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Climate change is exacerbating the vulnerability of temperate forests to severe disturbances, potentially increasing tree mortality rates. Despite the significance of this issue, there has been a lack of comprehensive research on tree survival across extensive forest areas under the background of global climate change. To fill this gap, we conducted a detailed analysis of tree survival within a canopy nitrogen and water addition experimental platform in central China, utilizing data from two censuses and evaluating contributing factors. Our findings revealed 283 dead trees within the plots, predominantly of very small diameters (1–10 cm). The distribution of these dead trees varied among subplots, influenced by both biotic and abiotic factors. Notably, three dominant tree species were responsible for 64.8% of the deadwood biomass. The study determined that both the breast diameter and the quantity of dead trees, affected by surrounding trees and environmental conditions, played a critical role in deadwood biomass accumulation. This research offers an in-depth examination of deadwood biomass patterns in a temperate forest, highlighting the need to consider both experiment treatments and abiotic elements like topography in studies of forest ecosystem carbon. The insights gained from this study enhance our understanding of warm temperate forests' role in the global carbon cycle and offer valuable guidance for forest conservation and management strategies. [ABSTRACT FROM AUTHOR]

Although recent years have witnessed a considerable increase in studies on the economic value of ecological products, the extant literature has overlooked the multidimensional enhancement of ecological product value from the perspective of farmers' well-being. This research aims to examine how the Realization of Ecological Product Value (EPVR) serves as a crucial pathway to promoting the overall well-being of farmers in developing countries. Through a case study of a village in southeastern China, this research reveals that EPVR can enhance farmers' well-being via various mechanisms as follows: (1) Economic solution to enrich farmers' livelihood diversity, achieved by leveraging rural resource endowments and comparative advantages; (2) Fair social protection program enabling farmers to enjoy ecological benefits and further achieving urban-rural integration; (3) Environmental protection plan that balances production, living, and ecology; and (4) Grassroots governance tool promoting the governance ability to form collaborative governance model in a community of shared interests. This study offers theoretical support for enhancing human well-being through the realization of ecological product value in rural areas. [ABSTRACT FROM AUTHOR]

Understanding needle biomass turnover rates in Scots pine (Pinus sylvestris L.) stands is crucial for modelling forest ecosystem dynamics and nutrient cycling. This study examined needle litterfall and biomass turnover in Scots pine stands of varying ages in temperate forests (western Poland). The research focused on determining how stand age affects needle biomass, litterfall and the associated turnover rates. Data were collected from 20 Scots pine stands aged 26 to 90 years, and needle litterfall was measured and analysed in relation to stand characteristics such as age, density and biomass. The average annual needle litter production of the sampled Scots pine stands was 2008 kg·ha−1·year−1, similar to the values previously reported for this tree species in other temperate forests in Europe. The average needle biomass turnover rate for sampled Scots pine stands was 23.4%. We could not support the hypothesis that this parameter depended on the age of the Scots pine stand. The needle biomass turnover rate showed a positive correlation with crown length and a negative correlation with stand density due to the very weak correlations; however, further research is needed to confirm these relationships. Despite this, the parameter can be used to estimate needle litterfall and can be applicable to conditions corresponding to those of temperate forests in Central and Western Europe. This study also highlights the need for further research on needle biomass turnover in temperate forests to improve the accuracy of carbon and nutrient cycling models. This work contributes to a deeper understanding of the role of needle litterfall in maintaining soil fertility and forest productivity, offering insights into sustainable forest management and conservation strategies. [ABSTRACT FROM AUTHOR]

Based on forest management inventory data, this study applies machine learning algorithms to explore the relationships between forest water conservation capacity and forest management inventory factors, thus providing more extensive insights into forest water conservation services. By integrating the InVEST model and machine learning algorithms, this study identifies the key factors related to water conservation services based on forest management inventory factors and investigates the differences in and accuracy of forest water conservation models using the random forest algorithm. The results are as follows: (1) The determination coefficients (R2) of the three machine learning models range from 0.508 to 0.869, with root mean square errors (RMSEs) ranging from 28.380 to 69.339. The performance of these models is generally satisfactory, with the random forest algorithm showing superior results. (2) By leveraging the advantages of the three machine learning algorithms in handling categorical data, this study analyzes the contributions of forest management inventory factors, revealing the impact mechanisms of forest-type water conservation services. (3) The integration of machine learning algorithms allows for better processing of the scale and correlation of independent variables, providing more objective information on the main controlling factors of forest water conservation. (4) Predictions of water conservation capacity using machine learning are consistent with that of the InVEST model. The water conservation per unit area shows a variation trend as follows: slow-growing broadleaf forests > shrub forests > middle-growing broadleaf forests > cunninghamia lanceolata forests > fast-growing broadleaf forests > pine forests > bamboo forests. (5) Since this study considers only the factors available in the forest management inventory, which does not encompass all relevant influencing factors, it is difficult to fully address the complexities of how forest water conservation services interact with forest structure. Therefore, further research is needed to investigate the intrinsic mechanisms underlying the interactions between water conservation and forest management inventory factors. [ABSTRACT FROM AUTHOR]

Flammable litter such as Pinus koraiensis needle accumulation increases the risk of wildfire. In the event of a high-intensity fire, forest resources can be severely damaged. To reduce the occurrence of forest fires, it is important to reduce loads and modify structures. This study conducted 270 indoor degradation experiments to determine physicochemical properties of Pinus koraiensis during the combustion degradation process. Combustion degradation treatment variables were constructed with different durations, Trichoderma fungi, and doses. The results show that the physicochemical properties of flammable litter changed significantly after degradation, with a maximum degradation rate of 11. The degradation rate was affected by time and microbial agents, but there was no significant difference between different doses. Principal component analysis was used to determine overall combustibility, and it was found that a dose of 4 mL of Trichoderma harzianum had the best effect on degradation for 42 days, reducing combustibility by 203%. It was found that the 6ml composite mould had the best inhibitory effect on fire spread rate, reaching the lowest value. After 42 days, the flame intensity of 4 mL Trichoderma harzianum reached its lowest value of 57.17 kw/m, which represents a decrease of 54% compared to the initial value. Similarly, the flame's length reached its lowest value of 4.91 cm, which represents a decrease of 31% compared to the same period last year. The aim of this study is to establish the relationship between time, microbial agents, dosage, flammable physical and chemical properties, overall flammability, and potential fire behaviour. The values of the goodness-of-fit index and the comparative fit index are both >0.98, and the values of the standardised root mean square residual and the approximate root mean square error are both <0.05. This study has a positive effect on accelerating the decomposition of combustibles, reducing the content of flammable components, reducing flammability and potential fire behaviour, and reducing the risk of forest fires. It is of great significance for strengthening natural resource management and forest ecological conservation. [ABSTRACT FROM AUTHOR]

The logging intensity often does not take into account the spatial heterogeneity of the forest volume of commercial native species in the Brazilian Amazon. This study aims to evaluate the spatio-temporal heterogeneity distribution by assessing logging intensity and its effects on the volumetric stock and abundance of commercial species, with a focus on sustainable management practices. This study was conducted in the Saracá-Taquera National Forest in the Brazilian Amazon. Forest inventory data, elevation, and PlanetScope satellite images were integrated into a geographic information system. The information was aggregated into regular 1-hectare cells for the times before, during, and after logging (t0, t1, and t2). The unsupervised classification algorithm k-means with four clusters was used to analyze heterogeneity. Before logging, areas with higher commercial volumes were distant from water bodies, while areas with lower elevation had lower wood stocks. Logging intensity was generally low, concentrating on a few trees per hectare. Logging in the study area revealed a heterogeneous spatial distribution by intensifying in areas with the highest wood stocks. These results suggest that, in addition to the recommended logging intensity according to legislation, forest heterogeneity should be considered by the manager, promoting adaptive strategies to ensure the conservation of forest resources. [ABSTRACT FROM AUTHOR]

