Large Forest Areas Research Articles

Restoration Evaluation of National Forest Park in Greater Khingan Mountains Region, China

As an important part of ecological civilization construction and harmonious coexistence between man and nature, the importance of restorative environment construction in national forest parks is self-evident. In this paper, the national forest park in the Greater Khingan Mountains region covering a large area of primary forest is taken as the research object. Based on visual perception, PRS, skin conductance level, and eye tracking technology are used as evaluation indexes to conduct restoration experiments on individuals. Among 60 participants, the PRS total scores for lawn space, shady space, dense forest space, and hard space were 166.63, 164.63, 168.43, and 158.93, respectively, indicating good restorative benefits, with hard space scoring lower. SCR decreases for dense forest space (M = 0.52) were significantly greater than for hard space (M = 0.38), suggesting better stress reduction. Eye tracking data showed that dense forest space had the longest total fixation duration (M = 42.57) and hard space the highest fixation count (M = 42.73). The results show that the national forest park is beneficial to the recovery of individual psychology and physiology, and can reduce the pressure of people’s visual perception. The psychological and physiological restorative benefits of different spatial scene types are different. Moreover, there is correlation between the eye movement index, psychological evaluation index, and physiological evaluation index, which confirms the possibility of introducing the eye movement index into the study of restorative environments.

Lantana Camara : A Medicinal Plant

Large areas of dry-moist forests and other culturable wastelands have been overrun by lantana on the Indian subcontinent, which may have changed ecosystem services, biodiversity, and landscape ecology. In addition to forest and barren areas, it has infiltrated the majority of Indian grazing lands (13.2 million hectares), and its control is expected to cost US$70 per hectare. Rapid spread, severe infestation, allelopathy, opportunistic growth habit, reproductive biology features, and a persistent resistance to burning and cutting make this invasive weed unique.(1) With an emphasis on its ecological characteristics, including biomass productivity, reproductive biology, invasiveness, allelopathy, eradication strategies, and commercial applications documented from India, this research examines the state of knowledge regarding Lanthana camara. The literature evaluation indicates that lantana’s economic benefits and advantages outweigh its drawbacks, necessitating more research on cost-benefit analysis when making decisions about its management and eradication.(1).

Seven new species from Eocene Baltic amber reveal surprising diversity and suggest possible speciation scenarios in the relictual family Collohmanniidae (Acari: Oribatida)

This study provides a comprehensive morphological analysis of ten fossil specimens from the genus Collohmannia, found in Eocene Baltic amber, with nine representing different species. Collohmanniidae, a unique monogeneric family of oribatid mites are among the largest oribatids. They feed on leaf litter and present significant sexual dimorphism and courtship behavior, which includes the transfer of nuptial food from male to female. Extant Collohmannia species inhabit isolated mountain forest areas. A fragmentation hypothesis has been proposed to account for their highly disjunctive distribution, suggesting that climate change divided a large forest area previously occupied by a parent species, leading to the allopatric evolution of several species of Collohmannia. However, with the discovery of a significant number of fossil species in Eocene Baltic amber we propose an alternate hypothesis with the Baltic amber forest serving as an arena for speciation of Collohmanniidae. According to it, the driving force of speciation was dietary and was based on gut microbiome specificity, reinforced by the female choice during the courtship ritual. In this paper we describe seven new fossil species of Collohmannia (C. sellnicki sp. nov., C. albertii sp. nov., C. nortoni sp. nov., C. groehni sp. nov., C. kerneggeri sp. nov., C. weiterschani sp. nov., C. clavata sp. nov.), re-describe C. schusteri based on study of the holotype, synonymize Embolacarus with Collohmannia, propose and describe the neotype of C. pergrata comb. nov., and provide a key to all described Collohmanniidae. Together with the hypotheses of speciation we propose verification experiments to be performed on extant Collohmannia mites.

