Forest Structure Research Articles

An Eye Movement Classification Method based on Cascade Forest.

Eye tracking technology has become increasingly important in scientific research and practical applications. In the field of eye tracking research, analysis of eye movement data is crucial, particularly for classifying raw eye movement data into eye movement events. Current classification methods exhibit considerable variation in adaptability across different participants, and it is necessary to address the issues of class imbalance and data scarcity in eye movement classification. In the current study, we introduce a novel eye movement classification method based on cascade forest (EMCCF), which comprises two modules: (1) a feature extraction module that employs a multi-scale time window method to extract features from raw eye movement data; (2) a classification module that innovatively employs a layered ensemble architecture, integrating the cascade forest structure with ensemble learning principles, specifically for eye movement classification. Consequently, EMCCF not only enhanced the accuracy and efficiency of eye movement classification but also represents an advancement in applying ensemble learning techniques within this domain. Furthermore, experimental results indicated that EMCCF outperformed existing deep learning-based classification models in several metrics and demonstrated robust performance across different datasets and participants.

Multi-scale forest heterogeneity promotes occupancy of dusky-footed woodrats in the Sierra Nevada

Forested landscapes are naturally heterogeneous, with the distribution of resources influencing animal habitat selection at multiple spatial scales. However, anthropogenic activities and changing disturbance regimes have reorganized how forests are structured from fine- to landscape-scales, generally with unknown consequences for forest-associated wildlife. For instance, fire suppression and selective logging in the western US has led to more homogeneous forests with fewer small patches of early-successional vegetation. As forest management aims to improve forest resilience to extreme fire and drought by restoring historical disturbance regimes and modifying forest structure through fuel management, there is a need for studies that evaluate how animals respond to forest heterogeneity at multiple scales. Here, we estimated occupancy for the dusky-footed woodrat (Neotoma fuscipes), an important prey species for many forest predators including the California spotted owl (Strix occidentalis occidentalis), relative to forest structure and composition at site-, patch-, and landscape-scales within landscapes where forest heterogeneity was created by even-aged timber management. Woodrats were more likely to occupy sites with greater canopy cover, understory cover, and hardwoods – particularly tanoak (Notholithocarpus densiflorus) – and smaller patches of young forest. Woodrats were also more likely to occupy mature forests in close proximity to younger forests, suggesting that young forest patches with more favorable local conditions can produce populations that recruit into adjacent, lower-quality mature forests. Our results suggest that creating small (∼2 ha) patches of high-quality woodrat habitat (i.e., young forests with dense understory and hardwoods) could provide “fishing holes” for spotted owls and other predators by supporting higher woodrat densities in surrounding mature forests managed for fuels – thus helping to meet both spotted owl conservation and forest resilience objectives. More broadly, we highlight the benefits of multi-scale studies and demonstrate that restoring landscape heterogeneity, including the creation of small early-successional forests, may benefit species conservation without compromising efforts to improve resilience in forest ecosystems globally.

Tree demographic drivers across temperate rain forests, after accounting for site-, species-, and stem-level attributes.

Diverse drivers such as climate, soil fertility, neighborhood competition, and functional traits all contribute to variation in tree stem demographic rates. However, these demographic drivers operate at different scales, making it difficult to compare the relative importance of each driver on tree demography. Using c. 20,000 stem records from New Zealand's temperate rain forests, we analyzed the growth, recruitment, and mortality rates of 48 tree species and determined the relative importance of demographic drivers in a multilevel modeling approach. Tree species' maximum height emerged as the one most strongly associated with all demographic rates, with a positive association with growth rate and negative associations with recruitment and mortality rates. Climate, soil properties, neighborhood competition, stem size, and other functional traits also played significant roles in shaping demographic rates. Forest structure and functional composition were linked to climate and soil, with warm, dry climates and fertile soil associated with higher growth and recruitment rates. Neighborhood competition affected demographic rates depending on stem size, with smaller stems experiencing stronger negative effects, suggesting asymmetric competition where larger trees exert greater competitive effects on smaller trees. Our study emphasizes the importance of considering multiple drivers of demographic rates to better understand forest tree dynamics.