Forests and grasslands play an important role in carbon cycling. They not only absorb CO2 from the air through vegetation biomass and soil carbon sinks, but also reduce and control the horizontal transport of soil carbon (i.e., reinforcing soil carbon storage via soil conservation), thus avoiding erosion-induced CO2 emissions. In this study, vegetation biomass and soil carbon sinks, soil carbon reinforcement and reduced carbon emissions via soil conservation by forests and grasslands were quantified on the scale of the whole of China. The analysis was based on the distribution of biomass and the soil carbon pool and soil erosion rates derived from national surveys, as well as carbon density values from field surveys and literature. In 2021, forests and grasslands in China generated 394.18 Mt C/year (y) of steady-state carbon sinks through vertical biomass and soil absorption. The biomass carbon sinks of grasslands, and those of leaves, twigs, flowers and fruits of the forests, were not taken into account when quantifying the stable biomass sink, because they can become net producers of CO2 due to seasonal withering and carryover, or they can form soil organic carbon as potential soil carbon sinks. The amount of horizontal soil carbon reinforcement in China's forests and grasslands in 2021 was 20.31 Mt C/y, which was positively correlated with the reduction in the water erosion area; consequently, vertical emissions of approximately 14.89–29.78 Mt of CO2 into the atmosphere were avoided. Overall, in 2021, China's forests and grasslands absorbed atmospheric CO2 and reduced emissions by 1.46–1.47 Gt CO2/y, equivalent to approximately 13% of China's annual fossil CO2 emissions. This study demonstrates the fact that the adoption of forest and grassland measures sequesters carbon in soil and biota and reduces the risks of CO2 emissions by both vertical and horizontal paths, which is important for achieving carbon neutrality and mitigating climate change. [ABSTRACT FROM AUTHOR]

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China. [ABSTRACT FROM AUTHOR]

Lianas are a crucial component of karst seasonal rainforests, yet research on them has predominantly focused on non-karst regions. Consequently, their abundance and species richness remain relatively understudied within karst ecosystems. We aimed to document the abundance and species richness of lianas and investigate their relationships with abiotic and biotic factors, based on data from a fully mapped 15 ha plot in a karst seasonal rainforest of Nonggang (SW China). Structural equation models (SEMs) were employed to estimate the path coefficients and variation of dependent variables, enabling a comprehensive analysis of the factors affecting the abundance and species richness of liana. Within the 15 ha plot, a total of 23,819 lianas were identified, encompassing 113 species from 34 families. These lianas constituted 24.16% of the total woody plant density and 33.44% of the species present, but only 4.32% of the total woody plant basal area. Lianas are primarily influenced by abiotic factors, especially elevation and phosphorus (P), with less impact from biotic factors. Our findings reveal that lianas, despite constituting a relatively small percentage of the total woody plant basal area, significantly contribute to the density and diversity of the forest. Notably, abiotic factors such as elevation and phosphorus availability predominantly shape the distribution and richness of lianas, highlighting the importance of these environmental variables. The findings offer valuable insights for future liana studies and the preservation of karst forests' biodiversity. [ABSTRACT FROM AUTHOR]

The persistent conflict between strict conservation and community welfare highlights the growing need to address sustainable livelihoods in forest protection programs. The Natural Forest Protection Program (NFPP) is a comprehensive forest protection program spearheaded by the Chinese government. It is designed to facilitate the conservation and restoration of forest ecosystems through a range of interventions, including logging ban, management, tending, and afforestation efforts. Drawing upon longitudinal micro-level household survey data spanning five consecutive years from 2017 to 2021, this research quantifies the sustainable livelihood levels of frontline participants in the NFPP by examining two dimensions: livelihood capital stock and livelihood transformation capacity. Additionally, it investigates the internal differentiation phenomenon within this cohort. The findings suggest that forest worker households engaged in tasks related to forest management, tending, and afforestation are the frontline participants in the NFPP, in contrast to management, technical, and service personnel. Moreover, these forest worker households exhibit a pattern characterized by a higher livelihood capital stock but a lower livelihood transformation capacity compared to non-forest worker households. Furthermore, within forest worker households, there is a significant group differentiation phenomenon, resulting in inter-group differentials in the sustainable livelihood levels based on geographical and seniority stratification criteria. The developers of the global forest protection program should prioritize addressing the sustainable livelihood issues of frontline participants in the program, especially the real problem of mismatches between livelihood capital stock and livelihood transformation capacity. This can be achieved through designing income incentives, stimulating consumption, and other means to enhance the relatively disadvantaged position of frontline participants while balancing the coordination and fairness of the protection program based on the aspects of both protection and development. [ABSTRACT FROM AUTHOR]

Forest-dwelling residents frequently collect non-timber forest products (NTFPs) for various reasons, such as food, medicine, firewood, religious reasons, or handicrafts. This study examines the multiple benefits derived from NTFPs and how they contribute to the livelihoods of Ghanaian communities to alleviate poverty. Resources and services provided by NTFPs have an essential role in providing economic, nutritional, medicinal, cultural, and environmental benefits. This study examines the diverse range of NTFPs locals collect, such as chewing sticks, games, herbs, honey, leaves, mushrooms, pestles, raffia and palms, snails, and straws. In addition to serving different purposes, these NTFPs contribute to income generation, food security, health care, cultural practices, and the protection of the environment. A qualitative research approach was used in this study to collect data through semi-structured interviews and focus groups with members on multiple collections of NTFPs and their multiple benefits. We interviewed 732 residents regarding their multiple NTFPs and their potential to improve the livelihood of the locals. The data for the study were analysed using descriptive statistics. As a result of the survey, the frequency and percentages of responses were analysed for each indicator related to multiple values of NTFPs. Both males and females collected a list of NTFPs during the survey. Regarding NTFPs used for sale, 275 were reported, representing 37.57% of the total. In addition to emphasising the importance of sustainable management practices and equitable distribution of benefits, the study explores the multiple benefits of NTFPs for poverty alleviation in Ghanaian communities. It is essential to ensure the conservation of forest ecosystems and promote inclusive policies to harness the potential of NTFPs and maximise their positive impact on livelihoods. NTFPs provide multiple benefits for Ghanaian communities, including income, nutrition, healthcare, cultural preservation, and environmental sustainability. To promote sustainable development, it is imperative to understand the role of NTFPs and implement appropriate strategies. [ABSTRACT FROM AUTHOR]