Optimizing Low-Efficiency Robinia pseudoacacia Forests on the Loess Plateau Based on an Evaluation of the Ecological Functions of Soil and Water Conservation

The vegetation for water and soil conservation on the Loess Plateau has resulted in the formation of large areas of low-quality and inefficient forests during its growth process, thereby hindering the high-quality development of such vegetation and compromising the sustainability of the ecological functions of soil and water conservation. This study focuses on Robinia pseudoacacia forests in the Caijiachuan watershed in the Loess region of western Shanxi. A comprehensive evaluation system for the ecological functions of soil and water conservation has been established to conduct a comprehensive assessment of the Robinia pseudoacacia forests. Additionally, the study identifies low-efficiency Robinia pseudoacacia forests and proposes reasonable regulation and control measures. The main research conclusions are as follows: (1) There are significant differences in the water and soil conservation functions among different Robinia pseudoacacia forests. The degree of inefficiency in these forests increases with forest density, and Robinia pseudoacacia forests with slopes between 35° and 40° are all low-efficiency forests. (2) The average density of high-efficiency Robinia pseudoacacia forests is 1655 trees per hectare, medium-efficiency forests average 1780 trees per hectare, and low-efficiency forests average 1927 trees per hectare. By substituting forest density, diameter at breast height (DBH), tree height, and crown width into the low-efficiency forest discrimination function, low-efficiency forests can be identified. The main influencing factors of low-efficiency forests are the Margalef richness index of shrubs, the total soil nitrogen content; and the nitrate nitrogen content. (3) Regulation measures for low-efficiency forests include controlling forest density to 1655 trees per hectare, increasing the richness of understory shrubs, and enhancing total soil nitrogen and nitrate nitrogen content by planting leguminous plants such as Lespedeza bicolor, Sophora davidii, and Vicia sepium. The research results can construct a comprehensive evaluation system for the ecological functions of Robinia pseudoacacia forests for water and soil conservation, identify low-efficiency forests, and provide precise regulation based on the causes of inefficiency, ensuring the sustainable function of Robinia pseudoacacia forests for water and soil conservation.

Miniature GPS tags reveal extensive movements by a threatened narrow‐space bat and highlight sensitivity to forest clearing

AbstractAn understanding of fine‐scale movements made by animals is critical for assessing localized habitat preferences and informing the scales over which land management should be applied. However, for cryptic species such as insectivorous bats that are highly mobile and active at night, it is difficult to collect these data. Nyctophilus corbeni serves as a case study for narrow‐space species as it is listed as threatened and nothing is known about its localized habitat preferences. We used miniature GPS tags to assess fine‐scale movements across three sites and 2 years (autumn 2020 and 2021) in the Pilliga forests in northwest New South Wales. Each site consisted of a mosaic of forest disturbances (e.g. timber harvesting / thinning regrowth, prescribed fires and wildfires, and cleared farmland). Bats made large movements in the non‐maternity season, with a maximum of ~21 km recorded as a one‐way movement on a given night. On average, the maximum distance travelled by tagged bats was 6.2 ± 1.4 km per night. Short‐term home (9884 ha) and core (2157 ha) ranges were extensive, but varied by sex, with ranges of females ~15 times larger than males. Bats crossed cleared land that was within close proximity (<2.3 km, but typically <~600 m) of large forest blocks (>100 ha). Based on availability of habitat types, bats selected for older regrowth and burnt forest and avoided cleared areas with scattered trees and also thinned forest, though the availability for the latter was low (<3 %). Flight speeds and the lack of clustering of GPS fixes in cleared landscapes suggest that limited foraging may occur there. Overall, our results highlight the sensitivity of the species to forest clearing and potentially to thinning, but also demonstrate the importance of maintaining large areas of forest and where possible, reforesting cleared areas that occur between smaller, more isolated forest blocks.

Propagation Characteristics and Influencing Factors of Meteorological Drought to Soil Drought in the Upper Reaches of the Shiyang River Based on the Copula Function

Drought propagation is a complex process, and understanding the propagation mechanisms of meteorological drought to soil drought is crucial for early warning, disaster prevention, and mitigation. This study focuses on eight tributaries in the upper reaches of the Shiyang River. Based on the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSMI), the Drought Propagation Intensity Index (DIP) and Copula function were applied to quantify the intensity and time of drought propagation from meteorological to soil drought and explored the drought propagation patterns at different temporal and spatial scales in these tributaries. Results showed that, in the 0–10 cm soil layer, the propagation intensity of meteorological drought to soil drought was peer-to-peer, with a propagation time of one month. In the middle (10–40 cm) and deep (40–100 cm) soil layers, propagation characteristics differed between the eastern and western tributaries. The western tributaries experienced stronger drought propagation intensity and shorter propagation times (2–4 months), while the eastern tributaries exhibited peer-to-peer propagation intensity with longer times (4–10 months). The large areas of forests and grasslands in the upper reaches of the Shiyang River contributed to strong land–atmosphere interactions, leading to peer-to-peer drought propagation intensity in the 0–10 cm soil layer. The eastern tributaries had extensive cultivated land, where irrigation during meteorological drought enhanced soil moisture, resulting in peer-to-peer propagation intensity in the middle (10–40 cm) and deep (40–100 cm) soil layers. In contrast, the western tributaries, with larger forest areas and widespread permafrost, experienced high water consumption and limited recharge in the 10–40 cm and 40–100 cm soil layers, leading to strong drought propagation.

