.0014 (SE = 0.0002)	96.6	29.1	43.3	3406	***	84	***
	Clay	0.0017 (SE = 0.0002)	97.3	36.6	29	5194	***	49.9	***
	Sand	0.0019 (SE = 0.0003)	97.5	40.3	21.8	6206	***	35.8	***
	Precipitation	0.0016 (SE = 0.0002)	97	33.8	34.5	4634	***	63.3	***
	Temperature	0.0011 (SE = 0.0002)	95.6	22.8	56.1	2961	***	139.8	***

Notes: τ² = amount of heterogeneity; I² and H² = statistics of the amount of heterogeneity; Q = test for heterogeneity; Q_M = omnibus test of moderators; df = degrees of freedom (130 and 1 for Q and Q_M tests, respectively); *** = significant at p-value < 0.0001; SE = standard error.

). To be more specific, the moderators of soil texture (type, clay, and sand contents) played a high influence on vegetation recovery, as shown by the change in spectral indices. Fine-textured soils are more sensitive to the treatments (R² ranging between 57% and 64% for GCI, respectively) (Table 5). Moreover, soil pH also explained a considerable amount of heterogeneity in the treatment effect (R²-value between 40% for MSI and 79% for GCI) and higher pH was associated with a higher post-fire recovery in the treated areas (Table 2). Climatic variables also noticeably contributed to the mean effect size, explaining up to 71% of the total heterogeneity of GCI for precipitation and about 65% in NBR and MSI for temperature.

Figure 8 depicts the regression models between the MD in the four spectral indices between control and treated sites, considered independent variables, and precipitation, temperature, soil water content, and texture as dependent variables. All values of SIs are positives, except MSI, which means a negative value of the MD for each moderator is a higher value for the treatment in all indices except MSI, that is opposite when the positive value of MD for MSI indicates the same. The intercepts of the regression models interpolating the effect size (MD) and the interval-defined moderators for the different indices are zero, which indicates the threshold for the treatment effect (Figure 8,

Table 6. Intercepts (reported with their standard error and 95% confidence intervals) of the regression model fit between the effect size (C-T) of the different spectral indices and the significant moderators (interval-defined) identified in the meta-analyses. The intercept is for the model moderator = incercept + b* (C-T), which gives the value of the moderator for an effect size of b. Spectral indices: GCI, MSI, NBR, and NDVI and the sample size in each process is 132 observations. Texture: soil texture that was coded numerically (12: sand, 11: loamy sand, 10: silt, 9: sandy loam, 8: silt loam, 7: loam, 6: sandy clay loam, 5: silty clay loam, 4: clay loam, 3: sandy clay, 2: silty clay, 1: clay); SWC: soil water content (%); Temp: diurnal temperature (°C) and Precip: annual precipitation (mm). In all the cases the models were significant and the full models are provided as SM1.

Spectral Indices	Moderators		Coefficient	Standard Error (SE)	Lower 95%	Upper 95%	R2
GCI	Texture	Intercepts	6.36	0.08	6.20	6.52	0.53
		b	2.74	0.22	2.29	3.18
	SWC	Intercepts	33.07	0.33	32.41	33.73	0.11
		b	3.74	0.93	1.89	5.58
	Temp	Intercepts	21.84	0.18	21.48	22.19	0.30
		b	−3.78	0.50	−4.78	−2.79
	Precip	Intercepts	685.02	21.52	642.44	727.61	0.57
		b	794.11	60.22	674.97	913.24
MSI	Texture	Intercepts	6.44	0.09	6.26	6.62	0.45
		b	−4.92	0.48	−5.87	−3.97
	SWC	Intercepts	33.90	0.26	33.38	34.41	0.50
		b	−15.74	1.37	−18.44	−13.03
	Temp	Intercepts	21.42	0.14	21.15	21.69	0.63
		b	10.66	0.72	9.23	12.08
	Precip	Intercepts	683.53	28.80	626.56	740.51	0.30
		b	−1122.56	151.59	−1422.46	−822.66
NBR	Texture	Intercepts	6.44	0.08	6.27	6.60	0.52
		b	8.25	0.70	6.87	9.63
	SWC	Intercepts	33.78	0.26	33.26	34.30	0.49
		b	24.06	2.17	19.77	28.35
	Temp	Intercepts	21.47	0.13	21.21	21.73	0.65
		b	−16.90	1.09	−19.05	−14.75
	Precip	Intercepts	687.79	26.66	635.04	740.53	0.38
		b	1981.01	221.29	1543.21	2418.80
NDVI	Texture	Intercepts	6.61	0.10	6.41	6.82	0.42
		b	16.49	1.69	13.14	19.84
	SWC	Intercepts	34.21	0.33	33.56	34.85	0.36
		b	45.66	5.37	35.03	56.29
	Temp	Intercepts	21.16	0.18	20.81	21.51	0.49
		b	−32.40	2.92	−38.17	−26.63
	Precip	Intercepts	735.00	30.83	674.00	796.00	0.33
		b	4111.42	508.62	3105.17	5117.66