Examining the efficacy of current assessment methodologies for forest conservation and restoration initiatives to align with global and national agendas to combat deforestation and facilitate restoration efforts is necessary to identify efficient and robust approaches. The objective of this study is to understand forest dynamics (1996–2021) and assess restoration implementations at the Urra's supplying basin hydroelectric reservoir in Colombia. The processing approach integrates optical and radar Earth Observation (EO) data from Sentinel-2 and Landsat for forest mapping and multi-temporal forest change assessment (1996–2021), and a Sentinel-1 backscatter time-series analysis is conducted to assess the state of forest restoration implementations. The processing chain was scaled in a cloud-based environment using the Nebari and SEPPO software and the Python language. The results demonstrate an overall substantial decrease in forested areas in the 1996–2000 period (37,763 ha). An accuracy assessment of multi-temporal forest change maps showed a high precision in detecting deforestation events, while improvements are necessary for accurately representing non-forested areas. The forest restoration assessment suggests that the majority of the 270 evaluated plots are in the intermediate growth state (82.96%) compared to the reference data. This study underscores the need for robust and continuous monitoring systems that integrate ground truth data with EO techniques for enhanced accuracy and effectiveness in forest restoration and conservation endeavors. [ABSTRACT FROM AUTHOR]

Diaspore dispersal is crucial in shaping plant population dynamics, biodiversity, and ecosystem functions. The effect of the vegetation structure on the secondary wind dispersal of diaspores with different appendage types is not well understood. Using a wind tunnel and a high-definition video camera, we accurately measured the lift-off and dispersal velocities of diaspores from sixteen plant species across six wind velocities (2, 4, 6, 8, 10, and 12 m s−1) under six simulated vegetation structures. Vegetation structure and appendage type were pivotal factors, explaining 41.1% and 42.3% of the variance in lift-off velocity and accounting for 12.0% and 25.3% of the variability in diaspore dispersal velocity, respectively. Vegetation coverage was the main factor influencing near-surface wind velocity, and the lift-off and dispersal velocities of diaspores changed significantly when vegetation coverage exceeded 40%. Diaspores with one wing, having high lift-off velocities and low dispersal velocities, adopt the anti-long-distance wind dispersal strategy, whereas diaspores with pappus, having low lift-off velocities and high dispersal velocities, adopt the long-distance wind dispersal strategy. In contrast, diaspores with thorn, discoid, balloon, and four wings adopt the non-long-distance wind dispersal strategy, suitable for environments such as low-coverage deserts and desert steppes but not high-coverage typical steppes. This study could help comprehend the effect of the vegetation structure on the dispersal process of diaspores, which facilitate habitat restoration and biodiversity conservation of grassland and forest ecosystems. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing effective strategies for climate change mitigation, and sustainable forest management and conservation. A total of 110 field plots (circular 0.02 ha plots with a 9 m radius), and airborne laser scanning (ALS)-light detection and ranging (LiDAR) data were collected in 2021. The random forest (RF) model was employed to predict the AGB at a 30 m × 30 m resolution based on 32 LiDAR metrics derived from ALS returns. The study assessed the relationships between the AGB distribution and nine independent variables using statistical techniques like the random forest model and partial dependence plots. Results showed that the mean value of the estimated AGB was 120 tons/ha, ranging from 0 to 446.42 tons/ha. AGB showed higher values in the northeast and southeast regions, gradually decreasing towards the northwest. Land use land cover, mean annual temperature, and mean annual precipitation were identified as the primary factors influencing the variability in AGB distribution, accounting for 64% of the variability. Elevation, slope, and distance from rivers were positively correlated with AGB, while proximity to roads had a negative correlation. The increase in precipitation and temperature contributed to the initial rise in AGB, but beyond a certain lag, these variables led to a decline in AGB. This study showed the efficiency of the random forest model and partial dependence plots in examining the relationship between the AGB and its driving factors within managed forests. The study highlights the importance of understanding the AGB driving factors and utilizing LiDAR data for informed decisions regarding the region's sustainable forest management and climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation. [ABSTRACT FROM AUTHOR]

The traditional knowledge of sunggau rafters for Apis dorsata nesting is essential for livelihood and forest conservation. We determine the plant species needed to support the conservation of A. dorsata in Belitung Islands, Indonesia, by investigating the distribution of the sunggau in the three types of forests, the traditional knowledge regarding the plant species used for sunggau, and the vegetation structure of forests in which the sunggau is installed. Distribution of sunggau were explored on heath, swamp heath, and mangrove forests. We conducted a vegetation survey in those forests and an ethnobotanical survey by interviewing bee farmers regarding the plants used for sunggau and other uses. We found 95 sunggau distributed in the heath, swamp heath, and mangrove forest. Based on interviews, we recorded 65 plant species for sunggau and six other uses, including bee forages. Calophyllum sp., Cryptocarya sp., Melaleuca cajuputi, and Syzygium urceolatum are the most important plants according to bee farmers. The last two species dominate all forests, except mangroves, which are dominated by Lumnitzera littorea, according to vegetation surveys. However, the availability of several plants for sunggau is declining. Therefore, the conservation of A. dorsata needs the sustainability of sunggau and bee forage plants. [ABSTRACT FROM AUTHOR]

Tropical forest management has a potential role in forest conservation if it is sustainable. This study of a forest under management in Bolivian Amazonia strongly suggests that the management project is not sustainable and that no potential changes in management would be likely to make it so. In a 216.41 ha harvested area, 278 commercial trees from 10 families, 15 genera, and 15 species were measured. The density of commercial species with diameter at breast height (DBH) ≥ 50 cm was 1.28 trees ha−1, and the harvestable commercial volume was 12.40 m3 ha−1. Due to market restrictions, the actual amounts harvested were much lower: 96 trees were harvested with commercial boles totaling 2.7 m3 ha−1. Of the total impact on biomass and carbon (above- and belowground), the logs removed from the area represented only 13.4%, while 86.6% was from losses in the forest as follows: 14.5% from the stumps, crowns, and roots of harvested trees (DBH ≥ 50 cm) plus 72.1% from the trees (DBH ≥ 10 cm) in the forest lost to roads, log landings, and skid tracks and the gap openings caused by felling the harvested trees. The estimated expenses exceeded the gross revenue of the management company (USD 519.15 ha−1), a fact confirmed by the company's subsequent bankruptcy. The project's low harvest intensity reduces the environmental impact per hectare but increases the impact per cubic meter of wood harvested because producing a given volume of wood requires disturbing a larger area and because more kilometers of access roads and skid tracks have to be installed to extract a given volume of wood. Because many costs are fixed regardless of harvest intensity, small harvest volume can render such projects financially unfeasible, increasing the likelihood that they will be abandoned and not provide long-term "sustainable" forest protection. However, this does not mean that higher harvest intensity results in sustainability, as other constraints apply to high-intensity projects. We conclude that conservation alternatives to maintain the forest would be more beneficial than management for timber. [ABSTRACT FROM AUTHOR]

Tropical forests provide habitats for diverse flora and fauna, in addition to playing a crucial role in climate regulation. They are being recognized for their roles as nature-based solutions to many sustainable development challenges, as shown by increased political commitment and global promises to reduce the rates of deforestation and boost the restoration of degraded forest ecosystems. Understanding tropical forest dynamics and their conservation status is therefore important. This study analysed the forest stand structure, the tree species composition and the regeneration status of Londiani Forest. In the three blocks of Londiani Forest, which are Kedowa, Chebewor and Londiani, belt transects that were 25 m wide and 1 km long were established. At every 200 m along the transects, 25 m × 25 m quadrats were set up in which an inventory of all the tree species was determined. Diameter tape was used to measure the diameter at breast height (DBH) 1.3 m above the ground. With the use of a Suunto angular clinometer, the tree height was measured. A nested 5 m × 5 m quadrat within the 25 m × 25 m quadrat was used to sample the saplings, while a 1m × 1 m quadrat was used to sample the seedlings. The quantities of seedlings and saplings were used to determine the state of regeneration. The data were entered into Microsoft Excel. The total stem density, species density, basal area, species basal area, relative density and species diversity were determined and extrapolated per hectare. A total of 1308 distinct trees from 34 different species and 24 families were counted. Kedowa recorded the highest (27) species richness, followed by Chebewor (19) and then Londiani (14). There was a statistically significant difference in the species richness among the three forest blocks (p < 0.05). Within the three forest blocks, there were no statistically significant variations in the basal area distribution (p > 0.005) or in the mean DBH (F = 0.560; p = 0.729) or height class distribution (F = 0.821; p = 0.558). There was a statistically significant difference in the stem density (F = 12.22; p = 0.005) and woody species diversity (F = 0.32; p = 0.001) within the three forests blocks. The similarity index ranged from 0.34–0.47. The presence of substantial numbers of seedlings and saplings in all forest blocks was an indication that there was regeneration. [ABSTRACT FROM AUTHOR]