Environmental Indicator Plants in Mountain Forests: A Review.

Plant indicators are important in studies related to the environment, and mountain forests are rich in such plants. We conducted this study using the Web of Science Core Collection tools and the VOSviewer program combined with a classic review, based on the specialty literature. The topic of indicator plants in mountain forests has been (the first article published in a prestigious journal dates back to 1980) and remains relevant (after 2015, between 40 and 60 articles were published annually), with the total number of publications found being 665 articles and 22 review articles. These publications were authored by researchers from 96 countries, the most representative being the USA, China, and Germany (which also have large areas of mountain forests), and were published in 306 journals, with the most important being Ecological Indicators, Forest Ecology and Management, Forests, Journal of Vegetation Science, and Plant Ecology. They belong to the main scientific fields of Ecology, Forestry, Plant Sciences, and Environmental Sciences. The most frequently used keywords are vegetation, diversity, biodiversity, and forests. Their evolution over the past decade shows that the focus has shifted from keywords specific to this topic to those related to biodiversity and conservation, and more recently to climate change. Indicator plants in mountain forests are extremely varied but can be used successfully in the monitoring activity. Climatic conditions or human interventions lead to the dynamic of these indicator plants.

Growth and globalization of the Central African wildlife economy: Insights from a 23-year study of wild meat markets on Bioko Island, Equatorial Guinea

The commercial trade in wild meat is booming in Central Africa. Addressing this issue is a global priority because the trade poses a major threat to biodiversity and human health. We investigated the impact of socioeconomic factors, public health emergencies, and conservation efforts on the wild meat trade using daily surveys of wild meat markets on Bioko Island, Equatorial Guinea (EG), from 1997 through 2021. Bioko is an ideal location for examining how external factors impact the wild meat market trade. Although small, the island has large areas of intact forest that host populations of commercially valuable wildlife; low-cost protein substitutes are available; and Malabo, the island’s only large metropolitan area and wild meat trading hub, hosts a wealthy class of urbanites. We found significant associations between global market trends and the wild meat trade, especially China’s foreign investment and oil production in the US and EG. Economic crises like EG’s 2009 economic downturn that followed a global crash in oil prices and reduced production, redirected demand towards cheaper mainland wildlife carcasses amid reduced consumer demand. Public health emergencies had the most comprehensive impact on the wild meat trade. The 2014 Ebola outbreak and the COVID-19 pandemic both induced shifts in market demand, and the COVID-19 pandemic disrupted trade routes, affecting both urban and rural markets. Internally, we observed market decentralization over the last decade and changes in wildlife supply chains during public health emergencies. Conservation policies, including anti-poaching measures and educational outreach, temporarily influenced wildlife market trends, sometimes leading to trading surges in endangered primate carcasses. Our study highlights the importance of monitoring global market trends, public health campaigns, and adapting conservation strategies to disrupt wildlife supply chains and curb consumer demand for wild meat.

Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data

Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented.

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).

Palawan Peacock Pheasant (Polyplectron Napoleonis) Species Distribution Modeling: A Review of the Impact of Typhoon Rai/Odette on the Diversity and Distribution of an Endemic Bird Species

This study quantified the habitat requirements and, consequently, described the remaining suitable habitats through species distribution modeling of Polyplectron napoleonis within Cleopatra’s Needle Forest Reserve and Puerto Princesa Subterranean River National Park after the onslaught of Typhoon Odette in the Palawan region. This was accomplished using MaxEnt software, a statistical machine-learning algorithm that creates niche models with presence-only (PO) data and surrounding environmental variables (Dudik et al., 2007; Merow et al., 2013). Previous studies have identified precipitation, elevation, and land cover as the top environmental variables influencing tandikan habitat suitability, as revealed by their percent contributions. There were earlier claims of the noticeable movement of habitat-suitable areas to the fringes and beyond Cleopatra's Needle Forest Reserve. The migration seems to be going towards highland forests. Two factors could be behind this migration: the tandikan are responding to climate change or avoiding human disturbance. As this species is known to have a high tolerance for temperature change, the latter could be the more plausible factor. For 2022, post-Odette, MaxEnt analysis revealed a shift of percent contributions with elevation and land cover classification outranking aspect and slope. Environmental layers were limited to four, as these were the only currently available data. This shift in percent contributions could be linked to the direct damage to forests brought by the Typhoon in December of 2021. The land cover map generated for this year has shown large areas of forests that were converted into non-forests. Conservation efforts, therefore, for the tandikan, a forest species, should be recalibrated in response to this current development. Further, measures to mitigate the impact of solid typhoons on forests should be reviewed and evaluated.

