Although many people affected by COVID-19 suffer from some form of psychological distress, access to proper treatment or psychosocial interventions has been limited. This study aimed to examine the feasibility and preliminary effects of a therapeutic gardening program conducted during the COVID-19 pandemic. The program consisted of 30 sessions and was conducted at 10 nationwide sites in Korea from June to November 2021. Mental health and well-being were assessed using the Mental Health Screening Tool for Depressive Disorders, Mental Health Screening Tool for Anxiety Disorders, Engagement in Daily Activity Scale, brief version of World Health Organization Quality of Life, and Mindful Attention Awareness Scale. Cohen’s d value was calculated for the effect size, and a multilevel analysis was used to determine the longitudinal effects of therapeutic gardening. The effect sizes for depression, anxiety, daily activities, quality of life, and mindfulness were 0.84, 0.72, 0.61, 0.64, and 0.40, respectively. Multilevel analyses showed that all five mental health variables improved significantly over time as the therapeutic gardening program progressed. Therapeutic gardening is promising and applicable as a nature-based intervention to improve the mental health of individuals experiencing psychological distress especially in the COVID-19 pandemic.

The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

Forest fires pose significant threats to ecosystems, economies, and human lives. However, existing forest fire risk assessments are over-reliant on field data and expert-derived indices. Here, we assessed the nationwide forest fire risk in South Korea using a dataset of 2289 and 4578 fire and non-fire events between 2020 and 2023. Twelve remote sensing-based environmental variables were exclusively derived from Google Earth Engine, including climate, vegetation, topographic, and socio-environmental factors. After removing the snow equivalent variable owing to high collinearity, we trained three machine learning models: random forest, XGBoost, and artificial neural network, and evaluated their ability to predict forest fire risks. XGBoost showed the best performance (F1 = 0.511; AUC = 0.76), followed by random forest (F1 = 0.496) and artificial neural network (F1 = 0.468). DEM, NDVI, and population density consistently ranked as the most influential predictors. Spatial prediction maps from each model revealed consistent high-risk areas with some local prediction differences. These findings demonstrate the potential of integrating cloud-based remote sensing with machine learning for large-scale, high-resolution forest fire risk modeling and have implications for early warning systems and effective fire management in vulnerable regions. Future predictions can be improved by incorporating seasonal, real-time meteorological, and human activity data.

This study evaluates the applicability of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model for assessing wildfire-induced changes in water-related ecosystem services at a localized scale. Wildfires significantly alter hydrological processes by reducing vegetation cover, which in turn affects water-yield dynamics. To quantify these changes, we applied the InVEST annual water-yield model to a 4.95 km² wildfire-affected area and validated its outputs against the physically based SWAT model. The study utilized Sentinel-2 imagery to create pre- and post-wildfire land cover maps, which served as key inputs for the InVEST model. The results showed a 7.05% increase in water yield after the wildfire. Validation using SWAT confirmed that InVEST could capture localized hydrological changes with accuracy. While InVEST simplifies hydrological processes by relying primarily on land cover data, it remains a valuable tool for rapid and low-resource assessments in wildfire-prone regions. This study highlights the potential of InVEST for rapid post-fire evaluations, offering a practical decision-support model for post-fire land and water resource management in the context of climate change.

The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.

Background and objective: Urban forests have increasingly integrated recreational and welfare facilities to enhance the quality of life for urban residents. This study explores the behaviors, perceptions, and satisfaction of visitors to major forests in Incheon Metropolitan City, aiming to offer guidelines for the improvement of recreational facilities within these urban forests. By understanding visitor needs and preferences, the study seeks to contribute to the development of more user-friendly and sustainable forest environments that cater to the well-being of city dwellers.Methods: The study focused on five major forests in Incheon: Munhaksan Mountain, Cheongnyangsan Mountain, Manwolsan Mountain, Wonjeoksan Mountain, and Gyeyangsan Mountain. Data were collected through field surveys and analyzed using cross-tabulation, one-way ANOVA, and regression analysis. Based on the results, targeted recommendations were made for the enhancement of recreational facilities in each forest.Results: For Munhaksan Mountain, expanding recreational spaces and offering trail-specific programs are key priorities, along with developing a maintenance plan to improve visitor satisfaction. In Cheongnyangsan Mountain, expanding popular facilities like barefoot walking paths and optimizing the forest structure for air purification and noise reduction are needed. Manwolsan Mountain requires a more efficient maintenance system, focusing on security, amenities, and visitor services to address the issue of low satisfaction. Wonjeoksan Mountain should enhance trail safety with more CCTVs and improve amenities such as restrooms and trash disposal. Gyeyangsan Mountain needs more rest areas and amenities, addressing congestion in the northwest while preserving natural scenery and providing an information platform for younger visitors.Conclusion: This study contributes to optimizing the use of urban forests and improving the quality of life for residents by proposing a strategic framework for the development of recreational welfare in forests. The findings are expected to serve as a valuable resource for local governments in formulating rational forest management and usage plans.