• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Journal of Forest and Environmental Science
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• v.38 no.1
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• pp.64-73
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• 2022

Increasing carbon and PM_2.5 concentrations have been emerging as serious environmental issues worldwide. The purpose of this study was to quantify carbon and PM_2.5 reduction by urban parks in Gangneung, Korea. A total of 35 parks were sampled by applying a random sampling method to survey tree planting structures and the areal distribution of land cover types of urban parks. These survey data and the Green Evaluation Technique (GET) computer program were used to estimate carbon and PM_2.5 reduction by trees. Mean tree density and cover in the study parks were 3.5±0.2 tree/100 m² and 44.5±3.0%, respectively. Annual carbon uptake and PM_2.5 deposition per unit area by trees averaged 2.8±0.2 t/ha/yr and 30.2±2.8 kg/ha/yr. Gangneung's urban parks annually offset the carbon emissions by 3.4% and the PM_2.5 emissions by 3.5%. Thus, urban parks played a significant role in reducing atmospheric carbon and PM_2.5 concentrations. Total annual carbon uptake and PM_2.5 deposition of urban parks in Gangneung were about 1,338.2 t/yr and 14,433.2 kg/yr. This study is expected to contribute to raising awareness of the role and importance of urban parks regarding carbon and PM_2.5 reduction.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.543-549
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• 2008

Human activities and land-use practices are intensely widening the urban areas. High impervious surface areas cover much of urban landscapes and are the primary pollutant sources which can lead to water quality and habitat degradation in its watershed. As the urban areas expand, transportation land-use such as parking lots, roads, service areas, toll-gates in highways and bridges also increase. These land-uses are significant in urban pollution due to high imperviousness rate and vehicular activities. To regulate the environmental impacts and to improve the water quality of rivers and lakes, the Ministry of Environment (MOE) in Korea developed the Total Pollution Load Management System (TPLMS) program. The main objective is to lead the watershed for a low impact development. On a local scale, some urban land surfaces can be emitting more pollution than others. Consequently, in urban areas, the unit loads are commonly employed to estimate total pollutant loadings emitted from various land-uses including residential, commercial, industrial, transportation, open lands such as parks and golf courses, and other developed land like parking areas as a result of development. In this research, unit pollutant loads derived specifically from transportation land-uses (i.e. branched out from urban areas) will be provided. Monitoring was conducted over 56 storm events at nine monitoring locations during three years. Results for the unit pollutant loads of transportation land-use are determined to be $399.5kg/km^2-day$ for TSS, $12.3kg/km^2-day$ for TN and $2.46kg/km^2-day$ for TP. The values are higher than those of urban areas in Korean MOE and US highways. These results can be used by MOE to separate the pollutant unit load of transportation landuses from urban areas.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.401-410
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• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.471-479
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• 2011

Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a hydrological factor that has an important role in water cycle. However, there is a limitation to understand it due to heterogeneity of land cover and vegetation. In this study, Mapping EvapoTRanspiration with Internalized Calibration (METRIC) model, one of the energy balance models, and MODerate resolution Imaging Spectroradiometer (MODIS) satellite based well-known Penman-Monteith algorithm were compared. Two ET maps were categorized and compared by land cover classification. The results represented overall applicability of the two models with the highest correlation coefficients in needleleaf and broadleaf forests. This study will be useful to estimate remote sensing based ET maps with high resolution and to figure out spatio-temporal variability and seasonal changes.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.361-368
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• 2004

Hydraulic models has a module to calculate SCS-CN values in order to estimate amount of water flow, which can be done with remotely sensed data and GIS data. The choice of the ancillary data tends to determine the range of SCS-CN values. We compare the results of SCS-CN value with satellite data of different spatial resolution and with soil maps of different scale. Mokhyun river basin was chosen,partly because of availbility of water quality and quantity data, partly because of rapid changes in land use and land cover since last ten years. The average CN values were calculated with spatial resolutions of 2.5 meter and 30 meter, We could not find any different result due to spatial resolution of CN resolution but due to both soil maps and to land cover maps. Further studies should be done for more than two kinds of satellite data.

