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Eucalypt forests are vital for biodiversity and fire dynamics, with fine fuels serving as the primary ignition source and critical factors in determining fire intensity. Accurate measurement of fine fuel via destructive field methods is location and time-restricted, hindering the tracking of their dynamic variations across diverse landscapes. In response, this study utilised forest inventory data collected by the Victorian Forest Monitoring Program (VFMP) in Victoria, southeastern Australia, along with allometric models, to estimate fine fuel loads across four vertical layers (surface, near-surface, elevated, and canopy). The average total fine fuel load in Victorian Eucalypt forests was estimated at 18.45 t ha−1 (ranging from 2.23 to 39.54 t ha−1), with surface litter contributing 58.2% of this total, followed by canopy (22.1%), near-surface (13.4%), and elevated (6.6%) fine fuels. We also conducted a comprehensive investigation and found distinct variations in fine fuel loads across different bioregions, forest covers, and vegetation types. Furthermore, stepwise multiple regression was employed to develop predictive models for estimating fine fuel loads from forest structural and environmental variables. Results show that total and canopy fine fuels can be reasonably explained by forest height, density, and climate-related variables (with R 2 = 0.38 and 0.51, respectively). However, the explanatory power of these models diminished when applied to elevated, near-surface, and surface fine fuels. This study underscores the intricacies of fine fuel distribution in Eucalypt forests, emphasising the necessity for comprehensive factors in fire management planning. Further research is needed to better comprehend the relationship between forest characteristics and fine fuel dynamics to enhance wildfire risk assessment and mitigation strategies. • Forest inventory data was utilised to estimate and analyse fine fuel loads across large areas. • Fine fuels were estimated across four vertical layers (surface, near-surface, elevated, canopy). • Fine fuel loads exhibit significant variations across different spatial strata. • Fine fuel loads show moderate correlations with forest structural and environmental variables. • Modelling fine fuel loads from forest structural and environmental variables. [ABSTRACT FROM AUTHOR]

Recent landscape‐scale wildfires in eastern Australia have made apparent the need for a greater understanding of the flammability dynamics of forested ecosystems. Fire severity is a measure of the impact of a fire on vegetation, but little is known about the landscape‐scale response of the fire‐prone dry sclerophyll forests of eastern Australia to different levels of fire severity. Species in these forests have multiple responses to fire, which can be dependent on the fire severity. In this study, we aimed to determine the effect of fire severity on the vegetation structure, and therefore flammability, of these forests. We addressed two hypotheses that 1) High severity fire would result in a denser understory than low severity fire after 5 years and that 2) High severity fire would reduce the vertical separation between understory and canopy after 5 years. Field surveys of 38 forest sites with differing fire severity but standardised time since fire and forest type, in Sydney region of New South Wales, Australia, were used to test these hypotheses. We found lower canopy cover and greater understory cover (0.5–4 m height) after high severity fire compared with sites which burnt at low severity. Vertical separation was less between the canopy and understory at sites after high severity fire than after low severity fire. The greater quantity of understory fuel and greater vertical continuity in fuel structure observed suggests a potential increase in forest flammability after high severity fire compared with lower severity fires in these forest types. [ABSTRACT FROM AUTHOR]

The recent episodes of forest fires in Brazil and Australia of 2019 are tragic reminders of the hazards of forest fire. Globally incidents of forest fire events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using a forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest fire. Model validation criteria like ROC curve, correlation coefficient, and Cohen's Kappa show a good predictive ability (AUC = 0.95, COR = 0.81, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures. [ABSTRACT FROM AUTHOR]

Aim: Forest carbon storage is the result of a multitude of interactions among biotic and abiotic factors. Our aim was to use an integrative approach to elucidate mechanistic relationships of carbon storage with biotic and abiotic factors in the natural forests of temperate Australia, a region that has been overlooked in global analyses of carbon‐biodiversity relations. Location: South‐eastern Australia. Time period: 2010–2015. Major taxa studied: Forest trees in 732 plots. Methods: We used the most comprehensive forest inventory database available for south‐eastern Australia and structural equation models to assess carbon‐storage relationships with biotic factors (species or functional diversity, community‐weighted mean (CWM) trait values, structural diversity) and abiotic factors (climate, soil, fire history). To assess the consistency of relationships at different environmental scales, our analyses involved three levels of data aggregation: six forest types, two forest groups (representing different growth environments), and all forests combined. Results: Structural diversity was consistently the strongest independent predictor of carbon storage at all levels of data aggregation, whereas relationships with species‐ and functional‐diversity indices were comparatively weak. CWMs of maximum height and wood density were also significant independent predictors of carbon storage in most cases. In comparison, climate, soil, and fire history had only minor and mainly indirect effects via biotic factors on carbon storage. Main conclusions: Our results indicate that carbon storage in our temperate forests was underpinned by tree structural diversity (representing efficient utilisation of space) and by CWM trait values (representing selection effects) more so than by tree species richness or functional diversity. Abiotic effects were comparatively weak and mostly indirect via biotic factors irrespective of the environmental range. Our study highlights the importance of managing forests for functionally important species and to maintain and enhance their structural complexity in order to support carbon storage. [ABSTRACT FROM AUTHOR]

