• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.771-783
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• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of Korean Society of Forest Science
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• v.36 no.1
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• pp.9-25
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• 1977

As a successive work of the variation studies of natural Pinus densiflora stands, some characteristics of individual trees of the three natural populations selected from the Kwang-won Province, the middle-east part of Korean peninsula, as shown in the location map, were investigated. And the statiscal differences between individuals within population, and between populations were analysed. Twenty trees from each population were selected for this study purpose. Doing this, those trees lagged in growth, usually showing poorer form, were eliminated. The results obtained are summarized as follows: 1. Though the average population ages had the ranage between 50 and 63, the growth of height or diameter was similar. Population No.9 is, however, considered to have better tree forms at glance. Population No.8 showed the heighest value not only in the clear-stem-length ratio. 0.53 but also in the crown-index 0.91. The higher value can be result from those trees having long lateral branches and relatively short crown height, meaning undesirable crown shape. In regard to the fine branchedness and the acuteness of branching angle, the population No.9. is considered to be a better one, whereas there was almost no difference in crown height among populations. 2. Checking the frequency distributions of the ratio of the clear-stem-height to the total height and the crown-indices, some difference between populations are considered. These might be attributed to the previous way of stand mangement which alters the density. 3. In the serration density, the average number of 54 per 1cm needle length, the significant differences exist between individual trees within population but not between populations. A few trees which extremly high serration density were observed. As in serration, so tendencies were in the number of stomata row and resin duct. 4. The population 8 had the resin duct index value of 0.074 as the highest which was twice or triple of the other ones. 5. The patterns of increasing process of the average 10-year-ring-segment were not similar till the 30 years of age, but beyond this, the tendency lines were aggregated. 6. Regading the average summer wood ratio, no diffrence between populations, but in the ranges, i.e. 23 to 30 in population No.8. and 16 to 36 in population No.9., with regad to the specific gravity of wood, there were hardly observed any difference between populations even in the ranges values. As the increase of tree ages, the increase of specific gravity was followed but the increasing patterns were not similar between populations. 7. No significant differences between populations in the average tracheid length and the range were detected. However, the length was increased according to the age increase. The increasing pattern was same between populations.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.645-651
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• 2007

In general, contours in digital vector maps, which represent terrain characteristics and shape, are created by 3D digitizing the same height points using aerial photographs on the analytical or digital plotters with stereoscopic viewing. Hence, it requires lots of task, and subjective decision and experience of the operators. DTMs are generated indirectly by using contours since the national digital maps do not include digital terrain model (DTM) data. In this study, model key points which depict the important information about terrain characteristics were extracted from the contours. Further, determination of the efficient and flexible grid sizes were proposed to generate optimal DTM in terms of both quantitative and qualitative aspects. For this purpose, a progressive sampling technique was implemented, i.e., the smaller grid sizes are assigned for the mountainous areas where have large relief while the larger grid sizes are assigned for the relatively flat areas. In consequence, DTMs with multi-grid for difference areas could be generated instead of DTMs with a fixed grid size. The multi-grid DTMs reduce computations for data processing and provide fast display.

• Korean Journal of Environment and Ecology
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• v.34 no.3
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• pp.235-248
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• 2020

The purpose of this study was to quantitatively analyze and investigate changes in the structural characteristics of the warm-temperate evergreen broad-leaved forest community in Mt. Cheomchalsan on Jindo Island. The Mt. Cheomchalsan has high conservation value because the representative warm temperate species such as Quercus acuta and Castanopsis sieboldii are distributed there. The community classification with TWINSPAN and DCA identified 4 communities: C. sieboldii community (I), C. sieboldii-Q. Salicina community (II), Q. acuta-C.sieboldii community (III), and deciduous broad-leaved trees-evergreen broad-leaved trees community (IV). According to the results of the mean importance percentage (MIP) analysis, C. sieboldii, Q. salicina, and Q. acuta were dominant species in the canopy layer, Camellia japonica, Ligustrum japonicum, and Cinnamomum yabunikkei were dominant in the understory layer, and Trachelospermum asiaticum, C. japonica, and C. sieboldii were dominant in the shrub layer. The comparison of the results of the diameter of breast height (DBH) analysis with the past data showed that the ratio of large-sized trees in the C. sieboldii and Q. acuta, which dominated the canopy layer, increased. However, there was no difference in the distribution of C. japonica and L. japonicum in the understory layer. In the future, it is necessary to generate a precision inhabiting vegetation map around the Natural Reserve to understand the actual habitation of evergreen broad-leaved trees and rezone the protective districts of evergreen broad-leaved trees forest with the watershed concept to preserve the evergreen broad-leaved forests of Mt. Cheomchalsan in Jindo.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.4
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• pp.365-369
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• 2016

