Search results for: forest cover-type dataset

Authors: Amri Yusoff, Shahrizuan Shafiril, Ashardi Abas, Norma Che Yusoff

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Nowadays, illegal logging has been causing much effect to our forest. Some of it causes a flash flood, avalanche, global warming, and etc. This comprehensibly makes us wonder why, what, and who has made it happened. Often, it already has been too late after we have known the cause of it. Even the Malaysian Royal Belum forest has not been spared from land clearing or illegal activity by the natives although this area has been gazetted as a protected area preserved for future generations. Furthermore, because of its sizeable and wide area, these illegal activities are difficult to monitor and to maintain. A critical action must be called upon to prevent all of these unhealthy activities from recurrence. Therefore, a remote monitoring device must be developed in order to capture critical real-time data such as temperature, humidity, gaseous, fire, and rain detection which indicates the current and preserved natural state and habitat in the forest. Besides, this device location can be detected via GPS by showing the latitudes and longitudes of its current location and then to be transmitted by SMS via GSM system. All of its readings will be sent in real-time for data management and analysis. This result will be benefited to the monitoring bodies or relevant authority in keeping the forest in the natural habitat. Furthermore, this research is to gather a unified data and then will be analysed for its comparison with an existing method.

Keywords: remote monitoring system, forest data, GSM, GPS, wireless sensor

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Susan Mwai, Mary Muchane, Peter Wachira, Sheila Okoth, Muchai Muchane, Halima Saado

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Tropical forests harbor a wide range of biodiversity and rich macro-fungi diversity compared to the temperate regions in the World. However, biodiversity is facing the threat of extinction following the rate of forest loss taking place before proper study and documentation of macrofungi is achieved. The present study was undertaken to determine the effect of converting indigenous habitat to plantation forest on macrofungi diversity. To achieve the objective of this study, an inventory focusing on macro-fungi diversity was conducted within Kereita block in Kikuyu Escarpment forest which is on the southern side of Aberdare mountain range. The macrofungi diversity was conducted in the indigenous forest and in more than 15 year old Patula plantation forest , during the wet (long rain season, December 2014) and dry (Short rain season, May, 2015). In each forest type, 15 permanent (20m x 20m) sampling plots distributed across three (3) forest blocks were used. Both field and laboratory methods involved recording abundance of fruiting bodies, taxonomic identity of species and analysis of diversity indices and measures in terms of species richness, density and diversity. R statistical program was used to analyze for species diversity and Canoco 4.5 software for species composition. A total number of 76 genera in 28 families and 224 species were encountered in both forest types. The most represented taxa belonged to the Agaricaceae (16%), Polyporaceae (12%), Marasmiaceae, Mycenaceae (7%) families respectively. Most of the recorded macro-fungi were saprophytic, mostly colonizing the litter 38% and wood 34% based substrates, which was followed by soil organic dwelling species (17%). Ecto-mycorrhiza fungi (5%) and parasitic fungi (2%) were the least encountered. The data established that indigenous forests (native ecosystems) hosts a wide range of macrofungi assemblage in terms of density (2.6 individual fruit bodies / m2), species richness (8.3 species / plot) and species diversity (1.49/ plot level) compared to the plantation forest. The Conversion of native forest to plantation forest also interfered with species composition though did not alter species diversity. Seasonality was also shown to significantly affect the diversity of macro-fungi and 61% of the total species being present during the wet season. Based on the present findings, forested ecosystems in Kenya hold diverse macro-fungi community which warrants conservation measures.

Keywords: diversity, Indigenous forest, macro-fungi, plantation forest, season

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Authors: Zhila Hemati, Seyed Sajjad Hosseni, Sohrab Zamzami

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In nature, forests serve a multitude of functions. They stabilize and nourish soil, store carbon, clean the air and water, and support biodiverse ecosystems. A natural disaster that can affect forests and ecosystems locally or globally is wildfires. Iran experiences annual forest fires that affect roughly 6000 hectares, with the Arasbaran forest being the most affected. These fires may be generated unnaturally by human activity in the forests, or they could occur naturally as a result of climate change. These days, wildfires pose a major natural risk. Wildfires significantly reduce the amount of property and human life in ecosystems globally. Concerns regarding the immediate and longterm effects have been raised by the rise in fire activity in various Iranian regions in recent decades. Natural ecosystem abundance, quality, and health will all be impacted by pasture and forest fires. Monitoring is the first line of defense against and control for forest fires. To determine the spatial-temporal variations of these occurrences in the vegetation regions of Arasbaran, this study was carried out to estimate the areas affected by fires. The findings indicated that July through September, which spans over 130000 hectares, is when fires in Arasbaran's vegetation areas occur to their greatest extent. A significant portion of the nation's forests caught fire in 2024, particularly in the northwest of the Arasbaran vegetation area. On the other hand, January through March sees the least number of fire locations in the Arasbaran vegetation areas. The Arasbaran forest experiences its greatest number of forest fires during the hot, dry months of the year. As a result, the linear association between the burned and active fire regions in the Arasbaran forest indicates a substantial relationship between species abundance and plant species. This link demonstrates that some of the active forest fire centers are the burned regions in Arasbaran's vegetation areas.

