Forest Management Decisions Research Articles

Two decades of stakeholder voices: Exploring engagement in Romania's FSC forest management certification

Stakeholders' engagement in forest management decision-making is a critical tool for enhancing the business acceptability of the forest sector, and the Forest Stewardship Certification (FSC) scheme proactively involves stakeholders in its implementation. Based on the stakeholder theory, this study examined stakeholder's engagement in the FSC forest management certification in Romania over the past two decades to identify the extent and inputs of their involvement in the process. Using in-depth content analysis and text mining techniques, we explored publicly available information from 209 public reports covering 45 forest management certificates. According to data available in 178 reports, the average number of stakeholders notified per audit was 50.7. However, response evidence was found in 128 reports, with an average of 1.2 responses per audit, suggesting a limited engagement in the process. Additional stakeholders' feedback was received as part of the on-site audits, with an average of 10.2 responses per audit. Given the inconsistencies in the reporting format between certification bodies, we set a structured framework to analyse the findings on stakeholders' engagement from different report sections. The employed analytical framework sheds light on stakeholder and legitimacy theories in forest certification. Our findings highlighted stakeholders' predominantly positive feedback tone, evenly distributed across all stakeholder types and the four analysis domains. Negative feedback accounted for only 9.4% of the findings, highlighting areas for improvement for certificate holders, companies in the timber supply chain and public authorities. This study's findings may assist FSC in refining its stakeholder engagement practices, contributing to enhanced participation and better risk mitigation of non-compliance with standard requirements.

A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China

Accurately assessing tree mortality probability in the context of global climate changes is important for formulating scientific and reasonable forest management scenarios. In this study, we developed a climate-sensitive individual tree mortality model for Masson pine using data from the seventh (2004), eighth (2009), and ninth (2014) Chinese National Forest Inventory (CNFI) in Hunan Province, South–Central China. A generalized linear mixed-effects model with plots as random effects based on logistic regression was applied. Additionally, a hierarchical partitioning analysis was used to disentangle the relative contributions of the variables. Among the various candidate predictors, the diameter (DBH), Gini coefficient (GC), sum of basal area for all trees larger than the subject tree (BAL), mean coldest monthly temperature (MCMT), and mean summer (May–September) precipitation (MSP) contributed significantly to changes in Masson pine mortality. The relative contribution of climate variables (MCMT and MSP) was 44.78%, larger than tree size (DBH, 32.74%), competition (BAL, 16.09%), and structure variables (GC, 6.39%). The model validation results based on independent data showed that the model performed well and suggested an influencing mechanism of tree mortality, which could improve the accuracy of forest management decisions under a changing climate.

Modelling Climate Effects on Site Productivity and Developing Site Index Conversion Equations for Jack Pine and Trembling Aspen Mixed Stands

Forest site productivity estimates are crucial for making informed forest resource management decisions. These estimates are valuable both for the tree species currently growing in the stands and for those being considered for future stands. Current models are generally designed for pure stands and do not account for the influence of climate on tree growth. Consequently, site index (SI) conversion equations were developed specifically for jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) trees grown in naturally originated mixed stands. This work involved sampling 186 trees (93 of each species) from 31 even-aged mixed stands (3 trees per species per site) across Ontario, Canada. Stem analysis data from these trees were utilized to develop stand height growth models by incorporating climate variables for each species. The models were developed using a mixed effects modelling approach. The SI of one species was correlated with that of the other species and climate variables to establish SI conversion equations. The effect of climate on site productivity was evaluated by projecting stand heights at four geographic locations (east, center, west, and far west) in Ontario from 2022 to 2100 using the derived stand height growth models. Height projections were made under three emissions scenarios reflecting varying levels of radiative forcing by the end of the century (2.6, 4.5, and 8.5 watts m−2). Climate effects were observed to vary across different regions, with the least and most pronounced effects noted in the central and far western areas, respectively, for jack pine, while effects were relatively similar across all locations for trembling aspen. Stand heights and SIs of jack pine and trembling aspen trees grown in naturally originated mixed stands can be estimated using the height growth models developed here. Similarly, SI conversion equations enable the estimation of the SI for one species based on the SI of another species and environmental variables.