Abstract: Treatment of fuel (e.g. prescribed fire, logging) in fire-prone ecosystems is done to reduce risks to people and their property but effects require quantification, particularly under severe weather conditions when the destructive potential of fires on human infrastructure is maximised. We analysed the relative effects of fuel age (i.e. indicative of the effectiveness of prescribed fire) and logging on remotely sensed (SPOT imagery) severity of fires which occurred in eucalypt forests in Victoria, Australia in 2009. These fires burned under the most severe weather conditions recorded in Australia and caused large losses of life and property. Statistical models of the probability of contrasting extremes of severity (crown fire versus fire confined to the understorey) were developed based on effects of fuel age, logging, weather, topography and forest type. Weather was the primary influence on severity, though it was reduced at low fuel ages in Moderate but not Catastrophic, Very High or Low fire-weather conditions. Probability of crown fires was higher in recently logged areas than in areas logged decades before, indicating likely ineffectiveness as a fuel treatment. The results suggest that recently burnt areas (up to 5–10 years) may reduce the intensity of the fire but not sufficiently to increase the chance of effective suppression under severe weather conditions. Since house loss was most likely under these conditions (67%), effects of prescribed burning across landscapes on house loss are likely to be small when weather conditions are severe. Fuel treatments need to be located close to houses in order to effectively mitigate risk of loss. [Copyright &y& Elsevier]

New sensor-based approaches for assessing the quantity, quality, and value of timber are being developed with the goals of improving the accuracy and economics of forest measurements. One new approach is based on terrestrial laser scanning (TLS). Thirty-three plots in six radiata pine (Pinus radiata D. Don) stands were scanned using TLS. Tree locations were automatically detected. Stem profiles were measured using three methods: (i) TLS scans, (ii) Atlas Cruiser inventory procedures, and (iii) manual measurement after harvesting. Stems were optimally bucked based on log specifications and prices for Australian markets. Tree values and log product yields were estimated for the TLS data and compared with estimates based on Cruiser and actual manual measurements of stem profiles. TLS volume and value recovery were within 8% and 7%, respectively, of actual harvester recovery for five of the six stands in which it was used. Cruiser volume and value estimates were both within 4% of actual harvester recovery. Plot preparation procedures, tree characteristics, and taper equations used to model diameters on hidden stem sections affected the accuracy of automated stem detection and profile measurements for the TLS system. Improvements in data capture and analytical procedures should improve the accuracy of TLS-based volume and value estimates. Dans le but d'améliorer la précision et l'économie des mesures dendrométriques, de nouvelles approches basées sur les capteurs sont en cours d'élaboration pour évaluer la quantité, la qualité et la valeur du bois. Une nouvelle approche est basée sur le balayage laser terrestre (BLT). Trente trois placettes établies dans six peuplements de pin de Monterey (Pinus radiata D. Don) ont été numérisées par BLT. L'emplacement des arbres a été détecté automatiquement. Le défilement des tiges a été mesuré en utilisant trois méthodes : (i) le BLT, (ii) l'inventaire Atlas Cruiser et (iii) la mesure réelle après la coupe. Les tiges ont été tronçonnées de manière optimale à l'aide des spécifications des billes de bois et des prix du bois sur les marchés australiens. La valeur des arbres et le rendement des produits de transformation des billes ont été estimés par BLT et comparés aux estimations basées sur l'inventaire Atlas Cruiser et aux mesures réelles du défilement. Les estimations du volume par BLT et de la valeur des produits de transformation ne dépassaient pas respectivement 8 et 7 % des résultats réels de l'abatteuse pour cinq des six peuplements où le BLT a été utilisé. Les estimations du volume et de la valeur des produits de transformation basées sur l'inventaire Atlas Cruiser ne dépassaient pas 4 % des résultats réels de l'abatteuse. Les procédures de préparation des placettes, les caractéristiques des arbres et les équations du défilement utilisées pour prédire le diamètre des sections de tige cachées par les arbres voisins ont affecté la précision de la détection automatique des tiges et la mesure du défilement dans le cas du BLT. L'amélioration de la saisie des données et des procédures d'analyse devrait améliorer la précision de l'estimation du volume et de la valeur du bois par BLT. [ABSTRACT FROM AUTHOR]

This article describes the design of a sampling quadrant frame constructed as a part of long-term study of leaf-litter-dwelling Stag Beetles being undertaken by the Forest Practices Authority, Tasmania. Information on the materials used and methods followed in constructing the lightweight sampling frame for ecological research and monitoring is presented. Application of this flexible frame made from fiberglass or carbon-fiber in grassland sampling and in restoration treatments is also discussed.

