Mobility models are used to evaluated the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility which is usually used for performance evaluation of ad-hoc mobile network. Mobile nodes have the dynamic mobility in the ad hoc network so the mobility model plays an important role to evaluate the protocol performance.In this article, we developed modify random waypoint mobility (MRWM) model based on random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence on the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) condition. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end to end delay and normalized routing overhead. The obtained results revealed that proposed modify random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realist.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

The construction of random fillings from the excavation of medium hardness rocks, with high particle sizes, presents limitations in compaction control. This research applies new control techniques with revised test procedures in the construction of the random fillings core, which constitutes the main part of the embankment, with the bigger volume and provides the geotechnical stability to the infrastructure. The maximum layer thickness researched was 800mm. As there are many types of rocks, this research is applied to metamorphic slates. Quality control has been carried out by applying new research associated with the revision of wheel impression test, topographic settlements and plate bearing test (PBT). A statistical analysis of the core of 16 slate random fillings has been carried out, with a total of 2250 in situ determination of density and moisture content, 75 wheel impression tests, 75 topographic settlement control and 75 PBT. The strong associations found between different tests have allowed to simplify the quality control.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

Amazon tropical forest is actually subject to strong deforestation, generally originated from illegal logging, resulting in ecological, environmental and economic problems. Aiming stop deforestation and timber commercialization of illegal logging of tropical forest, new laws has been introduced in many countries. Here we investigated the utility of DNA fingerprinting of nuclear and cytoplasmatic SNPs to timber tracking the intensive logged and commercialized of the Amazonian Neotropical tree Jacaranda copaia. Samples of 832 individuals from 43 populations from Bolivia, Brazil, French Guiana, and Peru were used to investigate the power of 113 nuclear SNPs, 11 CpSNPs, and four MtSNP loci to determine the country and population origin. The genetic differentiation among all populations and contries was high (0.233–0.942), specialy for CpMtSNP (generally>0.6) loci, and there is a strong isolation by distance pathern among populations, favoring the group or individual samples tracking to correct site. For self-assignment tests, we were able to 100% correct determine country and population origin of all samples using all SNPs. Our results show that the use of 128 SNP markers is suitable to correct determination of country and population site of J. copaia timber origin and very useful tool for customs and local and international policies.

Abstract: Community forests are being promoted in Togo as an alternative to the degradation of protected areas and as a means of restoring forest landscapes. The present study is carried out within Kouampante Community Forest (KCF) in Ogaro (Togo, West Africa). It intends to contribute to the sustainable management of KCF. Specially, it aims at: (i) mapping forest ecosystems and analysing their dynamic and (ii) characterising the floristic diversity of the KCF. Forest ecosytems are mapped and their dynamic evaluated based on Google Earth images of 2010 and 2020. The floristic and silvicultural analyses are based on inventories carried out in 13 plots of 1,000 m² each. The KCF is made up mainly by tree/shrub savannahs (58.73%) and croplands/fallow (41.27%) in 2010. These two land use types have undergone changes over the period. In 2020, the KCF had two land use types: tree/shrub savannahs (84.30%) and croplands/fallows (15.70%). The flora census consisted of 59 species grouped in 6 families. The most important plant species are Detarium microcarpum and Vitellaria paradoxa. The Fabaceae were the most abundant and diversified families, followed by the Combretaceae. The demographic structure showed a high abundance of individuals with small diameter and height. This indicates the possibility of a rapid restoration of the native vegetation. Protecting the forest from anthropogenic threats could be crucial for achieving the desired objective.

The 2022-2023 Chilean summer showed increased temperatures and similar burned area, compared to the 2016-2017 season, where more than 500,000 hectares were compromised, mainly in the rural areas. After a brief review, it is revealed that the effects of forest fires on soil and hydrological properties are barely debated in Chile. Here, we showed a climatological analysis where temperature records in the 2016-2017 season were unusual, as well as another unexpected increase in the summer of 2022-2023, resulting in high-severity fires known as ‘mega-fires’ or “storm-fires”. Mega-fires affect forest plantations and native forests mainly from 33º S (Maule Region) to 39º S (Los Ríos Region) and they are expected to become frequent due to climate change, moving from the north to the south. We present an overview of the influence of wildfires on soil components in the most affected areas (inland, Coastal, and Andes ranges), their hydrological impacts, and potential erosion risk due to high winter precipitation. We propose several management practices that could help to prevent or mitigate these events, including pre-and post-fire interventions, such as afforestation and seeding, selective logging, mulching, erosion barriers, soil preparation, and dam monitoring. We argue that any effective plan in fire-prone and affected areas should include a combination of actions taken at the hillslope scale at integral ecosystem management, whose effectiveness should be monitored and verified regionally at the watershed scale.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

Two sets of initial conditions are used in the investigation of capital return rate and carbon storage in boreal forests. Firstly, a growth model is applied in young stands as early as the inventory-based model is applicable. Secondly, the growth model is applied to observed wooded stands. Four sets of thinning schedules are investigated in either case. First, the capital return rate is aspired without any restriction. Second, the number of thinnings is restricted to at most one. Third, thinnings are restricted to the removal of only trees thicker than 237 mm. Fourth, commercial thinnings are omitted. The two sets of initial conditions yield similar results. The capital return rate is a weak function of rotation age, which results in variability in the optimal number of thinnings. Reducing the number of thinnings to one increases timber stock but induces a capital return rate deficiency. The deficiency per excess volume unit is smaller if the severity of any thinning is restricted by the removal of large trees only. Omission of thinnings best applies to spruce-dominated stands with stem count less than 2000/ha. Restricted thinning intensity applies to deciduous stands and dense pine stands. The albedo effect increases the benefits of restricted thinnings and increased clearcuttings instead of contradicting the carbon storage.

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

Immersion in forest environments was shown to produce beneficial effects to human health, in particular psychophysical relaxation, so much that this practice is increasingly recognized as a form of integrative medicine. Limited evidence exists about both statistical significance and size of the effects conditioned on personal characteristics, as well as on the main external variables. The primary purpose of this study was to substantiate the very concept of forest therapy by means of the quantification and significance of the psychological effects, stratified by gender, age groups and place of residence. A preliminary qualitative analysis of the main determinants, in particular the method of conducting, the meteorological comfort and the concentration of volatile organic compounds in the forest atmosphere, was afforded. Seven forest therapy sessions were performed in late summer though early fall, resulting in 150 psychological self-assessment questionnaires administered before and after each session. The results were comparable or even better than others reported in the international literature. Moreover, preliminary evidence arose about different functionality towards specific psychological indexes conditioned at least on gender and age groups, as well as meteorological comfort, structured programs and, possibly, volatile organic compounds showed an impact on the outcomes.

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

The research objectives was to determine the plants vegetation profile in forest revegetation and Mount Arjuna springs. The method used was descriptive method. While based on the techniques and tools used to research, the author uses the survey method to obtain facts that occur in the research area, namely in the area around the springs of Arjuna mount, Pasuruan, East Java, Indonesia. Data obtained in the field are then processed and analyzed using GIS, ArcView 3.3 and Google Earth programs. The results of the study explain the profile of plant vegetation, namely plant stratification, bird wealth, taxonomic wealth and plant density have a significant effect on forest revegetation and springs in Arjuna mount. The forest vegetation profile has a positive and significant effect on the preservation of springs. The better the profile of forest vegetation, the preservation of springs will be better, and vice versa.

Hyperspectral images are an important tool to assess ecosystem biodiversity both on terrestrial and benthic habitats. To obtain more precise analysis of biodiversity indicators that agree with indicators obtained using field data, analysis of spectral diversity calculated from images have to be validated with field based diversity estimates. The plant species richness is one of the most important indicators of biodiversity. This indicator can be measured in hyperspectral images considering the Spectral Variation Hypothesis (SVH) which states that the spectral heterogeneity is related to spatial heterogeneity and thus to species richness. The goal of this research is to capture spectral heterogeneity from hyperspectral images for a terrestrial neo tropical forest site using Vector Quantization (VQ) method and then use the result for prediction of plant species richness. The results are compared with that of Hierarchical Agglomerative Clustering (HAC). The validation of the process index is done calculating the Pearson correlation coefficient between the Shannon entropy from actual field data and the Shannon entropy computed in the images. Terrestrial dry forest and marine coastal hyperspectral images with different resolutions have been used for spectral diversity feature validation.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.

Human-elephant conflict poses serious conservation challenge in Asia and Africa. We deployed an AI-based camera-alert system in a conflict hotspot in West Bengal, India that accurately detected elephants near villages (266 events) and transmitted real-time alerts ( = 42 seconds). Among detections of separate age and sex classes, lone adult bulls were the most prone to conflict. Real-time alerts permitted rapid response by field personnel to prevent conflict at the site ( = 18.2, SD = 12.4 min to arrival). Frequent detections of humans (33,217 events) on elephant trails highlighted the value of real-time alerts. Four human fatalities and five serious injuries occurred in the study area over a two-year period prior, but none during the study (2023-2024) after deploying the AI-alerting system. Loss of lives and livelihoods underscore the urgency of applying this approach to other elephant ranges and adapting AI models to other conflict-pronespecies to promote coexistence.

This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also per-formed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. The challenge is to apply it to a surface with high temperatures, owing to the greater probe contact and the limitation of ultrasonic properties. This literature review provides a more precise direction for future research to evaluate PACM on piping that can be utilized in inspection during plant operation at elevated temperatures. Detecting and monitoring corrosion growth without shutdown.