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.

Evaluating the potential for continuous update of enhanced forest inventory attributes using optical satellite data

Abstract Timely and detailed inventories of forest resources are of critical importance to guiding sustainable forest management decisions. As forests occur across large spatial extents, remotely sensed data are often used to augment conventional forest inventory measurements. When combined with field plot measurements, airborne laser scanning (ALS) data can be used to derive detailed enhanced forest inventories (EFIs), which provide spatially explicit and wall-to-wall characterizations of forest attributes. However, these EFIs represent a static point in time, and the dynamic nature of forests, coupled with increasing disturbance and uncertain future conditions, generates a need for the continuous updating of forest inventories. This study used a time series of optical satellite data to update an EFI generated for a large (~690 000 ha) forest management unit in Ontario, Canada, at a two-week interval. The two-phase approach involved first building a relationship between single-year EFI attributes (2018) and spectral variables representing within-year slope, amplitude, and trend of a time series (2000–21) of 14 spectral bands and indices. For each of the 20 strata representing different species groups and site productivity classes, a k-nearest neighbor (kNN) model was developed to impute seven common EFI attributes: aboveground biomass, basal area, stem density, Lorey’s height, quadratic mean diameter, and stem volume. Across all strata, models were generally accurate, with relative root mean square error ranging from 11.47% (canopy cover) to 31.82% (stem volume). In the second phase of the approach, models were applied across the entire study area at two-week intervals in order to assess the capacity of the methodology for characterizing change in EFI attributes over a three-year period. Outputs from this second phase demonstrated the potential of the approach for characterizing changes in EFI values in areas experiencing no change or non-stand replacing disturbances. The methods developed herein can be used for EFI update for any temporal interval, thereby enabling more informed decisions by forest managers to prescribe treatments or understand the current state of forest resources.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Timber Harvesting Economics in Early Commercial Thinning and Group Selection Prescriptions

Forest management decisions that consider both short- and long-term objectives are becoming more challenging and costly, due to market uncertainty, labor shortages, and the evolving forest industry. Under such unpredictable circumstances, a better understanding of the production economics of various silvicultural prescriptions is crucial in implementing sound forest management activities. The objectives of this study were to: a) estimate stump to landing productivity and cost for Early Commercial Thinning (ECT) and Group Selection (GS) prescriptions; and b) compare the harvest system cost ($/PMH) using machine rate and PATH (Planning and Analysis in Timber Harvesting) methods. Detailed time-motion studies were conducted in the spruce-fir forest of Northern Maine, USA, during the summer of 2019. The results, as expected, indicated the harvesting costs were 23% higher for ECT (21.08 $/m3) compared to GS (16.32 $/m3). The average productivity of the harvester and forwarder were reduced by 15 and 37%, respectively, for ECT compared to GS. The estimated harvesting system cost ($/PMH) was 4% higher as indicated by PATH compared to machine rate method. Higher operational productivity in GS can be attributed to the larger volume of logs, and shorter travel loaded distance than ECT. These findings can help forest managers make more informed decisions while implementing appropriate silvicultural prescriptions under varying conditions based on management objectives and market conditions.

How forests may support psychological restoration: Modelling forest characteristics based on perceptions of forestry experts and the general public

