• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.123-134
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• 2014

Identification of methods to optimize the growth of a plant community, including the capacity of the soil to further sequester carbon, is important in urban design and planning. In this study, to construct and manage an urban park to mitigate carbon emissions, soil organic carbon of varying biomass, different park construction times, and a range of vegetation types were analyzed by measuring aboveground and belowground carbon in Seoseoul Lake Park and Yangjae Citizen's Forest. The urban parks were constructed during different periods; Seoseoul Lake Park was constructed in 2009, whereas Yangjae Citizen's Forest was constructed in 1986. To identify the differences in soil organic carbon in various plant communities and soil types, above and belowground carbon were measured based on biomass, as well as the physical and chemical features of the soil. Allometric equations were used to measure biomass. Soil total organic carbon (TOC) and chemical properties such as pH, cation exchange capacity (CEC), total nitrogen (TN), and soil microbes were analyzed. The analysis results show that the biomass of the Yangjae Citizen's Forest was higher than that of the Seoseoul Lake Park, indicating that older park has higher biomass. On the other hand, TOC was lower in the Yangjae Citizen's Forest than in the Seoseoul Lake Park; air pollution and acid rain probably changed the acidity of the soil in the Yangjae Citizen's Forest. Furthermore, TOC was higher in mono-layered plantation area compared to that in multi-layered plantation area. Improving the soil texture would, in the long term, result in better vegetation growth. To improve the soil texture of an urban park, park management, including pH control by using lime fertilization, soil compaction control, and leaving litter for soil nutrition is necessary.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.