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.

This study was carried out in the southern zone of Tigray to identify and characterize traditional common agroforestry practices and understand the existing knowledge of farm households on the management of trees under different agroforestry in different agroecologies. We conducted reconnaissance and diagnostic surveys by systematically and randomly selecting 147 farming households in the three agroecologies of the study area. A logit regression model was employed to determine how these factors influence farmers’ adoption decision. The findings indicate that a majority of the households (46.3%) were engaged in homestead agroforestry practices (AFP), followed by live fence (25.9%) and farmland or parkland (15%) agroforestry practices. The study identified Carica papaya, Malus domestic, Persea americana, Mangifera indica, Ziziphus spina-christi, and Balanites aegyptiaca as the most dominant fruit tree species found in the home garden agroforestry. In total, 68% of the households had some of these fruit trees around their home gardens. We also established the three most dominant agricultural production systems as: i) Agricultural production system, composed of fruit tree + cereal crops + Ziziphus spina-christi + Balanites aegyptiaca and/or acacia species; ii) agricultural production system, consisting of cash crops, like Coffee arabica and Catha edulies + fruit trees + Cordia africana + Balanites aegyptiaca and/or acacia species; and iii) agricultural production, composed of fruit trees + vegetables within a boundary of Sesbania sesban and other acacia species in the modern irrigated land. Furthermore, 90.16% of the households in the highlands reported a shortage of farmland for planting trees as the main constraint. About 34.44% farmers reported using leaves of Cordia africana, Balanites aegyptiaca, pods of acacia species, and crop residue as the main source of animal fodder. In total, 86.4% of the households also recognized the importance of multipurpose trees for soil fertility enhancement, control of runoff, microclimate amelioration, environmental protection, and dry season animal fodder. According to the logit model analysis, sex, family size, educational level, and landholding significantly (p < 0.05) influence the household’s role in the adoption of agroforestry practices. Based on these findings, farmers used different adaptation strategies, such as planting of multi-purpose trees (34.7%), conservation tillage to minimize both erosion and runoff potentials as soil conservation strategies (27.2%), varying planting dates, use of drought tolerant crop varieties (16.3%), and others based on farmers’ indigenous knowledge passed down from generation to generation. We conclude that agroforestry practices are important components of farming systems in Tigray, resulting in diversified products and ecological benefits that improve socio-ecological resilience. Therefore, we recommend that agroforestry practices are mainstreamed into development plans, especially in agriculture.

To assess which data type is more effective for spatial modeling in the Republic of Korea, we conducted geostatistical analysis based on frequency, intensity, and spatial autocorrelation using two types of forest fire occurrence data: that collected through field survey of the Korea Forest Service (KFS) and satellite active fire data of Moderate Resolution Imaging Spectroradiometer (MODIS). The maximum entropy (MaxEnt) model was used with environmental factors in the spatial modeling of fire probability to compare the accuracy of the two data types based on 10 years of historical data. The results showed a clear difference in fire frequency and similar fire intensity patterns. The spatial autocorrelation between the fire frequency and intensity of the two data types was analyzed using a semi-variogram. Fire intensity was significantly correlated, with the MODIS data having a higher correlation than the KFS data. Examination of the spatial autocorrelation and related factors by fire source also indicated that MODIS data had higher spatial autocorrelation, with remarkable distinction found in climate factors. In spatial the modeling, MODIS data showed a similar outcome to that of hotspot analysis, with higher accuracy and better model performance attributable to high spatial autocorrelation. Even though the KFS data were collected from post-fire surveys, they resulted in low spatial autocorrelation and reduced model accuracy owing to the wide distribution of data. MODIS had many detection errors. With spatial filtering, however, the model accuracy can be improved with relatively high spatial autocorrelation.

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.

Air pollution is one of the most significant environmental hazards. The elderly, young, and poor are more vulnerable to air pollution. The risk of air pollution was assessed based on the risk framework published by the Intergovernmental Panel on Climate Change (IPCC) in terms of three aspects: hazard, exposure, and vulnerability. This study determined the concentrations of hazardous pollutants using satellite images from 2015 at 1 km² spatial resolution. In addition, the study identified vulnerable groups who are exposed to hazardous air pollutants. The study highlighted the degree of vulnerability based on environmental sensitivity and institutional abilities, such as mitigation and social adaption policies, using statistical data. Based on the results, Seoul City and Gyeonggi Province have low air pollution risk owing to good institutional abilities, while the western coastal area has the highest air pollution risk. Three adaption pathway scenarios were assessed in terms of the effect of increases in the budget for social adaptation policies on the level of risk. The study found that the risk can be reduced when the social adaptation budget of 2015 base level is increased by 20% in Gyeonggi Province and by 30% in the western coastal area. In conclusion, this risk assessment can support policy-making to target more vulnerable groups based on scientific evidence and to ensure environmental justice at the national level.