Conformal Conductive Polymer Deposition on Carbon Nanotube Forests

Carbon nanotube (CNT) forests are highly conductive and possess a large surface area, making them promising as electrode support materials for electrochemical applications including energy storage and water desalination. However, these forests often suffer from mechanical collapse when exposed to liquids, limiting their effectiveness in wet environments. In this study, we explore the use of vapor-phase oxidative molecular layer deposition (oMLD) to deposit thin, redox-active polymer films onto CNT forests to enhance their mechanical and electrochemical properties. We evaluate the mechanical behavior of the polymer-coated CNT forests using nanoindentation compression tests and solvent evaporation tests. The modulus and buckling stress of the forests increase with the thickness of the polymer coating, indicating enhanced mechanical strength conferred by the oMLD process. Furthermore, solvent evaporation tests demonstrate that the morphology of the polymer-coated CNT forests remains undisturbed by surface tension forces, in contrast to uncoated CNT forests which collapse and densify. This preservation of morphology underscores the effectiveness of the polymer coating in protecting the CNT forests from structural degradation in liquid environments. To gain deeper insights, finite element simulations analyze the mechanical properties of both coated and uncoated CNT forests. The simulations reveal that the increase in buckling load scales with the bending stiffness of individual CNTs and their coatings, highlighting the synergistic effect of the polymer coating on the overall forest structure. Finally, electrochemical characterization in aqueous electrolyte indicates an increase in charge capacity with polymer thickness for thin films, and a decrease in charge capacity at high film thicknesses arising from restricted pore volume. This work indicates that oMLD polymer coatings enhance the mechanical strength and electrochemical activity of CNT-based substrates. The findings from this study contribute to the development of more efficient and durable CNT-based electrodes for water desalination and energy storage applications.

A novel approach for snow depth retrieval in forested areas by integrating horizontal and vertical canopy structures information

Snow cover significantly influences the Earth’s climate system and global hydrological cycle through its thermal insulation properties and high albedo, and is an important component of the cryosphere. Currently, the most efficient means of quantifying snow depth at both global and regional scales is through passive microwave remote sensing. However, the accuracy of passive microwave remote sensing inversion of snow depth in forested areas is affected by the forest canopy. In this study, a normalized maximum stem volume (NMSV) index was constructed by combining canopy height and tree cover data obtained through remote sensing techniques. The NMSV index was then incorporated into development of snow depth retrieval algorithm to improve accuracy of passive microwave snow depth estimation in forested areas. Compared to the Chang algorithm and the AMSR-E snow depth product, this study demonstrated higher accuracy in the mid- to high-latitude forested areas of Eurasia, with an R value approximately twice as high and a reduction in the overall root mean square error (RMSE) by 2.3 cm and 7.2 cm, respectively. The relative mean bias of this study in the Western Russia, the Eastern Siberian Mountains and the Northeast China is significantly reduced than that of the existing remote sensing algorithms. Against the ERA5 and GlobSnow datasets, with the exception of the Western Russia, the performance of this study in the mid- to high-latitude forested areas of Eurasia is comparable to the ERA5 dataset and superior to the other datasets. Based on the performance of the algorithms in different NMSV values, we observe a decline in the accuracy of this algorithm when the value exceeds 0.8, which was caused by small size of high NMSV values among the ground observation sites involved in the development of snow depth retrieval algorithm. Overall, the NMSV index proposed in this study, which integrates information from both the horizontal and vertical structures of forest, can better characterize the microwave radiation properties of sparse and moderately dense forests, facilitating improvements in the accuracy of passive microwave snow depth retrieval in global forested areas.

Comparison of selected structural elements of managed and unmanaged forest to guide integrated forest management

Traditional clear-cutting of even-aged plantations has become less desirable, with forestry shifting towards more diverse, multi-purpose integrated forest management. This study compared easily selected forest structures in managed forests and forest reserves to guide integrated forest management. The study area includes six managed forest areas and six forest reserves, with management types compared over approximately 50 years. The evaluated areas are part of the university forests of Mendel University in Brno, covering approximately 30 km² of natural deciduous temperate forests, predominantly composed of sessile oak and European beech. A total of 599 inventory plots were surveyed, assessing the presence or absence of selected forest structures in six categories. Twelve logistic generalized mixed models were developed to estimate the probability of occurrence of the observed forest structures based on their management status. Significant differences were found in the categories of dead standing and lying wood, microhabitat trees, and non-native tree species, with all variables showing a higher probability in forest reserves compared to managed forests. The results support the shift towards integrative management by incorporating elements of retention forestry. However, certain variables, such as natural regeneration and species diversity, showed little variation between management types, suggesting that some forest attributes are resilient to management practices.