Forests currently cover over 20% of the Australian continent and are an important resource, subject to a wide range of economic and environmental pressures. These lands support substantial numbers of forest-dependent species with national forest inventories providing important information on biodiversity. National scale information on these forests has been collected or collated since 1988 under the National Forest Inventory (NFI) programme, but substantial problems with the ‘snap shot’ approach have been recognized, particularly with respect to monitoring change and a consequent move towards a permanent and sample-based continental forest monitoring framework (CFMF) has been proposed. CFMF is proposed to consist of three Tiers: (1) satellite imagery of the continent to identify forest and change in forest cover; (2) systematic high-resolution remotely sensed data and (3) permanent ground points at 20×20 km grid interception points. The CFMF approach is in line with the international trend of national forest inventories in developed countries although the Tier 2 approach offers a useful extension. An alternative inventory approach is provided by the National Carbon Accounting System (NCAS) which models the mass of carbon and nitrogen in seven separate living and dead biomass pools for any point under forest or agriculture land use since 1970. The NCAS approach allows fine spatial and temporal monitoring of changes in these carbon and nitrogen biomass pools, and predictions of changes that result from policy or management decisions. This paper briefly reviews NFI, NCAS and the proposed CFMF, with particular emphasis on issues of use and potential for monitoring biodiversity in this biologically very diverse country. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems are targeted for many conservation and rehabilitation efforts due to their ability to store large amounts of carbon in their living biomass and soil. Traditional methods to monitor above-ground biomass (AGB) rely on on-ground measurements, which are expensive, labour intensive and cover small spatial scales. Structure from Motion and Multi-View Stereo reconstructions from Unmanned Aerial Vehicles imagery (UAV-SfM) have the potential to increase fieldwork efficiency by providing a greater amount of spatial information in less time. However, there is still a need to assess the ability of UAV-SfM to retrieve structural information of mangrove forests, which could pose challenges in areas of high forest complexity and density. In this study we successfully used UAV-SfM data to estimate height, canopy diameter and AGB of natural and rehabilitated mangrove forests across two regions of the southeastern coast of Australia. We used a variable window filter algorithm to detect trees with an 80% detection rate when considering the top canopy. Individual tree canopy segmentation was performed using a marker-controlled watershed segmentation with two sets of constraining markers: treetops and a minimum height below which a pixel is not considered part of a tree. Direct comparison with on-ground measurements at the regional level showed no significant difference in tree height and AGB medians when only top canopy was considered. Similarly, median canopy diameters were not significantly different in natural areas of both regions, but significant differences were found in rehabilitated areas. UAV-SfM estimates of AGB were on average 15% lower in natural areas and 10% higher in rehabilitated areas when compared to on-ground measurements and followed a strong linear relationship close to the ideal one-to-one relationship. Additionally, we performed a cost-benefit analysis of the two methodologies. UAV-SfM methods can save almost AU$ 50,000 per ha when compared to on-ground measurements and become cost-effective (based on total costs) after just 15 days of surveys. The methods described in this study open the possibility for easily repeatable, low-cost UAV-SfM surveys for local managers by providing a faster, more cost-effective approach for monitoring mangrove forests over larger areas than traditional on-ground surveys while maintaining forest inventory data accuracy in both natural and rehabilitated mangrove forests. • UAV imagery can be used to assess AGB of natural and rehabilitated mangrove forests. • Mangrove tree height and canopy diameter accurately estimated • UAVs are more cost-effective than on-ground surveys (AU$ 50,000 savings per ha). [ABSTRACT FROM AUTHOR]

Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia. [ABSTRACT FROM AUTHOR]

The use of forest biomass for bioenergy in Australia represents only 1% of total energy production but is being recognized for having the potential to deliver low-cost and low-emission, renewable energy solutions. This review addresses the potential of forest biomass for bioenergy production in Australia relative to the amount of biomass energy measures available for production, harvest and transport, conversion, distribution and emission. Thirty-Five Australian studies on forest biomass for bioenergy are reviewed and categorized under five hierarchical terms delimiting the level of assessment on the biomass potential. Most of these studies assess the amount of biomass at a production level using measures such as the allometric volume equation and form factor assumptions linked to forest inventory data or applied in-field weighing of samples to predict the theoretical potential of forest biomass across an area or region. However, when estimating the potential of forest biomass for bioenergy production, it is essential to consider the entire supply chain that includes many limitations and reductions on the recovery of the forest biomass from production in the field to distribution to the network. This review reiterated definitions for theoretical, available, technological, economic and environmental biomass potential and identified missing links between them in the Australian literature. There is a need for further research on the forest biomass potential to explore lower cost and lowest net emission solutions as a replacement to fossil resources for energy production in Australia but methods the could provide promising solutions are available and can be applied to address this gap. [ABSTRACT FROM AUTHOR]