• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.760-762
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• 2003

This study confirmed the usefulness of short-wavelength infrared (SWIR) in the discrimination and classification of evergreen forest types. A forested area near Hisayama and Sasaguri in Fukuoka Prefecture, Japan, served as the study area. Warm-temperate forest vegetation dominates the study site vegetation. Coniferous plantation forest, natural broad-leaved forest, and bamboo forest were analyzed using LANDSAT5/TM and SPOT4/HRVIR remote sensing data. Samples were extracted for the three forest types, and reflectance factors were compared for each band. Kappa coefficients of various band combinations were also compared by classification accuracy. For the LANDSAT5/TM data observed in April, October, and November, Bands 5 and 7 showed significant differences between bamboo, broad-leaved, and coniferous forests. The same significant difference was not recognized in the visible or near-infrared regions. Classification accuracy, determined by supervised classification, indicated distinct improvements in band combinations with SWIR, as compared to those without SWIR. Similar results were found for both LANDSAT5/TM and SPOT4/HRVIR data. This study identified obvious advantages in using SWIR data in forest-type discrimination and classification.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.89-104
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• 2015

In order to effectively manage forested areas in South Korea on a national scale, using remotely sensed data is considered most suitable. In this study, utilizing Land coverage maps and Forest type maps of national geographic information instead of collecting field data was tested for conducting supervised classification on SPOT-5 and KOMPSAT-2 imagery focusing on forested areas. Supervised classification were conducted in two ways: analysing a whole area around the study site and/or only forested areas around the study site, using Support Vector Machine. The overall accuracy for the classification on the whole area ranged from 54.9% to 68.9% with kappa coefficients of over 0.4, which meant the supervised classification was in general considered moderate because of sub-classifying forested areas into three categories (i.e. hardwood, conifer, mixed forests). Compared to this, the overall accuracy for forested areas were better for sub-classification of forested areas probably due to less distraction in the classification. To further improve the overall accuracy, it is needed to gain individual imagery rather than mosaic imagery to use more spetral bands and select more suitable conditions such as seasonal timing. It is also necessary to obtain precise and accurate training data for sub-classifying forested areas. This new approach can be considered as a basis of developing an excellent analysis manner for understanding and managing forest landscape.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.64-69
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• 2005

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is developing the forest fire occurrence probability model by means of forest site characteristics such as soil type, topography, soil texture, slope, and drainage and forest fire sites. Conditional probability analysis and GIS were used in developing the forest fire occurrence probability model that was used in the classification of forest fire occurrence risk regions.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.535-542
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• 2001

The objective of this study is to develop a spatial analysis modeling technique to evaluate the functional suitability of forest lands for land slide prevention. The functional suitability is classified into 3 categories of high, medium and low according to the potential of land slide on forest lands. The potential of land slide hazards is estimated using the measurements of 7 major site factors : slope, bed rock, soil depth, shape of slope, forest type and D.B.H. class of trees. The analytic hierarchical process is applied to determining the relative weight of site factors in estimating the potential of land slides. The spatial analysis modeling starts building base layers for the 7 major site factors by $25m{\times}25m$ grid analysis or TIN analysis, reclassifies them and produces new layers containing standardized attribute values, needed in estimating land slide potential. To these attributes, applied is the weight for the corresponding site factor to build the suitability classification map by map algebra analysis. Then, finally, cell-grouping operations convert the suitability classification map to the land unit function map. The whole procedures of the spatial analysis modeling are presented in this paper.

• Journal of Forest and Environmental Science
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• v.9 no.1
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• pp.57-66
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• 1993

The purpose of this study is to offer detailed topographic information for substantially selecting the yarding machine for mechanized yarding operations, classifying the forest site by cluster analysis and principal component analysis, and investigating simultaneously the variables which give much influence on the classification of forest site in forestry build-up region (21, 477ha) of Chunchon Gun, Kwangweon Do. Ten topographic variables were used for the analysis. The results of study were as follows : 1) Gosung region (2, 252ha) was classified into hilly terrain (57%) and steep terrain (43%) and required the tractor prehauling system for the former one and the medium skyline system for latter one, respectively. 2) 65% of Gajung region (2,306ha) and 67% of Kwangpan region (2, 627ha) were classified into steep terrain fitted for the medium skyline system and the portion of both region showed the hilly terrain for the tractor prehauling system. 3) Jiam region (4, 591ha), consisted only of steep terrain, required the medium skyline system. 4) Gunja region (3, 400ha), Sudong region (3, 984ha) and Sinpo region (2, 340ha) were classified into steep terrain, requiring the medium skyline system, with 85%, 75%, and 75%, respectively.