Abstract Spending time in forests benefits human well‐being, but the importance of forest characteristics on well‐being is unclear. This knowledge could help guide forest management decisions to improve outcomes for both people and nature. The overall aim of this study was to investigate how psychological restoration, defined as psychological recovery processes in nature, may be supported by forest characteristics. We (1) investigated how perceptions of restoration (perceived restorativeness) were linked to specific forest characteristics. More specifically, we selected attributes included in nature protection legislation in Germany (beauty, diversity and uniqueness) as the basis to evaluate how forest characteristics were related to perceived restorativeness. Additionally, we (2) tested differences in the assessments of these attributes between forestry experts and people from the general public. Based on the results of the first two objectives (1, 2), we (3) predicted how forest management that affects forest characteristics may impact psychological restoration today and in the future. We developed a perceived restorativeness model based on attributes stated in the German Nature Conservation Act and specific forest structure variables. Drawing from the literature, we included perceived naturalness as an additional key predictor for restoration. Forestry experts and participants from the general public were then asked to rate computer‐generated forest stand pictures on these attributes and restorativeness. We found that all attributes were positively associated with perceived restorativeness, but perceived beauty was most important. Perceived uniqueness was statistically significant, but the strength of the relationship was weak. Mixed forests were rated as most beautiful, while coniferous forest stands were rated as least beautiful. The general public gave higher ratings than forestry experts on all attributes, but the pattern was similar. Based on participant ratings, forests left without management (Set‐aside), followed by forests with management aiming for resilience to climate change (Adaptation forestry), both supporting biodiversity conservation, showed the highest perceived restorativeness over the course of 100 years. Based on our results, it could be recommendable to increase forest diversity, especially in areas with many visitors. However, more nuanced knowledge involving diverse stakeholders is needed to inform forest management decisions on landscape level. Read the free Plain Language Summary for this article on the Journal blog.

Investigating Dual-Source Satellite Image Data and ALS Data for Estimating Aboveground Biomass

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

Effects of spruce budworm defoliation on in-stream algal production and carbon use by food webs

Insect defoliation is a widespread cause of forest disturbance in Canada, and it has the potential to alter terrestrial carbon contributions to stream consumers and thus affect stream ecosystems. Although defoliation by spruce budworms ( Choristoneura fumiferana) is considered a natural disturbance with importance to forest succession, the magnitude and duration of outbreaks are increasing, and there is a growing need to understand their downstream consequences. To examine this, algal productivity and autochthonous (in-stream) energy supporting macroinvertebrate and fish consumers (using δ13C, δ15N) were examined in 12 streams in the Gaspé Peninsula, Québec, Canada, that ranged in watershed defoliation from spruce budworm. Hierarchical partitioning models were used to assess the contributions of watershed and stream variables to these endpoints. Total seasonal algal production ranged fourfold among sites, and cumulative defoliation was the strongest predictor. Consumers from streams with greater defoliation (cumulative or annual) had lower reliance on autochthonous carbon, despite increased algal production, and this effect was greater for brook trout than macroinvertebrates. These findings demonstrate for the first time that insect outbreaks—which alter forest structure and function—also similarly affect downstream aquatic ecosystems by causing changes in the production and contributions of carbon sources. This suggests that insect control strategies and forest management decisions should also consider implications for downstream aquatic ecosystems.

Managing Forests for Biodiversity Conservation and Climate Change Mitigation.

We include biodiversity impacts in forest management decision making by incorporating the countryside species area relationship model into the partial equilibrium model GLOBIOM-Forest. We tested three forest management intensities (low, medium, and high) and limited biodiversity loss via an additional constraint on regional species loss. We analyzed two scenarios for climate change mitigation. RCP1.9, the higher mitigation scenario, has more biodiversity loss than the reference RCP7.0, suggesting a trade-off between climate change mitigation, with increased bioenergy use, and biodiversity conservation in forests. This trade-off can be alleviated with biodiversity-conscious forest management by (1) shifting biomass production destined to bioenergy from forests to energy crops, (2) increasing areas under unmanaged secondary forest, (3) reducing forest management intensity, and (4) reallocating biomass production between and within regions. With these mechanisms, it is possible to reduce potential global biodiversity loss by 10% with minor changes in economic outcomes. The global aggregated reduction in biodiversity impacts does not imply that biodiversity impacts are reduced in each ecoregion. We exemplify how to connect an ecologic and an economic model to identify trade-offs, challenges, and possibilities for improved decisions. We acknowledge the limitations of this approach, especially of measuring and projecting biodiversity loss.

Response of beech and fir to different light intensities along the Carpathian and Dinaric Mountains.

