Earticle

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R2 value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m3 lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

Directional felling is a felling method practised by the Forestry Department in Peninsular Malaysia as prescribed in Field Work Manual (1997) for Selective Management Systems (SMS) in forest harvesting. Determining the direction of tree felling in Peninsular Malaysia is conducted during the pre-felling inventory 1 to 2 years before the felling operation. This study aimed to predict and analyze the direction of tree felling using the vector-based path distance back link method in Geographic Information Systems (GIS) and compare it with the felling direction observed on the ground. The study area is at Balah Forest Reserve, Kelantan, Peninsular Malaysia. A Path Distance Back Link (spatial analyst) function in ArcGIS Pro 3.0 was used in predicting tree felling direction. Meanwhile, a binary classification was used to compare the felling direction estimated using GIS and the tree felling direction observed on the ground. Results revealed that 61.3% of 31 trees predicted using the vector-based projection method were similar to the felling direction observed on the ground. It is important to note that dynamic changes of natural constraints might occur in the middle of tree felling operation, such as weather problems, wind speed, and unpredicted tree falling direction.

As abnormal weather events due to climate change continue to rise, landslide damage is also increasing. Given the substantial time and financial resources required for post-landslide recovery, it becomes imperative to formulate a proactive response plan. In this regard, landslide susceptibility analysis has emerged as a valuable tool for establishing preemptive measures against landslides. Accordingly, this study conducted an annual landslide susceptibility analysis using the history of landslides that occurred over many years in the Jeolla region, and analyzed areas with a high potential for landslides in the Jeolla region. The analysis employed an ensemble model that amalgamated 10 data-based models, aiming to mitigate uncertainties associated with a single-model approach. Furthermore, based on the cumulative data regarding landslide susceptible areas, this research identified regions vulnerable to recurring landslide damage in Jeolla region and proposed specific strategies for utilizing this information at various levels, including local government initiatives, adaptation plan development, and development approval processes. In particular, this study outlined approaches for local government utilization, the determination of adaptation plan types, and considerations for development permits. It is anticipated that this research will serve as a valuable opportunity to underscore the significance of information concerning regions vulnerable to recurring landslide damage.

This study verified the effect of a combined forest/thermal therapy (FT/TT) program on participants with depressive symptoms and smoking or alcohol dependence. The participants included 20 people in the experimental group and 18 people in the control group. The experimental group participated in the FT/TT program for 2 nights and 3 days at the National Center for Forest Healing, in Hoengseong (National Hoengseong Supchewon), and a charcoal kiln operated by the Gangwon Charcoal Farming Association. The control group carried out their daily life for the same period. Psychological, physiological, and biochemical tests were conducted to determine the effect of the program. The results showed that there were significant improvements in stress, anxiety, depression, sleep quality, sleep satisfaction, and IL-6 levels. However, no significant difference was found in the cortisol, serotonin and melatonin levels between the experimental and control groups. Thus, the FT/TT therapy had psychological and biochemical effects on individuals with depression and nicotine or alcohol dependency. Further studies should be conducted to verify the effect of forest healing in various conditions.

Forest fuel moisture content is a crucial factor influencing the combustion rate and fuel consumption during forest fires, significantly impacting the occurrence and spread of wildfires. In this study, meteorological data were gathered using a meteorological measuring device (HOBO data logger) installed in the south and north slopes of Kangwon National University Forest, as well as on bare land outside the forest, from November 1, 2021, to October 31, 2022. The objective was to analyze the relationship between meteorological data and fuel moisture content. Fuel moisture content from the ground cover on the south and north slopes was collected. Fallen leaves on the ground were utilized, with a focus on broad-leaved trees (Prunus serrulata, Quercus dentata, Quercus mongolica, and Castanea crenata) and coniferous trees (Pinus densiflora and Pinus koraiensis), categorized by species. Additionally, correlation analysis with fuel moisture content was conducted using temperature (average, maximum, and minimum), humidity (average, minimum), illuminance (average, maximum, and minimum), and wind speed (average, maximum, and minimum) data collected by meteorological measuring devices in the study area. The results indicated a significant correlation between meteorological factors such as temperature, humidity, illuminance, and wind speed, and the moisture content of fuels. Notably, exceptions were observed for the moisture content of the on the north slope and that of the ground cover of Prunus serrulata and Castanea crenata.

Salix gracilistyla is widely distributed along riversides in Korea and very good for biomass production by SRC because of its excellent germination ability, but it is necessary to measure drying tolerance for cultivation. The drought tolerance of S. gracilistyla was tested using cuttings, and growth and physiological analysis were performed after irrigation was stopped. The growth inhibition of S. gracilistyla was observed from the day irrigation was stopped, and the soil moisture content decreased to less than 10% on the 25th day after irrigation was stopped. Over 50% of the seedlings turned brown 25 days after watering was stopped. The chlorophyll content of S. gracilistyla decreased dramatically after 25 days of stopping of irrigation. RWC values were unchanged until day 12 after irrigation was stopped but decreased rapidly until day 21, but there was a slightly decreasing trend after that. RWL levels increased slightly during irrigation stops. The proline content of plants subjected to drought stress was 0.91-2.63 mg/0.05 g, 2.75 times higher than that of the control treatment. The sugar content of the drought stress treatment group was 29.77 to 350.66 mg/0.05 g, which increased 12.24 times that of the control treatment. As a result of this study, S. gracilistyla was found to have a drought tolerance almost comparable to that of evergreen broad-leaved trees growing on the land. This study is expected to contribute to the resource utilization S. gracilistyla, a native willow tree of Korea, and the mass production of biomass by SRC.

A sensor system has been developed that uses an ultrasonic sensor to detect the downhill slope on the side of a forest road and prevents a vehicle-type forest machine from rolling down a mountainside. A specular reflection of ultrasonic wave might cause severe issues in measuring distances to targets. By investigating the installation angle of the sensor to minimize the negative effects of specular reflection, the installation angle of lateral monitoring ultrasonic sensor could be determined based on the width of road shoulder. Obstacles such as small rocks or piece of log in a forest road may cause the forest machine to be overturned while the machine riding over due to excessive its posture change. It was determined that the laser sensor could be a part of a sensor system capable of specifying the location and size of small obstacles. Not only this sensor system including ultrasonic and laser sensors can issue a warning of dangerous sections to drivers in forest forwarders currently in use, but also it can be used as a driving safety sensor in autonomous forest machine or remote-control forest machine in the future.