• Journal of Plant Biology
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• v.9 no.1_2
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• pp.7-16
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• 1966

In order to determine the factors related to site quality, 13 areas of Larch growing in the Kwangung and its vicinity forest as sample plots, were examined. Sample plots included various site classes as well as age classes. Three were divided into two groups (major and minor trees). Average height of dominant trees was determined through messurement of 5 to 6 dominant tree in each sample plots. Average height of dominant 30 year-old trees was the basis for site index. A Standard Yield Table for the larch produced in Kwangnung forest was made by various data, which included age class 5, ranging from 10 to 45 years. The relationship of the height of the trees, the site conditions, and ground vegetation are investigated in this paper. The site indexes of 40 forest class age in 28-B and 28-G forest classes of the larch associations for ground vegetation had comparatively rarge differences due to the sampled areas. The relation of the direction of forest communities to the height and the diameter of the tree shwoed that its communiteis of northest and northwest parts appeared higher valueof the height and the diameter. The diameter and the height of trees were closely realted to each other. The samller the occupied area per tree and the smaller the average distance among trees, the more density was increased. The larger the density was the lower height of the trees. In the ground vegetation of the larch communities, there seems to be a definite correlation between the height of trees and the occupied area per tree or the average distance among the trees. The height of trees and site index of two larch communities were as follow: 28-B forest class site index 20.8, height 24.0m, 28-G forest class site index 18.4, height 20.9m. The ground layer was analyzed by the method of Quadrat(20/20sq. cm) with an interval of 1M. It set up 40 Quadrats of the larch communiteis. The community structure of the ground vegetation of two larch was analyzed, and important value was calculated and then evaluated. The ground vegetation under the larch had developed Burmannii Beauv stratal society below the 28-B and 28-G the forest class. Accordingly, the first important value of Burmannii Beauv was found in two ground vegetation below the larch. Therefore, this species could be quantitatively considered as the forest indicator species. Common species of each community appeared 18 species out of 34 species in the ground vegetation under two larch communities. The ground vegetation of the 28-B forest class showed more than that of the 28-G forest class. the similarity of the ground vegetation was measrued by the Frequency Index Community Coefficient. The differences between the associations were lcearly manifested by the ground vegetation tested by Gleason's Frequency Index of Community Coefficient for the analysis of each stratal society of all associations. According to F.I.C.C. the ground vegetation under two larch(28-B and 28-G) forest classes showed higher value. An investigation into the relationship of physical and chemical properties of soil and site was considered the next step to be taken in the study of the larch site classification.

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.65-78
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• 2013

Since pursuing the pleasant life for people, there is an increase of desire to appreciate outstanding scenery with the difference in certain level for perception and understanding of human on landscaping, However, the quality of landscaping has become artificial with the pleasance to be declining due to the urbanization. This study was applied at the site around High One Resort area in Gohan-eup, Jeongseon-gun Gangwon-do for analyzing the areas sensitive to the landscaping change as well as degree of requirement for landscape management for forest landscape management with the focus on presenting the zoning method and the management class classification method. Even if the forest is the same, the function of it is different depending on land use or what resource is placed that the forestry function is found out to present the management plan for each forestry function in the subject site and the result of the management grade classification is analyzed in overlapping to the forestry function level. As a result, from the landscaping management requirement and visual absorption analysis, the result formulated for upper, middle and lower zones to classify the final forestry landscape management degree into 1-4 grades and the management plan is presented on the respective 1-4 grade area for each forestry function. By applying the technique to set the management grade, it was possible to formulate the result to provide the means for integrated management in consideration of the forestry function and management of forestry landscape and resources.

• Journal of Forest and Environmental Science
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• v.36 no.3
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• pp.199-206
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• 2020

Satchari National Park is one of the most biodiverse forest in Bangladesh and home of many endangered flora and fauna. 206 tons of CO₂ per hectare is sequestrated in this national park every year which helps to mitigate climate issues. As people living near the area are dependent on this forest, degradation has become a regular phenomenon destroying the forest biodiversity by altering its forest cover. So, it is important to map land cover quickly and accurately for the sustainable management of Satchari National Park. The main objective of this study was to obtain information on land cover change using remote sensing data. Combination of unsupervised NDVI classification and supervised classification using maximum likelihood is followed in this study to find out land cover map. The analysis showed that the land cover is gradually converting from one land use type to another. Dense forest becoming degraded forest or bare land. Although it was slowed down by the establishment of 'National Park' on the study site, forecasting shows that it is not enough to mitigate forest degradation. Legal steps and proper management strategies should be taken to mitigate causes of degradation such as illegal felling.