Edge effect and species richness modulate biomass stocks and change over time in secondary forest fragments in the subtropical Atlantic Forest

ABSTRACT Background Above-ground biomass (AGB) and its temporal dynamics are key parameters of forest ecosystems, related to their resilience and capacity to fix atmospheric carbon. AGB is related to species richness, composition, forest structure, soils and climate. In human-modified landscapes, fragmentation and human pressure may change the nature of these relationships. Aims Our aim was to quantify how species richness and composition, leaf area index, and edge effect were related to biomass stocks and growth in fragmented sub-tropical secondary forests. Methods We tested the relationship between leaf area index, tree species richness and composition, edge effect and AGB stocks in secondary forest stands in the Atlantic Forest using multiple linear models on data from 104 sites. Results Our results show that biomass stocks were related to species richness, distance to forest edge and the interaction of both (R2 = 0.25; p < 0.05). Biomass change showed positive relationships with pioneer species richness and distance to edge, and negative relationship with the interaction of total species richness and distance to edge (R2 = 0.15; p < 0.05). Conclusion Edge effect can affect AGB dynamics directly and indirectly, by weakening the positive effects of species richness and composition on biomass. AGB loss at some sites suggests that some fragments are under chronic disturbance or may be experiencing delayed mortality due to fragmentation.

Divergent trajectories of regeneration in early-successional forests after logging and wildfire.

Increases in forest disturbances have altered global forest demography rates, with many regions now characterized by extensive areas of early-successional forest. Heterogeneity in the structure, diversity, and composition of early-successional forests influence their inherent ecological values from immediately following disturbance to later successional stages, including values for biodiversity and carbon storage. Here, using 14 years of longitudinal data, we describe patterns in the structure, richness, and composition of early-successional forests subject to one of three different disturbance types: (1) clearcut logging followed by slash burn, (2) severe wildfire followed by salvage logging, and (3) severe wildfire only, in the Mountain Ash (Eucalyptus regnans) and Alpine Ash (Eucalyptus delegatensis) forests of southeastern Australia. We also documented the influence of disturbance intervals (short, medium, and long) on early-successional forests. Our analyses revealed several key differences between forests that regenerated from wildfire versus two different anthropogenic perturbations. Most ash-type plant communities were resilient to wildfire within historical fire-regimes (75-150 years), exhibiting temporal trends of recovery in plant structure, richness, and composition within the first decade. In contrast, the richness, occurrence, and abundance of some plant lifeforms and life history traits were negatively associated with clearcut logging and salvage logging, relative to forests disturbed by wildfire alone. These included resprouting species, such as tree ferns and ground ferns. However, Acacia spp. and shrubs were more abundant after clearcut logging. Our findings also provide evidence of the pronounced negative impact of salvage logging on early-successional plant communities, relative to that of both clearcut logging and wildfire. Notably, plant richness declined for over a decade after salvage logging, rather than increased as occurred following other disturbance types. Early-successional forests provide the template for the stand structure and composition of mature forests. Therefore, altered patterns of recovery with different disturbance types will likely shape the structure and function of later-successional stages. Predicted increases in wildfire will increase the generation of early-successional forests and subsequent salvage logging. Therefore, it is pertinent that management consider how different disturbance types can produce alternate states of forest composition and structure early in succession, and the implications for mature stands.

Harnessing Biomass and Blue Carbon Potential: Estimating Carbon Stocks in the Vital Wetlands of Eastern Sumatra, Indonesia

Global warming is a critical factor driving climate change, impacting every aspect of life on Earth. The escalating concentration of greenhouse gasses in the atmosphere, the primary contributor to global warming, necessitates immediate action through effective climate mitigation strategies. This study aimed to quantify the biomass and blue carbon stocks in the eastern coastal mangrove forests of North Sumatra and Aceh Provinces in Indonesia, focusing on key sites in Langkat, Deli Serdang, Batu Bara, Tanjung Balai, and Aceh Tamiang Regencies. We measured carbon stock in three carbon pools: biomass (above and below ground), necromass, and soil. By analyzing tree stands using parameters such as tree height and diameter at breast height within circular plots (7 m in radius, 125 m apart), we gathered fundamental data on forest structure, species composition, and above- and below-ground biomass. Additionally, we collected soil samples at various points and depths, measuring the amount of wood, stems, or branches (necromass) that fell to or died on the forest floor. Data were collected in plots along a line transect, comprising three transects and six circular plots each. Sixteen diverse mangrove species were found, demonstrating rich mangrove biodiversity. The mangrove forests in the five regencies exhibited significant carbon storage potential, with estimated average above-ground carbon ranging from 96 to 356 MgC/ha and average below-ground carbon from 28 to 153 MgC/ha. The estimated average deadwood carbon varied between 50 and 91 MgC/ha, while soil carbon ranged from 1200 to 2500 MgC/ha. These findings underscore the significant carbon storage potential of these mangrove forests, highlighting their importance to global carbon cycling and climate change mitigation. This research contributes to a broader understanding of mangroves as vital blue carbon ecosystems, emphasizing the necessity of conservation efforts such as forest restoration and rehabilitation to enhance their role in stabilizing coastal areas and improving global climate resilience.

