Post-fire Vegetation Research Articles

Environmental factors influencing the distribution of the Kangaroo Island dunnart (

Determining the factors that drive the distributions of threatened species is often critical for informing effective conservation management actions. Species distribution models can be used to distinguish common habitat features shared by limited historical records and identify other areas where a species might persist. In this study, we built a species distribution model for the Endangered and cryptic Kangaroo Island dunnart (Sminthopsis fuliginosus aitkeni). We fitted generalised linear models using incidental records and presence-absence data from surveys between 1969 and 2018. In the models we included the variables rainfall, percentage native vegetation in the surrounding 2 km2, and post-fire vegetation age. The modelling suggested that rainfall and to a lesser extent post-fire vegetation age are good predictors of dunnart occurrence, with dunnart occurrence greatest in areas of high rainfall (>600 mm) and vegetation age classes <30 years post fire. Potentially suitable habitat for the KI dunnart was predicted to be on the central-western side of Kangaroo Island. These results suggest that careful fire management could benefit the dunnart, and that decreased rainfall (as projected by Australian climate models), will be a threat in the long term. Extensive recent fires on western Kangaroo Island suggest that climate-related threats are already being realised.

Does a trade-off between growth plasticity and resource conservatism mediate post-fire shrubland responses to rainfall seasonality?

Growth plasticity may allow fire-prone species to maximize their recovery rates during temporary, sporadic periods of rainfall availability in the post-fire environment. However, moisture-driven growth plasticity could be maladaptive in nutrient-limited environments that require tighter control of growth and resource use. We investigated whether a trade-off between plasticity and conservatism mediates growth responses to altered rainfall seasonality in neighbouring shrubland communities that occupy different soils. We monitored post-fire vegetation regrowth in two structurally similar, Mediterranean-type shrublands for 3years. We investigated the effects of experimentally altered rainfall seasonality on post-fire species' growth rates. We found that moisture-driven growth plasticity was higher among species occupying the fertile soils of the renosterveld site relative to those occupying the nutrient-poor soils of the fynbos site. This resulted in higher overall responsiveness of post-fire recovery patterns in renosterveld to experimental shifts in rainfall seasonality. In post-fire shrubland communities, the trade-off between moisture-dependent growth plasticity and resource conservatism could be mediated by soil nutrient availability. Therefore, edaphic differences between structurally similar shrublands could lead to differences in their sensitivity to post-fire rainfall seasonality.

Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin’anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.

Physiological and Regenerative Plant Traits Explain Vegetation Regeneration under Different Severity Levels in Mediterranean Fire-Prone Ecosystems

In Mediterranean fire-prone ecosystems, plant functional traits and burn severity have decisive roles in post-fire vegetation recovery. These traits may reflect plant fitness to fire regimes in the Mediterranean Basin. The aim of this study was to evaluate the effect of burn severity on post-fire vegetation regeneration through plant functional (physiological and regenerative) traits in two Mediterranean ecosystems: one more humid and colder (Cabrera in León province, NW Spain), and another characterized by a longer summer drought (Gátova in Valencia province, SE Spain). A total of 384 and 80 field plots (2 m × 2 m) were fixed in Cabrera and Gátova, respectively. In each burned plot, we quantified burn severity by means of the composite burn index (CBI), differentiating three severity levels (low, moderate, and high), and evaluated post-fire vegetation regeneration one and two years after wildfires. We measured the percentage cover of each species and classified them according to physiological (specific leaf area and N2-fixing capacity) and regenerative traits (reproductive strategy, bud bank location, and heat-stimulated germination). The main results showed that in Cabrera, burn severity had significant effects on vegetation cover independently of plant functional traits. In Gátova, burn severity effects differed among functional traits. In this site, the cover of plants with low specific leaf area and without heat-stimulation and N2-fixing capacity was negatively related to burn severity. On the contrary, the cover of N2-fixers and species with resprouting ability and heat-stimulated germination rose with increasing burn severity. In general, vegetation cover showed a more pronounced increased over time in the more humid area, mainly under the effect of high severity. The results of this research highlighted the importance of the use of plant functional traits as a driver to understand the response of different ecosystems to current fire regimes, which could be relevant for pre- and post-fire management.

Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

How Do Plants Respond Biochemically to Fire? The Role of Photosynthetic Pigments and Secondary Metabolites in the Post-Fire Resprouting Response

Resprouting is one of the main regeneration strategies in woody plants that allows post-fire vegetation recovery. However, the stress produced by fires promotes the biosynthesis of compounds which could affect the post-fire resprouting, and this approach has been poorly evaluated in fire ecology. In this study, we evaluate the changes in the concentration of chlorophylls, carotenoids, phenolic compounds, and tannins as a result of experimental burns (EB). We asked whether this biochemical response to fire could influence the resprouting responses. For that, we conducted three EB in three successive years in three different experimental units. Specifically, we selected six woody species from the Chaco region, and we analyzed their biochemical responses to EB. We used spectrophotometric methods to quantify the metabolites, and morphological variables to estimate the resprouting responses. Applying a multivariate analysis, we built an index to estimate the biochemical response to fire to EB per each species. Our results demonstrate that photosynthetic pigment concentration did not vary significantly in burnt plants that resprout in response to EB, whereas concentrations of secondary metabolites (phenolic compounds and tannins) increased up to two years after EB. Our main results showed that phenolic compounds could play a significant role in the resprouting responses, while photosynthetic pigments seem to have a minor but significant role. Such results were reaffirmed by the significant correlation between the biochemical response to fire and both resprouting capacity and resprouting growth. However, we observed that the biochemical response effect on resprouting was lower in tree species than in shrubby species. Our study contributes to the understanding of the biochemical responses that are involved in the post-fire vegetation recovery.

Medium-term effects of straw helimulching on post-fire vegetation recovery in shrublands in north-west Spain

Straw mulch is commonly applied to land after high-severity wildfires because of its effectiveness in reducing post-fire runoff and erosion. However, information about the effect on vegetation recovery is still scarce and usually limited to the first 2 years after wildfire. In this study, the effects of straw helimulching on vegetation recovery and species composition were assessed in 30 experimental plots established in four shrubland areas in north-west Spain 5 years after wildfire. The influence of the treatment on biomass accumulation in the medium term was also assessed. The relationships between soil burn severity, site characteristics (altitude, aspect, soil depth and percentage of stoniness) and the vegetation variables (total vegetation cover, weighted mean height of vegetation, total fuel load and litter and duff load) were also explored. Overall, the mulching treatment did not have significant effects on the variables studied. In the mulched plots, no non-native species were recorded 5 years after the wildfire. Site characteristics significantly affected the vegetation complex, but soil burn severity did not have any residual effect. The study findings indicate that straw helimulching has neutral effects on vegetation cover and composition in coastal shrublands in NW Spain in the medium term.

Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data

The estimation of post-fire vegetation recovery is essential for forest management and wildfire policy-making. In the last few decades, vegetation indices have been widely used to monitor post-fire vegetation recovery by comparison with the pre-fire state. In this study, vegetation recovery is estimated using Solar-Induced chlorophyll Fluorescence (SIF), which is a by-product of photosynthesis and can reflect the physiological characteristics of a plant. We found that 20 years is insufficient for vegetation recovery, as the SIF within burned areas exhibited a significant increasing trend, which was most notable within the first 6 to 10 years after a wildfire. When comparing the SIF within and outside burned areas, we found that, during the first 3 to 6 years, SIF values outside burned areas were larger than that within burned areas; however, after ~6 years, the SIF within the burned areas exceeded that outside burned areas owing to the different carbon sequestration intensities of different vegetation recovery stages. Field photos of recovering vegetation were then compared with the Enhanced Vegetation Index (EVI) trend within the burned area, and it was found that, although the EVI reached pre-fire levels or stabilised, vegetation recovery was continuing.

Post-Fire Vegetation Response in a Repeatedly Burned Low-Elevation Sagebrush Steppe Protected Area Provides Insights About Resilience and Invasion Resistance

Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush (Artemisia tridentata) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass (Pseudoroegneria spicata) and Thurber's needlegrass (Achnatherum thurberianum) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.

Assessing the Relationship between Litter + Duff Consumption and Post-Fire Soil Temperature Regimes

The immediate effects of wildland fire on soil have been well documented. However, we know much less about the longer-term effects and their implications for plants. Post-fire soil temperature regimes, for example, have received relatively little research attention, despite potential effects on plant phenology and establishment. Using portable temperature datalogger units (iButtons), we conducted an experimental study to assess how fire severity (measured in terms of litter and duff consumption) influences biologically relevant temperature parameters such as diel minimums, maximums, means, and ranges. We also used these data to calculate cumulative soil growing degree days (GDDs). The study was conducted during the early to mid-spring to capture the transition from dormant season to growing season. Results indicate that mean and max soil temperatures increase in the weeks after fire, with the most pronounced effects in the higher severity treatments. By the end of the 40-day study period, soils in the high severity burn treatment had accumulated 72 GDDs, compared to 17.9, 13.6, and 1.4 in moderate, low, and control treatments, respectively. These findings indicate that fire severity has significant and persistent effects on post-fire soil temperature regimes, and this likely has implications for the post-fire vegetation response.

Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.

