As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.

Afforestation of forests in South Korea may provide an example of the benefit of afforestation on precipitation storage and erosion control. In this study, we presented the effects of afforestation on water supply and soil loss prevention. A spatio-temporal simulation of forest water yield and soil loss was performed from 1971–2010 using InVEST water yield and SWAT models. A forest stock change map was produced by combining land cover data and National Forest Inventory data. The forest water yield increased about twice with changes in forest stock and climate from 1971–2010 and showed a spatially homogeneous water supply capacity. In the same period, the soil loss decreased more than three times, and the volatility of soil loss, in the 2010s, was smaller than before. The analysis of the change in forest stock without considering climate change showed an increase of 43% in forest water yield and a decrease of 87% in soil loss. An increase in precipitation increased the water yield, but also increased the soil loss volume. A change in forest stock led to positive changes in both. This study presents functional positive effects of the afforestation program in South Korea that can be useful in various afforestation programs in other countries.

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning.

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.

Carbon stored on land is discharged into rivers through water flow, which is an important mechanism for energy transfer from land to river ecosystems. The goal of this study was to identify the relationship between land cover and carbon flux mediated through precipitation. In order to clarify the general relationship, research was conducted on a range of national scales. Eighty-two watershed samples from an area where the urban land cover area was less than 10% and with a water-quality measurement point at an outlet were delineated. Carbon flux and soil organic carbon of the watershed was estimated using the Soil and Water Assessment Tool model, Forest Biomass and Dead Organic Matter Carbon model, and other data. Finally, the data were analyzed to determine the relationship between soil organic carbon and carbon flux. As a result, it was concluded that the carbon flux of the watershed increased with increasing area of the watershed. Under the same area condition, it was revealed that the greater the forest soil organic carbon, the less the carbon flux released from the watershed. Through this study, it was observed that as the above-ground biomass of forest increased, the carbon flux from watershed to river outlet decreased logarithmically.