This article discusses the impact of climate change on forest ecosystems and the importance of understanding these effects for effective conservation and management strategies. The research presented in this Special Issue covers various aspects of forest dynamics under climate change, including vegetation seasonality, carbon sink capabilities, biomass decline, soil respiration, forest restoration, timber tree distribution, forest monitoring, species distribution, thinning practices, and site productivity. The studies highlight the complex interplay between climate factors and vegetation types, the need for adaptive forest management practices, and the importance of tailored approaches for different forest types. The findings provide valuable insights for policy-making and actions to sustain forest ecosystems in a warming world. [Extracted from the article]

The assessment of a forest's structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model's generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as "good" quality comprised 11.18% of the total, while "medium" quality forests constituted the majority at 76.77%. In contrast, "poor" quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies. [ABSTRACT FROM AUTHOR]

We used tree-ring width data of Larix gmelinii and Pinus sylvestris var. mongolica from the northern region of the Daxing'an Mountains, China; normalized difference vegetation index (NDVI) data; and microtopographic information (elevation, slope direction, slope gradient, and topographic location index) to assess spatiotemporal dynamics in the growth of the boreal forest and topographic patterns of forest decline under the background of climate warming. Forest growth trends were determined based on tree growth decline indicators and NDVI time series trends, and topographic patterns of forest decline were analyzed using the C5.0 decision tree model. More climatic information was present in the radial growth of the trees at higher elevations, and P. sylvestris var. mongolica was influenced strongly by climatic factors of the previous year. Since 1759, tree radial growth trends in the study area have experienced two recessions during 1878–1893 and 1935–1943, which were characterized by persistent narrow whorls of tree rings of below-average growth. Changes in NDVI and tree-ring information were similar, and they together indicate a high risk of declining forest growth in the northern Daxing'an Mountains after 2010, especially at higher elevations. The NDVI time series showed that the high temperatures in 2003 negatively affected forest growth in the study area, which was confirmed by the tree-ring data. The decision tree terrain model results had an accuracy of 0.861, and elevation was the most important terrain factor affecting forest decline. The relative importance of elevation, topographic position index, aspect, and slope was 58.41%, 17.70%, 16.81%, and 7.08%, respectively. Classification rule-based decision tree models can be used to quantify the effects of terrain factors on tree growth. This research methodology can aid the management of regional forestry resources and the conservation of forest resources under the background of climate change, which increases the risk of forest decline. [ABSTRACT FROM AUTHOR]

The rapid expansion of tea plantations (Camellia sinensis), which replace local natural forests, decreases the water conservation function in the headwater region and threatens regional water security. However, studies focusing on the comprehensive evaluation and improvement of the water conservation function of natural forests and tea plantations are lacking. In this study, we developed a comprehensive evaluation method for the water conservation function by investigating, monitoring, and conducting process model simulations of 221 tea plantation and natural forest (bamboo forest) samples obtained in the field. We also quantified the trade-off between water conservation and carbon sequestration. The results showed that the tea plantations had lower annual water conservation than the bamboo forests. Slope was the most critical influential variable affecting water conservation in tea plantations. Strict control should be exercised in the development of steep slopes above 20° for tea plantations, to enhance the regional water conservation capacity. Water conservation in bamboo forests increased to the maximum level at a planting density (SD) of approximately 2500–2700 tree/ha and then decreased quickly. The optimal SD values of bamboo forests were 2500, 2600, and 2650 trees/ha at slopes of 10°, 15°, and 20°, respectively, which can maintain higher water conservation and adequate carbon sequestration. Overall, water conservation and carbon sequestration increased by approximately 22.6% and 13.7%, respectively, compared with the average values for bamboo forests. However, at slopes >20°, a strong trade-off was observed between water conservation and carbon sequestration, and competing ecosystem services accompanied those, which were hard to balance and which resulted here in disproportionate water conservation at the cost of carbon sequestration loss. [ABSTRACT FROM AUTHOR]

Climate change and land use/land cover (LULC) change have received widespread attention as the two main factors contributing to the shrinking of plant habitats. However, the different effects of these factors on understory economic tree species are not clear. This is not conducive to the conservation and exploitation of forest resources. Here, we used species distribution modeling to predict the extent to which climate change and LULC change will affect changes in suitable habitats for A. elata under different scenarios in the future. The results showed the suitable habitat to be located in the Changbai Mountain Range in northeast China. The current area is 110,962 km2. The main variables that affect the suitable habitat are annual precipitation, LULC, slope, and mean diurnal range. The percentage contributions are 31.2%, 16.8%, 12.8%, and 12.3%, respectively. In the 2070s, the area of high-quality (moderately and highly) suitable habitat was reduced by an average of 6.05% when climate alone changed, and by an average of 10.21% when land use alone changed. When both factors changed together, there was an average decrease of 9.69%. When climate change and land use change acted together, the shrinking area of suitable habitat did not suddenly increase. These findings help to identify potentially suitable habitats for A. elata and to carry out conservation and exploitation efforts to ensure sustainability. [ABSTRACT FROM AUTHOR]

The distribution map of groundwater-dependent ecosystems (GDEs) is generally used for the scientific management of vegetation and groundwater resources, and is instructive for forest resource conservation. The groundwater table in the Loess zone has declined over the past few years, but no study has yet been conducted to assess the impact of this decline on GDEs. This study used data from the GRACE gravity satellite to delineate groundwater fluctuation periods from 2002 to 2021, to develop a method to identify whether vegetation is potentially associated with groundwater using three criteria, and to verify the accuracy of this method. Study results show that the groundwater changes in the Loess zone can be divided into two periods, 2002–2014 and 2015–2021, with groundwater declines becoming more rapid after 2015. We did not observe the spatial variation pattern of GDEs in the Loess areas, but there was a significant change in the area of GDEs during the two periods studied, with a 13.56% decrease in the very likely GDEs' group area and an 11.68% increase in the unlikely GDEs' group area between 2015 and 2021 compared to 2002–2014, with little change in the neutral, likely, and very unlikely group areas. This study provides a reference for exploring the relationship between vegetation and groundwater, as well as for the scientific management of water resources. [ABSTRACT FROM AUTHOR]