The process of establishing secure communication between devices in the Internet of Things (IoT) can be approached by using a One-time-Pad (OTP) protocol. We propose using a known secure multiparty sum protocol for generating a One-time-Pad key using true (physical) random numbers in each device (party). We implemented the proposal using ZeroC-Ice, a middleware for distributed computing. The protocol security properties were analyzed under the assumptions of the Dolev-Yao threat model.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from time series data. This paper proposes the combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves respectively an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fiber, and wireless power amplifiers can be handled by deep learning equalization algorithms. Besides, the PS technique is considered an effective measure to further increase the capacity of the modulation-constraint channel. However, since the proba-bilistic distribution of m-QAM varies with the amplitude, there have been difficulties in learning valuable information from the minority class. This limits the gain of nonlinear equalization. To overcome the imbalanced machine learning problem, we propose a novel two-lane DNN (TLD) equalizer with the random over-sampling (ROS) technique in this paper. The combination of PS at the transmitter and ROS at the receiver improves the overall performance of the W-band wireless transmission system, and our 4.6-km ROF delivery experiment verifies its effectiveness for the W-band mm-wave PS-16QAM system. Based on our proposed equalization scheme, we realize single-channel 10-Gbaud W-band PS-16QAM wireless transmission over 100 m optical fiber link and 4.6-km wireless air-free distance. The results show that compared with the typical TLD without ROS, the TLD-ROS can improve the receiver sensitivity by 1dB. Furthermore, a reduction of 45.6% in complexity is realized, and training samples can be reduced by 15.5%. Considering the actual wireless physical layer with its requirements, there is much to be gained from the joint use of deep learning and balanced data pre-processing techniques.

The concept of out-of-time-ordered correlation (OTOC) function is treated as a very strong theoretical probe of quantum randomness, using which one can study both chaotic and non-chaotic phenomena in the context of quantum statistical mechanics. In this paper, we define a general class of OTOC, which can perfectly capture quantum randomness phenomena in a better way. Further we demonstrate an equivalent formalism of computation using a general time independent Hamiltonian having well defined eigenstate representation for integrable supersymmetric quantum systems. We found that one needs to consider two new correlators apart from the usual one to have a complete quantum description. To visualize the impact of the given formalism we consider the two well known models viz. Harmonic Oscillator and one dimensional potential well within the framework of supersymmetry. For the Harmonic Oscillator case we obtain similar periodic time dependence but dissimilar parameter dependences compared to the results obtained from both microcanonical and canonical ensembles in quantum mechanics without supersymmetry. On the other hand, for one dimensional potential well problem we found significantly different time scale and the other parameter dependence compared to the results obtained from non-supersymmetric quantum mechanics. Finally, to establish the consistency of the prescribed formalism in the classical limit, we demonstrate the phase space averaged version of the classical version of OTOCs from a model independent Hamiltonian along with the previously mentioned these well cited models.

Recently, the concept of participatory democracy developed in the early 1970s has come back into fashion to revitalize the public involvement in political decision-making processes. Public par-ticipation in forest policy has been fully conceptualized by the scientific community in the late 1990s and early 2000s, but in many contexts the practical application remains unfulfilled. The aim of this study is to identify and analyze the participatory techniques used in literature to increase knowledge and facilitate its transferability into forest policies and strategies. A literature review was carried out to offer an overview of the participatory techniques adopted in the deci-sion-making process. At the end of literature review, 25 participatory techniques were identified based on over 2,000 publications. Afterwards, the participatory techniques were assessed using seven indicators (degree of participation, type and number of participants, time scale, cost, po-tential influence on policy). The results showed that the type of actors involved in the participa-tory technique is a key variable for the complexity and usefulness of the process, while the number of participants influences how information is disseminated. The Principal Component Analysis highlighted that the participatory techniques can be divided in three groups: the first group includes those techniques with a high degree of participation and a contextual high po-tential influence on policies, the second one includes techniques with a costs in terms of re-sources, while the third one consists of those with a large number of participants and reduced time requirements.

The growing deforestation of tropical forests requires the implementation of restoration actions capable of assisting the recovery of biodiversity and functioning of these ecosystems. This research aimed to identify the environmental factors that influence woody plant recruitment abundance and diversity in an Andean forest restoration project in Medellin (Colombia). Data from all individuals taller than 80 cm was collected in 22 plots of 200 m2. The environmental factors selected were edaphic variables, plantation structure, slope, elevation, prior land use and landscape forest cover. A generalized linear model was adjusted for recruitment density, and linear mixed models were built for recruited species richness, diversity and dominance. Woody plant recruitment attributes in our study area were similar to a typical secondary succession in an Andean forest, but planted trees contributed little to these attributes. While recruitment density was affected by slope, canopy closure and landscape forest cover, recruitment diversity was influenced by physical (bulk density) and chemical (pH, aluminum, CEC) edaphic factors, planted trees diversity (species richness and composition), canopy closure and mortality rate of planted trees. We conclude that a good development of planted trees enhances both recruitment attributes, indicating a synergy between active restoration and passive regeneration processes.

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation, maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from imputation methods due to potential bias. Drought quantification involved the computation of the Aridity Index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann-Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting geographical location and elevation does not strongly influence observed dryness trends.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

Tropical forests provide habitats for diverse flora and fauna. In addition, they are essential in regulating the climate while at the same time millions of people depend on them for their livelihoods. As seen by rising political commitment and worldwide promises to reduce rates of deforestation and boost restoration of degraded forest ecosystems, forests are becoming more widely acknowledged for their role as a nature-based solution to many sustainable development concerns. Understanding tropical forest dynamics and their conservation have so become more important. This study analyzed, the forest stand structure, tree species composition, abundance and diversity and the regeneration status of Londiani Forest. Londiani Forest is divided into three blocks, namely Kedowa, Chebewor and Londiani and from each of these blocks belt transects 25 m wide and 1 km long were established 100 m from the edge of the forest to reduce edge effects. At every 200 m along the transect, 25 m × 25 m quadrats were set up in which, an inventory of all tree species was done. For tree species that could not be identified in the field, the local name was provided by para-taxonomists who participated in the data collection and the species name was later identified with the help of a taxonomist or a manual of woody tree species of Londiani Forest. The diameter at breast height (DBH) was measured at 1.3 m from the ground using a diameter tape. Tree height was measured using a Suunto angular clinometer. A nested quadrat 5 m x 5 m quadrat within the 25 m x 25 m quadrat was used to sample saplings while a 1 m x 1 m quadrat was used for sampling seedlings. Regeneration status was assessed using the number of seedlings and saplings. Stumps of trees cut were counted including identifying tree species from which the tree stump was derived. Data from the quadrats were entered into Microsoft Excel. Total stem density, species density, basal area, species basal area, relative density and species diversity were determined. A total of 1,308 individual trees belonging to 34 different species from 24 families were recorded. Kedowa had the highest (27) species richness followed by Chebewor (19) then Londiani (14). There was a statistically significant difference in species richness among the three forest blocks (p<0.05). There were no statistically significant differences in the mean DBH (F=0.560; p=0.729) and height class distribution (F= 0.821, p=.558) among the three forest blocks. There was a statistically significant difference in stem density (F=12.22; p=0.005) and woody species diversity (F=0.32; p=0.001) within the three forests blocks. There was no statistically significant difference in the basal area distribution within the three forest blocks p>0.005. The similarity index ranged from 0.34 to 0.47. In all the forest blocks, there was evidence of seedlings and saplings indicating that there was regeneration. Results clearly show that while the Londiani Forest shows variation in characteristics, there is clear evidence that it is regenerating.

Peri-urban forests often have extensive networks of greenways that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of flora and fauna and often overlook the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is not as high as expected. Having a quantification of their naturalness would be useful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on Geographic Information Systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index, while this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analysing the degree of naturalness of the trails, but if used together, it would be possible to add flexibility to the type of variables incorporated in the analysis.

Insect outbreaks are major drivers of natural disturbances in forest ecosystems. Outbreaks can have both direct and indirect effects on the composition of soil arthropod communities, through canopy opening, nutrient addition and predator-prey interactions. In this study, we aim to understand the effects of forest tent caterpillar (Malacosoma disstria; FTC) outbreaks on ant communities in both temperate and boreal forests in Canada. Using pitfall traps and Berlese funnels, we compared the ant communities as well as the surrounding arthropod communities between control and outbreak sites in boreal andboreal and temperate forests (in Québec, Canada). Using the Sørensen dissimilarity index, we determined the alpha and beta diversity of the ant community. Other arthropods collected in the traps were counted to evaluate the richness and abundance of potential prey for the ants and other potential predators of the FTC. We used an indicator species analysis to examine the species associated with sites defoliated by the outbreak. In the boreal forest, we found that FTC outbreaks caused decreases in species richness and increases in the evenness of ant communities in defoliated sites. In the boreal forest sites, species composition varied significantly between control and outbreak sites. This pattern was driven by the presence of other predators. We also saw no changes in beta diversity in the boreal forest but did see a significant decrease in the temperate forest between the outbreak sites and the control sites. A similar, but weaker pattern was observed in the temperate forest. Ant species in the boreal forest tended to exhibit a more marked preference for either control or previously defoliated sites than species in the temperate forest. Our study showed that disturbances like insect outbreaks can drive changes in the ant community. While we saw small effects of outbreaks, manipulation experiments using resource addition could help us validate the mechanisms behind these relationships.