Economic valuation of carbon sequestration in Quito’s Metropolitan Park using Sentinel-2 and neural networks

This study introduces an innovative method for assessing the economic value of carbon stored by the urban park forest in Quito, Ecuador. The method uses Sentinel-2 remote sensing data and advanced deep learning techniques. A large forest study area was chosen, and detailed field measurements were taken to gather biomass and carbon data. Sentinel-2 satellite images were processed to correct for radiation and atmospheric conditions, and vegetation indices such as NDVI and EVI were calculated. To model forest biomass, a convolutional neural network (CNN) was created and trained using Sentinel-2 spectral bands and vegetation indices. The model was validated with independent field data and demonstrated high accuracy in estimating biomass and carbon, as indicated by evaluation metrics like RMSE and R². The findings include detailed maps showing the spatial distribution of biomass and carbon in the study area, which can be a valuable tool for forest management and the implementation of policies for valuing stored carbon. This approach combines the high resolution of Sentinel-2 data with the predictive power of neural networks, providing a robust and scalable method for estimating carbon across large forest areas. The study's conclusions emphasize the feasibility and accuracy of this approach and its potential for application in various forestry and geographical contexts.

Modelling of atmospheric concentrations of fungal spores: a 2-year simulation over France using CHIMERE

Abstract. Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models.

Early wildfire detection using different machine learning algorithms

Early detection of wildfires is essential for mitigating their impact on forests and surrounding areas. In this study, we propose a wireless sensor node system that combines multiple low-cost sensors with an artificial intelligence-based detection method for early wildfire detection. The system architecture includes temperature, humidity, and smoke sensors, as well as a wireless communication module. Four machine learning classifiers, including decision trees, random forests, support vector machines, and k-nearest neighbors, were evaluated for their effectiveness in predicting wildfire detection using a dataset collected in a forest area. The results showed that the random forest algorithm with optimum hyperparameters had the highest accuracy in classifying fire and non-fire samples (77.95% correctly classified). The proposed system provides an effective and cost-efficient solution for early wildfire detection in large forest areas.

The Sensory Profile of 5 Types of Sumatran Forest Honey

Sumatra has a very large forest area so it has great potential to become a place for honey bee cultivation. The various honey produced has different characteristics depending on the source of the nectar. Research on sensory testing of Sumatran forest honey appears limited, with studies focusing more broadly on honey quality and properties. It is necessary to collect data on the sensory profile of Sumatran forest honey as a database that will help control the quality and ensure the authenticity of the honey. The purpose of this study is to determine the sensory profile of five types of Sumatran forest honey traded around Lampung Province. The method used a scoring test on color, aroma, taste, and viscosity. 30 trained panelists were used who had been selected and trained before conducting sample assessment. Data processing using ANOVA and further test Duncan Multiple Range Test (DMRT) with SPSS 27.0 software. The results showed that the five types of Sumatran forest honey tested had the same viscosity but had different color, aroma, and taste profiles. These distinctive and unique characteristics can enrich the biodiversity in Indonesia so it needs to be optimized for its benefits and preserved.

Characterizing Forest Plot Decay Levels Based on Leaf Area Index, Gap Fraction, and L-Moments from Airborne LiDAR