). For instance, an annual precipitation lower than 700 mm, a diurnal temperature over 21.4 °C, soils with texture finer than sandy clay loam (six in the adopted classification), and water content under 33% are quantitative limits of the treatment impact on vegetation recovery as exploratory approximations thresholds.

4. Discussion

4.1. Effects of Post-Fire Treatments on Vegetation Recovery from Spectral Indices

Post-fire treatments significantly increased vegetation recovery after the wildfires, as clearly indicated by the significant differences in all SIs between treated and untreated sites for all study areas. This result implies that the post-fire treatments are beneficial for the short-term restoration of the ground cover in burned sites.

Among the six SIs adopted in this study, GCI showed the highest differences be-tween treated and control areas, which indicates that this index is the most sensitive to the post-fire treatments. According to other studies [43,48], GCI is very robust in capturing the differences in vegetation, leaf structure, and canopy architectures since this index is well correlated with the chlorophyll content of vegetation and green LAI.

While EVI2 has demonstrated lower sensitivity in detecting differences in vegetation greenness between control and treated areas, this may be attributed to the absence of the blue band, which reduces sensitivity when soil backgrounds are heterogeneous [44]. Various studies have shown that EVI2 tends to saturate more rapidly, limiting its ability to discriminate subtle variations in chlorophyll content or leaf area index [49]. Furthermore, its formulation lacks the structural sensitivity of indices such as GCI, which leverage the green band to enhance chlorophyll detection and are less affected by soil reflectance interference [50]. Consequently, although EVI2 offers cross-sensor compatibility and robustness over snow-covered or highly reflective surfaces, its performance in post-fire vegetation recovery or early regrowth stages is generally inferior to indices with broader spectral coverage and higher chlorophyll sensitivity.

Post-fire vegetation recovery can be disentangled in semi-burned areas by a com-bination of completely burned and vegetated pixels defining the spectral space. In these cases, the NIR, Red, and SWIR are the most important channels for estimating the alternating burned and vegetation areas in fire-affected soils [20]. NIR ground reflectance plays a more significant role in estimating the distribution of burned areas, while SWIR seems more effective at estimating the vegetation cover. Moreover, pixels of completely burned areas show low and high variance in NIR and SWIR, respectively, whereas the opposite conditions are observed in completely vegetated areas. Both satellite spectral vegetation indices have demonstrated a good capacity to characterize forest dynamics after wildfires. The presence of bare soil in burned areas alters the spectral signals, increasing the spectral similarity between uncovered areas (in NIR) on one side and vegetated or burned areas (in SWIR) on the other side. Given these spectral characteristics, GCI (that uses NIR and Green) is the most suitable index for assessing post-fire recovery due to its high sensitivity to green vegetation in our study. In contrast, NBR (that uses NIR and SWIR) is more oriented to assess fire severity [51], and NDVI can be affected by soil background and the presence of non-vegetation spaces [52,53].

The different performance of the analyzed SI indexes is in close agreement with [54], who found that differential spectral response of both soil and vegetation types affect the selection of widely used SIs. According to this author, NDVI is sensitive to soil background and produces larger values for the same vegetation amount over dark backgrounds [54]. This could have affected the difference in the means of our analysis since the burned areas with more dark patches of soil can have a higher NDVI value compared to the adjacent treated areas. Therefore, GCI, by focusing on active green vegetation, minimizes the interference between the soil and non-vegetative cover, resulting in a more reliable indicator for vegetation recovery in our study.

