• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.289-294
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• 2004

본 연구의 목적은 고해상도 위성 영상을 이용하여 식생도 제작 기법을 연구하는 것이며, 식생도에는 활엽수, 혼효림, 침엽수의 군집 경계를 표현하고자 하였다. 본 연구는 고해상도 위성영상을 활용하여 객체지향분류 기법을 적용하였다. 객체지향 분류기법은 크게 세그멘테이션의 과정과 세그멘트를 분류하는 과정으로 나눌 수 있다. 세그멘테이션 과정을 통해서 식생군집의 경계를 추출하고, 영상을 이용하여 상록침엽수를 분류하여 식생조사시 침엽수군락의 위치를 파악함으로써 조사의 효율성을 증대하였다.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.169-185
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• 2017

The result of forest vegetation classification could be quite different and dependant on analysis methods. The purpose of this study was to compare the analyzed results for three kinds of methods (physiognomy dominant species, floristic composition and interspecific association) related to vegetation classification. Vegetation data were collected by the 80 quadrates in Mt. Oseo, Chungcheongnam-do from September to October in 2016. We carried out community type classification using above three methods. As a result, the vegetation according to physiognomy dominant species was classified into ten communities such as Pinus densiflora community, Quercus mongolica community, Zelkova serrata community, Quercus acutissima community, Cornus controversa community, Quercus serrata community, Larix kaempferi community, Pinus rigida community, Castanea crenata community and Liriodendron tulipifera community. The vegetation according to floristic composition was classified into 4 vegetation units. It was totally represented by Lindera erythrocarpa community group. And L. erythrocarpa community group was classified into the Rhododendron mucronulatum community (subdivided R. mucronulatum typical group and Styrax obassia group) and Zelkova serrata community (subdivided Larix kaempferi group and Pseudostellaria palibiniana group). As a result of interspecific association, forest vegetation was divided into two groups. And it was considered that the vegetation type by floristic composition and interspecific association significant could be affected by topography. There were lots of vegetation groups or units in the order like 10 types of communities by the physiognomy dominant species, 8 species group and 4 vegetation types by the floristic composition, and 2 types by the interspecific association. In conclusion, vegetation classification methods elicited diverse vegetation groups or units with lots of correlations of environmental factors.

• Journal of Korean Society of Forest Science
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• v.107 no.3
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• pp.245-257
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• 2018

The study was carried out to discover the forest vegetation structure in Hongdo Island, Jeonnam province. Vegetation data were collected by total of forty one quadrate plots using Z-M phytosociological method from June to August in 2017, and analyzed by vegetation classification, mean importance value and species diversity. As a result of vegetation type classification, Castanopsis sieboldii community group was classified at a top level of vegetation hierarchy. In the level of community, it was classified into Neolitsea sericea community and Carpinus turczaninowii community. N. sericea community was subdivided into Ficus erecta group(Vegetation unit 1) and Arisaema ringens group(VU 2). C. turczaninowii community was subdivided into Fraxinus sieboldiana group(VU 3) and C. turczaninowii typical group(VU 4). Therefore, it was classified into total of four vegetation units(one community group, three communities and four groups). As a result of mean importance value, Castanopsis sieboldii was the highest in VU 1, VU 2, VU 4, and C. turczaninowii in VU 4, respectively. In case of species diversity, VU 3 showed the highest among four units in species diversity index. In conclusion, the forest vegetation of Hongdo Island was classified into four units and seven species groups. Hongdo Island could be conclusively managed by community ecological approach for the units and groups. Also it was considered that a research for the succession to the evergreen broad-leaved forest should be more intensively proceeded near future.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.156-161
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• 2007