Assessing the landscape health of suburban forest parks is a prerequisite for achieving the dual objectives of forest resource conservation and recreational services. However, studies that analyze landscape health in suburban forest parks at a landscape scale by subdividing the landscape into multiple sampling units and adopting a multi-functional perspective have been limited. This study focuses on Xiqiao Mountain National Forest Park and establishes a landscape classification system and indices, taking into account its multifunctionalities. The study employs the entropy weight method to determine indicator weights and utilizes grid analysis and spatial interpolation to analyze the spatial distribution of landscape health under multiple sampling units and functionalities, along with the differences in its impact and influencing factors. The results indicate that: (1) regions with "very poor" and "poor" landscape health grades under multiple sampling units and functionalities exhibited a sheet-like distribution pattern, covering approximately 69.46% to 98.86% of the total area. In contrast, regions with "very good" and "good" grades are primarily located in block-like or linear patterns in the northern, central, and southern regions of the park, accounting for approximately 1.07% to 17.20% of the total area. (2) The area ratios of "very good" and "good" landscape health grades for recreational landscapes under varying sampling units were consistently higher than those of the same grades for eco-conservational landscapes, with a 5.03%–15.43% difference. This suggests a greater emphasis on recreational functionality. (3) The impact of three different sampling unit sizes on the landscape health of Xiqiao Mountain National Forest Park under multifunctionalities is not significantly different; however, the forest/non-forest area ratio and quantity ratio are vital factors influencing its landscape health. The landscape health assessment results, considering multiple sampling units and functionalities in this study, serve to provide technical method support and practical case references for the planning, construction, and management decision-making of suburban forest parks. [ABSTRACT FROM AUTHOR]

This study focused on assessing the impact of climate change on Cupressus funebris, one of the important endemic conifer species in China. The impact of fluctuations in temperature on the potential distribution and extinction risk of the C. funebris was evaluated using species distribution modelling. The outcomes of this current study revealed that the species was mainly distributed in mountainous forest areas, and climate variables played a major role in the distribution of this conifer tree. Under climate change, the threatened conifer will be mainly concentrated in mountainous forests and is projected to undergo contraction in distribution and shift northward. The conservation status of C. funebris is projected to be uplisted to "Near Threatened" status under the SSP5_8.5 scenario by 2040 and to the "Least Concern" status under all the other investigated climate and dispersal scenarios. Despite the high capacity of the species for adaptation to environmental changes, part of its AOO might be lost under severe climate change conditions. Key conservation areas were identified, and suggestions for redesigning some of the current natural reserves in the forested area where the species is found were proposed for the conservation of this key coniferous species. The stable area in the northwestern part of Yunnan in the Hengduan Mountain Forest can serve as a promising area for C. funebris reintroduction through afforestation programs. [ABSTRACT FROM AUTHOR]

The conservation and management of forest areas require knowledge about their extent and attributes on multiple scales. The combination of multiple classifiers has been proposed as an attractive classification approach for improved accuracy and robustness that can efficiently exploit the complementary nature of diverse remote sensing data and the merits of individual classifiers. The aim of this study was to develop and evaluate multiple classifier systems (MCSs) within a cloud-based computing environment for multi-scale forest mapping in Northeastern Greece using passive and active remote sensing data. Five individual machine learning base classifiers were used for class discrimination across the three different hierarchy levels, and five ensemble approaches were used for combining them. In the case of the binary classification scheme in the upper level of the hierarchy for separating woody vegetation (forest and shrubs) from other land, the overall accuracy (OA) slightly increased with the use of the MCS approach, reaching 94%. At the lower hierarchical levels, when using the support vector machine (SVM) base classifier, OA reached 84.13% and 74.89% for forest type and species mapping, respectively, slightly outperforming the MCS approach. Yet, two MCS approaches demonstrated robust performance in terms of per-class accuracy, presenting the highest average F1 score across all classification experiments, indicating balanced misclassification errors across all classes. Since the competence of individual classifiers is dependent on individual scene settings and data characteristics, we suggest that the adoption of MCS systems in efficient computing environments (i.e., cloud) could alleviate the need for algorithm benchmarking for Earth's surface cover mapping. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is the most dynamic carbon pool in forest ecosystems and is sensitive to biotic and abiotic factors. Previous studies on AGB have mostly focused on tropical and temperate forests, while studies on AGB and its determinants in subalpine coniferous forests are lacking and the mechanisms are not yet clear. Here, we systematically investigated all woody plants in 630 subplots (20 m × 20 m) in the Wanglang Plot (25.2 ha) to explore the spatial distribution of AGB and the effects of topography, soil, and stand structure on AGB. The results showed that AGB varied remarkably among different subplots with an average of 184.42 Mg/ha. AGB increased significantly with aspect, soil organic matter, maximum DBH, and important value of spruce–fir, while it decreased significantly with slope, total phosphorus, and stem density. Stand structure exerted greater influences than topography and soil factors, and especially maximum DBH determines the variation of AGB. Our results are of great significance to accurately estimate and predict the productivity of this forest type, and can provide insights into the diversity maintenance of subalpine coniferous forests as well as the conservation and management of forest ecosystems. [ABSTRACT FROM AUTHOR]

Forecasts of the forest ecosystem dynamics are important for environmental protection and forest resource management. Such forecasts can support decisions about where and how to restore damaged forests and plan felling, and in forest conservation. Forest landscape models (FLM) are used to predict changes in forests at the landscape level. FLM initialization usually requires detailed tree species and age data; so, in the absence of forest inventory data, it is extremely difficult to collect initial data for FLM. In our study, we propose a method for combining data from open sources, including remote sensing data, to solve the problem of the lack of initial data and describe initializing the LANDIS-II model. We collected land cover classification and above-ground biomass products, climate, soil, and elevation data to create initial vegetation and ecoregion maps. Our method is based on some simplifications of the study object—some tree species are replaced by groups of species; the forest stand is considered homogeneous. After initialization, the natural dynamics without harvesting and disturbances were simulated by the Biomass Succession extension for 200 years. The study presents a detailed methodology that can be used to initialize other study areas and other FLMs with a lack of field data. [ABSTRACT FROM AUTHOR]

Trees in forests can obstruct falling rocks and serve as a natural barrier to reduce the velocity of falling rocks. Recently, there has been growing interest in utilizing forests to safeguard against potential rockfall. Nevertheless, there is a dearth of research regarding the impact of rock size and forest structure on forest preservation against rockfall. This study takes the Jiweishan rock avalanche that occurred in China in June 2009 as an example to discuss the protection mechanism of forests against rockfall in rock avalanche disasters. Three sizes of rockfalls from the Jiweishan rock avalanche were simulated and analyzed with and without forests using Rockyfor3D software. The findings indicate that forests can mitigate the energy impact of falling rocks. Especially in the debris flow area of rock avalanches, the protective effect of trees on small-sized falling rocks is most obvious, reducing the runout distance and damage range of the debris flow. Moreover, the protective effect of forest structures on rockfall risk was explored. It was found that broad-leaved forests had the best protection against falling rocks, followed by coniferous broad-leaved mixed forests, and coniferous forests had the worst protective effect. Furthermore, increasing forest planting density and tree diameter at breast height (DBH) can result in better protection against rockfall. Thus, rational planning of forest species and planting density in areas of rockfall can effectively reduce the threat of rockfall risk. The research ideas in this study can provide a basis for evaluating the mitigation of rockfall hazards by forests and provide a reference for constructing and planning protective forests in rockfall and rock avalanche hazard areas. [ABSTRACT FROM AUTHOR]

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies. [ABSTRACT FROM AUTHOR]