Keywords: wildfire, vegetation, plant species, forest

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Authors: Insook Lee, Heeseung Choi, Kyung-Sook Bang, Sungjae Kim, Minkyung Song, Buhyun Lee

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Backgrounds: A clearer and comprehensive understanding of the effects of forest therapy on depression is needed for further refinements of forest therapy programs. The purpose of this study was to review the literature on forest therapy programs designed to decrease the level of depression among adults to evaluate current forest therapy programs. Methods: This literature review was conducted using various databases including PubMed, EMBASE, CINAHL, PsycArticle, KISS, RISS, and DBpia to identify relevant studies published up to January 2016. The two authors independently screened the full text articles using the following criteria: 1) intervention studies assessing the effects of forest therapy on depression among healthy adults ages 18 and over; 2) including at least one control group or condition; 3) being peer-reviewed; and 4) being published either in English. The Scottish Intercollegiate Guideline Network (SIGN) measurement tool was used to assess the risk of bias in each trial. Results: After screening current literature, a total of 14 articles (English: 6, Korean: 8) were included in the present review. None of the studies used randomized controlled (RCT) study design and the sample size ranged from 11 to 300. Walking in the forest and experiencing the forest using the five senses was the key component of the forest therapy that was included in all studies. The majority of studies used one-time intervention that usually lasted a few hours or half-day. The most widely used measure for depression was Profile of Mood States (POMS). Most studies used self-reported, paper-and-pencil tests, and only 5 studies used both paper-and-pencil tests and physiological measures. Regarding the quality assessment based on the SIGN criteria, only 3 articles were rated ‘acceptable’ and the rest of the 14 articles were rated ‘low quality.’ Regardless of the diversity in format and contents of forest therapies, most studies showed a significant effect of forest therapy in curing depression. Discussions: This systematic review showed that forest therapy is one of the emerging and effective intervention approaches for decreasing the level of depression among adults. Limitations of the current programs identified from the review were as follows; 1) small sample size; 2) a lack of objective and comprehensive measures for depression; and 3) inadequate information about research process. Futures studies assessing the long-term effect of forest therapy on depression using rigorous study designs are needed.

Keywords: forest therapy, systematic review, depression, adult

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Authors: Sujata Upgupta, Prasoon Kumar Singh

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The expansion of large scale coal mining into forest areas is a potential hazard for the local biodiversity and wildlife. The objective of this study is to provide a picture of the threat that coal mining poses to the forests of the East Bokaro landscape. The vulnerable forest areas at risk have been assessed and the priority areas for conservation have been presented. The forested areas at risk in the current scenario have been assessed and compared with the past conditions using classification and buffer based overlay approach. Forest vulnerability has been assessed using an analytical framework based on systematic indicators and composite vulnerability index values. The results indicate that more than 4 km² of forests have been lost from 1973 to 2016. Large patches of forests have been diverted for coal mining projects. Forests in the northern part of the coal field within 1-3 km radius around the coal mines are at immediate risk. The original contiguous forests have been converted into fragmented and degraded forest patches. Most of the collieries are located within or very close to the forests thus threatening the biodiversity and hydrology of the surrounding regions. Based on the vulnerability values estimated, it was concluded that more than 90% of the forested grids in East Bokaro are highly vulnerable to mining. The forests in the sub-districts of Bermo and Chandrapura have been identified as the most vulnerable to coal mining activities. This case study would add to the capacity of the forest managers and mine managers to address the risk and vulnerability of forests at a small landscape level in order to achieve sustainable development.

Keywords: forest, coal mining, indicators, vulnerability

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Felipe Villegas-Velasquez, Harold Pantoja-Villota, Sergio Holguin-Cardona, Alejandro Osorio-Botero, Brayan Candamil-Arango

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Colombian electricity comes mainly from hydric resources, affected by environmental variations such as the El Niño phenomenon. That is why incorporating other types of resources is necessary to provide electricity constantly. This research seeks to fill the wind speed and global solar irradiance dataset for two years with the highest amount of information. A further result is the characterization of the data by region that led to infer which errors occurred and offered the incomplete dataset.