도시화에 따른 도시문제발생이라는 결과로 미루어 볼 때, 지속적인 도시 성장을 위한 도시 성장 관리는 필수적이며, 이것을 위해서 도시지역을 추출하는 것은 도시의 성장 추이를 파악할 수 있게 한다는 점에서 매우 의미 있는 일이다. 본 연구에서는 도시 성장 모니터링에 있어서 정규식생지수(NDVI)와 정규시가지화지수(NDBI)를 결합한 방법의 활용성을 규명하는데 목적을 두었다. 이를 위해 토지피복분류에 일반적으로 사용되는 감독 분류기법과 도시지역추출에 이용되는 NDVI와 NDBI를 결합한 방법(식생지수결합법)으로 1988년과 2000년 두 시기의 Landsat TM 영상을 이용하여 도시지역을 추출하고 일치도를 분석하였다. 분석 결과, 1988년 식생지수결합법과 감독분류기법으로 추출한 도시지역의 일치도는 98%, 식생지수결합법 비도시지역으로 추출된 지역이 감독분류기법으로는 도시지역으로 추출될 확률은 37.35%로 나타났고, 같은 경우 2000년은 각각 99.3%와 7.7%로 나타났다. 이를 통해 식생지수결합법을 사용한 도시지역 추출 결과와 감독분류기법을 사용한 도시지역 추출 결과의 일치도가 비교적 높게 나타남을 알 수 있었다. 또, 각 기법을 통한 도시지역 추출 결과와 실제 도시 검사점과의 일치도의 분석을 통해서도 도시지역 추출 결과의 일치도가 비교적 높게 나타났다. 따라서 분류를 통한 도시지역 추출 방법에 비해 식생지수결합법을 이용한 도시지역 추출이 절차상 수월한 점을 감안하면 도시지역 추출에 있어서 식생지수결합법의 효율성을 입증할 수 있었다.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.496-507
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• 2006

A hybrid approach integrating both high-resolution and hyperspectral data sets was used to map vegetation cover of a coastal lowland area in the Hawaii Volcanoes National Park. Three common grass species (broomsedge, natal redtop, and pili) and other non-grass species, primarily shrubs, were focused in the study. A 3-step, hybrid approach, combining an unsupervised and a supervised classification schemes, was applied to the vegetation mapping. First, the IKONOS 1-m high-resolution data were classified to create a binary image (vegetated vs. non--vegetated) and converted to 20-meter resolution percent cover vegetation data to match AVIRIS data pixels. Second, the minimum noise fraction (MNF) transformation was used to extract a coherent dimensionality from the original AVIRIS data. Since the grasses and shubs were sparsely distributed and most image pixels were intermingled with lava surfaces, the reflectance component of lava was filtered out with a binary fractional cover analysis assuming that tile total reflectance of a pixel was a linear combination of the reflectance spectra of vegetation and the lava surface. Finally, a supervised approach was used to classify the plant species based on tile maximum likelihood algorithm.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.289-302
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• 2018

The study investigated the forest vegetation in 59 plots between June 2017 and August 2017 to understand the forest vegetation structure of the protected zone for forest genetic resource conservation (forest genetic resource reserve area) in Heuksando Island. We classified the vegetation using the Z-M phytosociological method analyzed the importance value and species diversity of each vegetation classification. The analysis showed the Camellia japonica community group at a top level of forest vegetation hierarchy. In the level of community, it was classified into Dendropanax morbiferus community (Vegetation unit 1; VU 1), Carpinus turczaninowii community, and C. japonica typical community (VU 6). C. turczaninowii community was subdivided into Buxus koreana group (VU 2), Rhododendron mucronulatum group (VU 3), Vitis amurensis group (VU 4) and C. turczaninowii typical group (VU 5). Therefore, it was classified into a total of six vegetation units (one community group, three communities, and four groups). The analysis of the mean codominant value of each VU show that Quercus acuta was the highest in VU 1, C. turczaninowii in VU 2, Pinus thunbergii in VU 3, Pinus densiflora in VU 4, and Castanopsis sieboldii in VU 5 and VU 6. The analysis of species diversity showed that VU 2 was the highest among six units in species richness index, species diversity index, and species evenness index. VU 6 showed the highest among six units in species dominance index. In conclusion, a synecology approach to manage six units and twelve species groups was needed for the forest vegetation of Heuksando Island protected area for forest genetic resource conservation.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.384-401
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• 2016

The vegetation assessment indicator has been developed recently as a biological part of the integrated assessment system for river environment to improve the efficiency of river restoration projects. This study carried out to test the vegetation assessment indicator and to reset its grade criteria on experimental streams. We classified and mapped vegetation communities at the level of physiognomic-floristic composition by each assessment unit. A total of 204 sampling quadrats were set up on the 68 assessment units at 5 experimental streams. By analyzing the vegetation data collected, we examined the appropriate numbers of sampling quadrats, the criteria of vegetation index score, classification of vegetation community, and grade criteria for vegetation assessment. The developed vegetation assessment indicator composed with the vegetation complexity index (VCI), the vegetation diversity index (VDI), and the vegetation naturalness index (VNI) was proved to reflect the current conditions of the streams sufficiently. The contribution of vegetation naturalness index to grading by vegetation assessment indicator was larger, but three indexes were closely correlated to each other. Also there was more clearer discrimination of grading with the application of adjusted criteria of vegetation assessment indicator and the standardized classification of vegetation community, but the stream segment type did not influence the vegetation assessment grade significantly.