The compelling effects of climate change on forests may have been underestimated in the past few decades in practical forestry. Although the first attempts to draw attention to this complex problem appeared almost half a century ago, the debate has been conceptual rather than experimental and applicative. At first glance, the con-cerns were mainly related to sustainable forest management (SFM) issues, which obviously needed attention. Over time, the effects of climate change have been mainly considered in the context of the SFM; they started from various and somewhat different scales and goals. Over time, more research and awareness of the im-portance of SFM under the pressure of climate change have led to the development of a clearer field that can be defined as ‘adaptive forest management’ - to climate change. One of the characteristics of this discipline is to be featured by the absence of univocal methods and / or objectives to be pursued but to identify, verify, and adapt methods to the various climatic and forest types and conditions found in the field. Therefore, this work shows some phases of forest planning and management concepts and criteria over time and recalls some innovative and / or adaptive methods related to the approach to forest planning and management under climate change

The effect of capitalization premium in forest estate markets on forest management and climate change mitigation economics is investigated. It is shown that proportional goodwill in capitalization induces linear scaling of the financial return, without any contribution to sound management practices. However, there is a financial discontinuity as harvesting deteriorates goodwill. On the contrary, capitalization premium set on bare land as a tangible asset would increase timber storage and carbon sequestration. Observations indicate that the proportional goodwill is closer to reality within the Nordic Region, resulting in continuity problems but a reduced capital expense for carbon storage.

Recent expansion in data sharing has created unprecedented opportunities to explore structure-function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data’s potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30-year time frame, including discrete return Light Detection and Range (LiDAR) from an airborne observatory platform. LiDAR is a well-established and increasingly available remote sensing technology for measuring three-dimensional (3D) characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 – 25+ points/m² depending on instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the USA and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve out of sixteen structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m^2, but our results indicate that point densities above 7-8 pt/m² should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m² and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and users of these open-source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

The carbon stocks in the forests originated from the atmosphere and are accumulated in the organic matter of trees and soils. Forests play major roles in providing ecosystem services like climate change mitigation through carbon sequestration and nutrient flow dynamics. Therefore, the major objective of this study was to estimate carbon stocks of Biteyu forest by quantifying the aboveground biomass of trees, belowground carbon, soil carbon, and carbon stocks of litter pool. Systematic sampling technique was employed for vegetation and carbon data collection. The total of 10 line transects were laid along elevational gradients. The transects were 500 m apart and sampling plots were 300 m apart from each other. Each transect has comprised of a minimum of 4 plots to a maximum of six totaling 50 plots representing the forest for the investigation. A square sample plot of 900 m2 was used to collect vegetation data with a DBH ≥ 2.5 cm and a height of 2 m and above. To sample herbaceous vegetation in the forest floor, five smaller subplots of 1 m x 1 m = 1 m2 (four at the corner and one at the centre of the main plot) were established. The disturbance level was also determined using the cattle interference and selective cutting of trees. The appropriate allometric models were applied for both aboveground and belowground biomass estimations. The findings showed that cattle interference affects the forest understory from growing and recruitment. The mean of cattle interference was 4.77±2.12 per ha and the mean of wood stump was 26.67±9.37 per ha. The size class analysis showed that the smallest diameter class (2.5-10 cm) in the forests represented 37.05% of the total stem density. The diameter classes between 10 and 30 cm comprised a stem density of 41.08%. It was estimated that the total carbon stock of Biteyu forest was about 166.67 ± 16.4 ha-1. The carbon stock in AGB and BGB was estimated to be 87.13 ± 11.80 t ha-1 and 22.94 ± 2.84 t ha-1, respectively. Moreover, the contribution of soil and litter carbon pools to the total carbon in the forest ecosystem were 56.37 ± 1.73 and 0.26 ± 0.01 t ha-1 , respectively. From the present study it can be concluded that estimated mean carbon stock of the forest is smaller than that of other similar forests in the dry evergreen montane forest, which was attributed to the higher anthropogenic disturbances. Therefore, the interventions, which reduce the climate change effect, would be very important in the maintenance of forest ecosystem functioning.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

Surface activity of humic acid (HA) and its six sub-fractions isolated from forest soil were characterized by surface tension measurements, dynamic light scattering, and laser doppler electrophoresis. The surface tension of HA sub-fractions decreased from 72.4 mN·m-1 to 36.8 mN·m-1 with the increasing concentration from 0 to 20000 mg·L-1. The critical micelle concentration (CMC) of HA and its sub-fractions ranged from 216 to 1024 mg·L-1 with the multiple linear regression equation of CMC = 18896–6.9×C–296×alkyl-C–331×aromatic-C–17019×H/C+4054×HB/HI (p &lt; 0.05). The Z-average particle size ranges from 108.2 nm to 186.9 nm. The maximum particle size was 5000 nm after filtered with a membrane of 0.45 μm, indicating HA and its sub-fractions progressed self-assembly during 2000 mg·L-1, pH = 6.86. The number-base particle size distribution of HA sub-fractions was identified as four peaks by Gaussian model, and the aggregate sizes were mainly in six clusters including 2 ± 1 nm, 5 ± 2 nm, 10 ± 3 nm, 21 ± 8 nm, 40 ± 10 nm, and &gt; 50 nm indicating multiple aggregations of different particle sizes in the HA fractions solution.

Net CO2 emissions and sequestration from European forests are the result of removal and growth of flora. To arrive at aggregated measurements of these processes at a country's level, local observations of increments and harvest rates are up-scaled to national forest areas. Each country releases these statistics through their individual National Forest Inventory using their particular definitions and methodologies. In addition, five international processes deal with the harmonization and comparability of such forest datasets in Europe, namely the IPCC, SOEF, FAOSTAT, HPFFRE, FRA (definitions follow in the article). In this study, we retrieved living biomass dynamics from each of these sources for 27 European Union member states. To demonstrate the reproducibility of our method, we release an open source python package that allows for automated data retrieval and analysis, as new data becomes available. The comparison of the published values shows discrepancies in the magnitude of forest biomass changes for several countries. In some cases, the direction of these changes also differ between sources. The scarcity of the data provided, along with the low spatial resolution, forbids the creation or calibration of a pan-European forest dynamics model, which could ultimately be used to simulate future scenarios and support policy decisions. To attain these goals, an improvement in forest data availability and harmonization is needed.

Forest healing effects are increasingly valued for their contribution to human psychological and physiological health, motivating further advances aimed at improving the knowledge of the relevant forest resources. Biogenic volatile organic compounds, emitted by the plants and accumulating in the forest atmosphere, are essential contributors to the forest healing effects, and represent the focus of this study. Using a photoionization detector, we investigated the high frequency variability, in time and space, of the concentration of total volatile organic compounds, on a hilly site, as well as along forest paths and long hiking trails on Italian northern Apennines. The scale of concentration variability was found to be comparable to absolute concentration levels, within time scales of less than one hour, and spatial scales of several hundred meters. During daylight hours, the concentration peaked from noon to early afternoon, followed by early morning, with lowest levels in late afternoon. Based on a conceptual model, these results were related to meteorological variables, including the atmospheric vertical stability profile. Moreover, preliminary evidence pointed to higher concentration of volatile organic compounds in forests dominated by conifer trees, in comparison with pure beech forests.

We propose a new estimator for creating expansion factors for survey plots in the USDA Forest Inventory and Analysis program. This is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data in that it produces a single set of expansion factors that can have general purpose use to produce small area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such of GREG estimation, in that it is guaranteed to produce positive expansion factors. We extend the previous method here to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

1) Background: We analyzed the size structure, richness and diversity in eight permanent forest plots in the upper limit of the great Amazon basin located in the Ecuadorian parts of Cordilleras del Cóndor and Cutucú. Our hypothesis was that not all the forests of the eastern mountain ranges are homogeneous in characteristics, but rather present differences in their structure, richness and floristic diversity. In order to test this, our main objective was to classify the types of forests based on characteristics in diametric structure and species composition of the Amazonian forests of the eastern cordilleras in southern Ecuador, and also to determine the influence of critical edaphic-environmental and geomorphological factors. 2) Methods: Eight one-hectare permanent plots were installed in homogeneous and well-conserved forest stands. Four plots were located in the province of Zamora Chinchipe and four in the province of Morona Santiago. We identified and measured all trees> 10 cm at diameter breast height. For each plot, soil samples were taken, along with environmental and slope data. The multidimensional non-metric adjusted scaling (NMDS) was used to evaluate changes in climatic and geomorphological gradients. The relationship between the composition of the species at the plot level and the edaphoclimatic variables was tested using a canonical correspondence analysis (CCA). Finally, we modeled the change in species diversity (Fisher's alpha) in relation to the climatic gradients, altitudinal and geomorphological using a GLMM. 3) Results: Overall, we identified 517 species belonging to 76 families. We determined the existence of two different types of forest, the first one named Terra Firme, characterized by the presence of a larger number of species and individuals per plot, compared to the second type of forest, called Tepuy or Sandstones forest. Species richness was negatively correlated with soil phosphorus content and pH, mean annual temperature, annual precipitation and altitude. The CCA analysis showed differences in the species composition between the Terra Firme and Sandstone forests. The climatic seasonality and the concentration of cations in the soil influence the diversity of the Amazon forest communities of the southeastern cordilleras. The forests of Terra Firme, which are settled in more stable and nutrient-rich climatic areas, were more diverse. Sandstone forests are poor in nutrients and develop in areas with stronger seasonality

The research objectives was to determine the plants vegetation profile in forest revegetation and Mount Arjuna springs. The method used was descriptive method. While based on the techniques and tools used to research, the author uses the survey method to obtain facts that occur in the research area, namely in the area around the springs of Arjuna mount, Pasuruan, East Java, Indonesia. Data obtained in the field are then processed and analyzed using GIS, ArcView 3.3 and Google Earth programs. The results of the study explain the profile of plant vegetation, namely plant stratification, bird wealth, taxonomic wealth and plant density have a significant effect on forest revegetation and springs in Arjuna mount. The forest vegetation profile has a positive and significant effect on the preservation of springs. The better the profile of forest vegetation, the preservation of springs will be better, and vice versa.

