• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.457-466
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• 2011

The objectives of this study are to produce level 3 type LULC map and analysis of phenological features of North Korea, ISODATA clustering of the 88scenes of MVC of MODIS NDVI in 2008 and 8scenes in 2009 was carried out. Analysis of phenological phases based mapping method was conducted, In level 2 type map, the confusion matrix was summarized and Kappa coefficient was calculated. Total of 27 typical habitat types that represent the dominant species or vegetation density that cover land surface of North Korea in 2008 were made. The total of 27 classes includes the 17 forest biotopes, 7 different croplands, 2 built up types and one water body. Dormancy phase of winter (${\sigma}^2$ = 0.348) and green up phase in spring (${\sigma}^2$ = 0.347) displays phenological dynamics when much vegetation growth changes take place. Overall accuracy is (851/955) 85.85% and Kappa coefficient is 0.84. Phenological phase based mapping method was possible to minimize classification error when analyzing the inaccessible land of North Korea.

• Journal of Korean Society of Forest Science
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• v.83 no.4
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• pp.441-449
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• 1994

The two reflective channels(red and near infrared spectrum) of advanced very high resolution radiometer(AVHRR) data were used to classify primary vegetation cover types in the Korean Peninsula. From the NOAA-11 satellite data archive of 1991, 27 daytime scenes of relatively minimum cloud coverage were obtained. After the initial radiometric calibration, normalized difference vegetation index(NDVI) was calculated for each of the 27 data sets. Four or five daily NDVI data were then overlaid for each of the six months starting from February to November and the maximum value of NDVI was retained for every pixel location to make a monthly composite. The six bands of monthly NDVI composite were nearly cloud free and used for the computer classification of vegetation cover. Based on the temporal signatures of different vegetation cover types, which were generated by an unsupervised block clustering algorithm, every pixel was classified into one of the six cover type categories. The classification result was evaluated by both qualitative interpretation and quantitative comparison with existing forest statistics. Considering frequent data acquisition, low data cost and volume, and large area coverage, it is believed that AVHRR data are effective for vegetation cover type mapping at regional scale.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.2
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• pp.70-82
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• 2016

We investigated the vegetation structure in Cultural Heritage Site of Odaesan National Park using 52 quadrats for each type of land use to figure out some characteristics of plant biotope. As we classified vegetation communities, they are six groups of communities. distinguished species in two of them are Taraxacum officinal, Erigeron annuus and Poa pratensis which are common in urban areas. Distinguished species in one of them are Potentilla fragarioides var. major which is common in outskirt of forest. And Distinguished species in another 3 communities are Sasa borealis and Quercus mongolica which are common in forest. Using TWINSPAN and DCA, we are able to classify the six communities into 3 types biotope (temple-biotope, slope-biotope, forest-biotope) in Cultural Heritage Site. The dominant species of urban-biotope are Poa pratensis, Artemisia prinseps and that of slope-biotope is Tripterygium regelii. Also the dominant species of forest-biotope are Quercus mongolica, Abies holophylla and Ulmus davidiana var. japonica. We could see more species in slope-biotope than another biotope types. Moreover, in urban-biotope types, we could find many of naturalized plant species.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.249-258
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• 2020

The purpose of this study examined the succession by vegetation type after forest ecosystem restoration in the Baramjae area. Vegetation of the Baramjae area was classified using a survey of 81 sites from May to October 2019. The vegetation type was classified as Pinus densiflora community group with both Quercus mongolica community and P. densiflora typical community. The group unit was further classified as the Quercus dentata typical subgroup, Salix koreensis subgroup, and Q. mongolica typical subgroup. Such as Q. mongolica, Quercus variavilis in vegetation unit 1, Q. mongolica, Q. dentata in vegetation unit 2, P. densiflora in vegetation unit 3 and S. koreensis in vegetation unit 4 were shown a high importance value. The difference in species by vertical layer is explained by sere. Based on the vegetation type classification system, Detrended Correspondence Analysis was conducted to observe the trend of succession. Since restoration, vegetation unit 1 and vegetation unit 2 were considered to have developed the most extensive vegetation. In vegetation unit 2 and vegetation unit 4, many of the species found were in the early vegetation development in S. koreensis subgroup. Accordingly, vegetation in the Baramjae area can be categorized as a stepwise succession.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.111-122
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• 2014

