• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.2
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• pp.58-68
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• 1999

This study was carried out to verify the distribution patterns of Salix gracilistyla and Phragmites japonica communities known as obligatory riparian species according to physical factors such as micro-landforms, substrates, etc., at Soo-ip stream corridor. Firstly four vegetation types - Salix gracilistyla dominant type, Phragmites japonica dominant type, mixed type of two species, and mixed type of two species to other species, were classified by cluster analysis based on UPGMA-Euclidean distance. Also these vegetation types showed many different distribution patterns in response to the longitudinal and lateral view along the stream corridor and substrate composition. Salix gracilistyla was major component of dominant vegetation types developed at attack point of bending reach and on substrates composed of rock fragments, but contrastly Phragmites japonica was most important component of dominant vegetation types at point bar of bending reach and floodplain, and on substrates composed of soil materials. Secondly the species and environment biplot form CCA strongly supported the vegetation types divided by classification. Namely Salix gracilistyla was closely correlated with rock fragments and steep slope, which is resistant to physical action even though located near running water. But Phragmites japonica showed a high correlation with soil particles sedimented at floodplain by divergent flow.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.255-261
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• 2009

A new polarimetric SAR image classification technique based on the degree of polarization (DoP) and the co-polarized phase-difference (CPD) is presented in this paper. Since the DoP and the CPD of a scattered wave provide information on the randomness of the scattering and the type of scattering mechanisms, at first, the statistics of the DoP and CPD are examined with measured polarimetric SAR image data. Then, a DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification technique is verified using the JPL AirSAR and ALOS PALSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest.

• 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.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.233-244
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• 2021

This study investigated the application of terrestrial light detection and ranging (LiDAR) to inspect the defects of the vegetated levee. The accuracy of vegetation filtering techniques was compared by applying filtering techniques on photogrammetric point clouds of a vegetated levee generated by terrestrial LiDAR. Representative 10 vegetation filters such as CIVE, ExG, ExGR, ExR, MExG, NGRDI, VEG, VVI, ATIN, and ISL were applied to point cloud data of the Imjin River levee. The accuracy order of the 10 techniques based on the results was ISL, ATIN, ExR, NGRDI, ExGR, ExG, MExG, VVI, VEG, and CIVE. Color filters show certain limitations in the classification of vegetation and ground and classify grass flower image as ground. Morphological filters show a high accuracy of the classification, but they classify rocks as vegetation. Overall, morphological filters are superior to color filters; however, they take 10 times more computation time. For the improvement of the vegetation removal, combined filters of color and morphology should be studied.

• Journal of Environmental Impact Assessment
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• v.18 no.6
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• pp.387-392
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• 2009

As Our society's environmental awareness and concern the significant increases, the importance of the legal system for environmental conservation such as the Prior Environmental Review System, Environmental Impact Assessment is growing increasingly. but, still critical issues are present such as reliability. Though there could be various causes such as the system or procedures etc. Above all, basically the environmental data problem is the critical cause. Therefore, this study was trying to improve the environmental data accuracy using the high-resolution color aerial photography, LiDAR data and Object Oriented Classification method. And in this study, classification based on coverage percentage of a particular species was attempted through the multi-resolution segmentation and multi-level classification method. The classification result was verified by comparison with 11 points local survey data. All 11 points were classified correctly. And even though the exact coverage percentage of the particular species did not be measured, It was confirmed that the species was occupied similar portion. It is important that the environmental data which can be used for the conservation value assessment could be acquired.

• Korean Journal of Environment and Ecology
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• v.18 no.2
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• pp.231-235
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• 2004

Landsat satellite images were analyzed to study vegetation change patterns of warm-temperate forests from 1991 to 2002 in Wando. For this purpose, Landsat TM satellite image of 1991 and Landsat ETM image of 2002 were used for vegetation classification using ENVI image processing software. Four different forest types were set as a classification criteria; evergreen broadleaf, evergreen conifer, deciduous broadleaf, and others. Unsupervised classification method was applied to classily forest types. Although it was impossible to draw exact forest types in rocky areas because of differences in data detection time and rough resolution of image, 2002 data revealed that total 2,027ha of evergreen broadleaf forests were growing in Wando. Evergreen broadleaves and evergreen conifers increased in total areas compared to 11 years ago, but there was sharp decrease in deciduous broadleaves. GIS-based management system for warm-temperate forest was done using Arc/Info. Geographic and attribute database of Wando such as vegetation, soils, topography, land owners were built with Arc/Info and ArcView. Graphic user interface which manages and queries necessary data was developed using Avenue.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.671-679
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• 2020

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

• Korean Journal of Environment and Ecology
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• v.21 no.4
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• pp.356-365
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• 2007

This study was conducted to evaluate the changed vegetational community structure according to vegetational succession in Chiaksan National Park of Korea by applying ordination and classification method of floristic composition along with the actual vegetation by correlation. As for the ratio of actual vegetation, Mongolian oak forest(33.1%) was the highest, followed by mixed forest(16.2%), Japanese larch forest(15.6%), deciduous broad-leaved forest(14.7%), red pine forest(11.1%), Korean pine forest(2.3%) and Pitch pine forest(0.1%), respectively. The vegetation was classified into Acer pseudosieboidianum-Quercus mongolica community, Cornus controversa-Carpinus cordata community, Quercus sonata community, Pinus densiflora community and afforestation. The Acer pseudosieboldianum-Quercus mongolica community-a subordinately ranked community-was divided into Carpinus laxiflora-Sassa borealis community, Fraxinus rhynchophylla community and Symplocos chinensis for. pilosa-Carex siderosticta community. The results of community classification using by ordination and classification method of floristic composition were similar to each other. The vegetational succession, with the combination of Quercus mongolica, Acer pseudosieboldianum and Rhododendron schlippenbachii, was predicted to form a climax forest from above the hillside.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.55-60
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• 2009

By applying NDVI(Normalized Difference Vegetation Index) and TCT(Tasseled-Cap Transformation) image algorithm on the basis of KOMSAP-2 MSC(Multi Spectral Camera) image(Jun. 12, 2007) for Naenam-myeon, Gyeongju city in this study, DN distribution map was drawn up. Discrimination analysis of image algorithm for the accuracy improvement of forest type classification was conducted through the comparative analysis between the distribution maps of NDVI and TCT DN, and forest field surveying data, and finally, the accuracy of the forest type classification was verified through the overlay analysis with the forest field surveying data. Through this study, it is thought that low cost and high efficiency will be able to be expected in the process of the examination for the automation practicality of the forest type classification and of the production of the accurate forest type classification map by using KOMPSAT-2 MSC image.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.101-110
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• 2006

The accuracy of rural land cover using airborne multispectral images and LEAR (Light Detection And Ranging) data was analyzed. Multispectral image consists of three bands in green, red and near infrared. Intensity image was derived from the first returns of LIDAR, and vegetation height image was calculated by difference between elevation of the first returns and DEM (Digital Elevation Model) derived from the last returns of LIDAR. Using maximum likelihood classification method, three bands of multispectral images, LIDAR vegetation height image, and intensity image were employed for land cover classification. Overall accuracy of classification using all the five images was improved to 85.6% about 10% higher than that using only the three bands of multispectral images. The classification accuracy of rural land cover map using multispectral images and LIDAR images, was improved with clear difference between heights of different crops and between heights of crop and tree by LIDAR data and use of LIDAR intensity for land cover classification.