Regarding number of vehicles, forest roads are characterized by low traffic intensity, but on the other hand great values of ground pressure between wheels of timber truck units and forest road surface occur, often with pressures values above 80 kN which additionally causes damage of the upper and lower forest road layer. There are currently several methods for assessing condition of a forest road surface which are mainly used for assessing state of public roads, but can be used in forestry as well. Assessing condition of forest road surface was done by measuring vibrations with a specially developed software for Android OS installed on a Huawei MediaPad 7 Lite. Software measured vibrations in all three axes, coordinates of device, speed of the vehicle and time. Aim of this research was to determine accuracy of collected data so that this method can be used for scientific and practical purposes. Research was carried out on the segment of a forest road during driving a vehicle equipped with a measuring device. Tests were performed in both driving direction of the forest road segment with different measuring frequencies, tyre inflation pressures and driving speeds. Values of vibrations were classified and translated on a map of forest road together with devices’ measured coordinates. Vibration values were compared with places of recorded forest road surface damages. Research results show no significant difference in vibration values between 1 Hz and 10 Hz of measurement frequencies. Based on the analysis of collected data and obtained results, it is clear that it is possible to assess the condition of a forest road surface by measuring vibrations. The greatest values of vibrations were recorded on the most damaged parts of the forest road. Vibrations do not depend on tyre inflation pressure, but ranges of vibrations are decreasing with decreasing driving speed. Accuracy of collected data depends on GPS signal quality, so it is recommended that each segment of forest road is recorded twice so that location of damages on forest road can be confirmed with certainty.

Forest canopy gaps are important for the ecosystem dynamics. Depending on tree species, small canopy openings might be also associated to intra-crown porosity and to space between crowns. Yet, little is known on the relationships between the fine-scaled pattern of canopy openings and biodiversity features. This research explored the possibility of i)- mapping forest canopy gaps from a very high resolution orthomosaic (10 cm), processed from a versatile imaging platform such as unmanned aerial vehicles (UAV), ii)- to derive patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. This is attempted in a test area of 240 ha covered by temperate deciduous forest types in Central Italy and containing 50 forest inventory plots of about 530 m². Correlation and linear regression techniques were used to explore relationships between patch metrics and understorey (density, development and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m²) can be faithfully extracted from UAV RGB imagery, using the red band and contrast split segmentation. Highest correlations were observed in the mixed forest (beech and turkey oak), while beech forest had the poorest ones and turkey oak forest displayed intermediate results. Moderate to strong linear relationships were found between gap metrics and understorey variables in mixed forest type, with adjusted R² from linear regression ranging from 0.52 to 0.87. Equally good results, in the same forest types, were observed for forest habitat biodiversity variables (0.52<adjusted R²<0.79) with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

Forest certification is considered a viable market-based policy instrument to promote forest sustainability. It has an important role of play in meeting the objective of modern forestry development in China, which is to sustain ecological and environmental benefits of forests. To understand differences in attitudes, opinions, and interests in forest certification, this study segmented respondents of a landowner’s survey in Shandong, China based on their level of interest in participating in forest certification under different program requirements. Multivariate cluster analysis revealed three distinct groups: likely-, potential-, and unlikely-landowners. We further examined the heterogeneity of these groups in terms of their demographics, ownership characteristics, management objectives, and perceived benefits and challenges with adopting forest certification. The results suggested the necessity of differentiating landowners in formulating and designing specific motivation-based incentives and tailor outreach efforts and communication strategies to improve their interests in forest certification. Findings are useful and interesting to forest policymakers interested in promoting forest certification among landowners in China and other countries facing similar circumstances.

Rapid urbanization has changed the structure and function of natural ecosystems, especially the floodplain ecosystems in SE Asia. This paper describes the ecological structure of vegetation stands and the usefulness of satellite images to characterize a disturbed tropical urban forest located in the lower floodplain of the Chao Phraya River, Thailand. Nine representative plots were established in Bang Kachao peninsula in 4 tropical urban forest types: rehabilitation forest, home-garden agroforestry, mangrove and park. The correlation between NDVI and LAI obtained from satellite images and plant structure from field surveys were analyzed. The NDVI had the highest relationship with stand factors for the number of families, number of species, Shannon-Weiner’s diversity index and total basal area. The LAI had the highest correlation with total basal area, number of canopy layers, stand density and canopy density. Linear regression predicted the correlation between NDVI and LAI with stand factors as show above. The trend in NDVI and LAI reflected the urban forest type, being high in rehabilitation and mangrove forests, moderate in home-gardens and low in parks. Future urban planning of the Bang Kachao peninsula should focus on rehabilitation to increase the biodiversity and complexity of the urban forest.

A scenario-based approach to the impacts of land use and climate change can help in identifying future policy directions. This study models the impacts of different land use and climate change scenarios on the forest ecosystems of South Korea to identify national-scale forest policy options. Climatically suitable forest areas for 1,031 climate vulnerable plant species were identified for current time and for 2050. We calculated change in species richness under four climate projections. We built forest conversion models and created four 2050 forest scenarios: (1) forest loss continues at current rates; (2) similar loss, but with conservation in areas with suitable future climates; (3) a reduction of loss by 50%; and (4) a combination of preservation and overall reduction of loss by 50%. We then crossed the forest conversion models with the climate-driven change in species richness, and categorized current forest areas into four classes to offer forest policy alternatives. By deploying the scenarios which preserve climatically suitable forests, the average species richness where forests converting to other land uses reduced significantly. We suggest conserving forests with suitable climates for biodiversity conservation and the establishment of forest plantations targeted to areas where species richness will decline based on our results.

The purpose of this study was to systematically review forest therapy programs designed to decrease the level of depression among adults and subsequently identify the gaps in the literature. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors independently screened full-text articles from various databases using the following criteria: 1) intervention studies assessing the effects of forest therapy on depression in adults aged 18 years and over; 2) studies including at least one control group or condition; 3) been peer-reviewed; and 4) been published either in English or Korean before July 2016. The Scottish Intercollegiate Guideline Network (SIGN) measurement tool was used to assess the risk of bias in each trial. In the final sample, a total of 28 articles (English: 13, Korean: 15) were included in the present systematic review. This review concluded that forest therapy is one of the emerging and effective interventions for decreasing the level of depression in adults. However, the studies included in this review lacked methodological rigor. Future studies assessing the long-term effect of forest therapy on depression using rigorous study designs are needed.

In Mexico there is a deficit in the production of pine resin, because it depends on natural forests. Therefore, it is necessary to select provenances and phenotypes of potential species such as P. oocarpa. The objective was to determine the difference between provenances and the variation of resin components and quality, as well as the effect of geographic and climatic factors. Resin from five provenances was collected from southern Mexico. The percentage of rosin, turpentine and water was obtained, as well as the acidity and saponification index. P. oocarpa resin had 80.94 % rosin, 7.7 % turpentine and 11.49 % water. The saponification and acidity index was 125.47 and 117.49 mg KOH.g_1, respectively. All variables showed differences (p≤ 0.0001) between provenances. The provenance contributed between 6.44 and 11.71 % to the total variance, the error contributed between 88.29 and 93.56 %. Geographic and climatic variables only had an effect on the percentage of turpentine; the correlation was negative with altitude and longitude, but positive with temperature and precipitation. The results allow defining seed collection sites for resin plantations and orienting the selection for a P. oocarpa improvement program.

To establish simulated micro-gravity conditions, biological organisms are placed on a simulated microgravity system, which primarily rotate in two or three dimensions and rely on slow sedimentation with latency to detect gravity. Fluid dynamics arise as a consequence of rotation of the chambers with in which the cells are filled. To minimize the effects of centrifugal forces, it is necessary to position the samples as close to the center of rotation as possible. Strong shear forces and velocities within the vessel can have an adverse impact on cells, making this a critical topic in cell biology. During experimental investigation, this specific phenomenon is not apparent, but numerical modelling readily illustrates and is not widely analyzed in literature's. Furthermore, bulk flask are frequently employed on random positioning machines (RPM), not merely because they demand a substantial quantity of reagents. The flask presented in the study can be easily developed and fitted in conventional cell culture plates containing medium and reagents similar to those used in ground experiments, thus allowing it to remain close to the center of rotation. In the present study, a series of fluid dynamics simulations are performed to examine the impact of shear forces within the microvessel presented under varying conditions using a foreign medium condition, and these results are compared with existing vessels by observing the effects of shear forces and maximum velocity zones.