Keywords: energy, wind speed, global solar irradiance, Colombia, imputation

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Authors: Koo Ja-Choon, Seok Hyun-Deok, Park So-Hee

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Even though developed countries have supported aid projects for restoring degraded and deforested mountain, recent North Korean authorities announced that North Korean forest is still very serious. Last 12 years, more than 16 thousand ha of forest were destroyed. Most of previous researches concluded that food and fuel problems should be solved for preventing people from deforesting and degrading forest in North Korea. It means that mountain restoration projects such as A/R(afforestation/reforestation) and REDD(Reducing Emissions from Deforestation and Forest Degradation) project should be implemented with the agroforestry and the forest tending project. Because agroforestry and the forest tending can provide people in the project area with foods and fuels, respectively. Especially, Agroforestry has been operated well with the support of Swiss agency of Development and cooperation since 2003. This paper aims to find the priority sites for mountain restoration project where all types of projects including agroforesty can be implemented simultaneously. We tried to find the primary counties where the areas of these activities were distributed widely and evenly. Recent spatial data of 186 counties representing altitude, gradient and crown density were collected from World Forest Watch. These 3 attributes were used to determine the type of activities; A/R, REDD, Agroforestry and forest tending project. Finally, we calculated the size of 4 activities in 186 counties by using GIS technique. Result shows that Chongjin in Hamgyeongbuk-do, Hoeryong in Hamgyeongbuk-do and Tongchang in Pyeonganbuk-do are on the highest priority of counties. Most of feasible counties whose value of richness and uniformity were greater than the average were located near the eastern coast of North Korea. South Korean government has not supported any aid projects in North Korea since 2010. Recently, South Korea is trying to continue the aid projects for North Korea. Forest project which is not affected by the political situation between North- and South- Korea can be considered as a priority activities. This result can be used when South Korean government determine the priority sites for North Korean mountain restoration project in near future.

Keywords: agroforestry, forest restoration project, GIS, North Korea, priority

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Authors: Riko Wahyudi, Rezky Lasekti Wicaksono, Ayu Satya Damayanti, Ridhasepta Multi Kenrosa

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Implementing Forest Management Unit (FMU) is considered as the best solution for forest management in developing countries. However, when FMU has been formed, many parties then blame the FMU and assume it is not working on. Currently, there are two main issues that make FMU not be functional i.e. institutional and financial issues. This paper is addressing financial issues to make FMUs in Aceh Province can be functional. A mixed financing scheme is proposed here, both direct and indirect financing. The direct financing scheme derived from two components i.e. public funds and businesses. Non-tax instruments of intergovernmental fiscal transfer (IFT) system and FMU’s businesses are assessed. Meanwhile, indirect financing scheme is conducted by assessing public funds within villages around forest estate as about 50% of total villages in Aceh Province are located surrounding forest estate. Potential instruments under IFT system are forest and mining utilization royalties. In order to make these instruments become direct financing for FMU, interventions on allocation and distribution aspects of them are conducted. In the allocation aspect, alteration in proportion of allocation is required as the authority to manage forest has shifted from district to province. In the distribution aspect, Government of Aceh can earmark usage of the funds for FMUs. International funds for climate change also encouraged to be domesticated and then channeled through these instruments or new instrument under public finance system in Indonesia. Based on FMU’s businesses both from forest products and forest services, FMU can impose non-tax fees for each forest product and service utilization. However, for doing business, the FMU need to be a Public Service Agency (PSA). With this status, FMU can directly utilize the non-tax fees without transferring them to the state treasury. FMU only need to report the fees to Ministry of Finance. Meanwhile, indirect financing scheme is conducted by empowering villages around forest estate as villages in Aceh Province is receiving average village fund of IDR 800 million per village in 2017 and the funds will continue to increase in subsequent years. These schemes should be encouraged in parallel to establish a mixed financing scheme in order to ensure sustainable financing for FMU in Aceh Province, Indonesia.