Vegetation cover is known as one of the most dominant parameters in soil erosion that can considerably affect soil erosion drivers. This study aimed to assess the effects of vegetation cover on soil conservation in Arasbaran Forests, Iran. A part of Arsbaran forests has been protected for 45 years. The other part has not been under protection during these years. This study was carried out in order to investigate the effects of forest protection management on the changes in the amount of soil erosion and compare it with the non-protection sector. To this end, 66 samples were grouped in the two selected elevation classes. Out of every three sample plots, one plot was randomly selected for collecting soil samples. Landsat 8 images and a Digital Elevation Model were utilized for sample collection via ENVI (Environment for Visualizing Images) and GIS (Geographic Information System), respectively. Then, the Revised Universal Soil Loss Equation (RUSLE) was employed to estimate the annual soil loss in the studied sites. The results showed the annual soil erosion of 9.84 and 10.06 tons per hectare/year for protected and non-protected areas, respectively. Moreover, the average annual soil erosion of 9.95 tons per hectare/year was calculated for the whole Arasbaran Forests. The results of the statistical test revealed no significant difference between protected and non-protected sites in terms of erosion rates (p > 0.05). Based on the findings, despite the non-significant and slightly lower soil loss per unit area in the protected site, there is a notable soil loss throughout the entire non-protected area. It appears necessary to conduct a thorough review of existing conservation laws and to closely monitor their effective implementation. This step is crucial for enhancing the effectiveness of forest conservation management in mitigating soil erosion. The results show that absolute forest protection alone cannot make a big difference in preventing soil erosion. In this regard, there is a need to carry silviculture measures to manage protected forest stands to increase the sustainability of the forest. Obviously, in the case of proper management along with protection, it is possible to have a greater effect in preventing soil erosion. [ABSTRACT FROM AUTHOR]

The State of the World's Forests 2020: Forests, Biodiversity and People; FAO: Rome, Italy. This results in a significant increase in forest cover and a decrease in forest fragmentation [[4]], with positive consequences for biodiversity conservation, as is proved with the recent increase in the distribution and abundance of Europe's emblematic carnivores [[6]]. Given the complexity of forest habitat types, new forest inhabiting species are constantly detected and described by science, such as the new arthropod species described in the study by Jang et al. [[33]]. Forests are extremely valuable ecosystems, associated with a number of ecosystem services that are of significant importance for human wellbeing. [Extracted from the article]

The European Union's current pressure on the Member States to adopt both the Green Deal and the Fit for 55 package is leading to an accelerated drive to put in place measures to meet the 2030 climate targets. At the same time, many discussions at the international bilateral meetings of EU country representatives raise the question of the realism of setting climate targets, and therefore, the ability to meet these commitments. The results elaborated in this analytical study offer a realistic picture of the possibilities of meeting strategic climate targets using the example of the use of forest dendromass. The data assess the possibility of achieving a 22% share of renewable energy sources (RES) in gross final energy consumption by 2030 in the Czech Republic. The study points out that at present, the use of forest dendromass from primary production is at its maximum and meeting the climate targets for increasing the share of RES in the energy mix represents a major problem in the long term. The findings published in this study also point to the objective threat of the increased use of dendromass in the energy sector to the maintenance of sustainable forest management and the preservation of forest quality. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system's capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. [ABSTRACT FROM AUTHOR]

The Natural Forest Protection Project (NFPP) is an ecological restoration project aimed at safeguarding natural forests, and is one of China's six main forestry initiatives. The upper reaches of the Yangtze River represent the main distribution area of natural forests in China, and are an important area for the implementation of the NFPP. A systematic assessment of forest ecosystem changes in the upper Yangtze River region before and after implementation of the NFPP is of great scientific significance for the improvement of the project implementation effect, regional ecological protection, and further protection and restoration of natural forest resources. This study uses the NFPP area in the Yangtze River's upper reaches as the study area; the data are primarily derived from the 1998 and 2020 forest resources category II survey data, long-term monitoring data from forest ecological stations, and public social data published by authoritative Chinese organizations. Based on the above data, we used the full index system of forest ecosystem services, continuous observation, and inventory system with the distributed measurement method to analyze the dynamic changes in forest ecosystem services in the study area in terms of three aspects: physical quality, value quality, and dominant function. The results of the study show that: (1) over the studied time scale, compared to the 1998 baseline, the physical quantities (soil erosion control, water regulation, and PM10 retention) and value of each service function of the forest ecosystem in the upper reaches of the Yangtze River project area in 2020 showed a significant increasing trend; (2) at the spatial scale, changes in forest ecosystem service functions in the upper reaches of Yangtze River under the NFPP showed significant spatial heterogeneity from 1998 to 2020; (3) in 2020, the total value of forest ecosystem services in the study area was 3,261,161,000,000 yuan/a, of which the functional value of biodiversity conservation was 1,294,426,000,000 yuan/a and the functional value of water conservation was 841,069,000,000 yuan/a, indicating that the forest ecosystem of the study area plays an important role as a "green gene pool" and "green water reservoir"; (4) the forest ecosystem service functions of the NFPP in the upper reaches of Yangtze River are intricately tied to forest resource features such as forest area, forest volume, age-group structure of arbor forest, etc., which influence the physical quantity and value of each forest ecosystem service function to varying degrees. In addition to illustrating the amazing efficiency of the natural forest preservation initiative, this study provides a scientific foundation for future natural forest resource conservation and restoration. It can serve as a reference for the project's subsequent development as well to provide scientific foundations and guidance for the development of the natural forest protection and restoration program and to promote the protection and restoration of more natural forest resources. [ABSTRACT FROM AUTHOR]

Vegetation, as a comprehensive ecological indicator of environmental change, plays an essential role in terrestrial ecosystems. Dramatic climate change and increasing anthropogenic disturbances have led to a profound change in vegetation cover. This study employed Moderate Resolution Imaging Spectroradiometer (MODIS) and other relevant data in Southwest China mountains, using Chongqing as a case study, to explore the variation in Normalized Difference Vegetation Index (NDVI) and its spatial differentiation mechanisms, quantify the relative contributions of climate change and human activities to these variations, and assess the effects of ecological policies, including afforestation, mountain closure forestry and natural forest conservation, and agricultural improvement. Results showed that the spatial distribution of annual NDVI, on average, varied 0.109 to 0.929, with the rate varying from −0.033 to 0.017 yr−1. NDVI was improved in 83.4% areas of Chongqing, and human activities contributed 69.1% of revegetation, whereas climate contributed 30.9%. On the other hand, human activities and climate change contributed 76.3% and 23.7% to vegetation degradation in 17.6% areas, respectively. Moreover, the explanatory power of anthropogenic factors on spatial differentiation of NDVI were stronger than that of climatic factors, and land use type was the dominant factor. The cumulative afforestation area, effective irrigation rate, fertilizer application, and total agricultural machinery power of the whole city was extremely significantly correlated with NDVI (p < 0.01), and the vegetation cover fluctuation in the region affected by natural forest protection and mountain closure forestry project was much more stable than that in non-protected areas, which to a certain extent reflected the effective implementation of local ecological protection, agricultural improvement, and other related policies. [ABSTRACT FROM AUTHOR]

