• Korean Journal of Ecology and Environment
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• v.53 no.4
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• pp.408-426
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• 2020

This study aims to establish the national strategy for biodiversity conservation by analyzing the current status of ecodiversity as the foundation of biodiversity conservation. Furthermore, this study has another purpose of preparing the measures for conservation and restoration of biodiversity. Ecodiversity was discussed as the basis for conserving biodiversity. Five climate zones and 14 climatic regions, eight plant geographic regions, three massifs and major geologic series, horizontal and vertical topographic conditions, 16 ecoregions, major ecosystems including forest, river and streams, wetlands, coast and marine, agriculture, and urban esosystems, and land use types were discussed as the element of the ecodiversity. In terms of biodiversity conservation, the actual conditions of each ecological unit were reviewed and measures were proposed to reduce biodiversity loss. Destruction and fragmentation of habitat, poor ecosystem management due to socioeconomic changes, the effects of exotic species and chemicals, and climate change were discussed as the major factors causing biodiversity loss. Systematic monitoring based on scientific principles and ecological restoration based on those monitoring results were recommended as measures for biodiversity conservation.

• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.561-569
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• 2013

This study examines locational environment factors that may affect the vegetation structure in the forests of Naejang National Park. To that end, we selected LAI (Leaf Area Index), diameter at breast height, and tree height as structural variables as well as altitude above sea level, gradient, slope direction, soil moisture, topographic location, and amount of solar radiation as locational environment factors, using the method of canonical correlation analysis in order to find out correlation between them. As to the simple correlation between the locational environment factors and structural variables, the correlation coefficient was relatively low (0.6). The values of LAI, measured along the ridge with higher altitudes, decreased as the soil moisture and solar radiation increased. However, LAI increased as the gradient increased and the slope direction faced the north (farther from the east). In respect of the diameter at breast height, the diameter decreased as the altitude and gradient increased. But the diameter increased as the moisture and solar radiation increased. The tree height decreased as the moisture increased and the site was closer to the ridge. These various correlations show a variety of locational environment factors in the national park, implying that the structural variables are affected by complex locational environment factors. This study conducted a canonical correlation analysis on locational environment factors which may affect the vegetation structure, and the result showed that LAI increased and tree height & diameter at breast height decreased as the solar radiation & moisture decreased and altitude increased. Although more factors that may affect vegetation structure (e.g. climate) should be taken into account, this study is significant in that the vegetation structure, which can adapt to more unfavorable conditions in terms of solar radiation, moisture, and higher altitudes, could be inferred in a statistical way. The results of this study, especially the locational environment factors based on DEM, can be used for assessing diversity of vegetation structure in a forest and for monitoring the structure in a national park on a regular basis so as to establish more effective maintenance plans of a park.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.59-65
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• 2005

This study was performed to estimate the soil loss on a national scale and grade regions with the potential risk of soil erosion. Universal soil loss equation (USLE) for rainfall and runoff erosivity factors (R), cover management factors (C) and support practice factors (P) and revised USLE for soil erodibility factors (K) and topographic factors (LS) were used. To estimate the soil loss, the whole nation was divided into 21,337 groups according to city county, soil phase and land use type. The R factors were high in the southern coast of Gyeongnam and Jeonnam and part of the western coast of Gyeonggi and low in the inland and eastern coast of Gyeongbuk. The K factors were higher in the regions located on the lower streams of rivers and the plain lands of the western coast of Chungnam and Jeonbuk. The average slope of upland areas in Pyeongchang-gun was the steepest of 30.1%. The foot-slope areas from the Taebaek Mountains to the Sobaek Mountains had steep uplands. Total soil loss of Korea was estimated as $50{\times}10^6Mg$ in 2004. The potential risk of soil erosion in upland was the severest in Gyeongnam and the amount of soil erosion was the greatest in Jeonnam. The regions in which annual soil loss was estimated over $50Mg\;ha^{-1}$ were graded as "the very severe" and their acreage was $168{\times}10^3ha$ in 2004. The soil erosion maps of city/county of Korea were made based on digital soil maps with 1:25,000 scale.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.721-738
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• 2018

