• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Korean Journal of Medicinal Crop Science
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• v.17 no.6
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• pp.421-426
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• 2009

The purpose of this study was to identify soil physical characteristics as guideline for high yield potential in ginseng cultivated field which produced 6 years root. Harvest yields of ginseng to be divided by parent rock was in order of phyllite and red shale 3.1 kg/$3.3\;m^2$ > granite and gneiss schist 3.0 kg > basalt 2.6 kg > porphyry 2.2 kg in upland and forest soil. Also, with classified by topography, it was in order of foot slope and alluvial fan 3.2 kg/$3.3\;m^2$ > valley 3.0 kg > low hill 2.9 kg > hill, lave flow and dilluvial terrace 2.8 kg in survey tilth. Class determination of soil texture, it was in order of sandy loam 3.1 kg/$3.3\;m^2$ > loam and silt loam 3.0 kg > clay loam 2.9 kg > silt clay loam 2.8 kg in survey tilth. Slope condition of farming land, in case of sloping (2~7%), it was 3.1 kg/$3.3\;m^2$ but deep sloping (15~30%) caused decreasing harvest yield. In drainage classes (excessively, well and moderately well), there was no significantly different in harvest yields. Relationship between harvest yield and soil series, Production sites as yielding 3.0 kg/$3.3\;m^2$ were seven sites, also it was contained 14 soil series. Production sites as yielding 2.5~3.0 kg/$3.3\;m^2$ were eleven sites, it was contained 16 soil series. Production sites as yielding 2.0~2.5 kg/$3.3\;m^2$ were 10 sites, it was contained 4 soil series.

• Korean Journal of Soil Science and Fertilizer
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• v.5 no.2
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• pp.1-38
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• 1972

This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) = $$-0.2491x^2+6.0388x-1.1251$$ y(subsoil or subsurface soil, clay content) = $$-0.31646x^2+7.84818x-2.50008$$ y(surface soil, organic carbon content) = $$-0.0089x^2+0.2192x+0.1366$$ y(subsoil or subsurface soil, pH) = $$-0.0178x^2-0.04534x+8.3531$$ Soil profile development, soil color, depositional and organic layers, soil texture and soil reaction etc. are thought to be the major items that should be considered in a paddy soil classification. It was found that most of the soils belonging to the moderately well, somewhat poorly and poorly drained fine and medium textured soils and moderately deep fine textured soils over coarse materials, produce higher paddy yields in excess of 3,750 kg/ha. and most of the soils belonging to the coarse textured soils, well drained fine textured soils, moderately deep medium textured soils over coarse materials and saline soils, produce yields less than 3,750kg/ha. Soil texture of the profile, available soil depth, salinity and gleying of the surface and subsurface soils etc. seem to be the major factors determining rice yields, and these factors are considered when establishing suitability groups for paddy land. The great group, group, subgroup, family and series are proposed for the classification categories of paddy soils. The soil series is the basic category of the classification. The argillic horizon (Bt horizon) and cambic horizon (B horizon) are proposed as two diagnostic horizons of great group level for the determination of the morphological properties of soils in the classification. The specific soil characteristics considered in the group and subgroup levels are soil color of the profile (bluish gray, gray or yellowish brown), salinity (salic), depositonal (fluvic) and muck layers (mucky), and gleying of surface and subsurface soils (gleyic). The family levels are classified on the basis of soil reaction, soil texture and gravel content of the profile. The definitions are given on each classification category, diagnostic horizons and specific soil characteristics respectively. The soils on these plains are classified in eight subgroups and examined under the existing classification system. Further, the suitability group, can be divided into two major categories, suitability class and subclass. The soils within a suitability class are similar in potential productivity and limitation on use and management. Class 1 through 4 are distinguished from each other by combination of soil characteristics. Subclasses are divided from classes that have the same kind of dominant limitations such as slope(e), wettness(w), sandy(s), gravels(g), salinity(t) and non-gleying of the surface and subsurface soils(n). The above suitability classes and subclasses are examined, and the definitions are given. Seven subclasses are found on these plains for paddy soils. The classification and suitability group of 15 paddy soil series on the Gimje-Mangyeong plains may now be tabulated as follows.