Keywords: forest management, public funds, mixed financing, village

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Authors: Hande Ünlü, Yu Morishita

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The total area of forest land in Japan accounts for 67% of the national land; however, despite this wealth and hundred years history of silviculture, today Japanese forestry faces socio-economic stagnation in forestry. While the growing gap in the people-forest relationship causes the depopulation of many forest villages, this paper introduces a methodology aiming to develop a place-specific approach in revitalizing this relationship. The paper focuses on a case study from Taiki town in the Hokkaido region to analyze the place's specific socio-economic requirements through interviews and workshops with the local experts, researchers, and stakeholders. Based on the analyzed facts, a master outline of design requirements is developed to produce locally sourced architectural embodiments that aim to act as a unifying element between the forests and the people of Taiki town. In parallel, the proposed methodology aims to generate a cycle of research feed and a researcher retreat, a definition given by Memu Earth Lab to the researchers' stay at Memu in Taiki town for a defined period to analyze local resources, for the continuous improvement of the introduced methodology to revitalize the interaction between people and forest through architecture.

Keywords: architecture, Japanese forestry, local timber, people-forest relationship, research platform

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Jerome Osentowski

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The objective at Central Rocky Mountain Permaculture Institute has been to put in practice a sustainable way of life while growing food, medicine, and providing education. This has been done by applying methods of farming such as agroforestry, forest farming, and perennial polycultures. These methods have been found to be regenerative to the environment through carbon sequestration, soil-building, climate change mitigation, and the provision of food security. After 30 years of implementing carbon farming methods, the results are agro-diversity, self-sustaining systems, and a consistent provision of food and medicine. These results are exhibited through polyculture plantings in an outdoor forest garden spanning roughly an acre containing about 200 varieties of fruits, nuts, nitrogen-fixing trees, and medicinal herbs, and two indoor forest garden greenhouses (one Mediterranean and one Tropical) containing about 50 varieties of tropical fruits, beans, herbaceous plants and more. While the climate zone outside the greenhouse is 6, the tropical forest garden greenhouse retains an indoor climate zone of 11 with near-net-zero energy consumption through the use of a climate battery, allowing the greenhouse to serve as a year-round food producer. The effort to source food from the forest gardens is minimal compared to annual crop production. The findings at Central Rocky Mountain Permaculture Institute conclude that agroecological methods are not only beneficial but necessary in order to revive and regenerate the environment and food security.

Keywords: agroecology, agroforestry, carbon farming, carbon sequestration, climate battery, food security, forest farming, forest garden, greenhouse, near-net-zero, perennial polycultures

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Authors: Yurike, Yonariza, Rudi Febriamansyah, Syafruddin Karimi

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Forest is a natural resource that has an important function as a supporting element of human life. Forest degradation enormous impact on global warming is a reality we have experienced together, that disruption of ecosystems, extreme weather conditions, disruption of water management system watersheds and the threat of natural disasters as floods, landslides and droughts, even disruption food security. Dharmasraya is a district in the province of West Sumatra, which has an area of 92.150 ha of forest, which is largely a former production forest concessions (Forest Management Rights) which is supposed to be a secondary forest. This study answers about the impact of land use in the former concession area Dharmasraya on the environment. The methodology used is the household survey, key informants, and satellite data / GIS. From the results of the study, the former concession area in Dharmasraya experienced a reduction of forest cover over time significantly. Forest concessions should be secondary forests in Dharmasraya, now turned conversion to oil palm plantations. Population pressures and growing economic pressures, resulting in more intensive harvesting. As a result of these forest disturbances caused changes in forest functions. These changes put more emphasis towards economic function by ignoring social functions or ecological function. Society prefers to maximize their benefits today and pay less attention to the protection of natural resources. This causes global warming is increasing and this is not only felt by people around Dharmasraya but also the world. Land clearing by the community through a process in slash and burn. This fire was observed by NOAA satellites and recorded by the Forest Service of West Sumatra. This demonstrates the ability of trees felled trees to absorb carbon dioxide (CO2) to be lost, even with forest fires accounted for carbon dioxide emitted into the air, and this has an impact on global warming. In addition to the change of control of land into oil palm plantations water service has been poor, people began to trouble the water and oil palm plantations are located in the watershed caused the river dried up. Through the findings of this study is expected to contribute ideas to the policy makers to pay more attention to the former concession forest management as the prevention or reduction of global warming.

Keywords: climate change, community, concession forests, environment

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Authors: Thierry Ganomanana, Dominique Hervé, Solo Randriamahaleo

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Management of Malagasy renewable natural re-sources allows, in the case of forest, the mobilization of several actors with norms and/or territory. The interaction in this socio-ecosystem is represented by a graph of two different relationships in which most of action chains, from individual activities under the continuous of forest dynamic and discrete interventions by institutional, are also studied. The fuzzy set theory is adapted to graduate the elements of the set Illegal Activities in the space of sanction’s institution by his severity and in the space of degradation of forest by his extent.