Predicting global change mitigations based on environmental variables, like temperature and water availability, although yielding insightful hypothesis still lacks the integration of environmental responses. Physiological limits should be assessed to obtain a complete representation of a species' fundamental niche. Detailed ecophysiological studies on the response of trees along the latitudinal gradient are rare. They could shed light on the behaviour under different light intensities and other studied traits. The forests of the Dinaric Mountains and the Carpathians represent the largest contiguous forest complexes in south-eastern Europe. In uneven-aged Carpathian (8 plots) and Dinaric Mountain (11 plots) forests, net assimilation (Amax) and maximum quantum yield (Φ) were measured for beech and fir in three predefined light intensity categories according to the indirect site factor (ISF%) obtained by the analysis of hemispherical photographs in managed and old growth forests, all located above 800 m a.s.l. The measurements were carried out under fixed environmental conditions in each light category per plot for three consecutive years. Data from the last 50-year average period from the CRU TS 4.01 dataset were used for the comparison between Amax, Φ, and climate. The highest Φ for beech were observed in the central part of the Dinaric Mountains and in the south westernmost and northwesternmost part of the Carpathians for both beech and fir, while they were highest for fir in the Dinaric Mountains in the northwesternmost part of the study area. The Φ-value of beech decreased in both complexes with increasing mean annual temperature and was highest in the open landscape. For fir in the Carpathians, Φ decreased with increasing mean annual temperature, while in the Dinaric Mountains it increased with higher temperature and showed a more scattered response compared to the Carpathians. Short-term ecophysiological responses of beech and fir were consistent to long-term radial growth observations observed on same locations. The results may provide a basis and an indication of the future response of two tree species in their biogeographical range to climate change in terms of competitiveness, existence and consequently forest management decisions.

Collaboration or Contravention? Ramifications For Effective Participatory Decision-Making in Collaborative Forest Management at Echuya Central Forest Reserve, South Western Uganda

Participatory decision-making has been extensively adopted worldwide as an essential mechanism and a good governance principle to reach a consensus in natural resource management. However, in most communities, local people who depend on forests are not involved in the decision-making process regarding conserving protected areas. This research assesses the ramifications of collaboration or contravention on effective participatory decision-making in collaborative forest management in Echuya Central Forest Reserve. It provides insights into how community consultations and participation of forest-adjacent communities in planning meetings influence the conservation of protected areas. We collected data from a total of 639 respondents, including Collaborative Forest Management (CFM) group members and non-CFM community members, government institutions, conservation organizations, and local community leaders, using semi-structured questionnaires, interview guides, and Focus Group Discussion guides. Results indicate that community consultations allow CFM stakeholders, particularly community members, to give ideas on how such projects can be implemented without destroying forest resources, promoting the goal of conserving the forest reserve. Community members’ participation in planning meetings allows them to contribute ideas about relevant and appropriate forest conservation approaches. However, the National Forestry Authority (NFA) and other non-community actor groups dominate the decision-making process mainly because the NFA has the legal mandate to manage the forest reserve on behalf of the Government. We concluded that the connivance of government forestry officials with unauthorized forest resource users undermines the spirit of participatory decision-making. Dishonesty and lack of accountability hampered effective participatory decision-making, and low attendance at meetings undermines effective participation in decision-making. Thus, community members’ consistent attendance of meetings is paramount for positive outcomes of participatory decision-making for the conservation of forests

Predicting the site productivity of forest tree species using climate niche models

Species occurrence-based climate niche models (CNMs) serve as valuable tools for predicting the future ranges of species’ suitable habitats, aiding the development of climate change adaptation strategies. However, these models do not address an essential aspect - productivity, which holds economic significance for timber production and ecological importance for carbon sequestration and ecosystem services. In this study, we investigated the potential to extend the CNMs to predict species productivity under various climate conditions. Lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.) were selected as our model species due to their comprehensive range-wide occurrence data and measurement of site productivity. To achieve this, we compared and optimized the performance of four individual modeling algorithms (Random Forest (RF), Maxent, Generalized Boosted Models (GBM), and Generalized Additive Model (GAM)) in reflecting site productivity by evaluating the effect of spatial filtering, and the ratio of presence to absence (p/a ratio) observations. Additionally, we applied a binning process to capture the overarching trend of climatic effects while minimizing the impact of other factors. We observed consistency in optimal performance across both species when using the unfiltered data and a 1:1.5 p/a ratio, which could potentially be extended to other species. Among the modeling algorithms explored, we selected the ensemble model combining RF and Maxent as the final model to predict the range-wide site productivity for both species. The predicted range-wide site productivity was validated with an independent dataset for each species and yielded promising results (R2 above 0.7), affirming our model’s credibility. Our model introduced an innovative approach for predicting species productivity with high accuracy using only species occurrence data, and significantly advanced the application of CNMs. It provided crucial tools and insights for evaluating climate change's impact on productivity and holds a better potential for informed forest management and conservation decisions.