• Journal of Korea Soil Environment Society
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• v.1 no.1
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• pp.47-54
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• 1996

Soil pollution indices (SPI) were designed for estimating quality of soil polluted with arsenic and heavy metals. Applying the quality reference value of soil based on its multifunctional purpose was a key step. For considereing multifunctions of soil, soil was classified into 4 groups-agricultural land, residential area, recreational area, factorial site. Then, each concentration of arsenic and each of five heavy metals (Cd, Cu, Hg, Pb, Zn) in soils grouped was transformed to a mathematical value based on the soil quality reference value which may stand for ecological impact. Soil pollution score (SPS) was the addition of the 6 values transformed, and the range of the SPS was divided into 4 Soil Pollution Classes (SPC). The SPC 1, 2, 3, and 4 were SPS <100, SPS 100-200, SPS >200-300, and SPS >300, repectively. SPS and SPC were evaluated with the results of the data from employing the Soil Network of 1994. Based on the soil quality reference values, SPS and SPC of the Soil Network's data were transformed and classified, respectively. Then, SPS and SPC were compared with arsenic and the 5 heavy metal contents of their reference values resulted from the Soil Network's. From this method, soil quality of most of the Soil Network area was estimated to be healthy. However, ca. 3.0~4.0% of the Soil Network area was determined in a range of slightly and heavily polluted. As the mean value of SPS of the Soil Network's was 66.2 which indicates most of soil evaluated was healthy. When the SPSs of the data were divided into 4 groups of SPC, Class 1 (Good quality of soil), Class 2 (Need to be checked area 1), Class 3 (Need to be checked area 2) and Class 4 (Polluted area) were 87.0, 9.4, 2.4, 1.2%, respectively. Using SPI were comparable to those of heavy metal contents in soils, and would be comprehenve to determine the status of soil qulity. Methodology of the developing SPI would be applicable to the other soil pollutants such as organic and inorganics than arsenic and 5 heavy metals used here.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.3
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• pp.242-250
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• 2016

Veterinary antibiotics (VAs) has been used to treat animal disease and to increase animal weight as growth promoter. However, abused usage of VAs can cause production of antibiotic resistance genes (ARGs) in the environment and additionally, residual of VAs in soil can be transferred into crops. Therefore, main objective of this research was to examine bioaccumulation of VAs in sprouts (red cabbage, Brassica Olearacea L. var. Capitata f. rubra and red radish, Raphanus sativus) with hydroponic method. Total of 7 VAs in 2 different classes of VAs (tetracyclcines: tetracycline, oxytetracycline, chlortetracycline, sulfonamides: sulfamethoxazole, sulfamethazine, sulfamethiazole, macrolides: tylosin) were evaluated and experiment was conducted with solid phase extraction (SPE)/high performance liquid chromatography tandem mass spectrometry (HPLC/MS/MS). Initial spiked concentration of 7 VAs was $5mg\;L^{-1}$ and cultivation period was 8 days. Result showed that growth of sprouts was inhibited about 23-27% when VAs was introduced. Amount of bioaccumulated VAs was also differed depending on class of VAs. The highest amount of bioaccmulated VAs was tetracycline and sulfamethoxazole in each class with a concentration of 4.05, $7.73mg\;kg^{-1}$ respectively. Calculated transfer ratio of VAs into crops was also ranged 0.38-54.27%. Overall, bioaccumulation of VAs in crops can be varied depending on crop species and class of VAs. However, further research should be conducted to verify bioaccumulation of VAs in crops in the soil environment.

• Journal of Korean Society of Forest Science
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• v.90 no.3
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• pp.257-265
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• 2001

This study was carried out to investigate composition and distribution of the soil microarthropods in Pinus rigida plantations following strip-cutting from May 1998 to April 1999. The results of this study were as follows; 1. The individuals of soil microarthropods found during the research periods were 181, 904 and were identified into 21 orders in 7 classes. The classes were Arachnida, Insecta, Malacostra, Pauropoda, Diplopoda, Chilopoda and Symphyla. 2. The population density of the soil microarthropods was 82,962 individuals at the control area, 62,688 individuals at the reserve area and 36,254 individuals at the cutting area. The microarthropods in the control area were 2 times higher than those in the cutting area. 3. Although the major dominant taxa, Acari and Collembola, decreased in densities at the cutting area, the density reduction in Acari was higher than that in Collembola. This resulted in the increase of Collembola in terms of the relative abundance at the cutting area. 4. Among the total soil microarthropods, Arachnida was 59.74%, followed by Insecta of 39.82%. Such two groups comprise 99.56% of soil microarthropods. 5. According to the relative population density, Acari was 99.18% in Arachnida and 59.25% in the total and Collembola 93.99% in Insecta and 37.42% in the total. Therefore, individuals of Acari and Collembola was 96.67% of the total individuals. And the next abundant groups were Hymenoptera (0.95%), Diptera (0.64%) and Pseudoscorpiones (0.39%). 6. The population density of the soil microarthropods fluctuated seasonally, showing the bimodal pattern, being high in fall and spring. The highest density occurred in November and the lowest in July.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1351-1361
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• 2019