• Journal of the Korean GEO-environmental Society
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• v.13 no.7
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• pp.39-47
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• 2012

Six land use data for a total of twenty five years were reviewed from 1975 to 2000 by dividing the period by 5-year unit; the land use variation was schematized; the watershed hydrological parameters were extracted by the representative rainfall years(maximum, average, driest year) by analyzing the recent thirty years'(from 1980 to 2010) climate data of the study region with SWAT model to investigate the effect of the precipitation change on the characteristics of groundwater recharge. In addition Markov Chain model was used to estimate the future land use; the predicted land use was applied to study the effect of the land use variation on the characteristics of groundwater recharge. For the research of this, long-term characteristics of groundwater recharge were estimated for the study region; the obtained results can be described as follows. The study region was divided into typical three area using SWAT model; yearly land use conditions were applied to the meteorological data of 1975 to 2010 and analyzed, producing the average rate of groundwater recharge of 30% for the applied period. This number is way lower than that of the earlier studies on the groundwater recharge for Jeju Island, which is 40-50%. Thirty percent (30%) is low considering the geological characteristics of Jeju, water-permeable vesicular strata, the reason of which must be the type of development is non-permeable paving.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.347-378
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• 2020

Based on monthly average groundwater recharge over a nearly 10 year period, results of fully integrated hydrologic modeling of SWAT-MODFLOW, land cover, land use, soil type and hydrologic response unit (HRU) was used to assess the dominant influencing factors of groundwater recharge spatial patterns in Jangseong district. As dominant factors, land cover was FRSE (forest-evergreen) and soil type was Samgag. Landsat-8 OLI imaging spectrometer data were acquired in the period 2003 to 2004 and seasonal bare soil lines (BSL) were estimated through NIR-RED plot. Extent of slope of BSL was from 1.092 to 1.343 and the intercept was from -0.004 to -0.015. To know correlation between spatial groundwater recharge and soil-vegetation indices (PVI, NDVI, NDTI, NDRI), this study employed frequency and regression analysis. On May, RED band increased up 3 to 4 times compared to other seasons and only one turning point appeared as recharge-index with upward parabola bell shape as results of existing research. Considering precipitation, if the various studies for relationship between groundwater recharge and soil-vegetation index just like NDVI are performed, it is possible to estimate groundwater recharge through analyzing remote sensing data.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.75-81
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• 2008

The water shortage in mega cities in Asia, which face a rapid growth in urban population, is an outstanding problem. It is important, therefore, to accurately estimate the water balance in each city in order to use the limited water resources effectively. In this study, we estimated the potential water resources in し sixteen mega cities in Asia. The target cities were Delhi and Calcutta, India; Colombo, Sri Lanka; Dhaka, Bangladesh; Yangon, Myanmar; Bangkok, Thailand; Kuala Lumpur, Malaysia; Singapore; Jakarta, Indonesia; Hanoi, Vietnam; Beijing and Hong Kong, the People's Republic of China; Seoul, the People's Republic of Korea; Manila, the Philippines, and Sapporo and Tokyo, Japan. The potential water resources were estimated by subtracting the actual evaporation from the amount of rainfall. The actual evaporation was estimated using the potential evaporation obtained by Hamon's equation which requires the air temperature and the possible hours of sunshine. When the results of Hamon's and Penman's evaporation equations were compared, a considerable error appeared in the low latitude region. The estimation using Hamon's equation was corrected with the linear regression line of Hamon's and Penman's equations. A classification of the land cover was carried out based on satellite photographs of the target cities, and the volume of surface runoff for each city was obtained using the runoff ratios which depended on the land cover. As a result, the potential water resources in the above mega cities in Asia were found to be greater than the world average. However, the actual water resources which are available for one person to use are probably very limited.