Fire intensity effects on post-fire fuel recovery in Eucalyptus open forests of south-eastern Australia

Fuel accumulation shapes post-fire fuel decomposition through soil heating effects on plants, fungi, and soil chemistry

Forty percent of terrestrial ecosystems require recurrent fires driven by feedbacks between fire and plant fuels. The accumulation of fine fuels in these ecosystems play a key role in fire intensity, which alters soil nutrients and shapes soil microbial and plant community responses to fire. Changes to post-fire plant fuel production are well known to feed back to future fires, but post-fire decomposition of new fuels is poorly understood. Our study sought to quantify how pre-fire fuel loading influenced post-fire fuel decomposition through soil abiotic properties, as well as plant and soil fungal communities. Prior to spring prescribed burns, we manipulated fine fuel loads in plots, both near (<10 m) and away (>10 m) from overstory pines, to modify soil heating in an old-growth longleaf pine savanna. We then assessed how fuel load and soil heating influenced post-fire plant fuel decomposition through changes to soil chemistry, vegetation, and fungi. Burning larger fuel loads made fires hotter, burn longer, and more completely combusted fuels. In these plots, decomposition of newly deposited fine fuels was slower in the eight months following fire. Decomposition changes from greater soil heating were mediated by greater shifts to postfire plant (2 and 4 months postfire) and fungal communities (4 and 6 months postfire). Soil properties (C: N ratios, soil pH, and P) controlled postfire decomposition throughout the year, but weakly responded to soil heating differences from fuels. Since the mechanisms for fuel effects on decomposition change over time, fire timing may be a future target for understanding fire feedbacks to fuel decomposition. Integrating these feedbacks with fuel production responses across fire-dependent ecosystems can help managers better set prescribed fire intervals and predict responses to reintroducing burning in fire-suppressed ecosystems.

A review of optimization and decision models of prescribed burning for wildfire management

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatment outcomes.

Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires

Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63-76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20-40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.

Mechanical treatments and prescribed burning can reintroduce low-severity fire in southern Australian temperate sclerophyll forests

The establishment of sustainable, low-intensity fire regimes is a pressing global challenge given escalating risk of wildfire driven by climate change. Globally, colonialism and industrialisation have disrupted traditional fire management, such as Indigenous patch burning and silvo-pastoral practices, leading to substantial build-up of fuel and increased fire risk. The disruption of fire regimes in southeastern Tasmania has led to dense even-aged regrowth in wet forests that are prone to crown fires, and dense Allocasuarina-dominated understoreys in dry forests that burn at high intensities. Here, we investigated the effectiveness of several fire management interventions at reducing fire risk. These interventions involved prescribed burning or mechanical understorey removal techniques. We focused on wet and dry Eucalyptus-dominated sclerophyll forests on the slopes of kunanyi/Mt. Wellington in Hobart, Tasmania, Australia. We modelled potential fire behaviour in these treated wet and dry forests using fire behaviour equations based on measurements of fuel load, vegetation structure, understorey microclimate and regional meteorological data. We found that (a) fuel treatments were effective in wet and dry forests in reducing fuel load, though each targeted different layers, (b) both mechanical treatments and prescribed burning resulted in slightly drier, and hence more fire prone understorey microclimate, and (c) all treatments reduced predicted subsequent fire severity by roughly 2-4 fold. Our results highlight the importance of reducing fuel loads, even though fuel treatments make forest microclimates drier, and hence fuel more flammable. Our finding of the effectiveness of mechanical treatments in lowering fire risk enables managers to reduce fuels without the risk of uncontrolled fires and smoke pollution that is associated with prescribed burning. Understanding the economic and ecological costs and benefits of mechanic treatment compared to prescribed burning requires further research.

Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors

The frequency of forest fires is increasing under global climate change, and forest fires can cause devastating disturbances to forest systems and varying degrees of recovery of forest ecosystems after a disaster. Due to the different intensity of forest fires and forest systems, and in particular the fact that forest ecological recovery is influenced by many topographical and climatic factors, the process of postfire vegetation recovery is unclear and must be studied in depth. In this study, the Greater Hinggan Mountain Range was taken as the study area. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat time-series images acquired from 2000 to 2018, this study used the spatiotemporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after forest fires. The vegetation restoration was characterized by disturbance index (DI) values, which eliminated phenological influence. Six types of topography and climatic factors (elevation, aspect, slope; temperature, precipitation, and wind speed) were coupled with DI. Through single-factor analysis of variance and multiple comparison statistical methods, it was found that there was a significant relationship between the six factors and DI, which indicated those factors had a significant impact on the restoration of forest vegetation in burned areas. The results will be useful as a reference for future monitoring and management of forest resources.

Effects of prescribed fire frequency on wildfire emissions and carbon sequestration in a fire adapted ecosystem using a comprehensive carbon model

Prescribed fire to reduce forest fuels has been routinely applied to reduce wildfire risk in many parts of the world. It has also been proposed that prescribed fire can be used to mitigate greenhouse gas (GHG) emissions. Although prescribed fire creates emissions, if the treatment also decreases the incidence of subsequent wildfires, it is possible for the net outcome to be an emissions decline. Previous studies have suggested prescribed fire, at the frequencies required to materially impact wildfire occurrence, generally leads to net emissions increases. A focus on emissions means any change in carbon storage within the ecosystem remains unaccounted for; because living, dead, and soil carbon pools are characterized by different residence times, a re-distribution of carbon amongst these pools may either reduce or increase long-term ecosystem carbon stores. A full ecosystem carbon model has been developed to investigate the implications of prescribed fire management on total Net Ecosystem Carbon Balance (NECB), inclusive of both emissions and carbon storage. Consistent with previous work, the results suggested limited potential for reducing net GHG emissions through applying prescribed fire, with higher emissions from prescribed fire approximately offset by lower emissions and avoided carbon losses from the subsequent reduction in wildfire frequency. For example, shortening the prescribed fire interval from 25 to 10 years resulted in a NECB sequestration that was typically less than ±0.4 Mg C ha^-1 yr^-1, or less than approximately 0.1% of the total ecosystem carbon storage. Hence, whilst there was limited opportunity for achieving emission abatement outcomes from changing prescribed fire management, there were no significant emission penalties for doing so. These results suggest land managers should be free to adopt prescribed fire regimes to target specific management outcomes, without significantly impacting net emissions or total ecosystem carbon storage over the long term.

Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.