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This study predicted changes in diameter distribution for Korean red pine (Pinus densiflora), Korean white pine (P. koraiensis), and Japanese larch (Larix kaempferi) plantations in South Korea using the stand table projection method. Growth index ratio over 3-year (GIR3) was calculated by species, age class, and stocking class to estimate upgrowth across diameter classes. GIR3 tended to be higher in Korean red pine and decreased with increasing age and stocking classes. Diameter distributions after 3, 6, and 9 years were predicted using the calculated GIR3. The predicted distributions generally shifted to the right while maintaining the shape of the present distribution. Prediction accuracy was assessed using KS quotient and Error Index, showing decreasing accuracy with longer prediction periods. The GIR table and stand table projection method can provide a simple and practical approach for predicting changes in growing stock, timber yield, and carbon sequestration in forest management planning.

Capparis decidua (kair), a multiple branched shrub, is an important species of desert and arid regions of India that provides vegetative cover in dry, hot, sandy desert areas, improves soil, and prevents soil erosion. Kair plays an important role in the rural economy of arid regions. It is one of the most important indigenous NTFP yielding shrub species of the region. Its fruits yield supplementary income to the rural people since it is used to make pickles, a value-added product, which is in very high demand. This article presents the ways to enhance the productivity of kair fruit for the food security and improving livelihoods of rural people. Various combinations of organic and inorganic fertilizers were tried to arrive at the appropriate combination which suited best to enhance fruit productivity. Integrated use of organic and inorganic fertilizers was helpful in enhancing the number of fruiting shrubs and per shrub yield. Leaf Compost Manure (LCM) in combination with inorganic fertilizer (potassium, phosphorus, and zinc) provided highest fruit yield. Moisture conservation also contributed significant fruit yield.

Tropical forests in Nigeria (such as Omo Biosphere Reserve, OBR) are rich in species diversity and play an essential role in conserving biodiversity and enriching livelihood. Despite numerous studies in OBR, the interconnected dynamics of forest structure, species diversity, and Above-Ground Biomass (AGB) across its distinct management zones remain understudied. This study examined the AGB, species diversity and diameter distribution across the three zones in Omo Biosphere Reserve, Nigeria. A line transect sampling design was employed, and all woody trees with diameter at breast height (DBH)≥10 cm were measured across zones. Tree parameters recorded included DBH, basal diameter, top diameter, and total height, while species-specific wood density values were obtained from the FAO wood density database. Diameter distributions were modeled using four probability density functions: Johnson (SB), Weibull (3P), Lognormal (3P), and Beta and they were ranked based on Kolmogorov Smirnov. Tree frequency per hectare (TF), Tree relative density (TRD), Tree Basal Area (TBA), Tree Relative Dominance (TRD), Shannon-Wiener Diversity (H’) and Family Important Value Indices (FVI) were used to estimate the species diversity, and Aboveground biomass (AGB) was estimated using the allometric model. The result revealed significant differences across the zones. The core zone had the highest biomass while the buffer zone exhibited slightly higher species diversity likely due to edge effects. Johnson SB provided the best fit for diameter distribution, capturing the inverse J-shaped pattern typical of regenerating tropical forests. Findings from this study gave insights that emphasize the importance of differentiated management zones within a biosphere reserve and the crucial zone of protected zone in biomass conservation. These insights support evidence-based strategies for enhancing biodiversity, improving carbon sequestration, and strengthening the ecological resilience of protected tropical forests.

This study aimed to improve the spatial prediction accuracy of Species Distribution Models (SDMs) by applying and comparing area-weighted sampling and simple random sampling for Korean Red Pine (Pinus densiflora). We employed Random Forest (RF) and Maximum Entropy Model (MaxEnt) as the SDMs and evaluated their performance differences and spatial suitability under each sampling regime. The results showed that simple random sampling improved the model’s generalization ability by uniformly reflecting presence points across the entire study area, thereby ensuring overall spatial homogeneity. Meanwhile, in the case of area-weighted sampling, samples were concentrated in areas with high actual occurrence density, such as the East coast region and southern forest zones. This enhanced spatial concentration and resulted in higher predictive reliability in these high-density regions, but it also led to a relative increase in True Negatives (TN) and False Positives (FP). These findings indicate that sampling alters not only the numerical balance of the data but also the representativeness of the spatial patterns learned by the model. Consequently, the selection of a sampling method requires a strategic choice, contingent on whether the objective prioritizes global predictive stability or precision within core distribution areas.

