• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.3
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• pp.1-18
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• 2024

The study aims to establish a direction for forest ecological restoration by classifying forest types and understanding the ecological characteristics of the Uljin forest area damaged by a large fire in 2022. Hierarchical cluster analysis and indicator species analysis were conducted on 78 survey plots located in the forest fire-affected area, and four forest cover types were derived: P. densiflora pure forests, P. densiflora dominant forests, mixed broad-leaved forests, and Q. variabilis dominant forests. As a result of visually comparing changes in forest types before and after forest fire damage, by classifying data according to whether or not upper dead trees are included, it was confirmed that pine forests, which have a high proportion of pine trees, spread widely due to forest fire damage. However, broad-leaved mixed forests and oyster oak dominant forests showed characteristics of maintaining concentration, indicating that pine forests were severely damaged. As a result of the important value analysis, during the process of natural recovery after a forest fire, the species that appear early in the lower layer are the sprouts of existing species such as Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume, Fraxinus sieboldiana Blume, Rhododendron mucronulatum Turcz. The distribution of diameter at breast height by forest cover type showed that among areas with extreme forest fire damage, the proportion of dead trees was relatively high and structural changes were large in P. densiflora pure forests and P. densiflora dominant forests where pine trees had a high distribution ratio. However, if continuous monitoring is carried out in the future with reference to the results of this study and plant data is collected and analyzed from a mid- to long-term perspective, it is believed that it will be used as useful data to promote forest ecological restoration projects in forest fire-affected areas.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.269-279
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• 2021

This study was carried out to introduce the forest cover types of the Baekdudaegan inhabiting the number of native tree species. In order to understand the vegetation distribution characteristics of the Baekdudaegan, a vegetation survey was conducted on the major 20 mountains of the Baekdudaegan. The vegetation data were collected from 3,959 sample points by the point-centered quarter method. Each mountain was classified into 4-7 forests by using various multivariate statistical methods such as cluster analysis, indicator species analysis, multiple discriminant analysis, and species composition analysis. The forests were classified mainly according to the relative abundance of Quercus mongolica. There was a total of 111 classified forests and these forests were integrated into the following nine forest cover types using the percentage similarity index and by clustering according to vegetation type: 1) Mongolian oak, 2) Mongolian oak and other deciduous, 3) Oaks (Mixed Quercus spp.), 4) Korean red pine, 5) Korean red pine and oaks, 6) ash, 7) mixed mesophytic, 8) subalpine zone coniferous, and 9) miscellaneous forest. Forests grouped within the subalpine zone coniferous and miscellaneous classifications were characterized by similar environmental conditions and those forests that did not fit in any other category, respectively.

• Journal of Environmental Science International
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• v.23 no.11
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• pp.1863-1879
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• 2014

This study aims at classifying and interpreting on the shrine forest vegetation located in Jirisan national park affiliated to an ecotone in southern part of Korea, foreseeing a vegetation change based on composition species and dominant species on canopy, and proposing the direction of vegetation management. The shrine forests were classified into the 7 community units as Chamaecyparis obtusa-Cryptomeria japonica afforestation, Pinus densiflora community, Pinus rigida afforestation, Quercus variabilis-Quercus serrata community, Zelkova serrata-Kerria japonica for. japonica community, Phyllostachys bambusoides forest, Camellia japonica community. This research is also expatiated on the analogous results of ordination analysis with phytosociological analysis. The constituents of deciduous broad-leaved forest in the warm temperate zone were appeared in the most vegetations. It emerged less that the constituents of evergreen broad-leaved forest in the warm-temperate zone and deciduous broad-leaved forest in the cold-temperature zone. The life form analyses were made use with the two ways: appearance species in total communities and each community. The species diversity of shrine forests is declined because the high dominances of Sasa borealis and Pseudosasa japonica emerged in the shrub and herb layers. These shrine forests will be succession to Q. variabilis-Q. serrata community as the representative vegetation of deciduous broad-leaved forest in the warm-temperate zone, owing to the temperature rise by global warming, and an evergreen broad-leaved forest will be able to be also formed if a temperate rise will be continued. The one of the artificial management of shrine forests is to consider the introduction of the constituents of evergreen broad-leaved forest in the warm-temperate zone.