Forests, serving as crucial custodians of our planet's ecological balance, also constitute a significant source of livelihood for humanity. Karst regions, recognized as some of the world's most susceptible landscapes, grapple with the dual predicaments of ecological restoration and resident impoverishment. To bridge the gap between environmental and economic concerns, this manuscript employs an amalgamation of remote sensing and socio-economic methodologies to devise a comprehensive assessment framework, thereby scrutinizing the alterations in forest ecosystems from 2001 to 2020.The investigation reveals that over the past two decades, forest rehabilitation within the study area has yielded commendable outcomes, substantially mitigating various ecological dilemmas instigated by rocky desertification in this region. The forested area has increased significantly, and the ecosystem service value has more than doubled. These improvements are largely attributed to compulsory forest conservation measures, demonstrating their decisive influence. The study advocates meticulous management and conservation strategies to safeguard these unique ecosystems and ensure their sustainability. This research underscores the significance of striking a balance between maintaining ecological integrity and fostering economic development, thereby contributing to the broader discourse on sustainable forest management in vulnerable landscapes. [ABSTRACT FROM AUTHOR]

Forest management aimed at the sustainable use of forest resources is an alternative land use to deforestation and can improve forest conservation in tropical regions. The construction of forest infrastructure, including forest roads, skid trails, and log-landings, is a key factor in minimizing the impacts and forest disturbances typically caused by selective logging activities in tropical forests. In this study, we used field and secondary data to assess the planned and implemented forest infrastructure in a study site of 5723 hectares under a forest concession in the Caxiuanã National Forest, located in the state of Pará, Brazilian Amazon. We tested alternative modeling approaches (the Tomlin and Spanning Tree models) by comparing them with the previously planned and implemented logging infrastructure by a concessionaire timber company (CEMAL Ltd.) in the study site. Our results indicate that the Tomlin model was the best approach for allocating forest roads in the study area, as it demonstrated the optimal balance between financial costs and forest disturbances for timber extraction. Additionally, Minimum Spanning Tree modeling achieved the most favorable results in delineating secondary roads and skid trails in the study site, despite slightly higher financial costs compared to the minimum acceptable costs. This alternative approach to modeling forest infrastructure can contribute to reducing forest disturbances and increasing the economic and ecological sustainability of forest management in tropical forests. [ABSTRACT FROM AUTHOR]

Deforestation is thought of as a huge threat to carbon neutrality and the development of contemporary society and it has brought wide interest and attention in the science community to develop new methods to identify and quantify the occurrence and extent of forest loss. Understanding the forest-loss patterns is essential for forest management and protection. With the help of a high-spatial-resolution remote-sensing dataset on forest loss, the spatial and temporal dynamics of deforestation patterns in forests of Southwest China (SWC) have been investigated. The major findings of this study indicated that small-scale (<5 ha) deforestation raised pervasively in the region from 2001 to 2019, and the number of large patches of forest loss (>5 ha) has decreased significantly during the same period. Moreover, the mean size of forest-loss patches showed an increase from 0.34 ha to 0.61 ha over time. With the alarming trend of increasing deforestation in the southern region of our study area, the growth of emerging forest-loss hotspots was clearly observed in Chongqing and Sichuan Province. The results promoted an indepth understanding of forest-loss patterns in SWC and can help provide more coherent guidance for further forest monitoring and conservation. [ABSTRACT FROM AUTHOR]

Radial growth of trees is closely related to canopy activity. Revealing the relationship between radial growth and canopy activity is of great significance for forest protection under climate change. In this study, we built tree-ring chronologies for two tree species, spruce (Picea aspruceerata) and Chinese pine (Pinus tabuliformis), from the Helan Mountains in the arid region of northwest China. Correlation coefficients were then calculated to reveal the relationships among tree rings, two kinds of gross primary productivity (GPP) indices, and climate data. The results demonstrated that the radial growth of both spruce and Chinese pine was positively correlated with GPP from late February to early March, and moisture conditions may be the driving factor of tree growth. However, radial growth of Chinese pine was also correlated with GPP from the end of March to the end of August. This study aimed to further supplement the relationship between trunk radial growth and canopy dynamics in the arid zone of northwest China and to provide theoretical guidance for vegetation restoration and forest conservation in the arid zone of northwest China under climate change. [ABSTRACT FROM AUTHOR]

Araucaria angustifolia (Bertol.) Kuntze, commonly known as Brazilian pine, is a significant tree species in the Brazilian flora that once covered an area of 200,000 km2 in the Southern region. During the 1970s, high-quality timber logs from this conifer became the primary export product of the country. However, the species is endangered due to uncontrolled exploitation and is subject to a harvesting ban. It is crucial, therefore, to explore sustainable cultivation methods for this species, which necessitates urgent research and scientific insights. In this study, we present a simulation of a management strategy for in situ conservation by manipulating growth space and crown size dynamics. Forest inventory data and mixed forest regression equations were employed to describe the horizontal dimensions of average and maximum potential crown growth, resulting in two management scenarios. The results presented in management diagrams show that both approaches required logging numerous trees to ensure adequate space for healthy tree growth and provide soil coverage and forest protection. Therefore, the absence of effective forest management initiatives for Araucaria forests may have further implications for the structure, production, conservation, and overall development. To address these challenges, we propose two hypotheses: firstly, that tree diameter depends on crown dimensions, which are in turn influenced by tree growth space, and, secondly, that crown dimensions serve as a reliable indicator of existing competition and can be utilized to simulate forest management practices. We urge that implementing sustainable forest management initiatives for Araucaria angustifolia at selected locations can contribute to expanding natural forest areas, mitigate deterioration caused by high competition, discourage illegal logging, and prevent overexploitation of their edible seeds, which hinders regeneration. Our results underscore the significant implications of the lack of forest management initiatives in rural properties, potentially resulting in irreversible deterioration. The exact consequences of this deterioration remain unclear, emphasizing the need for further studies to understand its eventual effects on the growth reaction of trees of different diameters, ages, and crown conditions after the liberation of their crowns. [ABSTRACT FROM AUTHOR]

Future climate forecasts predict major changes that will have negative impacts on the distribution, abundance, and dynamics of forest ecosystems. In Mexico, there is evidence of symptoms of massive forest decline; however, there is no consensus in terms of attributing these symptoms to climate change. This study aimed to provide evidence of forest decline possibly associated with climatic change in the highland pine (Pinus hartwegii Lindl.) populations of the Nevado de Toluca Flora and Fauna Protection Area. Using unmanned aerial vehicles (UAV) equipped with multispectral sensors, the study applied digital photogrammetry techniques, automated tree crown detection algorithms, and calculation of the normalized difference vegetation index (NDVI) and leaf chlorophyll index (LCI) to assess forest health across an altitudinal transect (from 3300 m to the timberline at 4040 m elevation). Climate analysis was conducted with TerraClimate data using mean annual temperature (MAT), April temperature, and Palmer Drought Severity Index (PDSI) from the studied altitudinal transect and its xeric limit. We found that lower altitude populations had significantly higher stress levels, indicating forest decline phenomena, while intermediate altitude populations showed greater vigor of the detected trees. Statistically significant differences in the NDVI and LCI values along the altitudinal gradient provided evidence of forest decline in terms of forest vigor and productivity, with the greatest disturbance found at the lower altitude of the examined forest species. The analysis of the climatic data revealed an increase in April temperature +1.4 °C of the xeric limit of the transect (low altitude) when comparing the reference period, 1961–1990 (mean: 12.17 °C), with the decade prior to our study (2011–2020; mean: 13.57 °C). This would be equivalent to an upward shift in elevation of 280 m of the xeric limit. In addition, the PDSI analysis revealed that droughts are becoming increasingly intense at a rate of 0.06 PDSI units per decade, with greater intensity in the last five years. These findings highlight the negative impacts of climate change on forest ecosystems and the urgent need for alternative forest management and conservation practices to increase resilience and adaptation in the temperate forests of Mexico. This study sets a precedent for further research to improve our understanding of the impacts of climate change on forest ecosystems and the development of sustainable management practices. [ABSTRACT FROM AUTHOR]