Are forest management practices to improve carbon balance compatible with maintaining bird diversity under climate change? A case study in Eastern North America

The combination of climate change and anthropogenic disturbance significantly impacts forest bird assemblages. Assessing the cumulative effects of forest management and climate change on biodiversity and ecosystem services, including carbon sequestration and storage and provisioning of wood products is key to informing forest management and conservation decision making. Specifically, we projected changes in forest composition and structure according to various forest management strategies under a changing climate using LANDIS-II for two case study areas of Quebec (Canada): a hemiboreal (Hereford Forest) and a boreal (Montmorency Forest) area. Then, we assessed projected bird assemblage changes, as well as sensitive and at-risk species. As part of an integrated assessment, we evaluated the best possible management measures aimed at preserving avian diversity and compared them with optimal options for mitigation of carbon emissions to the atmosphere. Forest management and climate change were projected to lead to significant changes in bird assemblages in both types of forest through changes in forest composition. We projected an increase in deciduous vegetation which favored species associated with mixed and deciduous stands to the detriment of species associated with older, coniferous forests. Changes were more pronounced in Hereford Forest than Montmorency Forest. In addition, Hereford’s bird assemblages were mainly affected by climate change, while those in Montmorency Forest were more impacted by forest management. We estimated that 25% of Hereford and 6% of Montmorency species will be sensitive to climate change, with projected abundance changes (positive or negative) exceeding 25%. According to the simulations, a decrease in the level of forest harvesting could benefit bird conservation and contribute to reduction of carbon emissions in the boreal forest area. Conversely, the hemiboreal forest area require trade-offs, as mitigation of carbon emissions is favored by more intensive forest management that stimulates the growth and carbon sequestration of otherwise stagnant stands.

Stratified burn severity assessment by integrating spaceborne spectral and waveform attributes in Great Xing'an Mountain

Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows for more objective assessment based on the strong relation between fire damages and spectral features. Importantly, burn severity generally characterizes differences between forest overstory and understory layers due to their discrepancies in vegetation structures and environmental conditions. Spatially explicit mapping of strata-level burn severity is vital for post-fire forest management and ecological function evaluation. Until now, almost no available spaceborne remote sensing method can concurrently assess overstory and understory burn severity over heterogeneous forests. In this study, we proposed a Hybrid Composite Burn Index (HCBI) that comprehensively indicates the fire-induced spectral and structural changes along the vertical profile by integrating spectral and waveform attributes from both active and passive remote sensing data. Firstly, we introduced two remote sensing-based rating factors namely relative spectral change (RSC) and relative waveform change (RWC), and established HCBI through weighting and scoring the rating factors. Subsequently, we evaluated the effectiveness and generality of overstory, understory, and total site HCBI based on various pairs of simulated remote sensing data of pre-fire and post-fire forest scenes. Thirdly, we derived the spatially discontinuous maps of overstory, understory, and total site HCBI of the Xiushan fire site in Great Xing'an Mountain using WorldView-2 (WV-2) multispectral imagery and Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR data. Finally, we produced wall-to-wall HCBI maps using multiple predictive variables from pre-fire and post-fire Sentinel-2 MultiSpectral Instrument (MSI) images with a Random Forest (RF) algorithm. The predicted overstory, understory, and total site HCBI were validated by field-surveyed CBI. The assessment results based on simulation data showed HCBI was sensitive to the fire damage regardless of burn severity levels and vegetation cover levels (R2 of larger than 0.97 and RMSE of <0.15). The prediction results based on RF models achieved reliable HCBI of the total site, overstory, and understory levels (R2 of around 0.85 and RMSE of around 0.30). We found the wall-to-wall maps of HCBI captured the subtle horizontal and vertical variation even in the case of understory burn alone. We concluded that the newly proposed HCBI can advance the remote sensing-based assessment of stratified burn severity, offering opportunities for making fine-scale forest management decisions.