• Journal of Korean Society of Forest Science
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• v.99 no.1
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• pp.111-124
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• 2010

This study has intended to develop an evaluation model to select suitable locations of recreation forests in accordance with their types, and to apply the models to the feasibility study of selecting suitable recreation forest locations of candidate sites. To reach this goal, this study employed a Delphi expert survey method and Analytic Hierarchy Process (AHP) for the whole process of model building. And the followings are what this study has found during model building and application process. First, the assessment criteria for classifying recreation forests and selecting suitable locations were initially identified through justification process with two rounds of expert review, after broken down into 2 categories, and then further divided into 6 items and 12 indicators accompanying with hierarchical structure. Second, in the third phase of Delphi expert survey, the relative weights of the assessment criteria were derived by employing AHP. Through overlaying two evaluation categories including resource and usability, 4 types of recreation forest were presented. In the forth phase of the Delphi survey, this study has developed an evaluation model to select suitable locations of recreation forests in accordance with their types using relative weights of the selected indicators through. This study has applied the models to the feasibility study of selecting suitable recreation forest locations of candidate sites, and found that the usability of recreation forest was severely affected by the distance from the capital region, that the closer the locations of natural recreation forests from the capital region, the more advantageous. The developed model can be used to designate recreation forests in accordance with their types.

• Journal of Korean Society of Rural Planning
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• v.14 no.4
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• pp.1-9
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• 2008

Many urban people are attracted to the recreational opportunities and attractions of rural areas, such as beautiful scenery, lakes, mountains, forests, and resorts. Furthermore, rural development planning increasingly emphasizes the integration of resource extractive industries with non-market-based recreational and amenity values. This article outlines a method to estimate an amenity level of rural areas such as natural amenities index and shows the relation of the natural amenity and population growth. The results revealed that natural amenity classifying each region can be explained with geographical characteristics, temperature-humidity, and sunshine duration time. In analysis with population, natural amenity does not exactly explain that but can be one of the important factor of population change.

• Journal of Environmental Science International
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• v.21 no.2
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• pp.135-144
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• 2012

This study aims at classifying and analyzing the vegetation structure of Castanopsis sieboldii forest, one of the evergreen broad-leaved forests found under the warm-temperate climate of Korea. It is also compared with the ones of the Castanopsis sieboldii forest in Japan where most similar such forest of Korea, to find unique vegetation structures of the only Korean forest. Vegetation structure of Korean Castanopsis sieboldii forest was divided into two units at the level of community units both of Ardisia japonica-Castanopsis sieboldii community and Ardisio-Castanopsietum sieboldii association. The association carries similar type with the vegetation system of Japan, but any subunits differentiated with the Japan were found vary much. Hierarchical cluster analysis brings in similar result with the analysis on the vegetation structure as well.

• 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.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.979-997
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• 2013

This study aims at classifying and interpreting on the vegetation structure, the correlation between a vegetation and an environment, a species diversity and a life-form of Evergreen Broad-Leaved Forest(EBLF) located in Gageo-do Island. It is also the objective that the estimation of vegetation change founded on the species composition and characteristics. The vegetation of EBLF was classified into three forests or four community units as Machilus thunbergii forest (Polystichum polyblepharon-M. thunbergii community and Phaenosperma globosum-M. thunbergii community), Ilex integra-Castanopsis sieboldii community, Quercus acuta community and Neolitsea sericea stand. The ordination analysis by DCA is analogous with the vegetation structure analysis. As a result of the correlation (Pearson's correlation coefficient) with environmental conditions, the Altitude has the significance with the distribution of communities. The total vegetation change by progress of succession will not be wandered away from the present vegetation structure practically, and the vegetation on the underlayers will be a little changed.

• The Korean Journal of Ecology
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• v.23 no.6
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• pp.453-460
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• 2000

The forest vegetation of Mt. seondal was classified into eight communities and one afforestation by the phytosocialogical method (Z-M method). In general, Quercus mongolica trees occupied most of the area, while afforestation forest was distributed on the lower slope, cultivated land, and at the vicinity of village. The vegetation on the top part of Mt. Seondal was comparatively well preserved, but that in the lower areas has been disturbed heavily by human activity and some had mixed forests composed of pine trees, oaks, ashes, and Rhododendron micrantum shrub. By cluster analysis method. nine groups were identified as follows : Quercus mongolica group, Q. mongolica - Pinus densiflora group, Q. mongolica - Rhododendron schlipen - bachii group, Q. mongolica - Symplocos chinensis for. pilosa group, P. densiflora group, Juglans mandshurica group, Fraxinus mandshurica group, Betula costata group and Larix leptolepis group. These groups showed differences in species composition, but Quercus mongolica, Q. mongolica - P. densiflora, Q. mongolica - R. schlippenbachii and Q. mongolica - S. chinensis for. pilosa groups among them showed very similar floristic composition to each other. In the relationship between polar ordination axes and environmental variables, altitude was the environmental factor determining variation in species composition along axis X and soil moisture was the environmental along axis Y. They were the main factors in determining forest vegetation. The result of cluster analysis and polar ordination for the forest vegetation were corresponded to those of phytosocialogical classification in classifying vegetation.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.