Effective forest management is essential for mitigating climate change effects. This is why understanding forest growth dynamics is critical for its sustainable management. Thus, characterizing forest plot deadwood levels is vital for understanding forest dynamics, and for assessments of biomass, carbon stock, and biodiversity. For the first time, this study used the leaf area index (LAI) and L-moments to characterize and model forest plot deadwood levels in the Bavarian Forest National Park from airborne laser scanning (ALS) data. This study proposes methods that can be tested for forests, especially those in temperate climates with frequent cloud coverage and limited access. The proposed method is practically significant for effective planning and management of forest resources. First, plot decay levels were characterized based on their canopy leaf area density (LAD). Then, the deadwood levels were modeled to assess the relationships between the vegetation area index (VAI), gap fraction (GF), and the third L-moment ratio (T3). Finally, we tested the rule-based methods for classifying plot decay levels based on their biophysical structures. Our results per the LAD vertical profiles clearly showed the declining levels of decay from Level 1 to 5. Our findings from the models indicate that at a 95% confidence interval, 96% of the variation in GF was explained by the VAI with a significant negative association (VAIslope = −0.047; R2 = 0.96; (p < 0.001)), while the VAI explained 92% of the variation in T3 with a significant negative association (VAIslope = −0.50; R2 = 0.92; (p < 0.001)). Testing the rule-based methods, we found that the first rule (Lcv = 0.5) classified Levels 1 and 2 at (Lcv < 0.5) against Levels 3 to 5 at (Lcv > 0.5). However, the second rule (Lskew = 0) classified Level 1 (healthy plots) as closed canopy areas (Lskew < 0) against Levels 2 to 5 (deadwood) as open canopy areas (Lskew > 0). This approach is simple and more convenient for forest managers to exploit for mapping large forest gap areas for planning and managing forest resources for improved and effective forest management.

Review of forest ecosystem services evaluation studies in East Africa

The East African region hosts diverse forest ecosystems, such as woodlands, highland forests, and coastal mangrove forests. These ecosystems are crucial for biodiversity and support various plant and animal species. They provide essential resources, benefiting livelihoods directly and indirectly. Examining regional and global variations in forest ecosystem valuation, especially regarding livelihood benefits, is crucial for sustainable resource management aligned with forest-based climate solutions. The aim of this review is to assess how regional contextual factors and global trends impact the valuation of ecosystem services (ES) in East Africa, with two main objectives (1) to identify the most evaluated ES in East Africa and (2) to identify the preferred ES valuation methods and approaches applied in East Africa. Data from the Web of Science/Clarivate platform was used in the systematic review process using the Reporting standard for Systematic Evidence Syntheses to retrieve and analyse 222 articles. The ES were categorized using the Millennium Ecosystem Assessment framework and the Common International Classification of Ecosystem Services in order to create a review database. Provisioning services i.e., wood biomass and food production were the most assessed at 55%, followed by 30% regulating services i.e., carbon storage, 8.6% support services i.e., habitat and 6.4% cultural services i.e., ecotourism which reflected communities’ direct reliance on forest resources. The preferred direct ES valuation techniques included market price at 14.9% for assessing provisioning services and travel cost methods at 10.4% for cultural services. Amongst the indirect ES valuation techniques, remote sensing at 14.7% were preferred as they proved efficient for large and remote tropical forest areas. Participatory methods such as interviews at 11.8% were also preferred because they offer holistic perspectives on community priorities. This review provides spatial context on the regional priorities of ES valuation which is vital for safeguarding natural resources for future generations.

Accurate Calculation of Tree Stem Traits in Forests Using Localized Multi-View Registration

Abstract. In recent years, there has been a high demand in forestry and forest research for the accurate measurement of tree traits from point clouds captured by terrestrial laser scanners. However, the reliability of the calculated values is not sufficient due to the difficulty of accurate registration of each tree over a large area of forest. To solve this problem, we introduce localized multi-view registration for correcting the registration matrix of each tree stem. In addition, we discuss methods for registering the whole point clouds of a forest by using the registration matrices locally calculated for tree stems. Especially, we discuss a method to align tree stem points that do not have sufficient overlapping points required in registration. The proposed method was applied to actual forest point clouds and diameter at breast height (DBH) was compared to the manually measured DBH. Experimental results showed that the proposed method was effective in reducing registration errors and in calculating tree stem traits with high accuracy.

Increase the production of giant mud crab (Scylla serata) through the Bieng Samudra Cage in Pauh Tanjung Village River Farmer Group, Langsa City

Potential for the development of the mangrove crab business in the coastal area of the city of Langsa, which has a fairly large mangrove forest area and produces abundant mangrove crab resources. However, some of the results of the study stated that the exploitation and the absence of restrictions on the size of the capture of mangrove crabs resulted in a decrease in the number of catchments by fishermen because the condition of crab stock in nature has decreased. The purpose of dedication is to provide education and training to fishermen about selling mangrove crabs, which is more prioritized than fattening results from direct catches from nature, and to increase the production of mud crabs by utilizing local materials. The next goal is the management of crab capture. Implementation of dedication is carried out through socialization and training for coastal communities and fishermen in Sungai Pauh Tanjung Village, Langsa City. The results of the implementation of community service activities contribute to increasing skills and added value, as well as increasing the production of mangrove crabs through the use of Samudra being cages and the use of local materials as potential feed for fattening mangrove crabs before being sold at the market.