• Journal of Korean Society of Forest Science
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• v.78 no.3
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• pp.255-262
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• 1989

Vegetation data of the mixed mesophytic forest in Mt. Chombong area were analyzed by the methods of classification and ordination. 'Weighted group average linkage cluster analysis' recognized five distinctive vegetation groups, based on the abundance data of 83 woody plant species in 70 sampling units. The species diversity was also examined for each group. The importance values of 42 tree species in the groups were subjected to principal component analysis (PCA). The PCA ordinated five vegetation groups on the first two axes, so as to compare similarity among them in terms of species composition. Acer palmatum, Fraxinus rhynchophylla, Quercus mongolica, and Acer mono had greatest influence on the determination of group scores with high eigenvectors (component loadings) in the first axis. Distribution of these four dominant species appeared to be important in determining community association in this diversified forest.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1341-1350
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• 2019

This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.504-521
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• 2011

This study was carried out phytosociological forest community analysis, the sampled dada were collected and studied by 1,456 plots from 1993 to 2009 for 17 years in the 22 mountain area of South Korea. Four opposed species groups were classified and 10 vegetation units were divided as a result of forest vegetation classification. The 10 units were closely correlated with major environmental factors such as geological features, climatic conditions, topographical configurations, and etc. Therefore the forest vegetation of South Korea could be conclusively abstracted by 10 vegetation units and 7 eco-types.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.311-318
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• 1997

On April 27,1993, a forest fire occurred in Morito-area, Manba-city, Gunma-prefecture Japan. Under the prevailing strong winds, the fire spread and extended to the largest scale ever in Gunma-prefecture. The author chartered a helicopter on May 5, one week after the fire was extinguished, and took aerial photos of tile damaged area, and investigated the condition. of the fire through field survey and data collection. The burnt area extended. over about 100 hectares, and the damage amounted to about 190 million yen (about two million dollar). The fire occurred at a steep mountainous area and under strong winds, therefore, md and topography strongly facilitated the spreading, It is the purpose of this paper to report a damage investigation of the fire and to develop the forecasting method of forest fires based on the topographical analysis and spreading speed of fire. In the first place, I analyze the topographical structure of the regions which became the bject of this study with some topographical factors, and construct a land form classification ap. Secondly, I decide the dangerous condition of each region in the land form classification map according to the direction of the wind and spreading speed of f'kre. In the present paper, I try to forecast forest fires in Morito area, and the basic results for the forecasting method of forest fires were obtained with the topographical classification system and spreading speed of fire.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.1-8
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• 2019

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.407-414
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• 2013

This study was conducted to classify forest cover types using the multivariate statistical analysis in the natural forest of western Mt. Jiri. On the basis of the vegetation data by point quarter sampling, the adopted analytical methods were species-area curve (SAC), hierarchical cluster analysis (HCA), indicator species analysis (ISA), and multiple discriminant analysis (MDA). SAC selected the outlier tree species which was likely to have no influence on the classification of forest cover types, excluded from all analytical process. Based on forest vegetative information, HCA classified the study area into 2 to 10 clusters and ISA indicated that the optimal number of clusters were seven. MDA was taken to test the clusters that classified with HCA and ISA. The seven clusters were classified appropriately as overall classification success were 91.3%. The classified forest cover types were named by the ratio of the dominant species in the upper layer of each cluster. They were (1) Quercus mongolica Pure forest, (2) Mixed mesophytic forest, (3) Q. mongolica - Q. serrata forest, (4) Abies koreana - Q. mongolica forest, (5) Fraxinus mandshurica forest, (6) Q. serrata forest, and (7) Carpinus laxiflora forest.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.494-503
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• 2011

This study was aimed at applying the IUCN category system to the Baekdudaegan Protected Area. A classification key was developed to apply the system to the overlapped designated protected areas inside of Baekdudaegan Protected Area. Korea national parks and forests managers' and experts' opinions were collected and they all agreed to the use of multiple classification in Baekdudaegan Protected Area. For example, the type of natural forests among the Forest Genetic Resources Reserves was classified to be IUCN Category Ia while other types of Forest Genetic Resources Reserve was classified to be Category IV. And the Protected Forest Landscape was classified to be Category V while the other types of protected forests were classified to be Category VI. The study suggests the need of classification of forest protected areas including Baekdudaegan Protected Area using IUCN system accompanying with protected areas management effectiveness evaluation.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.