We present results of the detected voltage distribution of a quantum random number generator based on the photonic integration of semiconductor laser, delay interferometer and photodetector. We find that the integrated system behaves as expected for random number generation from gain-switched laser sources. The biggest advantage is that only electrical connections are needed to operate the system without the need for tricky and expensive optical alignment to external circuitry. We supply results showing that random bit stream created from the random numbers passes the NISTS statistical test suite tests; thus demonstrating the feasibility to generate random number via quantum means at gigabit/s rates from a single photonic integrated circuit. The results are backed by numerical simulations.

Open-cell metallic foams used as catalyst supports exhibit excellent transport properties. In this work, a unique application of metallic foam, as pelletized catalyst in a packed bed reactor, is examined. By using a wall-segment Computational Fluid Dynamics (CFD) setup, parametric analyses are carried out to investigate the influence of foam morphologies (cell size ϕ=0.45-3 mm and porosity ε=0.55-0.95) and intrinsic conductivity on flow and heat transport characteristics in a slender packed bed (N=Ddp=6.78) made of cylindrical metallic foam pellets. The transport processes have been modeled using an extended version of conventional particle-resolved CFD, i.e., flow and energy in inter-particle spaces are fully resolved, whereas porous-media model is used for the effective transport processes inside highly-porous foam pellets. Simulation inputs include the processing parameters relevant to Steam Methane Reforming (SMR), analyzed for low (Rep~100) and high (Rep~5000) flow regimes. The effect of foam morphologies on packed beds has shown that the desired requirements contradict each other, i.e., increase in cell size and porosity favor the reduction in pressure drop, however lowering the heat transfer efficiency. A design study is also conducted to find the optimum foam morphology of a cylindrical foam pellet at higher Rep~5000, which yields ϕ = 0.45, ε = 0.8. Suitable correlations to predict the friction factor and the overall heat transfer coefficient in a foam packed bed have been presented, which considers the effect of different foam morphologies over a range of particle Reynolds number, 100≤Rep≤5000.

In this paper, a kind of single-walled carbon nanotube nonlinear model is developed, and the strongly nonlinear dynamic characteristics of such carbon nanotubes subjected to random magnetic field are studied. The nonlocal effect of microstructure is considered based on the theory of nonlocal elasticity. The natural frequency of the strongly nonlinear dynamic system is obtained by the energy function method, the drift coefficient and the diffusion coefficient are verified. The stationary probability density function of the system dynamic response is given and the fractal boundary of the safe basin is provided. Theoretical analysis and numerical simulation show that stochastic resonance occurs when varying the random magnetic field intensity. The boundary of safe basin has fractal characteristics and the area of safe basin decreases when the intensity of the magnetic field permeability increases.

Object segmentation on remotely-sensed images: aerial (or very high resolution, VHS) images and satellite (or high resolution, HR) images, has been applied to many application domains, especially road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts in applying deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction on remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve DCNN, a modern activation function, called exponential linear unit (ELU), is employed in our network resulting in a higher number of and yet more accurate extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, the state-of-the-art object segmentation technique on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F1.

Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.88), outperforming 99% of the polygenic scores obtained from sets of random SNPs (Monte Carlo p= 0.011). Its predictive power remains unaffected after controlling for spatial autocorrelation (Beta= 0.83). The largest polygenic score (161 SNPs) exhibits similar predictive power (Beta=0.8). Random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection using Monte Carlo simulations.

The search for natural product-based biopesticides from endophytic fungi is an effective tool to find new solutions. In this study, we studied a pre-selected fungal endophyte, isolate YCC4, from the paleoendemism Persea indica, along with compounds present in the extract and the identification of the insect antifeedant and nematicidal ones. The endophyte YCC4 was identified as Phyllosticta capitalensis by molecular analysis. The insect antifeedant activity was tested by choice bioassays against Spodoptera littoralis, Myzus persicae and Rhopalosiphum padi and the in vitro and in vivo mortality was tested against the root-knot nematode Meloiydogyne javanica. Since the extract resulted an effective insect antifeedant, a strong nematicidal and lacked phytotoxicity on tomato plants, a comprehensive phytochemical study was carried out. The chemical study resulted in the identification of two new metabolites, metguignardic acid (4) and (-)-epi-guignardone I (14), along the known dioxolanones guignardic acid (1), ethyl guignardate (3), guignardianones A (5), C (2), D (7) and E (6), phenguignardic acid methyl ester (8), and the meroterpenes guignardone A (9) and B (10) and guignarenone C (12), (-)-guignardone I (13) and the phyllomeroterpenoid B (15). Among these compounds, 1 and 4 were effective antifeedants against both insect species, while 2 was only active on the aphid M. persicae. The nematicidal compounds were 4, 7 and 8. This is the first report on the insect antifeedant or nematicidal effects of these dioxolanone-type compounds. Since the insect antifeedant and nematicidal activity of the P. capitalensis extract depends on the presence of dioxolanone components, future fermentation optimizations are needed to promote the biosynthesis of these compounds instead of the meroterpenes.

Machine-learning methods used for classification are challenged by the class imbalance problem, where a certain class is underrepresented. Over/under-sampling the majority/minority class observations or model selection for ensemble methods alone might not be effective if the class imbalance ratio is very high. To address this concern, a novel method is presented for generating synthetic data for minority class observations in conjunction with an optimal tree ensemble classifier (OTEC). This novel synthetic method first generates minority class instances to balance the training data. Then it applies OTEC, where models are selected based on their performance using out-of-bag observations and training subsamples. A total of 20 benchmark problems on binary classification with moderate to extreme class imbalance are used to assess the efficacy of the proposed method against other well-known methods, including optimal tree ensemble, smote random forest, under-sampling random forest, oversampling random forest, k-nearest-neighbor, support vector machine, tree, and artificial neural network, using classification error rate, sensitivity, specificity, precision, recall and F1-score as performance metrics to assess the efficacy of the proposed method. The analyses presented in this study revealed that the proposed method based on data balancing and model selection yielded better results than other methods.

Smartphone-based fundus camera and telemedicine are an opportunity for accessing ocular health inequalities in underresourced areas. The objective of this study is to evaluate the prevalence of retinal findings in a community in the Amazon and propose strategies to enhance ocular health. A cross-sectional study was conducted in a river side community. Retinal photos from the posterior pole and optic disc were captured using a portable fundus camera. All photos and data were analyzed remotely by a retina specialist. The final sample was 107 participants, aged 52±17. Retinal findings were detected in 37.4% (95%CI 28.7–46.8) of the sample, the three main retinal findings were epithelial changes (10.3%, 95%CI 5.6–17.1), chorioretinal scars (8.4%, 95%CI 4.2–14.8), and dry Age-related Macular Degeneration (7.5%, 95%CI 3.6–13.6). This study detected retinal alterations in similar prevalence of other underresourced areas. Telemedicine is an opportunity to address health inequities, especially in ophthalmology, through relatively low-cost portable devices, supporting clinical decisions in areas with low health access, however, maintaining assistance after implementation is a challenge. Enhancing medical education and training local non-specialized health professionals for risk assessment, device handling, and data base use is reasonable to ensure follow-up.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

Non-timber forest Products (NTFPs) are a broad category of natural resources harvested from forests that do not involve cutting down trees for their wood or timber. NTFPs play a vital role in the livelihoods and economies of many rural communities, particularly in forest-dependent regions. Despite the benefits obtained from NTFPS, little is known about its potential to alleviate poverty in the local communities. The objective of this review is to comprehensively assess the potential of NTFP's contribution to poverty reduction within forest fringe communities. The study employed the systematic review method to delve into the multifaceted relationship between NTFPs and poverty alleviation. We followed the preferred reporting items for systematic reviews and meta-analyses protocol (PRISM-P). In all, 58 research articles were reviewed. The results indicated that NTFPs hold significant promise as a tool for poverty alleviation. Globally, NTFPs have the potential to alleviate poverty and increase household incomes between 19% and 78% within forest fringe rural communities. In addition, the review indicated that countries in Africa and Asia depend highly on NTFPs and provide substantial amounts of income to the locals. However, the potential for NTFPs to reduce poverty is not uniform. It is highly contingent upon local ecological conditions, market accessibility, community involvement, and supporting policies and interventions. In conclusion, this systematic review demonstrates that NTFPs have a substantial role in poverty alleviation. This study underscores the need for continued research and targeted development initiatives to unlock the vast potential of NTFPs in addressing poverty challenges.

Carbon monoxide (CO) concentrations in wildfire plumes are easily measured from satellites. This gas can be used as a proxy for carbon dioxide. Forest fires play an important role in the carbon balance and in particular the CO balance. The most likely causes of mega-fires of 2003, 2012, 2021, and 2023 in the Northern hemisphere are heat waves and severe droughts associated with changes in general circulation. Here we analyze satellite data obtained by two different sounders, AIRS and TROPOMI. Different sensitivity to the lowest troposphere allows obtaining information about anthropogenic or pyrogenic contamination of the boundary layer. Shapes of areas polluted by mega-fires in 2019-2020 (Southeastern Australia) and 2021 (Central Siberia) coincide with the areas occupied by coal deposits. The Siberian Lena and Tunguska coal basins are the two largest coal fields in the world. In 2021, their combined area accounted for 90% of fire CO emission from the entire Russian Federation. So strong fires have not observed in this area before. Under- and sub-ground coal combustion may be included into the list of wildfire fuels, at least their role for ignition should be admitted. Further research is needed to assess the importance of coal fires to global climate.

