• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.330-339
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• 2017

In Korea, metropolitan cities and provinces are responsible for setting up their own Regional Forest Plans to manage the forests at regional scales distinguished by administrative boundaries, and the role of the plans are very crucial by linking the Forest Basic Plan for nationwide forest management policy with Forest Management Plans for local-level forest management practices. Thus, the analysis of forest resources at regional levels is required to make more efficient regional forest plans by properly reflecting regional forest situations. This study aims to present which species are concentrated at each individual metropolitan city or province, contributing to more efficiently establishing its regional forest plan. In order to measure the concentration levels of species for each region, Location Quotient and Relative-Specialization Index are computed using area- and volume-data for the major species selected in the Statistical Yearbook of Forestry. As a result, the ranks among the indices of the major species for each individual municipal city and province are presented. The results from this study can contribute to the selection of regional target species and establishment of regional forest management objectives. Further study regarding the differences between the results from area-based and volume-based indices will be helpful to consider regional level productivity by species into the regional forest plans.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.3
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• pp.179-187
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• 2007

This study was conducted to develop site index equations for some pine species by climatic zones based on the relationships between site index and environmental factors. The selected pine species were Pinus densiflora Sieb. et. Zucc., Pinus densiflora for, erecta, and Pinus thunbergii. A total of 28 environmental factors were obtained from a digital forest site map. The influence of 28 environmental factors on site index was evaluated by multiple regression analysis. Four to eight environmental factors were selected in the final site index equation for pine species by climatic zones. The site index equations developed in this study was then verified by three evaluation statistics such as model's estimation bias, model's precision and mean square error type of measure. We concluded that the site index equations for the pine species by climatic Bones were capable of estimating forest site productivity. Based on these site index equations, the amount of productive areas for the species by climatic zones was estimated by applying the GIS technique to digital forest maps.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.588-591
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• 2021

4차 산업혁명 시대가 도래하면서 세상이 빠른 속도로 변하고 있다. 특히 데이터·인공지능(AI, Artificial Intelligence)의 활용이 적극적으로 다양한 분야에서 적용되기 시작하고 있다. 하지만 산림수종을 분석하는 업무를 수행하는 과정은 수작업으로 진행하다 보니 오류가 다수 발생하고 있다. 따라서 본 논문에서는 수도권 항공사진을 이용하여 소나무, 낙엽송, 침엽수, 활엽수를 대상으로 자동으로 분석하는 AI 학습용 데이터 약 60,000장을 구축하고, 수종을 구분할 수 있는 AI 모델을 개발하였다. 이를 통해 산림변화탐지 및 산림 분야 주제도 제작 시 수종 분할 이미지를 기초자료로 활용함으로써 업무효율 증대를 기대할 수 있다.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.465-475
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• 2001

Based on the community structural attributes, such as species composition, diameter and height distribution, topographic position, and species diversity in the natural deciduous forest of Mt. Gari area, this study suggested desirable species and composition ratio to achieve ecological management of forests so as to maintain forest stability and enhance economical values. The results are as follows : 1. Twenty-five tree species were growing in the study forest. Of these Quercus mongolica, Pinus densiflora, Juglans mandshurica, Quercus serrata, Cornus controversa, Acer mono, Fraxinus rhynchophylla, and Tilia mandshurica were selected for desirable species through the evaluation of dominant and dominant potential. Kalopanax pictus, considered to be highly valuable species, was also included. 2. Taking account of different species composition pattern by topographic positions, we select as desirable species of J. mandshurica, C. controversa, Q. mongolica, A. mono, T. mandshurica, and F. rhynchophylla in the valley area, Q. mongolica, Q. serrata, A. mono, T. mandshurica, F. rhynchophylla, and K. pictus in the mid-slope area, and Q. mongolica, P. densiflora, Q. serrata, and Fraxinus rhynchophylla in the ridge area. 3. Based on the estimation of species diversity index for the overstory components, the reasonable forest stability levels of the indices were estimated at 1.96, 1.68, 1.94, and 1.27 for whole forest, valley, midslope, and ridge, respectively. 4. The recommended species composition ratios in the study forest were suggested Q. mongolica to be 30%, A. mono, F. rhynchophylla, Q. serrata, and T. mandshurica to be 10%~15%, J. mandshurica, P. densiflora, and C. controversa to be 5%~10%, and K. pictus to be 5%.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.21-30
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• 2012