Keywords: fuzzy set, graph, institution, renewable resource, system

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Authors: Dionisios Panagiotaras, Ioannis P. Kokkoris, Dionysios Koulougliotis, Dimitra Lekka, Alexandra Skalioti

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In the summer of 2021, Greece was affected by devastating forest fires in various regions of the country, resulting in human losses, destruction or degradation of the natural environment, infrastructure, livestock and cultivations. The present study concerns a pilot assessment of natural vegetation regeneration in the second, in terms of area, fire-affected region for 2021, at Ancient Olympia area, located in West Peloponnisos (Ilia Prefecture), Greece. A standardised field sampling protocol for assessing natural regeneration was implemented at selected sites where the forest fire had occurred previously (in 2007), and the vegetation (Pinus halepensis forest) had regenerated naturally. The results of the study indicate the loss of the established natural regeneration of Pinus halepensis forest, as well as of the tree-layer in total. Post-fire succession species are recorded to the shrub and the herb layer, with a varying cover. Present findings correspond to the results of field work and analysis one year after the fire, which will form the basis for further research and conclusions on taking action for restoration schemes in areas that have been affected by fire more than once within a 20-year period.

Keywords: forest, pinus halepensis, ancient olympia, post fire vegetation

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Authors: Hyppolite Aignon, Souleymane Yorou, Martin Ryberg, Anneli Svanholm

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Climate change and human actions cause the extinction of wild mushrooms. In Benin, the diversity of fungi is large and may still contain species new to science but the inventory effort remains low and focuses on particularly edible species (Russula, Lactarius, Lactifluus, and also Amanita). In addition, inventories have started recently and some groups of fungi are not sufficiently sampled, however, the degradation of fungal habitat continues to increase and some species are already disappearing. (Yorou and De Kesel, 2011), however, the degradation of fungi habitat continues to increase and some species may disappear without being known. This genus (Inocybe) overlooked has a worldwide distribution and includes more than 700 species with many undiscovered or poorly known species worldwide and particularly in tropical Africa. It is therefore important to orient the inventory to other genera or important families such as Inocybe (Fungi, Agaricales) in order to highlight their diversity and also to know their phylogenetic positions with a combined approach of gene regions. This study aims to evaluate the species richness and phylogenetic position of Inocybe species and affiliated taxa in West Africa. Thus, in North Benin, we visited the Forest Reserve of Ouémé Supérieur, the Okpara forest and the Alibori Supérieur Forest Reserve. In the center, we targeted the Forest Reserve of Toui-Kilibo. The surveys have been carried during the raining season in the study area meaning from June to October. A total of 24 taxa were collected, photographed and described. The DNA was extracted, the Polymerase Chain Reaction was carried out using primers (ITS1-F, ITS4-B) for Internal transcribed spacer (ITS), (LROR, LWRB, LR7, LR5) for nuclear ribosomal (LSU), (RPB2-f5F, RPB2-b6F, RPB2- b6R2, RPB2-b7R) for RNA polymerase II gene (RPB2) and sequenced. The ITS sequences of the 24 collections of Inocybaceae were edited in Staden and all the sequences were aligned and edited with Aliview v1.17. The sequences were examined by eye for sufficient similarity to be considered the same species. 13 different species were present in the collections. In addition, sequences similar to the ITS sequences of the thirteen final species were searched using BLAST. The nLSU and RPB2 markers for these species have been inserted in a complete alignment, where species from all major Inocybaceae clades as well as from all continents except Antarctica are present. Our new sequences for nLSU and RPB2 have been manually aligned in this dataset. Phylogenetic analysis was performed using the RAxML v7.2.6 maximum likelihood software. Bootstrap replications have been set to 100 and no partitioning of the dataset has been performed. The resulting tree was viewed and edited with FigTree v1.4.3. The preliminary tree resulting from the analysis of maximum likelihood shows us that these species coming from Benin are much diversified and are distributed in four different clades (Inosperma, Inocybe, Mallocybe and Pseudosperma) on the seven clades of Inocybaceae but the phylogeny position of 7 is currently known. This study marks the diversity of Inocybe in Benin and the investigations will continue and a protection plan will be developed in the coming years.