Background: The mental state is important in the health/illness process. Including stress, depression and anxiety. Nature immersion therapies have been implemented as a valid method for reducing these disorders. The aim will evaluate the effect of forest intervention therapies on levels of stress, depression, and anxiety. Methods: We searched for randomized clinical trials related to stress, anxiety and depression levels. Risk of bias was assessed according to the guidelines of the Cochrane Handbook for International Systematic Reviews. Results: We identified and synthesized eight publications. Two studies for cortisol showed significant differences between the pre-test and post-test levels in the intervention groups. One study including Natural killer cells, that reported a significant decrease in the intervention group compared to the control group. Two studies used the Stress Response Inventory, indicated a significant reduction in stress levels in the intervention group compared to the control group. Regarding Restorative Out-come Scale, no significant differences were found. Three studies were selected for the evaluation of anxiety and depression levels according to the Positive and Negative Affect Schedule. Four studies used The Profile of Mood States scale; no significant difference was observed. Conclusions: There is limited evidence to recommend forest bathing for reducing these disorders.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

Various studies on the physical characteristics of the canopy gaps in tropical and subtropical regions but that in the mangrove swamp forest in Malaysia is little known. The objectives of this study are to assess the canopy gaps physical characteristics (size, form, direction), to assess the changes in soil physical characteristics and microenvionment in and around the canopy gaps. Five canopy gaps were selected formed by lightning strikes, gaps size and shapes were determined using the portable Global Positioning System and photographic images were analysed using ForestCrown. Result shows that canopy gap opening has increased the total light reaching the forest floor, changed the canopy gap microenvironment. Strong correlation between canopy opening percentage and temperature of gaps explains the changes of canopy gaps characteristics. These changes have contributed to new seedling germination, regeneration and maintaining the canopy gap dynamics.

In tropical Africa, forests and savannas are the two most widespread biomes and potentially represent alternative stable states with divergent species composition. A classic, but untested, hypothesis posited by White (1983) suggests that the transition zones between forests and savannas contain a floristically impoverished assemblage with few representatives from each biome. Further, the evolutionary dimension of diversity has received limited attention, despite its importance for understanding the biogeographic history of biomes. Here, we quantify species richness and several measures of evolutionary diversity in 1° grid cells, using c. 300K occurrence records of trees and shrubs combined with biome affiliation data for 3,125 species. We find that assemblages in transition zones hold fewer woody species than assemblages in forest and savanna zones, as posited by White. However, transition zones hold more phylogenetic diversity than expected given their species richness, whether one considers forest and savanna assemblages separately or together. We also show that the Congo basin forest has low levels of phylogenetic diversity given the number of species and highlight south-eastern African savannas as a centre of savanna woody species richness and phylogenetic diversity. Regions with high phylogenetic diversity given the number of both forest and savanna species were centred around the Dahomey Gap and Cameroon, mainly in transition zones. Overall, our study shows that even if floristically impoverished, transition zones lead to unexpectedly high evolutionary diversity, suggesting they are important centres of evolutionary innovation and diversification.

Browsing of wild ungulates can have profound effects on the structure and composition of forests. In the Swiss National Park, the density of wild ungulates including red deer (Cervus elaphus), ibex (Capra ibex), and chamois (Rupicapra rupicapra) is exceptionally high due to strict protection and the absence of large predators. We examined count data of larch (Larix decidua), cembra pine (Pinus cembra), spruce (Picea abies), upright mountain pine (Pinus mugo subsp. uncinata), and mountain ash (Sorbus aucuparia) of four sampling years between 1991 and 2021 and modelled how different topographic and location factors affected the probability of browsing on saplings of larch, cembra pine, and spruce. Despite the high density of wild ungulates, the numbers of saplings and young trees increased over the past 30 years. The probability of browsing on saplings was highest for larch at a height of 10 – 40 cm and increased with increasing elevation. In our study area, open grasslands are mainly located above the tree line, which might explain the positive correlation between elevation and the probability of browsing. Other factors like exposition and slope, available food resources and disturbance by humans did not have clear effects on the probability of browsing.

Forests are a large reservoir of biodiversity on which riparian populations frequently rely. Non-timber forest products (NTFPs) are an important source of income for millions of people living in forest-adjacent communities. This study aims at characterizing the types and uses of NTFPs in order to determine whether their exploitation in the eastern Democratic Republic of the Congo is sustainable. Interviews and direct observation were carried out with NTFP stakeholders (harvesters, sellers, and consumers) in Kalonge, near the Kahuzi-Biega National Park. The results showed that 40 NTFPs of plant origin and 10 NTFPs of animal origin are commonly exploited and used in food and traditional medicine. The most common NTFP harvesting techniques are debarking, picking, digging up, felling, and wine extraction, which are all tailored to the plant part. A significant relationship (p-value&lt; 0.001) has been established between the types of NTFPs used and harvesting methods as well as between the types of NTFPs used and organs retrieved. NTFP products are mainly obtained from the natural forest KBNP but also in the woodland, trees grown outside of forest or through domestication. Their abundance, however, is hampered by the extraction of wood for charcoal, energy, and timber, as well as agricultural expansion. The supply of NTFPs is determined by the market demand for the products, the nature of the product, and the ease of disposal. The NTFPs value chain in Kalonge is important to the local economy, however exploitation of NTFP products remains uncontrolled and should be well managed to ensure sustainability.

Fossil-based emissions can be avoided through using wood in place of non-renewable raw materials as energy and materials. However, increasing wood harvest influences forest carbon stocks. This effect may reduce the overall climate benefit of wood use significantly but is widely overlooked. We carried out a systematic review of simulation studies and compared differences in forest carbon and amount of wood harvested between more and less intensive wood harvest scenarios for three different time perspectives; short (1-30 years), mid (31-70 years) and long (71-100 years). Out of more than 450 reviewed studies 44 provided adequate data. Our results show that increased harvesting reduced carbon stocks over 100 years in temperate and boreal forests roughly 1.6 (stdev 0.9) t_C per t_C harvested. The value proved to be robust when outliers explicitly influenced by other factors than change in harvest rate, such as increase in fertilization or forest area, were removed. Interestingly, no significant difference in carbon impacts was found for average values of boreal and temperate forests or between short and long time-horizons. However, impacts tend to be greatest in the mid-term. This carbon balance indicator that we estimated can be interpreted as carbon debit of wood harvest in forests. It is significant compared with the typical GHG credits in technosphere generated by avoiding fossil emissions in substitution and increase in carbon storage in harvested wood products, and should not be ignored. Our estimates provide default values that can directly be included in GHG balances of products or assessment of mitigation policies and measures related to wood use. However, more systematic scenarios and transparent data in which different factors influencing forest carbon stocks are separately studied are clearly required to provide better constrained estimates for specific forest types.

According to several evidence, forest environmental seems able to provide beneficial effects on functional and psychological parameters, related to cardiovascular, metabolic, respiratory functions as well depression and anxiety. The aim of this study is to investigate the effect of a one-day forest walking in Selva di Castelfidardo (AN, Italy) on 37 participants aged 21-68, most of them living in either urban or suburban areas of large cities. We observed a statistically significant effect on sympathovagal balance by the means of heart rate, systolic and diastolic blood pressure, body temperature, skin temperature, skin conductance, HRV parameters (AVNN, SDNN, rMSSD, pNN50, LF, HF, LF/HF ratio), oxygen oximetry, PEF, FEV1. A significant difference was also detected on the Perceived Stress Scale responses (19.27 pre vs 13.81 post-immersion, p=<0,05; -28,3% variation). Our data contribute to increase the body of literature about the effect of forest walking, adding data on an Italian area qualified for forest bathing.

Current satellite remote sensing methods struggle to detect and map forest degradation, a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height (hϕ) is a promising variable for measuring forest disturbances, as it is closely related to mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrain, despite the fact that much of the worlds’ remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between hϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising the level of multilooking in the interferometric processing chain strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available.

Despite the use of commercial forest carbon protocols (CFCPs) for more than two decades, claiming ~566 MMtCO₂e and a market value of ~USD $15.7 billion, comparative analysis of CFCP methodology and offset results is limited. In this study, five widely used biometric-based CFCPs are characterized, and common characteristics and differences are identified. CFCP claims of net forest carbon sequestration are compared with results of directly measured CO₂ by eddy covariance, a meteorological method integrating gross vertical fluxes of forest and soil carbon, and the only alternative non-biometric source of net forest carbon sequestration data available. We show here that CFCPs share a structural feature delimiting forest carbon values by zero-threshold carbon accounting (gC m^-2 ≤ 0), a pattern opposite to natural emissions of forest CO₂ exchange based on direct measurement and a fundamental biological constraint on net forest carbon storage (i.e., soil efflux, ecosystem respiration). Exclusion of forest CO₂ sources to the atmosphere precludes net carbon accounting, resulting in unavoidable over-crediting of CFCP project offsets. CFCP carbon results are significantly different from global forest CO₂ net ecosystem exchange population results (FluxNet2015 gC m^-2) at the 95% to 99.99% confidence levels, inferring an annual median error of ~247% (gC m^-2), consistent with over-crediting. Direct CO₂ measurement provides an urgently needed alternative method for commercial forest carbon products that has the potential to harmonize global markets and catalyze the role of forests in managing climate change through nature-based solutions.

