Seong Woo Jeon

Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS

Recently there has been an increasing occurrence of flooded area in Korea. Most of these flooded area occurred roadside in the city or residential areas. For predictive flooded area susceptibility mapping, this study applied and verified probability model, the frequency ratio at Busan, Korea, using a Geographic Information System (GIS) and Statistical methods. Flooded areas were identified in the study area of field surveys, and maps of the topography, geology, landcover and green infrastructure were constructed to spatial database. Using this analysis results, part of urban planning can find ideal locations for GIS which are needed. This result expects that this planning framework can bring flood mitigation of city.

Mapping urban growth probability in South Korea: comparison of frequency ratio, analytic hierarchy process, and logistic regression models and use of the environmental conservation value assessment

Rapid industrialization and economic growth in South Korea since the 1970s have resulted in severe environmental disturbance and pollution, problems aggravated by the imprudent expansion of urban areas. This paper analyzes and predicts urban growth patterns with the aim of contributing to more efficient urban planning. Urban growth probability index (UGPI) maps were prepared using the frequency ratio (FR), analytic hierarchy process (AHP), and logistic regression (LR) methods, with and without considering development restrictions based on the national environmental conservation value assessment map (ECVAM). Environmental and legal restrictions were associated with an average difference of 41.70% in conservation areas and an 81.32% average difference in agriculture and forest land use–land cover (LULC). Accuracy of the models was examined by area under the curve (AUC) analysis. Accuracies of UGPI maps produced with the ECVAM were higher than UGPI maps produced without the ECVAM. In addition, effectiveness and accuracy tests based on LULC showed that the UGPI maps produced with the ECVAM had a higher rate of accuracy that UGPI maps produces without the ECVAM. Using the ECVAM and assuming that urban and built-up areas will be 1.5 times greater than in 2005 and that environmental restrictions are removed, urban development can be expected to more than double in conservation areas and borderlands, increase by more than 1.5 times in developable areas, and decrease by half in old downtown areas. If legal restrictions are removed, urban development is expected to occur mostly in former conservation areas, followed by borderlands, old downtowns, and developable areas.

Estimation of forest carbon budget from land cover change in South and North Korea between 1981 and 2010

This paper quantified carbon budget in the past 30 years (1981–2010) and identified the impact of land cover change on carbon dynamics using vegetation integrated simulator for trace gases (VISIT) model. North Korea was converted from carbon sink to source with 10.72 ± 5.18 Tg C yr−1 of net ecosystem production (NEP) in the 1980s, 3.00 ± 7.96 Tg C yr−1 in the 1990s, and −0.46 ± 5.13 Tg C yr−1 in the 2000s. NEP in South Korea was 10.55 ± 1.09 Tg C yr−1 in the 1980s, 10.47 ± 7.28 Tg C yr−1 in the 1990s, and 6.32 ± 5.02 Tg C yr−1 in the 2000s, showing a gradual decline. In North Korea, NEP was decreased by 0.52 Tg yr−1 in the 1990s due to reduction of forest, and increased by 0.36 Tg yr−1 in the 2000s due to expansion of cropland. In South Korea, it was decreased by 0.24 Tg yr−1 in the 1990s as urban and built-up area expanded, and increased by 0.04 Tg yr−1 in the 2000s with the expansion of forest. These results suggest the importance of forest and land cover management against deforestation for ensuring national carbon balance.

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AICc). The use of three data sets was statistically significant at R2 = 0.75 (RMSE = 52.17 m3 ha−1), R2 = 0.84 (RMSE = 45.24 m3 ha−1), and R2 = 0.91 (RMSE = 31.48 m3 ha−1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R2 = 0.78, R2 = 0.53, and R2 = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.

Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data

Satellite data has been used to ascertain trends and correlations between climate change and vegetation greenness in Asia. Our study utilized 33-year (1982-2014) AVHRR-GIMMS (Advanced Very High Resolution Radiometer - Global Inventory Modelling and Mapping Studies) NDVI3g and CRU TS (Climatic Research Unit Time Series) climate variable (temperature, rainfall, and potential evapotranspiration) time series. First, we estimated the overall trends for vegetation greenness, climate variables and analyzed trends during summer (April to October), winter (November to March), and the entire year. Second, we carried out correlation and regression analyses to detect correlations between vegetation greenness and climate variables. Our study revealed an increasing trend (0.05 to 0.28) in temperature in northeastern India (bordering Bhutan), Southeast Bhutan, Yunnan Province of China, Northern Myanmar, Central Cambodia, northern Laos, southern Vietnam, eastern Iran, southern Afghanistan, and southern Pakistan. However, a decreasing trend in temperature (0.00 to -0.04) was noted for specific areas in southern Asia including Central Myanmar and northwestern Thailand and the Guangxi, Southern Gansu, and Shandong provinces of China. The results also indicated an increasing trend for evapotranspiration and air temperature accompanied by a decreasing trend for vegetation greenness and rainfall. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central China, North Korea, South Korea, and northern Japan, showing an indirect relationship (R=0.84-0.96).

Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data

Graphical abstract

Drought monitoring of the wetland in the Tumen River Basin between 1991 and 2016 using Landsat TM/ETM+

ABSTRACT Wetland areas are known as ‘the kidneys of the Earth’ because they provide important functions towards stabilizing the environment, long-term protection of water sources, effectively minimizing sediment loss, purifying surface water from industrial and agricultural pollutants, and enhancing aquifer recharge. The condition of water supply in wetlands directly affects the growth of wetland plants and local biodiversity. Therefore, drought monitoring is vital in wetlands. In this study, Vegetation Temperature Condition Index (VTCI) derived from normalized difference vegetation index (NDVI) and land surface temperature (LST) is used to observe the drought status of the wetland in the cross-border (China and North Korea) Tumen River Basin from 1991 to 2016. For this purpose, the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data for six periods were used for the analysis. Soil moisture maps acquired from the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) were then introduced for validating the reliability of the drought monitoring method. The results showed that most areas with a normal moisture level (decreased 25.8%) began experiencing slight drought (increased 29.7%). The coefficient of determination (R2) between VTCI and soil moisture showed values of 0.69, 0.32, and 0.2 for 0–5 cm, 0–10 cm, and 10–20 cm thicknesses, respectively. Although climate change probably contributes to the formation of drought by decreasing precipitation (50 mm decrease in Chinese section) and increasing temperature (0.5°C increase in North Korean section), human activities such as surges in daily water consumption appear as the main threats that leading to droughts in this wetland.

Valuation of Forest Habitat Functions of Endangered Mammals Using Species Distribution Model

Abstract It is estimated that there is a total of approximately 100,000 species in Korea. However, the number is currently about 30,000 and only 16,027 species are listed in the ‘Species Korea’ (as of December, 2014). Of the listed species, 51 species are designated as the Endangered Species Class I while 195 species are in the Class II, totaling 246 endangered species including 20 mammals. Under the circumstances that development (e.g., roads) is increasingly threatening the persistence of endangered mammals, it is significant to identify and preserve suitable habitats for them. In this context, evaluating the values of the suitable habitat environment would serve as essential information for development decision making. This study estimated the values of endangered mammals’ forest habitats through spatialization of habitat services. In doing so, a species distribution model, Maximum Entropy Model (MaxEnt) was utilized for a group of endangered mammals including, mountain goat, wildcat, marten cat, and flying squirrel. To calculate the values per unit area, a benefit transfer method was used based on the point-estimate technique with the best available values estimated previously. The range of discount rate of 3.0 to 5.5 percent was applied taking the notion of social discount rate into account. As a result, the province with the highest values for endangered mammal habitats appeared to be Gangwon, followed by Gyeongbuk and Gyeongnam. The monetary values of the endangered mammal habitats were estimated to be 330 billion to 421 billion won per year.Key Words: endangered mammals, habitat, values per unit area, benefit transfer method, social discount rate