The temporal analysis of control and treated soils shows that all SIs indicate increasing recovery of greenness over time. This is expected because grass covers the ground immediately after the fire [55]. In the first and second temporal surveys (three and six months after the fire), the SIs show the greatest differences between treated and untreated areas (Figure 5). In spring (i.e., nine months after the fire), the solar radiation over the soil enhances seed bank germination in both areas. These fire-adapted systems can withstand fire in treated and untreated areas [56]. All SIs well reproduce the post-fire greening-up of soil, but GCI shows the best capacity over the whole period. Good performances are also observed for this index when SIs are separately evaluated by ecoregions since GCI shows the greatest difference between control and treated sites. There is a possibility that the data derived from Landsat imagery may present certain biases due to pixel size and landscape heterogeneity. To minimize this, comparisons were made between classes with similar physiographic conditions (aspect and slope) and fire severity. In addition, the images used in each case and the outputs generated by the masking algorithm were carefully reviewed [34].

4.2. Influence of Physiography and Fire Severity on the Effectiveness of Post-Fire Treatments

The size effect of treatments increased on the steeper and south-facing slopes (GCI and NDVI, Figure 6), underlying different and beneficial impacts of the treatments under harsh conditions for ecosystem recovery. However, similar recovery rates were detected using NBR and MSI on both south- and north-facing slopes regarding the effectiveness of the treatments. In the literature, the effects of soil slope and aspect on post-fire vegetation recovery are characterized by a large spatial variability, showing that vegetation better recovers under wetter conditions and lower radiation [9,19,57,58,59,60]. In the Mediterranean study areas, landscape managers prioritized emergency actions on steeper slopes and south-facing slopes, which are more prone to erosion and other hydrogeological hazards compared to flat areas [9]. This choice enhanced the differences compared to the harsh and non-treated slopes.

Concerning the impacts of fire, higher recovery rates were detected after the treatments regardless of its severity class, although the mean effect decreased with more severe fires. Fire severity is a key factor for the short-term recovery of burned environments. The areas burned with low and medium severity can recover faster in the short term [61], although the opposite effect has also been observed [60]. In this study, the most beneficial effects of the experimental treatments were recorded on ground cover in the ecoregions for the lowest severities. According to the evidence of the study, in areas affected by wildfires with lower severity, the emergency measures promote a higher vegetation recovery in the short term. Moreover, divergent vegetation responses are presumably associated with the different forest types and species in the study areas. Much of this response was recorded in stands with Pinus pinaster Ait. or Pinus halepensis Mill. under different climatic and soil conditions in the Iberian Peninsula.

4.3. Influence of Ecoregion and Time Elapsed from Fire on the Effectiveness of Post-Fire Treatments

Including the ecoregion as a moderator changed the overall pattern in the Atlantic and Mediterranean areas, where the untreated sites did not show any differences in vegetation cover compared to the sites subjected to post-fire treatments. In the Atlantic ecoregion (Po_Ar site), the climate is mild and rainy, and thus vegetation regrowth can be fast regardless of post-fire treatments. Here, the effect size was even higher in the control zones compared to the areas treated with straw mulching. However, the monitoring plots installed in the treated area just after fire did not show a significant effect of the treatment on the recovery of vegetation [62]. This apparently contradictory result is due to the fact that spectral indices do not reflect well soil burn severity [63] and therefore the selection of an untreated control using only the dNBR without taking into account the soil burn severity is not appropriate in this case and may lead to inaccurate results. Moreover, soil burn severity has been found to affect the sprouting process during the first months after fire [64].

In the case of the Mediterranean Ecoregion (V_Ll), no differences between the control and treated areas were found in all indices except NBR, which revealed a higher vegetation cover in the untreated areas. In this case, pre-fire vegetation could have influenced the effectiveness of post-fire treatments [65]. Different plant species react to fire through different traits and mechanisms [13,19,66], such as resprouting or recolonization by seeds. The resprouting ability, whenever buds survive the fire, is the most widespread survival strategy in the Mediterranean [58], and these species are likely more resilient to changing fire regimes [67]. The Lluxent site is subjected to a high-frequency fire regime that has favored the dominance of resprouters, such as kermes and holm oaks. In this mild and wet site, oaks exhibit new vigorous shoots after the fire that engenders a rapid return to pre-fire conditions [14]. The authors of ref. [68], working in the same environment as the site of Hellín, found a fractional cover dominated by re-sprouting oak species, especially kermes oak, a highly fire-adapted species in both treated and untreated areas [58]. The construction of log and branch barriers can adversely affect this species [6].