Deforestation and forest degradation mostly caused by human interventions affects the capacity of forest ecosystem to provide ecosystem services and livelihood benefits. Forest Land Restoration (FLR) is an emerging concept which focuses on the improvement of ecosystem as well as livelihood of the people at the landscape level. Nepal has successfully recovered degraded forest land mainly from the hilly region through forest restoration initiatives especially community based forestry. However, the Terai region is still experiencing deforestation and forest degradation. This study navigated the gaps related to forest restoration in the existing policies and practices and revealed that the persistence of deforestation and forest degradation in Terai is a result of a complex socio-economic structure, limitation of government to implement appropriate management modality, unplanned infrastructure, and urban development. We suggest that forest restoration should focus on ecological and social wellbeing pathways at the landscape level, to reverse the trend of deforestation and forest degradation in the Terai regions of Nepal. The study provides a critical insight to the policy makers and practitioners of Nepal and other countries (with similar context) who are engaged in forest/ecosystem restoration enterprise.

Rubbish in a forest environment is a great threat to this ecosystem, but this threat may also apply to the lost benefits for visitors to the forest. Previous studies proved that forest areas have a positive effect on obtaining psychological relaxation in the people visiting them. However, it was not known whether this restorative experience could be disturbed in any way by the presence of an open dump in the forest. To check how the presence of a landfill affects the visitors, an experiment was planned in which the respondents observed a forest area with a landfill and a forest landscape without a landfill for 15 minutes (control). The respondents then assessed the landscape using the semantic differential method and the Perceived Restorativeness Scale (PRS). An analysis of these observations showed that the presence of a landfill in the forest significantly changed the appreciation of the landscape by the respondents, the values of positive experiences decreased, and the negative experiences increased. Restorativeness was also reduced. Based on the results, it can be concluded that the presence of garbage in the forest may interrupt the restorative experience of its visitors.

Truffles contribute to crucial dynamics in the soil systems, being involved in plentiful ecological functions important for ecosystems. Despite this, the interactions between truffles and surrounding mycobiota remain unknown. Here, we aimed to shed light on how much truffle species could affect its surrounding soil mycobiota. Using traditional chemical analysis and Illumina ITS amplicon sequencing, we compared soil nutrients and mycobiota surrounding two truffle species: Tuber indicum (Ti) and T. pseudohimalayense (Tp) inhabit in the same Pinus armandii forest in southwestern China. Tp soil was more acidic and had higher nutrients (total C, N, P contents) than Ti soil. Fungal richness and diversity of truffle ascomata and surrounding soils were significantly higher in Tp than in Ti. Redundancy analysis showed relationships between soil fungal taxa and soil properties had changed from negative (Tp) to positive (Ti) and shifted from a moisture-driving (Tp) to a total N-driving (Ti). Overall, our results showed that the interactions between truffle and soil system had been altered with species variation, although the causative peculiarity of these associations needs to be further studied.

In this study, the decision-making process management of forest tending in the forestry business is decentralized, and forest tending decision-making activities at different points in time are integrated by decision makers at different geographical locations. The decision-making process was analyzed and optimized from a system perspective. Based on the optimized decision-making process, a forest tending business group decision support system (FTGDSS) was established. We first reviewed and discussed the characteristics and development of the forest tending business and forestry decision support system. Business Process Modeling Notation was used to draw a current state flow chart of the forest tending business, to identify and discover important decision points in the process of tending decision-making. We also analyzed the content and attributes of each decision point, and described the system structure, functional framework, knowledge base structure, and reasoning algorithm of FTGDSS in detail. Finally, FTGDSS was evaluated from the two dimensions of the technology adoption model. FTGDSS integrates different levels of time-space decision-making activities, historical tending data, business plans, decision-makers' management tendencies into the decision-making process and automatically extracts decision-making data from the forest business process management enterprise resource planning system (Smartforest) that improves the ease of use of the decision support system (DSS). It also improves the quality of forest tending decisions, and enables the DSS to better support multi-target management strategies.

The import and export of tree seed carries with it risks of inadvertent introduction of pests and pathogens to hitherto unaffected regions. Although trade in seed of specified trees is regulated, phytosanitary requirements for most tree species are minimal, even those related to the most important forest tree species in a given region. A better understanding of the microbiome associated with seed intended for commercial production or ornamental use, and their potential risk with the transport from the source origin of distributors, will help regulatory agencies implement measures to prevent new and emerging risks. In this study we used high throughput sequencing to show that highly diverse fungal communities were associated with seed of 14 different Pinus species obtained from seed banks (seed orchards) and retail sources (online distributors) in North America and Europe. Fungal diversity differed among the 23 seedlots tested. Community composition did not relate to the species of Pinus nor the country of origin. Assigned potential functions based on sequence identity using FUNGuild provided an overall understanding of the likely life strategies associated fungal OTUs. Of those sequences classified to a trophic level, 453 were plant pathogens, with the Dothideomycetes having the highest prevalence. The most common plant pathogens detected included Sydowia polyspora, Lasiodiplodia theobromae, Diplodia intermedia and Diplodia sapinea. The evidence presented here illustrates an urgent need for plant protection authorities, practitioners and the general public to recognize the potential risk of introducing harmful pathogens through innocent transport of seed.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R²_adj=0.820), followed by basal area (BA) (R²_adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.

Deadwood represents a source of nutrients, carbon and water for metabolism within forest ecosystem. Nutrients are mobilized due to the decomposition of wood, which is a long-term process that can be best studied by analysing environmental data on a temporary scale. Our study provides physico-temporal data on the downed logs of three major tree species in European temperate forests: Abies alba Mill., Fagus sylvatica L. and Picea abies (L.) Karst. Time since death was obtained using tree censuses (repeated for 40 years) and dendrochronology for each single downed log, the oldest being 75 years old. Standard laboratory methods were used for the determination of wood density and moisture changes. F. sylvatica was decomposed rapidly in the initial phase – mass loss was 50% during the 5 years after death, while A. alba and P. abies lost 13% and 16%, respectively. Downed logs of F. sylvatica contained 391 kg of water per m³, while these of P. abies 279 kg. A log-transformed linear model was created that shows the dependence of time since death on mass loss. According to the model, F. sylvatica had the shortest total decomposition time (39 years), followed by A. alba (58 years) and P. abies (86 years).

This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.

Indirect imaging methods involve at least two steps, namely optical recording, and computational reconstruction. The optical recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical modulation functions and reconstruction methods developed in the past years for different applications. In most cases, a compatible pair of optical modulation function and reconstruction method gives optimal performance. A new reconstruction method termed non-linear reconstruction (NLR) was developed in 2017 to reconstruct the object image in the case of optical scattering modulators. During the years, it was revealed that the NLR could reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR has been investigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed.

The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.

This article takes an approach to explaining the behavioral manifestations of the decision making in US companies’ offer of health insurance that is grounded not only on their cost minimizing behavior, but also in a humanness dimension based on the African concept of Ubuntu. In this way, we define an Ubuntu based Random Utility modeling framework, describing the choice process as a tripartite decision making, and implemented using a nationally representative random sample of 1,061 American companies from the Dunn and Bradstreet Business data, supplied by Survey Sampling International to the Associated Press-NORC Center for Public Affairs Research. The results from the three sequentially implemented specifications showed that the relationship between management culture and health plan offering strategy is dependent on other relevant co-variates, which when left out, leads to the problem of omitted variables bias. However, when all variables are included but assumed to enter the relationship exogenously, this results in management culture not having any statistically significant effect on companies' decisions about scope of health plan offering. When the exogeneity assumption is relaxed through a recursively Bivariate Probit model, the system of two equations produces a highly significant management culture effect. In fact, in this later case we see that companies with groups and formal committee management culture are 1.58 times less likely to choose a multiple plan strategy over a single plan strategy, hence failing to show the more wholesome plan offering that would theoretically prevail under Ubuntu style management.

The escalating global population and subsequent demand for agricultural products have led to a surge in agricultural waste generation, posing significant disposal challenges. Conventional disposal methods such as burning and dumping not only harm the environment but also jeopardize human health and safety. Recognizing the urgent need for sustainable waste management, researchers have increasingly focused on repurposing agricultural plant waste as a valuable resource. This paper presents a comprehensive review of the potential of agricultural plant waste in wealth creation and sustainable development. It highlights the detrimental impacts of current disposal methods and emphasizes the necessity for alternative approaches. By analyzing the physical, mechanical, and chemical properties of plant fibers, particularly cellulose, hemicellulose, and lignin, this review underscores their suitability for diverse applications. Moreover, it explores the emerging trend of utilizing pineapple leaf fiber, a sustainable and lightweight material, in structural applications such as UAV construction. With its exceptional mechanical properties and biodegradability, pineapple leaf fiber holds promise as a viable alternative to traditional materials, contributing to a more sustainable future. In conclusion, this review advocates for a paradigm shift towards embracing agricultural plant waste as a valuable asset for economic prosperity and environmental sustainability. It underscores the importance of continued research and technological advancements to unlock the full potential of agricultural waste in fostering a circular economy and driving sustainable development globally.

The yield of logging in a forest management area using only the volume variable is not sufficient to assess effective wood productivity. The objective was to evaluate production using different logging variables to point out possible divergences between what was planned and what was actually carried out, in order to indicate adjustments to annual operating plans (AOPs) in the Amazon. The database comes from a community forest management area in the Tapajós National Forest, con-sidering three sources: 100% inventory, AOP planning and the post-exploration report. The pro-duction yield considering the number of trees felled and the effective volume were compared with the AOP planning. The volume estimated by the generic equation was compared with the cubed volume and the commercial height variable was evaluated. Of the 6,267 trees selected, 5,090 trees were harvested (3.2 trees.ha-1), indicating a yield of 85.0% of the estimated volume. Of the 25 commercial species logged, the most representative were: Manilkara huberi, Vochysia maxima, Lec-ythis lurida, Couratari guianensis and Hymenaea courbaril. It can be concluded that the number of trees, cubed volume and commercial height are post-exploration yield response variables and not just volume, as is currently used in SFMPs.