In this study, the annual surface soil erosion amount of before (2007 year) and after (2015 year) the river improvement projects were calculated using RUSLE (Revised Universal Soil Loss Equation) in the Geumgang basin (Daecheong-Dam to Geumgang Estuary-Bank). After the results were classified into five classes, the results were compared and analyzed with the results of the change in the land cover. In order to generate each factor of RUSLE, various spatial information data, such as land cover maps for 2007 and 2015 years, national basic spatial information, soil map, and average annual precipitation data were utilized. The results of the analysis are as follows: 1) annual surface soil erosion in the study area increased the area of class 1 in 2015 years compared to 2007, 2) the area of class 2, 3 and 5 decreased, 3) the area of class 4 increased. It is believed that the average annual amount of surface soil erosion decreased in most areas due to the reduction of annual average precipitation, the formation of ecological parks, the expansion of artificial facilities, and the reduction of illegal farmland.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.91-105
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• 2021

We conducted a study to identify the relationships between the investigated factors and provide a methodology and generate data by applying deterioration classes to the Seonjaryeong Forest Trail (4.3 km) in the Baekdudaegan Ridge. The average trail width (1.7 m) and bare width (1.4 m) were wider than those obtained in the previous studies. The frequency of trail deterioration was also high. Specific data on deterioration classes were obtained and evaluated using qualitative criteria. Specific data for heavy class stands at 20.1% in trail grade, 13.3 cm on average, and 16.1 cm in the center of erosion depth, 16.2 cm of CSA, 12.3 kg/cm (20.1 mm) on average and maximum 39.3 kg/cm (29.6 mm) of soil hardness. We observed a positive correlation between the deterioration class and trail grade, and the average and maximum soil erosion depths of the hill side were deeper than those of the ridge. The soil hardness data showed a statistically significant difference in terms of the transect site and calculation method (㎏/㎠, mm). Therefore, trail deterioration was observed at the sites having ≥20% trail grade; thus, continuous monitoring at fixed sites over time will be required for sustainability. Furthermore, the trail grade should be of the utmost priority in trail design and management.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.983-991
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• 2012

These days, agricultural products cultivated in facilities occupy the highest percentage of agricultural output price. Specifically cucumbers have been one of the crops that farmers prefer to growing, because their prices were high. However, cucumber crop is sensitive to the soil and environments and it requires the exact crop management. In order to establish cultivation techniques for cucumbers, the current situation of cucumber cultivation was surveyed from ten cucumber farmlands; five farmlands of cucumber cultivation in forcing and five of semi-forcing practicing systems, respectably. The soil conditions were alluvial or valley in soil topology, moderately or poorly drainage in soil drainage classes, coarse loamy in soil texture family. Soil was managed with deep plowing combined with application of basal fertilizers such as compost, rice straw, oil cake, wood chip and chemical fertilizer. The whole soil was prepared in uniformly with rotary. Three major nutrients ($N-P_2O_5-K_2O$)of basal application were 815-464-529 kg $ha^{-1}$ in forcing and 197-135-151 kg $ha^{-1}$ in semi-forcing cultivation. Top dressing of fertilizer was supplied in fertigation system of macro and micro elements in 2~3 day interval with water irrigation. The average yields of cucumbers were $168t\;ha^{-1}$ with 381,000 thousand won $ha^{-1}$ in average gross profit (AGP) in forcing cultivation and $115t\;ha^{-1}$ with 177,000 thousand won $ha^{-1}$ in AGP in semi-forcing cultivation. Cucumber production during the winter season was considered to increase the gross profit because cucumber price tends to stay in high level during this time. The accumulation of soil chemicals like EC, available $P_2O_5$ and exchangeable cations could be controlled by rice straw application. The rice straw application increased soil temperature during the winter season, in exchange of soil air, and in extension of plant roots. In addition, the rice straw application somewhat affected decrease of salts accumulation.