The response of vegetation in the sclerophyllous (Al_He) and conifer (J_Ca) ecoregions was enhanced by the post-fire treatments, and the surveys revealed different effects between forests of pine (the dominant pre-fire cover) and broadleaved re-sprouting forests of the other ecoregions [19]. This response agrees with previous results reported in studies in the same sites [9].

4.4. Influence of Climate and Soil Properties on the Effectiveness of Post-Fire Treatments

Among the climatic moderators, temperature was the most influential factor. In this regard, this study showed that sites with lower temperatures weakened the effectiveness of treatments (Figure 8). This confirms that sites with milder temperatures exhibit better recovery rates, whereas in sites with higher temperatures, vegetation struggles more to recover; however, the implementation of treatments helps improve those rates [69]. According to [60], post-fire climate is a key driver of vegetation recovery processes. The strongest effect of the treatments in Al_He has biased the temperature moderator towards a direct relationship with their effectiveness. Precipitation influenced the post-fire recovery of vegetation in Ar_Po, where, as shown by the dynamics of GCI, the regrowth of plant communities was faster under the rainy climate. For this reason, the lower precipitation in the remaining ecoregions was related to the effectiveness of the treatments. This indicates that the implementation of post-fire treatments for the specific purpose of increasing plant cover should not be prioritized in the coolest and wet climates, considering the covariates of temperature and precipitation together, since it is well known that it boosts post-fire regeneration compared to warmer and drier conditions [60,70].

Soil properties also impacted the beneficial impacts of post-fire management treatments, especially in sites with fine-textured and drier soils. This underscores the improvement of vegetation regeneration after burning with the treatments, especially under unfavorable conditions for plant growth, such as a combination of clayey texture and dry conditions. The literature has widely explored the influence of post-fire treatments on the physical, chemical, and biological properties, showing that the response of burned areas to post-fire management is site-specific [71,72]. In this regard, the Mediterranean soils, which are generally shallow and poor in organic matter, show a particular sensitivity to fire. Therefore, the choice of the most effective post-fire management technique must be carried out with caution, prioritizing the restoration of organic matter and nutrients the control of post-fire flooding and erosion hazards and favoring a quicker regrowth of vegetation [2,68].

5. Conclusions

The meta-analysis of the effectiveness of post-wildfire emergency works on early vegetation recovery in treated and untreated soils in four Spanish ecoregions showed that post-fire treatments significantly affected all vegetation indices (except EVI2). The index sensitivity was the highest for GCI and the lowest for NDII. All indices indicated a significant recovery of vegetation in the treated sites. The statistical analysis revealed that:

(i)

the post-fire management works were more effective for lower fire severity and on the steepest and south-facing slopes;

(ii)

a considerable effect of the ecoregion on the vegetation recovery was evident;

(iii)

the treatments were more effective in the short term, which indicates the need for a prompt implementation of post-fire management immediately after the fire;

(iv)

post-fire climate is a key driver of vegetation recovery, and sites with lower temperatures and higher precipitation weakened the effectiveness of the treatments;

(v)

soil texture plays a high influence on vegetation recovery, and fine-textured soils are more sensitive to the treatments; higher pH was associated with a higher post-fire recovery in the treated areas;

(vi)

finally, the regression analysis using random-mixed models showed that an annual precipitation lower than 700 mm, a diurnal temperature over 21 °C, soils with texture finer than loamy, and a water content under 33% are quantitative limits of the treatment positive effect on vegetation recovery.

Overall, the study suggested that different forest types and functionally diverging species may show contrasting responses to the same post-fire management strategy. Decisions about the most appropriate management actions to be adopted in burned forest areas should therefore be supported by further studies focusing on this aspect.