Post-fire vegetation succession in the Siberian subarctic tundra over 45 years

Wildfires are relatively rare in subarctic tundra ecosystems, but they can strongly change ecosystem properties. Short-term fire effects on subarctic tundra vegetation are well documented, but long-term vegetation recovery has been studied less. The frequency of tundra fires will increase with climate warming. Understanding the long-term effects of fire is necessary to predict future ecosystem changes.We used a space-for-time approach to assess vegetation recovery after fire over more than four decades. We studied soil and vegetation patterns on three large fire scars (>44, 28 and 12 years old) in dry, lichen-dominated forest tundra in Western Siberia. On 60 plots, we determined soil temperature and permafrost thaw depth, sampled vegetation and measured plant functional traits. We assessed trends in Normalized Difference Vegetation Index (NDVI) to support the field-based results on vegetation recovery.Soil temperature, permafrost thaw depth and total vegetation cover had recovered to pre-fire levels after >44 years, as well as total vegetation cover. In contrast, after >44 years, functional groups had not recovered to the pre-fire state. Burnt areas had lower lichen and higher bryophyte and shrub cover. The dominating shrub species, Betula nana, exhibited a higher vitality (higher specific leaf area and plant height) on burnt compared with control plots, suggesting a fire legacy effect in shrub growth. Our results confirm patterns of shrub encroachment after fire that were detected before in other parts of the Arctic and Subarctic.In the so far poorly studied Western Siberian forest tundra we demonstrate for the first time, long-term fire-legacies on the functional composition of relatively dry shrub- and lichen-dominated vegetation.

A critical review of fuel accumulation models used in Australian fire management

Various classifications of fuel accumulation models are used to describe the complex temporal relationship between fuel loads and vegetation dynamics. Fuel accumulation models are an important tool in wildfire management as fuel is the only component that can be directly controlled by fire managers. Here we discuss various strengths and limitations of analytical fuel accumulation models that exist in the literature, with a focus on those used in Australia. Early approaches to analytical or continuous models of fuel accumulation centred around the model introduced by Olson in 1963. This model assumes that the rate at which fuel accumulates is determined as a balance between the rate of fuel accession and the rate at which it decays. The Olson model has been shown to provide a reasonable description of litter accumulation, data sparsity issues notwithstanding, but can be of limited use in describing elevated fuels, or in forest stands that exhibit more complex post-disturbance dynamics. Interactions between species and other disturbances have the potential to change the dynamics of fuel accumulation and decay processes. Moreover, post-fire vegetation stands are usually dominated by an understorey layer that eventually senesces as the dominant vegetation grows. Motivated by the critical differences between the models presented, a more general approach featuring vegetation density is proposed. A generic result is presented to indicate how the theoretical predictions of the model are able to emulate patterns of fuel accumulation that have been reported, and that can not be accounted for by the models commonly used in Australia.

Effects of Post-Fire Vegetation Recovery on Soil Erosion in Vulnerable Montane Regions in a Monsoon Climate: A Decade of Monitoring

In areas with frequent fires and erosive soils under monsoon climate, we aimed to determine the long-term dynamics of soil loss during vegetation recovery and to examine elapsed time for soil stabilization. Runoff plots were installed in Pinus densiflora forests affected by stand-replacing fires on the East Coast of South Korea, which occurred in spring 2000. Runoff plots measured runoff and sediment yields for 11 years (2003–2013) during which time, vegetation cover of low-, intermediate-, and high-vegetation cover plots increased from 21 to 44%, 49% to 69%, and 87% to 95%, respectively. Vegetation was effective in preventing runoff and sediment yield. Nevertheless, to stabilize to below baseline load, it took 7 years at low and intermediate cover plots and only 3 years at high cover plots. For 7 years, 7.06 (5.2-fold) and 4.29 (3.1-fold) kg m−2 of soil were lost at the low and intermediate cover plots, respectively, compared to the high cover plots (1.37 kg m−2). Sediment loss fluctuated more than runoff following extreme rainfall events. We suggest that for the slow recovery area ( 70% cover).

Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

Vegetation response to wildfire and climate forcing in a Rocky Mountain lodgepole pine forest over the past 2500 years

Wildfire is a ubiquitous disturbance agent in subalpine forests in western North America. Lodgepole pine ( Pinus contorta var. latifolia), a dominant tree species in these forests, is largely resilient to high-severity fires, but this resilience may be compromised under future scenarios of altered climate and fire activity. We investigated fire occurrence and post-fire vegetation change in a lodgepole pine forest over the past 2500 years to understand ecosystem responses to variability in wildfire and climate. We reconstructed vegetation composition from pollen preserved in a sediment core from Chickaree Lake, Colorado, USA (1.5-ha lake), in Rocky Mountain National Park, and compared vegetation change to an existing fire history record. Pollen samples ( n = 52) were analyzed to characterize millennial-scale and short-term (decadal-scale) changes in vegetation associated with multiple high-severity fire events. Pollen assemblages were dominated by Pinus throughout the record, reflecting the persistence of lodgepole pine. Wildfires resulted in significant declines in Pinus pollen percentages, but pollen assemblages returned to pre-fire conditions after 18 fire events, within c.75 years. The primary broad-scale change was an increase in Picea, Artemisia, Rosaceae, and Arceuthobium pollen types, around 1155 calibrated years before present. The timing of this change is coincident with changes in regional pollen records, and a shift toward wetter winter conditions identified from regional paleoclimate records. Our results indicate the overall stability of vegetation in Rocky Mountain lodgepole pine forests during climate changes and repeated high-severity fires. Contemporary deviations from this pattern of resilience could indicate future recovery challenges in these ecosystems.

Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI)

Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future.

Increasing cuticular wax concentrations in a drier climate promote litter flammability

Several plant chemical traits (cellulose, tannins and terpenes) have been related to plant flammability. Contrastingly, no study has focused on the relationship between plant flammability and physico-chemical leaf litter traits with a focus on cuticular wax concentration. This study focuses on alkane cuticular waxes because of their relatively low flash point and storage in the cuticle of all vascular plant species. The sclerophyllous species Quercus coccifera is the model species since it is the main shrub species in the Mediterranean basin and all previously investigated sclerophyllous species feature a high cuticular wax content. Litter was collected in a Mediterranean garrigue where Q. coccifera grows under natural drought and recurrent aggravated drought (consisting of 5.5 years of rain restriction). These different drought conditions were expected to imply different alkane wax concentrations since one of the major roles of cuticular waxes is evapotranspiration limitation during drought. Litter flammability was assessed through ignition delay, flame residence time and flame height (assessed using an epiradiator) and gross heat of combustion (using an adiabatic bomb calorimeter). Results showed that the higher cuticular alkane concentrations reached under aggravated drought were associated with an increased leaf litter flammability as expected. These results confirm that all potentially flammable organic metabolites (terpenes as previously reported in other studies, and cuticular alkane waxes) are drivers of vegetation flammability. It is suggested that Q. coccifera flammability (considered as low to moderate), could increase under a drier scenario in the Mediterranean area. We hypothesize that fire severity would accordingly be intensified in shrubs dominated by this sclerophyllous species without necessarily increasing vulnerability of Q. coccifera to fire since this is a resprouter species after fire and is one of the main pioneer species during post-fire vegetation succession.

Development of post-fire vegetation response-ability model in grassland mountainous ecosystem using GIS and remote sensing

The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire severity), adaptive strategies (flowering months, water requirements, resprouter/seeders) and ecological status (increaser or decreaser) for the park. Vegetation recovery index derived from pre- and post-fire normalised difference vegetation index (NDVI) was used with correlation and regression analysis to validate the model. The results showed that amongst the environmental variables, elevation and fire were the most important factors influencing vegetation response-ability followed by soil, aspect, rainfall, and land surface temperature. Almost half (48%) of the park had a high vegetation response-ability, 43% medium, and 9% low. On the other hand, the vegetation recovery index showed 34% of the park fully recovered to pre-fire conditions, while 61% and 5% largely and slightly recovered, respectively. There was a strong correlation between vegetation response-ability and vegetation recovery index. The regression analysis showed a good relationship between vegetation response-ability and vegetation recovery index (R = 0.91) with 83.34% coefficient of determination.

Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm

Successful post-fire management depends on accurate burn severity maps that are increasingly derived from satellite data, replacing field-based estimates. Post-fire vegetation and soil changes, besides modifying the reflected and emitted radiation recorded by sensors onboard satellites, strongly alters water balance in the fire affected area. While fire-induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis (MESMA), changes in water balance are mainly registered by evapotranspiration (ET). As both types of variables have a clear physical meaning, they can be easily understood in terms of burn severity, providing a clear advantage compared to widely-used spectral indices. In this research work, we evaluate the potential of Landsat-derived ET to estimate burn severity, together with MESMA derived Sentinel-2 fraction images and important environment variables (pre-fire vegetation, climate, topography). In this study, we use the random forest (RF) classifier, which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate. Our study area is located in Central Portugal, where a mega-fire burned >450 km2 from 17 to 24 June 2017. We used the official burn severity map as ground reference. The RF algorithm identified ET as the most important variable in the burn severity model, followed by MESMA char fractions. When both ET and MESMA char fraction image were used as RF inputs, burn severity estimates reached higher accuracy than if only one of them was used, which suggests their potential synergetic interaction. In particular, when environmental variables were used in addition to ET and char fraction, the highest accuracy for burn severity was reached (κ = 0.79). Our main conclusion is that post-fire fine resolution ET is a useful and easily understandable indicator of burn severity in Mediterranean ecosystems, in particular when used in combination with a MESMA char fraction image. This novel approach to estimate burn severity may help to develop successful post-fire management strategies not only in Mediterranean ecosystems but also in other ecosystems, due to ease of generalization.