The advancement of mechanization in forestry has increased productivity in the forestry sector, bringing positive and negative impacts that require a deeper understanding for sustainable forest management. This study aimed to apply a simplified instrument for assessing damage and environmental impacts in forest harvesting of commercial eucalyptus plantations, using a combination of methodologies. The methodology used combined interaction networks and impact assessment matrices, carrying out field surveys, transposing them to interaction networks and weighting them through assessment matrices, resulting in environmental indices (ES) for prioritizing actions. The study was conducted on a commercial eucalyptus plantation in the municipality of São Pedro, São Paulo, Brazil. The mechanized harvesting of the area consists of the structure of a module with a mobile unit consisting of a harvester and forwarder. The results indicated that wood transport presented the highest ES, both positive and negative. The most significant negative impacts (ES) were the depletion of water resources and erosion, while the positive impacts included regional development and job creation. The most notable changes, positive and negative, were observed in the physical and anthropic environment, with a lesser impact on the biotic environment.

Forest resources are renewable,rational exploitation and utilization of forest resources are not only conducive to sustainable development of population, but also can obtain higher economic benefits. Based on actual timber harvesting problems,a combined harvesting model of timber and non-timber with nonlinear harvesting term was established.Firstly, using the qualitative theory of ordinary differential equations, the dynamic behavior of the model is studied, and the existence and stability of the equilibrium point of the model are proved .Secondly, the optimal control solution is obtained by using the optimal control theory.Finally, the optimal harvesting strategy of timber and non-timber products is given based on the numerical simulation results, which provides a certain theoretical basis for the sustainable development of forestry ecological economy.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

The Australian tropical montane cloud forest (TMCF) is home to approximately 18 percent of the nation’s total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and WTWHA is no exception. The current average annual temperature of WTWHA in FNQ is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species endemic to the WTWHA in FNQ, which are already experiencing climate change threats. Few of these plants are used in herbal medicines. This study comprehensively reviewed metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses: i) recent developments in plant metabolomics studies that can be applied to study and understand the interactions of wet tropical plants with climatic stress, ii) medicinal plants and isolated phytochemicals with structural diversity, and iii) reported biological activities of crude extracts and isolated compounds.

Since 2015, community forests have been promoted in Togo as an alternative to protected areas degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m x 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Hetopogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsis suberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.

Woodland bat assemblages are usually structured in space by distance from the ground, water and obstacles, features often defining chiropteran hunting tactics. Consequently, bat species composition differs strongly among various habitats even within the same forest patch. However, when conducting local bat survey in Wolin National Park (WPN), we revealed unexpected uniformity in qualitative and quantitative structure of bat assemblages, based on mist netting and ultrasound recording. In total, 10 vespertilionid species were detected. In all methods and sampled habitats a single species, Pipistrellus pygmaeus, predominated, while no Barbastella barbastellus, an old forest specialist, was detected, despite the abundance of preferred daily roosts. We also reviewed literature for mist netted bat samples in four different habitats of lowland Polish forests. Samples usually clustered based on habitats and the same habitat classes often clustered very closely, despite representing geographically distant forests. The exception was WPN, where all four habitat classes formed a tightly packed cluster. We suppose that P. pygmaeus might act as a hyperabundant native species, a successful generalist that reduces the contribution of more specialized taxa in the assemblage. It probably benefits from both forest renaturation and anthropogenic cross-boundary subsidy, the latter including both roost availability and prey abundance.

The author attempts to reconstruct the climatic vicissitudes of the Mediterranean to explain why only broadly distributed Eurasian forest species have penetrated the Mediterranean peninsulas. He has carried out a bibliographic survey of the status of breeding forest birds in Italy over 15 decades (1872-2022) in order to establish an objective long-term trend (stable, increasing, decreasing, etc.). The number of breeding forest birds in Italy amounts to 61; their distribution, with a few exceptions, indicates that they are widespread in Eurasia, but only a small percentage of Eurasian forest species have colonized Italy and the other Mediterranean peninsulas, namely 49 in Iberian, 61 in Italian and 64 in Balkan peninsulas; a small percentage of them (between 10.9 and 14.3%) belongs to trans-Saharan migrants. The similarity between the forest species on the three peninsulas (Iberian, Italian and Balkan) results between 0.45 and 0.48, indicating a certain differ-ence in the overall fauna on the three territories. Not all species have penetrated southwards into the three peninsulas; for example, some that stopped in the Italian Alps have instead arrived to the forests of Greece, at a latitude corresponding to southern Italy, or species that in Italy stopped in the northern Apennines in the other two peninsulas have instead arrived far south. Iberian peninsula and the island of Corsica hold three endemic species among breeding forest birds, Italian and Balkan peninsulas have not endemic species. Overall, the Mediterranean presently hosts mainly neo-endemic taxa among forest bird species; the only paleo-endemics can be considered the three species of nuthatches living in Corsica, Algeria, and Turkey (other than Caucasus and the islet of Lesvos) and Le Vaillant’s woodpecker in the Maghreb (North Africa). Italian forests cover ca. 40% of land surface and since 1980’ are increasing, but 22% of them do not have a natural origin. However, it is difficult to know the true increase of forests, because some of them are fired every year. The presence of some ecologically demanding forest birds depends on the age of the trees, permanent open spaces and other characteristics at the edge of woodland.

Deforestation brings vast and detrimental impact on the environment, economy, and social aspects of community. Thus, it is important to assess and analyze deforestation to inform the decision maker that oversees issued policy and development strategies. The present study aims to characterize deforestation in Central Kalimantan between 2006 - 2020. Utilized land cover map issued by Indonesian Ministry of Environment and Forestry, we analyzed the change in natural forest cover using Remote Sensing and Geographical Information System (GIS) to find the rate the trend, location, and land cover replacement of deforestation in Central Kalimantan Province, Indonesia. The research found that during the period Central Kalimantan lost 1.5 million ha of natural forest with the rate of deforestation 117,000 ha/year. In general, the deforestation shown decrease trend and fluctuated during the period. Deforestation majority takes place at secondary swamp and dry forest that are located at south part of the island. Most of deforestation resulted shrubs, plantations, and agriculture land. The finding of this research could be used as a base to determine the target location for rehabilitation strategy and approach to prevent further deforestation.

Depression is considered a widespread mental health problem worldwide. Moreover, anxiety symptoms are very closely related to depression patients, and it is known that the coexistence rate of depression and anxiety diagnosed simultaneously is high. Treatment and preventive management of depression and anxiety are essential for public health. Forest healing is attracting attention as a form of low-cost preventive medicine that is safe and has no side effects. However, although the physiological and psychological effects have been scientifically proven, it is insufficient to reveal a direct relationship between forest healing and depression. This study investigated the benefits of an urban forest healing program on depression and anxiety symptoms of depressive disorders. We employed a randomized controlled trial design. Forty-seven depression patients were randomly divided into an urban forest healing program group and a control group. Measures included the Montgomery-Asberg depression rating scale (MADRS), the Hamilton Anxiety Rating Scale (HARS), and the State-Trait Anxiety Inventory (STAI) questionnaires. Our results revealed that the combination of general treatment and forest healing programs for patients with depression is more effective in improving depression and anxiety than routine treatment alone. We expect our work to serve as a starting point for more sophisticated research in discussing the availability of non-pharmacological treatments in forest healing.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p&lt;0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

Tree ordinances can be an effective means of preserving urban forests in the face of development pressures. Despite this, they also have the potential to be divisive among the public - especially when applied to privately-owned land. In this study we surveyed 1,716 Florida urban residents to understand how they value regulation and management of the urban forest. Specifically, we asked about: tree protection ordinances, incentive programs to manage or plant trees, justification for tree removal, and development. Most respondents supported tree protections, even when applied to trees on their own property or when they had the potential to limit development activities. Additionally, there was limited support for removing healthy trees for development. Respondents supported the use of funds for urban forestry efforts – particularly at the local or state level.

Home-field advantage (HFA) encompasses all the processes leading to faster litter decomposition in the 'home' environment as compared to that of ‘away’ environments. To determine the occurrence of HFA in a forest and adjacent clear-cut, we set up a reciprocal litter decomposition experiment within the forest and clear-cut for two soil types (Cambisols and Gleysols) in temperate Germany. The forest was dominated by Norway spruce (Picea abies), whereas forest regeneration of European Beech (Fagus sylvatica) after clearcutting was encouraged. Our observation that Norway spruce decomposed faster than European beech in 70-yr-old spruce forest, was therefore most likely related to specialized litter-microbial interaction under existing spruce, and leading to a HFA. The ‘spruce’ microbial biome and soil conditions of original forest were then rapidly changed even after a short-term regeneration following clearcutting, resulting in faster beech decomposition also masking any HFA for spruce, particularly in moisture- and nutrient-deficient Cambisols. Divergence between forest and clear-cut in the Cambisol of their litter δ15N values beyond 9 months, implied litter N decomposition was only initially independent of soil and residual C status. We conclude that clearcutting modifies HFA and helps promote the establishment or regeneration of European beech in this and similar forest mountain upland areas.