The world is undergoing rapid development and urbanization as a result of indiscriminate timber harvesting, burning of residues, and unauthorized waste disposal. These has led to increased carbon emissions that have greatly contributed to greenhouse gases and global warming. Therefore, this study was carried out to assess the amount of carbon stock of avenue trees around the Western Gate of Adekunle Ajasin University, Akungba-Akoko, which contributed to the environment. Enumeration was considered for this study because it is possible to count all the trees in the study area and all the trees with DBH≥10 cm were assessed. The avenue trees were identified, enumerated, and measured for diameter at the base, middle, and top, along with tree height. Wood density values were sourced from existing literature to estimate biomass, carbon, and carbon dioxide content. A total of 124 tree species were identified and recorded, representing 18 species across 11 families. The results show that, Gmelina arborea exhibited the highest carbon stock at 25.34 kg and carbon dioxide sequestration at 93.00 kg, followed by Acacia auriculiformis, which sequestered 4.67 kg of carbon and 17.16 kg of carbon dioxide. In total, the avenue trees in the study area sequestered 94.14 kg of biomass, 47.07 kg of carbon, and 172.75 kg of carbon dioxide. Hence, the findings showed the significant potential and contributions of the avenue trees around the Western Gate in absorbing atmospheric (CO2). This has contributed immensely to climate change mitigation and amelioration efforts on the University Campus.

Timber harvesting in forested areas involves the simultaneous operation of heavy forest machines and manual workers, creating a high risk of collision-related accidents. To address these safety concerns, this study developed a deep learning-based human detection sensor system designed for installation on forest forwarders. The system integrates a Raspberry Pi 4 with a Pi-camera running a MobileNetV2-based detection model, optimized for real-time inference under low-power embedded conditions. In addition, ultrasonic sensors were incorporated to measure distances to detected person, enabling accurate localization around the machine. Model training utilized a filtered COCO dataset and achieved optimal performance through augmentation strategies, with the customized M3 configuration and our own test dataset reaching a mean average precision (mAP) of 0.71, precision of 0.95, and recall of 0.99. Experimental evaluations confirmed that the system successfully detected person across various postures, positions, and environmental conditions, with localization errors maintained within acceptable limits. Outdoor tests further demonstrated robust performance even under partial occlusion, although occasional false negatives in complex or low-light scenarios highlighted the need for dataset expansion and sensor fusion. The developed system transmits integrated detection and localization data via CAN bus, confirming its feasibility for deployment in actual forest forwarders. These findings suggest that the proposed sensor system offers a promising solution for enhancing worker safety in mechanized forestry operations and provides a foundation for future smart and autonomous forest machines.

Climate change directly influences air pollutant concentrations, making fine particulate matter (PM) a major environmental hazard. Regional-level analysis alone is insufficient, necessitating a micro-scale PM vulnerability assessment integrating climatic, atmospheric, and social factors. This study developed an integrated framework to assess PM vulnerability at the administrative district level in Chuncheon City. Vulnerability was defined by the IPCC framework, comprising exposure, sensitivity, and adaptive capacity. Indicators were selected based on previous research, and the final vulnerability index was calculated using expert-recommended weights (Exposure: 0.4, Sensitivity: 0.3, Adaptive Capacity: 0.3). The assessment results for 25 administrative districts showed substantial variation. Toegye-Dong and Namsan-Myeon emerged as the most vulnerable districts, driven by factors such as high exposure and critically limited adaptive capacity, respectively. Conversely, Hyoja-1-Dong and Hyoja-3-Dong were the least vulnerable. A comparative review with the Ministry of Environment’s VESTAP model revealed substantial discrepancies, demonstrating that uniform, nationally applied indicators often fail to reflect local characteristics. The findings confirm that atmospheric stagnation and demographic conditions are major determinants of PM vulnerability in Chuncheon City. Consequently, the city requires climate-responsive air quality management strategies and an integrated health-environment management system, with regular updates to administrative district-level vulnerability assessments.

Pteris nipponica W.C.Shieh, an evergreen perennial fern with high ornamental value, is an endangered and ecologically important plant in Korea. This study was conducted to optimize the culture conditions for spore germination, sporophyte formation, and subsequent growth, thereby establishing an efficient mass propagation system for P. nipponica. To investigate the effect of ammonium nitrate (NH4NO3) on sporophyte formation and growth, prothalli were cultured for 6 weeks on half-strength MS basal medium containing different concentrations of NH4NO3. The M-3 medium, containing half the standard concentration of NH4NO3, achieved the highest sporophyte formation rate of 58.8%, significantly surpassing M-1 (27.5%) and M-2 (23.8%). Statistical analysis revealed that the concentration of NH4NO3 had a significant effect on sporophyte formation (p=0.0039). Furthermore, the M-3 medium significantly promoted sporophyte growth indicators such as root number, root length, shoot length, and shoot width compared to the M-1 and M-2 media. Therefore, the optimal nitrogen source for effective sporophyte formation and development in P. nipponica was identified as a half concentration of NH4NO3 (5.15 mM) in medium. The formed young sporophytes were transplanted into trays containing sphagnum moss for 4 weeks of acclimatization before being moved to a greenhouse. After 12 weeks, a high survival rate of over 95% was observed. The culture conditions and acclimatization methods established in this study are expected to be practically applicable to the efficient mass production of P. nipponica and various other fern species.