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.

With the recent increase in food demand, urban agriculture has gained attention as a way of increasing food self-sufficiency and providing recreational spaces in cities. In this study, the suitability of rooftop greenhouses (RGs), a type of urban agriculture, was analyzed by combining the analytical hierarchy process (AHP) and geographic information systems (GIS) in Seoul, the capital city of Korea. To achieve this, we derived location suitability factors through expert consultations and calculated the weights of each factor through AHP. After building the spatial data according to these factors, they were weighted and summed then scaled to a score of 0–100. The highest weight of the RG location factors was for benefit (0.1782), followed by officially assessed land prices (0.0913) and supermarket density (0.0802). The weights of supermarket density and accessibility were high because they are considered the main distribution channels. When analyzing the location of RGs by linking these results with the spatial data according to factor, we revealed that Gangseo-gu (a district of Seoul) had relatively high location suitability scores. This trend was determined to be caused by the rather low officially assessed land price, high supermarket density, and productive population. This result could prove useful when selecting the approximate locations of RGs in Seoul and for promoting food self-sufficiency in cities.

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.

Although thermal tolerance along geographical gradients gives an insight into species’ response to climate change, current studies on thermal tolerance are strongly skewed towards global-scale patterns. As a result, intraspecific variations are often assumed to be constant, despite a lack of evidence. To understand population-specific responses to thermal stress, we investigated the presence of intraspecific variations in the critical thermal maximum (CTmax) of tadpoles in two anuran species, Rana uenoi and Bufo sachalinensis. The study was conducted across a five-degree latitudinal gradient in the Republic of Korea. We exposed the tadpoles to increasing temperatures and recorded the CTmax for 270 R. uenoi individuals from 11 sites, collected in rice paddies, and for 240 B. sachalinensis individuals from ten sites, collected in reservoirs. We also recorded the swimming performance and behavior of the tadpoles when placed in an experimental apparatus during CTmax measurements. We then used linear regressions to determine the relationship between abiotic factors and CTmax. In R. uenoi, we found a positive relationship between latitude and CTmax, but the tadpoles did not display specific thermoregulatory behaviors. In B. sachalinensis, none of the abiotic factors such as climate and geographic coordinates were related to CTmax, but we detected a tendency to swim close to the water surface when water temperature was increasing. For R. uenoi, we tentatively relate the CTmax variability across the latitudinal gradient to a physiological adaptive response associated with habitat characteristics that are assumed to be fluctuating, as the species inhabits small water bodies prone to drying out. In the case of B. sachalinensis, the behavior observed may be linked to oxygen depletion and thermoregulation, as it may buffer temperature changes in the absence of physiological adjustment. These findings suggest that intra-specific variations in CTmax are greater than generally accounted for, and thermal conditions of natural environments are important for understanding thermal tolerance in ectothermic species. Our results highlight that species’ specific responses to climate warmings need to be studied to better protect species against 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: This study investigated the occurrence characteristics of street tree management factors based on different special-purpose areas with emphasis on citizen participation in data collection.Methods: We collected growth and management data of street tree sections and individual trees within sections using a monitoring form developed for street trees in four special-purpose areas in the urban area of Chuncheon. We used a binary logistic regression model to analyze the probability of occurrence for each management factor (pruning traces, sign blocking, disease and pest, felled trees, piled objects, and broken branches).Results: The probability of occurrence of management factors varied by special-purpose area, which was related to the characteristics of each area.Conclusion: This study suggests the need to develop efficient street tree management strategies by prioritizing management factors in consideration of differences in special-purpose areas