The purpose of this study is to create landslide vulnerability using frequency ratio (FR) and evidential belief functions (EBF) model which are two methods of probability model and to select appropriate model for each region through comparison of results in Sacheon-myeon and Jumunjin-eup of Gangneung. 762 locations in Sacheon-myeon and 548 landscapes in Jeonju-eup were constructed based on the interpretation of aerial photographs. Half of each landslide point was randomly selected for modeling and remaining landslides were used for verification purposes. Twenty landslide-inducing factors classified into five categories such as topographic elements, hydrological elements, soil maps (1:5,000), forest maps (1:5,000), and geological maps (1:25,000) were considered for the preparation of landslide vulnerability in the study. The relationship between landslide occurrence and landslide inducing factors was analyzed using FR and EBF models. The two models were then verified using the AUC (curve under area) method. According to the results of verification, the FR model (AUC = 81.2%) was more accurate than the EBF model (AUC = 78.9%) at Jeonjun-eup. In the Sacheon-myeon, the EBF model (AUC = 83.6%) was more accurate than the FR model (AUC = 81.6%). Verification results show that FR model and EBF model have high accuracy with accuracy of around 80%.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.19-27
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• 2017

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.322-330
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• 1994

The Universal Soil Loss Equation (USLE) has been widely used to predict long-term soil loss by incorporating several erosion factors, such as rainfall, soil, topography, and vegetation. This study is aimed to introduce the LISLE within geographic information system(GIS) environment. The Kwangneung Experimental Forest located in Kyongki Province was selected for the study area. Initially, twelve years of hourly rainfall records that were collected from 1982 to 1993 were processed to obtain the rainfall factor(R) value for the LISLE calculation. Soil survey map and topographic map of the study area were digitized and subsequent input values(K, L, S factors) were derived. The cover type and management factor (C) values were obtained from the classification of Landsat Thematic Mapper(CM) satellite imagery. All these input values were geographically registered over a common map coordinate with $25{\times}25m^2$ ground resolution. The USLE was calculated for every grid location by selecting necessary input values from the digital base maps. Once the LISLE was calculated, the resultant soil loss values(A) were represented by both numerical values and map format. Using GIS to run the LISLE, it is possible to pent out the exact locations where soil loss potential is high. In addition, this approach can be a very effective tool to monitor possible soil loss hazard under the situations of forest changes, such as conversion of forest lands to other uses, forest road construction, timber harvesting, and forest damages caused by fire, insect, and diseases.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.115-125
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• 2013

The systematic management of river is difficult due to various environmental factors such as season and terrain deformation. Especially, river terrain are rapidly changing by natural and anthropogenic factors such as torrential rain during the summer and river development projects. Thus in this conditions, building the advanced river management system is an essential condition to support the ongoing management of survey data and to acquire data regularly through river terrain survey in order to maintain an active river. The need to build an efficient system have been increased through the enhancement and advancement of River Management Geographic Information Systems(RIMGIS). In this study, database design system and Riverbed Change Data Management Program was developed for systematic management of new river terrain survey data and the efficient use of river data dynamic changes. The key features are construction of river survey data, cross and longitudinal section monitoring and analysis of riverbed change data. Maintenance tasks which can be utilized in river-based architecture was constructed. The expected results are to be able to manage river systematically, and utilization of river topographic survey data efficiently for river maintenance work.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.329-337
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• 2013

This study was performed to evaluate the temporal and spatial variations of the water quality at stream flowing into the Suncheon bay in Suncheon city from October 2008 to August 2009 and to estimate the pollutant sources from the streams using multivariate analysis. Water qualities from Seo stream, Dong stream, Ok stream were evaluated as I grade(very good) that compared to the Water Quality Standard. But Haeryong stream and inlet site of Suncheon Bay in BOD were evaluated as a little bad and fair. Water quality at the stream flowing into the Suncheon Bay was could be explained up to 92.8% by three factors which were included in loading of nutrients, organic matter and total coliform group by the allochthonous matters(53.7%), Topographic Factors(25.0%), seasonal variation(14.2%). The concentrations of total nitrogen and phosphorus at sewage treatment plant and organic matters at Haeryong stream were higher than that of others, respectively. From principal component analysis and factor analysis, it could be suggested that it is very important to make an effort to reduce the nutrients and organic matters from sewage treatment plant and Haeryong stream to be in good conservation of the Suncheon bay.