The objective of this study was to comprehend inter-species association and factors affecting species composition by factor analysis and to classify forest types of natural forests in Dootasan. We examined the correlation (positive or negative) of the major species by correlation analysis, the selection of three factors affecting the species composition by factor analysis, cluster analysis on the basis of factor scores, and the evaluation of the results of forest type classification by ANOVA. The outputs of correlation analysis were closely associated with those of factor analysis. The first factor affecting species composition was found to be the decline phenomenon of Pinus densiflora during forest succession process. The second and third factors were growth environments in valley and slope, respectively. The cluster analysis was carried out based on three factors affecting the species composition. The results indicated that the study area was classified into four forest types as follows: Quercus mogolica-Acer mono-Fraxinus rhynchophylla community, Q. mongolica community, Q. mongolica-Tilia amunrensis community and Pinus densiflora community. The dominant species of each community in the four classified forest types were significantly different (p<0.05).

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.173-187
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• 2023

The forest protection policies implemented in South Korea have resulted in the significant accumulation of forest. Moreover, the associated public interest has also been closely evaluated. As forests mature, there arises a need for forest management (FM) practices, such as thinning and harvesting. It is therefore essential to perform a scientific analysis of the long-term effects of FM. In this study, conducted in Mt. Gariwang, the effect of FM on forest succession and wood production (WP) were evaluated based on changes in aboveground biomass (AGB) using the LANDIS-II model. The FM consists of three scenarios (Selection, Shelterwood, and Two-stories), characterized based on the harvest intensity, frequency, and period. The model was applied to changes in the forest over 200 years. All scenarios show that the total AGB decreased immediately after thinning and harvesting. However, AGB recovery time differed among scenarios, with recovery to preharvest level occurring from 15 to 50 years after harvest; further, after 200 years, harvested forests had a greater total AGB than forests without FMs In particular, the changes in AGB of each species was different depending on its shade tolerance. The AGB of currently dominant shade-intolerant and mid-tolerant species decreased dramatically after harvesting. However, shade-tolerant species, dominant in the understory, continued to grow but were not harvested due to their small size. The cumulative WP for each scenario was estimated at 545.6, 141.6, and 299.9 tons/ha in Selection, Shelterwood, and Two-stories, respectively. The composition of WP differed according to harvest intensity and period. Most WP originated from shade-intolerant and mid-tolerant species in the early period. Later, most WP was from shade-tolerant species, which became dominant. The modeling approach used in this study is capable of analyzing the long-term effects of FM on changes in forests and WP. This study can contribute to decision making to guide FM methods for a variety of purposes, including WP and controlling forest composition and structure.

• 산림경영
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• s.66
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• pp.18-22
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• 1991

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.347-354
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• 2022

Since the era of the 4th industrial revolution has been ripe, the use of artificial intelligence(AI) based on massive data is beginning to be actively applied in various fields. However, as the process of analyzing forest species is carried out manually, many errors are occurring. Therefore, in this paper, about 60,000 pieces of AI learning data were automatically analyzed for pine, larch, conifer, and broadleaf trees of aerial photographs and pseudo images in the metropolitan area, and an AI model was developed to distinguish tree species. Through this, it is expected to increase in work efficiency by using the tree species division image as basic data when producing forest change detection and forest field topics.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2002.11a
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• pp.101-105
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• 2002

다양한 오염물질들은 산림유전자원을 감소시키는 것은 물론 산림쇠퇴에도 큰 영향을 주는 것으로 보고되고 있다. 따라서 각종 오염물질로부터의 산림유전자원의 감소를 방지하고 훼손된 산림을 복원하기 위해서는 오염물질에 대한 내성을 지닌 유전자원을 확보하는 것이 급선무이다. 층층나무는 다양한 용도 개발이 기대되는 경제수종으로, 그 가운데 경관 수종으로서 용도 개발이 유망한데, 층층나무가 대기오염이 심각한 도심지 등에 조경수로 심겨지기 위해서는 대기오염에 대한 내성을 보이는 개체를 선발하여 식재하는 것이 바람직하다.(중략)