• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.250-255
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• 2002

This paper describes the study methods of identifying forest vegetation types, based on this study, forest vegetation classification method based on vegetation index is proposed. According to reflectance data of vegetation canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun, China, many vegetation index are calculated and analyzed. The relationships between vegetation index and vegetation types are that PVI identifies broadleaf forest and conifer forest the most easily, the next is TSAVI and MSAVI, but their calculation is complex. RVI values of different conifer trees vary obviously, so RVI can classify conifer trees. In a word, combination of PVI and RVI is evaluated to classify different vegetation types.

• The Korean Journal of Ecology
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• 제26권2호
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• pp.75-81
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• 2003

This study was performed to prove if the high resolution satellite imagery of IKONOS is suitable for preparing digital vegetation map which is becoming increasingly important in ecological science. Seven classes for forest area and five classes for non-forest area were taken for classification. Three methods, such as the pixel based classification, the segment based classification with majority principle, and the segment based classification with maximum likelihood, were applied to classify IKONOS imagery taken in April 2000. As a whole, the segment based classification shows better performance in classifying the high resolution satellite imagery of IKONOS. Through the comparison of accuracies and kappa values of the above 3 classification methods, the segment based classification with maximum likelihood was proved to be the best suitable for preparing the vegetation map with the help of IKONOS imagery. This is true not only from the viewpoint of accuracy, but also for the purpose of preparing a polygon based vegetation map. On the basis of the segment based classification with the maximum likelihood, a digital vegetation map in which each vegetation class is delimitated in the form of a polygon could be prepared.

• 한국환경복원기술학회지
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• 제4권2호
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• pp.36-44
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• 2001

The main purpose of this study was to classify forest vegetation effectively using Landsat Thematic Mapper data(June, 1994) in mountainous region. The research area was the Mt. Wolak Experimental Forest of Chungbuk National University, near Chungju and Jecheon city, Chungcheongbuk-do. To classify forest vegetation effectively, Normalized Difference Vegetation Index(NDVI) was used to reduce topographic effects. This NDVI was modified and transformed to the value of 0 to 255, and then the modified values were combined with other Landsat Thematic Mapper bands. To classify forest and land cover types, unsupervised classification method was used. The results of this study are summarized as follows. 1. Combinations of band "3, 5, NDVI" in Landsat Thematic Mapper data showed a good separation with high accuracy. The expected classification accuracy was 95.1% in Landsat Thematic Mapper data. 2. The Land Cover types were classified into six groups : coniferous forest, deciduous forest, mixed forest, paddy and grass, non-forest, and other undetectable areas. As these classified results were compared with the reconnaissance survey and aerial black and white infrared photographs, the overall classification accuracy was 76.5% in Landsat Thematic Mapper data. 3. The portion of non-forest in Mt. Wolak area was 1.9%. The percentages of coniferous, deciduous and mixed forests were 30.9%, 35.7% and 26.4%, respectively. 4. As these classified results were compared with other reference data, the percentages of coniferous, deciduous and mixed forests increased, but the portion of non-forest was exceedingly diminished. These differences are thought to be from the different research method and the different season of received Landsat Thematic Mapper data.

• The Korean Journal of Ecology
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• 제19권5호
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• pp.465-476
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• 1996

There are several environmental variables that may be influential to the spatial distribution of forest vegetation. To create a map of forest vegetation zone over southern part of Korean Peninsula, digital map layers were produced for each of environmental variables that include topography, geographic locations, and climate. In addition, an extensive set of field survey data was collected at relatively undisturbed forests and they were introduced into the GIS database with exact coordinates of survey sites. Preliminary statistical analysis on the survey data showed that the environmental variables were significantly different among the previously defined five forest vegetation zones. Classification of the six layers of digital map representing environmental variables was carried out by a supervised classifier using the training statistics from field survey data and by a clustering algorithm. Although the maps from two classifiers were somewhat different due to the classification procedure applied, they showed overall patterns of vertical and horizontal distribution of forest zones. considering the spatial contents of many ecological studies, GIS can be used as an important tool to manage and analyze spatial data. This study discusses more about the generation of digital map and the analysis procedure rather than the outcome map of forest vegetation zone.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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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.

• 한국환경과학회지
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• 제18권12호
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• pp.1427-1436
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• 2009

The purpose of this study was to explain community structure for actual vegetation and their environment variables in Anmyeon Island. Samples were collected for 106 plots using ZM phytosociology method and coincidence method. Actual vegetation were classified into three vegetations types(forest vegetation type, maritime vegetation type, lake vegetation type) and eleven community units. Pourthiaea villosa community in forest vegetation type was divided into 5 groups such as Carpinus coreana group, Pinus rigida group, Chamaecyparis obtusa group, Castanea crenata group and Typical group. Maritime vegetation type was divided into 3 communities, such as Vitex rotundifolia community, Koelreuteria paniculata community and Suaeda japonica community. V. rotundifolia community was subdivided into 2 groups, Rosa rugosa group and Diodia teres group. K. paniculata community was subdivided into 2 groups, Grewia biloba var. parviflora group and Typical group. Lake vegetation type was divided into 1 community, Nelumbo nucifera community. And it was entirely classified into 11 community units.