Keywords: Benin, diversity, Inocybe, phylogeny placement

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Authors: Carmen Avilés Palacios, Camilo Muñoz Arenas, Joaquín García Alfonso, Jesús González Arteaga, Alberto Alcalde Calonge

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There is a need for review of the consumption and production models of industrialized states in accordance with the Paris Agreement and the Sustainable Development Goals (1) (OECD, 2016). This constitutes the basis of the bioeconomy (2) that is focused on striking a balance between economic development, social development and environmental protection. Bioeconomy promotes the adequate use and consumption of renewable natural resources (3) and involves developing new products and services adapted to the principles of circular economy: more sustainable (reusable, biodegradable, renewable and recyclable) and with a lower carbon footprint (4). In this context, Urban Forest Innovation Lab (UFIL) grows, an Urban Laboratory for experimentation focused on promoting entrepreneurship in forest bioeconomy (www.uiacuenca.es). UFIL generates local wellness taking sustainable advantage of an endogenous asset, forests. UFIL boosts forest bioeconomy opening its doors of knowledge to pioneers in this field, giving the opportunity to be an active part of a change in the way of understanding the exploitation of natural resources, discovering business, learning strategies and techniques and incubating business ideas So far now, 100 entrepreneurs are incubating their nearly 30 new business plans. UFIL has promoted an ecosystem to connect the rural-urban world that promotes sustainable rural development around the forest.

Keywords: bioeconomy, forestry, innovation, entrepreneurship

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Authors: Samipraj Mishra

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Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which is climatically belongs to tropical humid and possessed high quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefit sharing, collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economical potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counter-productive to promote the equitable benefit sharing in the areas of heterogeneous socio-economic conditions with high value forests. The low pricing strategy has been increasing accessibility of better off households at higher rate than poor; as such households always have higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerment of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: community forest, livelihood, socio-economy, pricing system, Nepal

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Authors: Meilisha Putri Pertiwi, Mufti Petala Patria

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Mangrove forest often categorized as a productive ecosystem in trophic water and the highest carbon storage among all the forest types. Mangrove-derived organic matter determines the food web of fish and invertebrates. In Indonesia trophic water ecosystem, 80% commersial fish caught in coastal area are high related to food web in mangrove forest ecosystem. Based on the previous research in Panjang Island, Bojonegara, Banten, Indonesia, removed mangrove litterfall to the sea water were 9,023 g/m³/s for two stations (west station–5,169 g/m³/s and north station-3,854 g/m³/s). The vegetation were dominated from Rhizophora apiculata and Rhizopora stylosa. C element is the highest content (27,303% and 30,373%) than N element (0,427% and 0,35%) and P element (0,19% and 0,143%). The aim of research also to know the diversity of fish inhabit in mangrove forest. Fish sampling is by push net. Fish caught are collected into plastics, total length measured, weigh measured, and individual and total counted. Meanwhile, the 3 modified pipes (1 m long, 5 inches diameter, and a closed one hole part facing the river by using a nylon cloth) set in the water channel connecting mangrove forest and sea water for each stasiun. They placed for 1 hour at low tide. Then calculate the speed of water flow and volume of modified pipes. The fish and mangrove litter will be weigh for wet weight, dry weight, and analyze the C, N, and P element content. The sampling data will be conduct 3 times of month in full moon. The salinity, temperature, turbidity, pH, DO, and the sediment of mangrove forest will be measure too. This research will give information about the fish diversity in mangrove forest, the removed mangrove litterfall to the sea water, the composition of sediment, the total element content (C, N, P) of fish and mangrove litter, and the correlation of element content absorption between fish and mangrove litter. The data will be use for the fish and mangrove ecosystem conservation.

Keywords: fish diversity, mangrove forest, mangrove litter, carbon element, nitrogen element, P element, conservation

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Yeliz Karaca, Rana Karabudak

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Multifractal Denoising techniques are used in the identification of significant attributes by removing the noise of the dataset. Magnetic resonance (MR) image technique is the most sensitive method so as to identify chronic disorders of the nervous system such as Multiple Sclerosis. MRI and Expanded Disability Status Scale (EDSS) data belonging to 120 individuals who have one of the subgroups of MS (Relapsing Remitting MS (RRMS), Secondary Progressive MS (SPMS), Primary Progressive MS (PPMS)) as well as 19 healthy individuals in the control group have been used in this study. The study is comprised of the following stages: (i) L2 Norm Multifractal Denoising technique, one of the multifractal technique, has been used with the application on the MS data (MRI and EDSS). In this way, the new dataset has been obtained. (ii) The new MS dataset obtained from the MS dataset and L2 Multifractal Denoising technique has been applied to the K-Means and Fuzzy C Means clustering algorithms which are among the unsupervised methods. Thus, the clustering performances have been compared. (iii) In the identification of significant attributes in the MS dataset through the Multifractal denoising (L2 Norm) technique using K-Means and FCM algorithms on the MS subgroups and control group of healthy individuals, excellent performance outcome has been yielded. According to the clustering results based on the MS subgroups obtained in the study, successful clustering results have been obtained in the K-Means and FCM algorithms by applying the L2 norm of multifractal denoising technique for the MS dataset. Clustering performance has been more successful with the MS Dataset (L2_Norm MS Data Set) K-Means and FCM in which significant attributes are obtained by applying L2 Norm Denoising technique.