Red sorrel (Rumex acetosella L.), as one of exotic weeds in Korea, was dominated in grassland and reduced the quality of forage. Improving current pasture productivity by precision management requires practical tools to collect site-specific pasture weed data. Recent development in unmanned aerial vehicle (UAV) technology has offered cost effective and real time applications for site-specific data collection. To map red sorrel on a hill pasture, we tested the potential use of an UAV system with digital cameras (visible and near-infrared (NIR) camera). Field measurements were conducted on grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17, 2014. Plant samples were obtained at 20 sites. An UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number values of Red, Green, Blue, and NIR channels were extracted from aerial photos. Multiple linear regression analysis results showed that the correlation coefficient between Rumex content and 4 bands of UAV image was 0.96 with root mean square error of 9.3. Therefore, UAV monitoring system can be a quick and cost effective tool to obtain spatial distribution of red sorrel data for precision management of hilly grazing pasture.

• Journal of Korean Society of Forest Science
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• v.84 no.4
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• pp.447-455
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• 1995

Regeneration process of a mongolian oak forest in Bukam-Ryeong area, Mt. Jumbong, Kangwon-Do, was studied in relation to its structure. The dominant trees of the stands came up beyond 10m in height. The upper-tree layer was highly dominated by oaks, and they distributed horizontally in random. Oak trees of the middle layer and the lower layer were few in number and small in basal area, and tended to be distributed contagiously. In the trees of the upper layer, the distribution of the age tended to be two modal type which has the mode of 70 and 230 years in plot. In the horizontal distribution of these trees, some of the even-aged cluster which were constituted of several trees, were found. The rate of the stern diameter increment during first 25 years of the oaks in upper layer were higher than those of the same species in the middle layer. These results showed that after the forest canopy had been broken out, the seedlings which were established in dense there grow with the decreasing density and some of these, of which distribution became in random, would constitute the canopy.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.3
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• pp.309-319
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• 2011

We experimentally investigated lifted propane jet flames diluted with nitrogen to obtain flame-stability maps based on heat-loss-induced self-excitation. We found that heat-loss-induced self-excitations are caused by conductive heat loss from premixed flame branches to trailing diffusion flames as well as soot radiation. The conductive-heat-loss-induced self-excitation at frequencies less than 0.1 Hz is explained well by a suggested mechanism, whereas the oscillation of the soot region induces a self-excitation of lift-off height of the order of 0.1 Hz. The suggested mechanism is also verified from additive experiments in a room at constant temperature and humidity. The heat-loss-induced self-excitation is explained by the Strouhal numbers as a function of the relevant parameters.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.27-42
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• 2016

Forest accounts for almost 64 percents of total land cover in South Korea. For inventorying, monitoring, and managing such large area of forest, application of remote sensing and geographic information system (RS/GIS) technology is essential. On the basis of spectral characteristics of satellite imagery, forest cover and tree species can be classified, and forest cover map can be prepared. Using three dimensional data of LiDAR(Light Detection and Ranging), tree location and tree height can be measured, and biomass and carbon stocks can be also estimated. In addition, many indices can be extracted using reflection characteristics of land cover. For example, the level of vegetation vitality and forest degradation can be analyzed with VI (vegetation Index) and TGSI (Top Grain Soil Index), respectively. Also, pine wilt disease and o ak w ilt d isease c an b e e arly detected and controled through understanding of change in vegetation indices. RS and GIS take an important role in assessing carbon storage in climate change related projects such as A/R CDM, REDD+ as well. In the field of climate change adaptation, impact and vulnerability can be spatio-temporally assessed for national and local level with the help of spatio-temporal data of GIS. Forest growth, tree mortality, land slide, forest fire can be spatio-temporally estimated using the models in which spatio-temporal data of GIS are added as influence variables.