Golden camellia is a highly valued commercial plant owing to its flowers' medicinal substances. One species of golden camellia, Camellia quephongensis Hakoda et Ninh, is used as a non-timber forest product for value-chain forestry (VCF) in Que Phong District, Nghe An Province, North-Central Vietnam; its production and sale is an important business activity which contributes substantially to local livelihood improvement. According to previous studies and in situ observation, golden camellias, including C. quephongensis, inhabit regenerated forests, especially along streams and rivers. This encourages VCF with C. quephongensis to function as a driver of forest regeneration and conservation. However, the ecological aspect of this species in regenerated forests is poorly understood. Thus, this study aimed to determine this species' growth patterns and population expansion during forest regeneration. We surveyed the morphological characteristics of this species at different ages in several populations, and revealed that the plants reached a tree height of 150 cm and started blooming eight years after germination. Comparing C. quephongensis-populations inhabiting different succession-staged forests, we observed that the population in long-term regenerated forests was significantly larger and had a pyramid-shaped age distribution pattern to possibly expand its population size. Based on this, we conclude that C. quephongensis is a good indicator of forest regeneration and contributes to local livelihoods and forest regeneration and conservation. [ABSTRACT FROM AUTHOR]

This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud—referred to as Peak Returns (PR)—and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants. [ABSTRACT FROM AUTHOR]

In Hong Kong, as in many tropical areas, grasslands are maintained by fire on disturbed and abandoned land. However, Hong Kong's native forests are regenerating in many areas, alongside frequent burning of the hillsides, and are in different stages of structural succession to closed canopy forest patches. Understanding the major determinants of secondary succession is a vital input to forest management policies. Given the importance of forests for biodiversity conservation, watershed protection and carbon cycling. This study examines the relationship between burning regimes and structural forest succession over 42 years from 1973 to 2015, using an archive of satellite images, aerial photographs and field plot data. Overlay of a fire frequency map with maps of forest structural classes at different dates indicates the number of fires undergone by each successional class as well as the time taken to progress from one class to another under different fire regimes. Results indicate that the native sub-tropical evergreen forests, which are naturally fire intolerant, can regenerate alongside moderate burning, and once the shrub stage is reached, succession to closed forest is relatively rapid and can occur within 13 years. More than one burn, however, is more destructive, and twice-burnt areas were seen to have only one-third of the woody biomass of once-burnt plots. The most frequent fires occurred in areas where mono-cultural plantations had been destroyed by disease in the 1960s and were subsequently invaded by grasslands. These former plantation areas remained in early successional stages of grass and open shrubland by 2015. Other plantations from the 1970s and 1980s remain as plantations today and have acted as a barrier to natural forest succession, attesting to the greater effectiveness of fire control over re-afforestation measures. [ABSTRACT FROM AUTHOR]

Associated endogenous hormones were affected by forest fragmentation and significantly correlated with aboveground biomass storage. Forest fragmentation threatens aboveground biomass (AGB) and affects biodiversity and ecosystem functioning in multiple ways. We ask whether and how forest fragmentation influences AGB in forest fragments. We investigated differences in AGB between forest edges and interiors, and how plant community characteristics and endogenous hormones influenced AGB. In six 40 m × 40 m plots spread across three forest fragments, AGB was significantly higher in plots in the forest interior than in those at the edge of forests. The proportion of individuals with a large diameter at breast height (DBH > 40 cm) in the forest edges is higher than that in the forest interiors. Further, trees within a 15–40 cm DBH range had the highest contribution to AGB in all plots. Trees in interior plots had higher abscisic acid (ABA) and lower indole-3-acetic acid (IAA) concentrations than those in edge plots. In addition, AGB was significantly positively and negatively correlated with ABA and IAA concentrations at the community scale. In this study, we provide an account of endogenous hormones' role as an integrator of environmental signals and, in particular, we highlight the correlation of these endogenous hormone levels with vegetation patterns. Edge effects strongly influenced AGB. In the future, more endogenous hormones and complex interactions should be better explored and understood to support consistent forest conservation and management actions. [ABSTRACT FROM AUTHOR]

The phenological responses of plants to climatic variables are critical for conservation planning; however, it is less understood in an Afrotropical context. Here, we observed how flowering and fruiting phenophases of seven indigenous plant species are related to monthly rainfall and temperature for 24 months in Ethiopia. We employed linear and non-linear models to test the effects on flowering and fruiting intensity. The results of the linear model showed that flowering intensity decreased with increasing monthly temperature for Maytenus arbutifolia, Prunus africana, and Solanecio gigas, but increased for Bersama abyssinica, and decreased with increasing monthly rainfall for Maytenus arbutifolia. The results of the non-linear model indicated that the flowering intensity of Brucea antidysenterica, Dombeya torrida and Rosa abyssinica decreased, leveled off and increased with increasing monthly temperature. Moreover, the fruiting intensity of Brucea antidysenterica and Rosa abyssinica decreased with increasing monthly rainfall, but increased for Bersama abyssinica; The fruiting intensity increased with increasing monthly temperature for Brucea antidysenterica and Rosa abyssinica. Altogether, the effects of climatic variables not only vary among the species, but also among the phenophases of a plant species. Hence, considering these varied effects in forest conservation schemes is critical, especially during the epoch of this continuing climate change. [ABSTRACT FROM AUTHOR]

The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy ( a c c ), intersection over union ( I o U ), and F1-score ( F 1 s c o r e ), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of 2 × 2 , 4 × 4 , 6 × 6 , and 8 × 8 pixels that produced the highest a c c , I o U , and F 1 s c o r e of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas. [ABSTRACT FROM AUTHOR]

Confirmation of rights and collective trust (interpersonal and institutional) can act as primary factors for facilitating effective forest management and conservation. Collective forests are lands held collectively by either rural or indigenous communities based on a shared history, language, culture, or lineage. It is an institutional arrangement in which communities are involved wholly or partly in decision-making and contribute knowledge and labour to achieve healthy forests and social well-being. Based on the existing literature, the nature of collective forest rights and trust can be derived from social, rather than economic, prospects. Therefore, based on the institutional theory, this study constructs a theoretical framework to verify the impact of the confirmation of rights and trust on farmers' forestry management conservation behaviour. The empirical setup of the study was comprised of a data set of 682 collective forest farmers in Zhejiang and Jiangxi provinces, China. We utilized the negative binomial regression model to quantify the proposed framework. The main conclusions were as follows. Confirmation of rights and collective trust had a significant positive role in promoting farmers' forestry management and protection behaviour. Increasing the confirmation of rights and trusts by one unit increased the number of farmers' forest management and protection by 1.846 and 2.631 times, respectively. The interaction between confirmation and trust did not substantially affect farmers' behaviour. The diverse forest labour force, the total area of forest land, and the number of forest plots had a significant positive impact, while the head of the village and the number of migrant workers significantly and negatively impacted farmers' behaviour. Therefore, it is necessary to further improve the forestry land rights system and strengthen the trust mechanism so that it can become an effective institutional incentive for farmers to manage and protect forests. [ABSTRACT FROM AUTHOR]