Keywords: clinical decision support, clustering algorithms, multiple sclerosis, multifractal techniques

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Authors: Isaac Kwaku Adu, Jacob Doku Tetteh, John Joseph Puthenkalam, Kwabena Effah Antwi

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The continuous increase in population implies increase in food demand. There is, therefore, the need to increase agricultural production and other forest products to ensure food security and economic development. This paper employs the three-level intensity analysis to assess the total change of land-use in two-time intervals (1986-2002 and 2002-2017), the net change and swap as well as gross gains and losses in the two intervals. The results revealed that the overall change in the 31-year period was greater in the second period (2002-2017). Agriculture and forest categories lost in the first period while the other land class gained. However, in the second period agriculture and built-up increased greatly while forest, water bodies and thick bushes/shrubland experienced loss. An assessment revealed a reduction of forest in both periods but was greater in the second period and expansion of agricultural land was recorded as population increases. The pixels gaining built-up targeted agricultural land in both intervals, it also targeted thick bushes/shrubland and waterbody in the second period only. Built-up avoided forest in both intervals as well as waterbody and thick bushes/shrubland. To help in developing the best land-use strategies/policies, a further validation of the social factors is necessary.

Keywords: agricultural land, forest, Ghana, land-use, intensity analysis, remote sensing

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Authors: Maher J. Tadros, Yaseen Ananbeh

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The study area is located in Ajluon Forest Reserve northern part of Jordan. It consists of Mediterranean hills dominated by open woodlands of oak and pistachio. The aims of the study were to investigate the positive and negative relationships between the locals and the protected area and how it can affect the long-term forest conservation. The main research objectives are to review the impact of establishing Ajloun Forest Reserve on nature conservation and on the livelihood level of local communities around the reserve. The Ajloun forest reserve plays a fundamental role in Ajloun area development. The existence of initiatives of nature conservation in the area supports various socio-economic activities around the reserve that contribute towards the development of local communities in Ajloun area. A part of this research was to conduct a survey to study the impact of Ajloun forest reserve on biodiversity composition. Also, studying the biodiversity content especially for vegetation to determine the economic impacts of Ajloun forest reserve on its surroundings was studied. In this study, several methods were used to fill the objectives including point-centered quarter method which involves selecting randomly 50 plots at the study site. The collected data from the field showed that the absolute density was (1031.24 plant per hectare). Density was recorded and found to be the highest for Quecus coccifera, and relative density of (73.7%), this was followed by Arbutus andrachne and relative density (7.1%), Pistacia palaestina and relative density (10.5%) and Crataegus azarulus (82.5 p/ha) and relative density (5.1%),

Keywords: composition, density, frequency, importance value, point-centered quarter, structure, tree cover

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Authors: Samuel O. Akande

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The human-induced climate change is likely to have severe consequences on forest ecosystems in Nigeria. Recent discussions and emphasis on issues concerning the environment justify the need for this research which examined deforestation monitoring in Oban Forest, Nigeria using Remote Sensing techniques. The Landsat images from TM (1986), ETM+ (2001) and OLI (2015) sensors were obtained from Landsat online archive and processed using Erdas Imagine 2014 and ArcGIS 10.3 to obtain the land use/land cover and Normalized Differential Vegetative Index (NDVI) values. Ground control points of deforested areas were collected for validation. It was observed that the forest cover decreased in area by about 689.14 km² between 1986 and 2015. The NDVI was used to determine the vegetation health of the forest and its implications on agricultural sustainability. The result showed that the total percentage of the healthy forest cover has reduced to about 45.9% from 1986 to 2015. The results obtained from analysed questionnaires shown that there was a positive correlation between the causes and effects of deforestation in the study area. The coefficient of determination value was calculated as R² ≥ 0.7, to ascertain the level of anthropogenic activities, such as fuelwood harvesting, intensive farming, and logging, urbanization, and engineering construction activities, responsible for deforestation in the study area. Similarly, temperature and rainfall data were obtained from Nigerian Meteorological Agency (NIMET) for the period of 1986 to 2015 in the study area. It was observed that there was a significant increase in temperature while rainfall decreased over the study area. Responses from the administered questionnaires also showed that futile destruction of forest ecosystem in Oban forest could be reduced to its barest minimum if fuelwood harvesting is disallowed. Thus, the projected impacts of climate change on Nigeria’s forest ecosystems and environmental stability is better imagined than experienced.