Stakeholder collaboration, adaptive management and social learning: a perspective for community forestry in Nepal

This note highlights the findings of a recent research on Adaptive Collaborative Management (ACM) of forest in which an assessment of ACM situation in eight selected community forestry sites in Nepal is made. The objective of the research is to explore connection between ACM and outcomes in forest condition and livelihoods in different conditions. The research focused on two inter-linked elements: collaboration and adaptive management. In this research, collaboration among various interests groups within FUGs and between FUGs and six different types of stakeholders is assessed in relation to outcomes on forest condition andlivelihoods. Similarly, adaptive management of forest at FUG level is assessed in terms of four key interrelated components, namely: mental models, shared vision, collective learning and systems thinking.The evidences indicate that at all levels, decision-makers have overlooked the complexity and dynamism of the management issues and contexts. They have not only limited appreciation of monitoring the plans as they are put into practice but also limited tendency to challenge their own assumptions in which the plans are based. Forest User Groups (FUGs) as well as their collaborating institutions mostly follow "trial and error" or "unmonitored experiences" approaches to action and learning. As a result, the quality of individual as well as collaborative forest management decisions and actions are seriously affected, leading to sub-optimal outcomes on both forest condition and livelihoods. Key findings on collaboration and adaptive management are summarized also.&#x0D;

Three decades of spatiotemporal dynamics in forest biomass density in the Qinba Mountains

The forest ecosystem plays a pivotal role in the global carbon cycle and is crucial for investigating atmospheric carbon exchanges. Forest biomass, a fundamental quantitative measure of the forest ecosystem, serves as a critical indicator for forest carbon stocks and carbon sequestration capacity. This study utilizes the GIMMS NDVI3g dataset to downscale forest inventory data spanning from 1989 to 2018, creating a 1 km resolution map of forest biomass density in the Qinba Mountains. The density initially decreased but has been increasing since 2004. The northern region of the Qinba Mountains exhibits a high forest biomass density (>100 Mg/hm2), while the southern region has relatively lower biomass density. This study provides the longest-term estimation of forest biomass density in the Qinba Mountains to date. It serves as a foundation for regional-scale forest carbon sequestration management and carbonization decision-making. This research is of significant importance for enhancing understanding of regional carbon cycling and supporting sustainable ecological development.

Cascading impacts of overstory structure in managed forests on understory structure, microclimate conditions, and Ixodes scapularis (Acari: Ixodidae) densities.

Forest management practices designed to meet varied landowner objectives affect wildlife habitat and may interrupt the life-cycle stages of disease vectors, including the black-legged tick, Ixodes scapularis Say (Acari: Ixodidae). Ixodes scapularis transmits multiple pathogens including Borrelia burgdorferi, the causative agent of Lyme disease, which is the most common tick-borne disease in the United States. There is evidence that a range of active forest management practices (e.g., invasive plant removal, prescribed burning) can alter tick densities and pathogen transmission. However, few studies have investigated relationships between forest stand structural variables commonly manipulated by timber harvesting and tick ecology. Foresters may harvest timber to create certain forest structural conditions like the mean number of trees, or basal area, per hectare. This study used a spatially replicated experiment in a blocked design to compare forest stands with a range of overstory structures and document variations in the midstory, understory, and forest floor, as well as microclimate conditions within tick off-host habitat. Greater numbers of trees or basal area per hectare correlated with greater canopy closure but less understory cover, stabilized microclimate temperature, higher microclimate humidity, and greater I. scapularis nymph densities. A random forest model identified understory forest structure as the strongest predictor of nymph densities. There was no relationship between the number of trees or basal area per hectare and daily deer (Odocoileus virginianus Zimmermann) activity or nymphal infection prevalence. These findings provide a deeper understanding of tick-habitat associations within a forest stand and have the potential to inform forest management decisions.