Integrated multi-satellite data and machine learning approach in mapping the successional stages of forest types in a tropical montane forest

Understanding the successional stages in tropical montane forests (TMF) is crucial for its conservation and management. This study integrated Sentinel-1, Sentinel-2, InSAR, GEDI, and machine learning to map the categorical successional stages of different forest types in a Philippines’ TMF. Field data collected from December 2022 to January 2023 were used to create and validate successional stages models. Sentinel-1 interferogram, unwrapped interferogram, and coherence exhibited moderate positive correlations with canopy height (r = 0.43). Incorporating GEDI with InSAR to predict canopy height yielded less accurate predictions (r = −0.20 to 0.04; RMSE = 12–13 m). Results show that canopy height, a widely accepted attribute for forest structure, appears secondary to other biophysical variables. Integrating optical, radar, and auxiliary variables achieved an overall accuracy of 79.56% and a kappa value of 75.74%. Feature importance analysis using Random Forest enhanced the overall accuracy (84.22%) and kappa value (81.19%). The integration of multi-satellite data with machine learning has proven effective for studying TMFs successional stages. Elevation emerged as the most significant predictor of forest type distribution, with mature and young pine forests dominating lower elevation (700–1,400m) and mossy forests dominating above 1,400m. Given the observed disturbances, the study underscores the need for robust conservation strategies and sustainable TMF management. Future research should focus on time-series analyses of successional stages, further optimization of machine learning models, and integrating additional data sources, such as LiDAR, to enhance canopy height predictions and forest monitoring efforts. The findings also provide valuable knowledge applicable to TMFs globally, supporting informed conservation and policies intended to protect biodiversity.

Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity.

Driving Factors and Future Trends of Wildfires in Alberta, Canada

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts.

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R2 of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

How can oak regeneration in the Leipzig Floodplain Forest be effectively supported by femel plantations? Application of a demographic forest model

Like many temperate floodplain forests in Europe, the Leipzig Floodplain Forest (LFF) has been impacted by anthropogenic disturbances, such as historical forestry practices and hydrological alterations. As a result, the forest's tree composition has changed, as evidenced by the declining abundance of typical floodplain species—like pedunculate oak (Quercus robur)—which play an important role in maintaining biodiversity. Notably, oak is not naturally regenerating in the mature forest, situating a need for management intervention to assist oak regeneration. One such management intervention is to plant oak in femels, i.e. small-scale clearings, or canopy gaps. However, it remains unclear whether small femels (<0.5 ha) can ensure effective oak regeneration. Moreover, city foresters aim at reaching 40 % average oak cover in the forest overstory by planting oaks in femels, but uncertainty remains regarding how much of the area would have to be planted with oaks per year to reach this goal.To explore these questions, we collected forest inventory data from oak femels ranging from 0.1 to 0.6 ha in size and compared oak density, size, growth, and mortality across femel sizes. Furthermore, we used the demographic Perfect Plasticity Approximation (PPA) model to simulate oak overstory cover over time under a femel management regime to assess the percentage of the forest area that should be planted with oaks per year accounting for different groundwater conditions. To parameterise the model, we used forest inventory data to quantify growth rates of oaks in the femels and growth, mortality, and recruitment rates of all common tree species in the Leipzig Floodplain Forest that may regenerate under the oak canopy over time.Oak density, size, growth, and mortality were similar across femel sizes, indicating that smaller femels are as effective as larger ones in promoting oak regeneration, while minimising the impact on the forest structure. To reach the foresters’ goal of 40 % average oak overstory cover, it is recommended that approximately 0.3 % of the hardwood floodplain forest area is planted with oaks each year, independent of the groundwater conditions. This study illustrates how ecological modeling can assist conservation planning in the context of forest management decision-making and these results may also be relevant for other European floodplain forests where oak regeneration requires support through active management to conserve long-standing biodiversity.