Keywords: deforestation, ecosystems, normalized differential vegetative index, sustainability

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: annotated facial expression dataset, gesture recognition, sequenced facial expression dataset, sign language recognition

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Pennapa Palapin

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This study aimed to examine the management and development of forest tourism Kamchanoad. Ban Dung, Udon Thani sustainability. Data were collected by means of qualitative research including in-depth interviews, semi-structured, and then the data were summarized and discussed in accordance with the objectives. And make a presentation in the form of lectures. The target population for the study consisted of 16 people, including representatives from government agencies, community leaders and the community. The results showed that Guidelines for the Management and Development of Forest Tourism Kamchanoad include management of buildings and infrastructure such as roads, water, electricity, toilets. Other developments are the establishment of a service center that provides information and resources to facilitate tourists.; nature trails and informative signage to educate visitors on the path to the jungle Kamchanoad; forest activities for tourists who are interested only in occasional educational activities such as vegetation, etc.; disseminating information on various aspects of tourism through various channels in both Thailand and English, as well as a website to encourage community involvement in the planning and management of tourism together with the care and preservation of natural resources and preserving the local cultural tourist area of Kamchanoad.

Keywords: guidelines for the management and development, forest tourism, Kamchanoad, sustainability

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Authors: Mohan Bikram Thapa

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Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which climatically belongs to tropical humid and possessed high-quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefits sharing, the collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economic potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counterproductive to promote the equitable benefit-sharing in the areas of heterogeneous socio-economic conditions with high-value forests. The low pricing strategy has been increasing accessibility of better off households at a higher rate than poor, as such households always have the higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerments of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: benefit sharing, community forest, livelihood, pricing mechanism, Nepal

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Authors: Xiaoying Zhang

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Forest school – an outdoor educational experience that is undertaken in an outdoor environment with trees – becomes an emerging field of early childhood education recently. In China, the benefits of natural outdoor education to children and young people’s wellness have raised attention. Although different types of outdoor-based activities have been involved in some pre-school of China, few study and practice have been conducted in terms of the notion of forest school. To comprehend the impact of forest school for children and young people, this study aims to systematically review articles on the topic of forest school in preschool education from an ecological perspective, i.e. from individual level (e.g., behavior and mental health) to microsystem level (e.g., the relationship between teachers and children) to ecosystem level. Based on PRISMA framework flow, using the key words of “Forest School” and “Early Childhood Education” for searching in Web-of-science database, a total of 33 articles were identified. Sample participants of 13 studies were not preschool children, five studies were not on forest school theme, and two literature review articles were excluded for further analysis. Finally, 13 articles were eligible for thematic analysis. According to Bronfenbrenner's ecological systems theory, there are some fingdings, on the individual level, current forest school studies are concerned about the children behavioral experience in forest school, how these experience may relate to their achievement or to develop children’s wellbeing/wellness, and how this type of learning experience may enhance children’s self-awareness on risk and safety issues. On the microsystem/mesosystem level, this review indicated that pedagogical development for forest school, risk perception from teachers and parents, social development between peers, and adult’s role in the participation of forest school were concerned, explored and discussed most frequently. On the macrosystem, the conceptualization of forest school is the key theme. Different forms of presentation in various countries with diverse cultures could provide various models of forest school education. However, there was no study investigating forest school on an ecosystem level. As for the potential benefits of physical health and mental wellness that results from forest school, it informs us to reflect the system of preschool education from the ecological perspective for Chinese children. For instance, most Chinese kindergartens ignored the significance of natural outdoor activities for children. Preschool education in China is strongly oriented by primary school system, which means pre-school children are expected to be trained as primary school students to do different subjects, such as math. Hardly any kindergarteners provide the opportunities for children and young people to take risks in a natural environment like forest school does. However, merely copying forest school model for a Chinese preschool education system will be less effective. This review of different level concerns could inform us that the localization the idea of forest school to adapt to a Chinese political, educational and cultural background. More detailed results and profound discussions will be presented in the full paper.

Keywords: early childhood education, ecological system, education development prospects in China, forest school

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