The provided habitat of many services from natural capital is important. But because most ecosystem services tools qualitatively evaluated biodiversity or habitat quality, this study quantitatively analyzed those aspects using the species distribution model (MaxEnt). This study used location point data of the goat(Naemorhedus caudatus), marten(Martes flavigula), leopard cat(Prionailurus bengalensis), flying squirrel(Pteromys volans aluco) and otter(Lutra lutra) from the 3rd National Ecosystem Survey. Input data utilized DEM, landcover classification maps, Forest-types map and digital topographic maps. This study generated the MaxEnt model, randomly setting 70% of the presences as training data, with the remaining 30% used as test data, and ran five cross-validated replicates for each model. The threshold indicating maximum training sensitivity plus specificity was considered as a more robust approach, so this study used it to conduct the distribution into presence(1)-absence(0) predictions and totalled up a value of 5 times for uncertainty reduction. The test data's ROC curve of endangered mammals was as follows: growing down goat(0.896), otter(0.857), flying squirrel(0.738), marten(0.725), and leopard cat(0.629). This study was divided into two groups based on habitat: the first group consisted of the goat, marten, leopard cat and flying squirrel in the forest; and the second group consisted of the otter in the river. More than 60 percent of endangered mammals' distribution probability were 56.9% in the forest and 12.7% in the river. A future study is needed to conduct other species' distribution modeling exclusive of mammals and to develop a collection method of field survey data.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.25-34
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• 1997

The optimum global natural vegetation mapping(GNVM) system was selected as a series of the study to estimate potential forest area of the globe. To select the system, three types of GNVM systems which are simple system with Light Climatic Dataset(LCD), altitude-allowed system with LCD and altitude-allowed system with Heavy Climatic Dataset(HCD) were established and compared. The three GNVM systems spherically interpolate such spotty climate data as those observed at weather stations the world over onto $1^{\circ}{\times}1^{\circ}$ grid points, product vegetation type classification, and produce a potential natural vegetation(PNV) map and a PNV area. As a result of comparison with three GNVM systems, altitude-allowed LCD system represented natural vegetation distribution better than other versions. The difference between the simple system versus the one with altitude allowance indicated that the simple version tends to over-represent the warmer climate areas and under-represent cold and hostile climate areas. In the difference between altitude-allowed versions of LCD and HCD, HCD version tended to overestimate moist climate areas and to underestimate dry climate areas.

• Korean Journal of Environment and Ecology
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• v.26 no.5
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• pp.770-779
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• 2012

In order to verify the characteristics of vegetation structure in Jeolgu valley, Nakdongjeongmaek, we set up 29 plots(each plot area is $10m{\times}10m(100m^2)$. The survey site is located in around the valley and its range is about 3km section. The forest vegetation communities were analysed by TWINSPAN classification. The results of communities were classified 5 types such as Pinus densiflora community, deciduous broad-leaved tree community, Quercus variabilis community, Quercus mongolica community, Larix leptolepis community. The deciduous broad-leaved tree which prefer to moist environment and Quercus spp. has dominant in around the valley and the northern slope. In addition, Larix leptolepis community expect to maintain the present status for a while. However, the under story of Larix leptolepis community have expanded the influence of deciduous broad-leaved tree such as Fraxinus mandshurica, Morus bombycis, Acer mono and so on. Therefore, there will be developed next ecological succession by species of deciduous broad-leaved tree. The diversity index showed form 0.9665 to 1.2450. It were analyzed that diversity index of Jeolgu valley was higher than other places in Nakdongjeongmaek.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.105-117
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• 2012

Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.37-43
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• 2021

This study proposed an effective automatic classification technique to identify dropout patterns of university students, and based on this, an intelligent recommender system to prevent dropouts. To this end, 1) a data processing method to improve the performance of machine learning was proposed based on actual enrollment/dropout data of university students, and 2) performance comparison experiments were conducted using five types of machine learning algorithms. 3) As a result of the experiment, the proposed method showed superior performance in all algorithms compared to the baseline method. The precision rate of discrimination of enrolled students was measured to be up to 95.6% when using a Random Forest(RF), and the recall rate of dropout students was measured to be up to 80.0% when using Naive Bayes(NB). 4) Finally, based on the experimental results, a method for using a counseling recommender system to give priority to students who are likely to drop out was suggested. It was confirmed that reasonable decision-making can be conducted through convergence research that utilizes technologies in the IT field to solve the educational issues, and we plan to apply various artificial intelligence technologies through continuous research in the future.