• 한국산림과학회지
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• 제100권3호
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• pp.504-521
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• 2011

본 연구는 우리나라의 산림식생에 대한 대표 군락유형 분류를 위하여 1993년부터 2009년까지 약 17년 동안 22개 산림지역에서 1,456개소의 식생자료를 수집하여 식물사회적으로 분석하였다. 그 결과, 4쌍의 대립종군이 나타났고 최상위 수준에서 신갈나무군강(class)이 분류되었다. 식생분류를 수행하기 위하여 4쌍의 대립종군 중에서 굴참나무군락형과 청시닥군락형을 기준으로 분석하여 총 10개의 식생단위로 분류되었다. 또한 각 식생단위는 지질, 기후, 지형 등의 환경인자에 의해 매우 높은 상호관계를 가지고 있었다. 따라서 우리나라 산림식생은 10개 군락단위와 7개 생태형으로 정리되었다.

• 한국산림과학회지
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• 제106권2호
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• pp.169-185
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• 2017

산림식생은 분류방법에 따라 식생유형의 결과가 다를 수 있다. 본 연구의 목적은 세 종류의 식생분류 방법론을 적용하여 결과를 비교하기 위해 충남 오서산 산림식생을 대상으로 2016년 9월부터 10월까지 총 80개소의 식생조사를 수행하였다. 얻어진 자료를 토대로 상관우점종에 의한 군락유형분류, 종조성에 따른 군락유형분류, 종간연관 분석을 실시하였다. 상관우점종에 의한 군락유형분류를 실시한 결과, 소나무군락, 신갈나무군락, 느티나무군락, 상수리나무군락, 층층나무군락, 졸참나무군락, 일본잎갈나무군락, 리기다소나무군락, 밤나무군락, 백합나무군락의 총 10개의 군락유형으로 구분되었다. 종조성에 따른 유형분류를 실시한 결과, 총 4개의 식생단위와 8개의 종군 유형으로 분류되었다. 종조성 체계의 최상위 수준에서 비목나무군락군으로 대표되어졌으며, 비목나무군락군은 진달래군락(진달래전형군, 쪽동백나무군), 느티나무군락(일본잎갈나무군, 큰개별꽃군)으로 분류되어, 1개 군락군 2개 군락 2개 군의 분류체계를 나타냈다. 종간연관 분석 결과는 크게 두 개의 그룹으로 나누어졌으며, 종조성에 의한 군락유형과 종간연관에 의한 유형은 지형적인 영향이 크게 작용하는 것으로 판단되었다. 조사지의 산림식생은 상관우점종에 의해 10개의 군락유형, 종조성에 의해 8개의 종군단위와 4개의 식생단위, 종간연관에 의해 2개의 유형으로 분류되어, 상관우점종, 종조성(종군유형${\rightarrow}$식생단위), 종간연관 순으로 식생단위가 단순화되는 것을 알 수 있었다. 결론적으로 산림식생은 분류방법론에 따라 다양한 식생유형이 분류되었고 또한 많은 환경요인들의 영향이 작용하고 있는 것으로 판단되었다.

• The Korean Journal of Ecology
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• 제19권5호
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• pp.403-416
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• 1996

Global natural vegetation mapping (GNVM) system was developed for estimating potential forest area of the globe. With input of monthly mean temperature and monthly precipitation observed at weather stations, the system spherically interpolates them into 1°×1°grid points on a blobe, converts them into vegetation types, and produces a potential vegetation map and a potenital vegetation area. The spherical interpolation was based on negative exponential function fed from the constant radius stations with oval weighing method which is latitudinally elongated weighing in temperature and longitudinally elongated weighing in precipitation. The temperature values were corrected for altitude by applying a linear lapse-rate (0.65℃ / 100m) with reference to a built-in digital terrain map of the globe. The vegetation classification was based upon Koppen’s sKDICe. The potential forest area is estimated for 6.96 Gha (46.24％) of the global land area (15.05 Gha).

• Journal of Forest and Environmental Science
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• 제39권3호
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• pp.167-179
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• 2023

The extent of change in the Land use/Land cover (LULC) of Okomu National Park (ONP) and fringe communities was evaluated. High resolution Landsat imagery was used to identify the major vegetation cover/land use systems and changes around the national park and fringe communities while field visits/ground truthing, involving the collection of coordinates of the locations was carried out to ascertain the various land cover/land use types identified on the images, and the extent of change over three-time series (2000, 2010 and 2020). The change detection was analyzed using area calculation, change detection by nature and normalized difference vegetation index (NDVI). The result of the classification and analysis of the LULC Change of ONP and fringe communities revealed an alarming rate of encroachment into the protected area. All the classification features analyzed had notable changes from 2000-2020. The forest, which was the dominant LULC feature in 2000, covering about 66.19% of the area reduced drastically to 36.12% in 2020. Agricultural land increased from 6.14% in 2000 to 34.06% in 2020 while vegetation (degraded land) increased from 27.18% in 2000 to 38.89% in 2020. The magnitude of the change in ONP and surroundings showed the forest lost -247.136 km² (50.01%) to other land cover classes with annual rate change of 10%, implying that 10% of forest land was lost annually in the area for 20 years. The NDVI classification values of 2020 indicate that the increase in medium (399.62 km² ) and secondary high (210.17 km² ) vegetation classes which drastically reduced the size of the high (38.07 km² ) vegetation class. Consequent disappearance of the high forests of Okomu is inevitable if this trend of exploitation is not checked. It is pertinent to explore other forest management strategies involving community participation.