Disentangling direct and indirect effects of forest structure on biodiversity: Bottom‐up and top‐down effects between forestry, bats and their insect prey

Abstract Timber‐oriented forest management profoundly alters forest structure and composition, with complex effects on associated biodiversity. However, while species' responses to forest management and resulting structural characteristics have been the subject of numerous studies, direct and indirect effects that cascade through trophic levels are rarely disentangled. As insectivorous bats are particularly sensitive to changes in forest structure, that shape their available flight space, we investigated how forest structure, composition and management also indirectly modify their habitats, for example, by affecting important insect prey groups. We used structural equation models (SEMs) to test bat responses to forest composition, structure (forest heterogeneity, old‐growth attributes) and management intensity, quantifying direct and indirect prey‐mediated effects. For that, three bat guilds—short‐ (SRE), mid‐ (MRE) and long‐range echolocating (LRE) bats—and their prey insects (moths and ground beetles) were analysed from 64 sites in the Black Forest, Germany. We found guild‐specific effects on bats: While the structural heterogeneity of forests directly influenced the activity of bat guilds, the main influence of forest management, composition and structure was mediated through their prey‐groups. SRE activity responded to moths and LRE activity was associated with ground beetles, with positive effects of the insect groups' abundance, but negative effects of the same group's species richness. In addition, the SEM approach revealed a negative top‐down relationship between MRE activities and moths, suggesting predation or avoidance behaviour of moths. While forest management directly or indirectly increased prey insect abundance, it negatively affected the availability of roosting structures for bats. Synthesis and applications. The results highlight the indirect and positive effects of forest management on bats and support the important role of bats in insect regulation within continuous cover forests. Although forest management created small gaps that improved foraging habitats for most bats, it compromised the roosting functionality for bats. The ‘close‐to‐nature forestry’ currently prevalent in Europe mainly promotes continuous‐cover forests in mid‐successional stages. Expanding the forest management portfolio towards open and old‐growth forests would increase roosting opportunities and provide complementary foraging habitats for different bat species, while promoting high biodiversity in managed forest landscapes.

Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627

Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al. (2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE; (2) the higher temporal resolution of ORCHIDEE; and (3) the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.

Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions.

Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province

The acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We combined six commonly land cover datasets and forest age, canopy height, above-ground biomass, and tree species distribution datasets to reconstruct 30 m spatially accurate forest refinement dataset (FRD) for Guangdong Province. In addition, the distribution characteristics of forest structure and function were evaluated using forest morphological spatial pattern analysis. The results show that the overall accuracy of FRD of the Guangdong Province in 2020 reached 86.07 %. Forest types in the Guangdong Province were mainly dominated by evergreen needle-leaf forests. Tsuga chinensis, Red cedar, and Pinus sylvestris were more commonly planted. Older and taller trees were found in northern and eastern Guangdong. In addition, forest above-ground biomass (AGB) was larger in the coastal areas of northern and western Guangdong. The core and perforation had the oldest age and the highest tree height, and the islet had the lowest for all forest structure and function indicators. Based on multi-source datasets, this study contributes to a better understanding of the attributes characterizing the structure and function of forests. The refined dataset and research framework will effectively enhance forest management efficiency and policy making, as well as provide case references for research on climate change response, forest conservation and biodiversity assessment.

Modeling the interaction between wildfires and windthrows: A pilot case study for Italian Alps

Wildland fires and windthrows represent relevant disturbances for forest ecosystems worldwide. In this context, especially for Italian catchments, the interaction between windthrows and changes in wildfire behaviour starting from ALS data processing is scarcely investigated. Therefore, this research aims to compute a multi-temporal analysis of the interaction between windthrows and wildfire behaviour in a forested area (Veneto region, northern Italy), recently affected by the renamed Vaia windstorm. The semi-empirical FlamMap model was applied, starting from ALS data processing implemented in R for mapping the spatial distribution of forest attributes and fuels within the catchment. The role of windthrows in altering wildfire behaviour was investigated considering ALS point clouds acquired before and after the occurrence of the storm Vaia. Digital Terrain Models (DTMs), Canopy Height Models (CHMs), topographic data and metrics describing forest structure were extracted from ALS data for both scenarios at 5 m resolution, to compare changes in wildfire behaviour over time. Differences in Rate of Spread (RoS), flame length (FL), midflame windspeed (WS) and arrival time (AT) were assessed, and their correlation with windstorm damages was investigated at the catchment detail. , An increase of RoS, FL, and WS greater than 30 m/min, 3 m and 1.1 m/s were respectively estimated in windthrown areas, as well as a decrease of AT greater than 30 min, attesting the key role of windthrows in altering wildfire behaviour over time. The correlation between windthrows and changes in wildfire attributes was finally modeled by computing regression analysis, with R2 of 0.86, 0.93, 0.62, and 0.91 resulted for RoS, FL, WS and AT. This research represents a pilot case study for better detecting changes in wildfires behaviour due to windthrows occurrence, therefore proposing and carrying out effective planning and management strategies for disturbed forest stands over time.