• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.547-555
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• 2015

The vegetation index is a crucial parameter in many biophysical studies of vegetation, and is also a valuable content in ecological processes researching. The OVIs (Optical Vegetation Index) that of using multispectral and hyperspectral data have been widely investigated in the literature, while the RVI (Radar Vegetation Index) that of considering volume scattering measurement has been paid relatively little attention. Also, there was only some efforts have been put to fuse the OVI with the RVI as an integrated vegetation index. To address this issue, this paper presents a novel FVI (Fusion Vegetation Index) that uses multispectral and full-PolSAR (Polarimetric Synthetic Aperture Radar) data. By fusing a NDVI (Normalized Difference Vegetation Index) of RapidEye and an RVI of C-band Radarsat-2, we demonstrated that the proposed FVI has higher separability in different vegetation types than only with OVI and RVI. Also, the experimental results show that the proposed index not only has information on the vegetation greenness of the NDVI, but also has information on the canopy structure of the RVI. Based on this preliminary result, since the vegetation monitoring is more detailed, it could be possible in various application fields; this synergistic FVI will be further developed in the future.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.673-678
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• 2002

A satellite remote sensing is useful for vegetation monitoring. But it has some problem. One of these, it is difficult to find a difference of vegetation surface roughness using satellite remote sensing. Each vegetation type has unique surface roughness, for example needle leaves forest, broad leaves forest and grassland. Difference of vegetation surface roughness can be detected by satellite multiangular observation. In this study, objective is to propose index of vegetation surface roughness using BRF property. General vegetation indices are calculated from nadir data of satellite data. A proposed index is calculated from two different observation zenith angle data. Two different zenith data can provide BRF (Bi-directional Reflectance Factor) property of satellite observation data. A proposed index was able to detect different value on where NDVI shows similar high value areas of rice field and forest. This index is useful for vegetation monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.607-615
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• 2014

RVI (Radar Vegetation Index) has shown some promise in the vegetation fields, but its relationship with MVI (Multispectral Vegetation Index) is not known in the context of various land covers. Presented herein is a comparative analysis of the MVI values derived from the LANDSAT-8 and RVI values originating from the RADARSAT-2 quad-polarimetric SAR (Synthetic Aperture Radar) data. Among the various multispectral vegetation indices, NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were used for comparison with RVI. Four land covers (urban, forest, water, and paddy field) were compared, and the patterns were investigated. The experiment results demonstrated that the RVI patterns of the four land covers are very similar to those of NDVI and SAVI. Thus, during bad weather conditions and at night, the RVI data could serve as an alternative to the MVI data in various application fields.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.21-35
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• 2020

Vegetation information is a very important factor used in various fields such as urban planning, landscaping, water resources, and the environment. Vegetation varies according to canopy density or chlorophyll content, but vegetation vitality is not considered when classifying vegetation areas in previous studies. In this study, in order to satisfy various applied studies, a study was conducted to set a threshold value of vegetation index considering vegetation vitality. First, an eBee fixed-wing drone was equipped with a multi-spectral camera to construct optical and near-infrared orthomosaic images. Then, GIS calculation was performed for each orthomosaic image to calculate the NDVI, GNDVI, SAVI, and MSAVI vegetation index. In addition, the vegetation position of the target site was investigated through VRS survey, and the accuracy of each vegetation index was evaluated using vegetation vitality. As a result, the scenario in which the vegetation vitality point was selected as the vegetation area was higher in the classification accuracy of the vegetation index than the scenario in which the vegetation vitality point was slightly insufficient. In addition, the Kappa coefficient for each vegetation index calculated by overlapping with each site survey point was used to select the best threshold value of vegetation index for classifying vegetation by scenario. Therefore, the evaluation of vegetation index accuracy considering the vegetation vitality suggested in this study is expected to provide useful information for decision-making support in various business fields such as city planning in the future.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.97-110
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• 2021

Vegetation index information is an important figure that is used in many fields such as landscape architecture, urban planning, and environment. Vegetation may vary slightly in vegetation vitality depending on photosynthesis and chlorophyll content. In this study, a range of vegetation worth preserving in the Taehwa River water system was determined, and hyperspectral images of drones were acquired (August, October), and the results were presented through DVI(Normalized Defference Vegetation Index), EVI(Enhanced Vegetation Index), PRI(Photochemical Reflectance Index), ARI (Anthocyanin Reflectance Index) index analysis. In addition, field spectral data and VRS-GPS(Virtual Reference System-GPS) surveys were performed to ensure the quality and location accuracy of the spectral band. As a result of the analysis, NDVI and EVI showed low vegetation vitality in October, -0.165 and -0.085, respectively, and PRI and ARI increased to 0.011 and 7.588 in October, respectively. For general vegetation vitality, it was suggested that NDVI and EVI analysis were effectively performed, and PRI and ARI were thought to be effective in analyzing detailed characteristics of plants by spectral band. It is expected that it can be widely used for park design and landscape information modeling by using drone image information construction and vegetation information.

• Proceedings of the KSRS Conference
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• 2002.10a
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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.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.80-83
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• 2008

Diverse researches using vegetation index have been carried out to monitor spring droughts that have frequently occurred since 2000. The strength of the drought monitoring using vegetation index lies in that it can reflect characteristics of satellite images: large area coverage, cyclicity, and promptness. However, vegetation index involve uncertainly caused by diverse factors that affect vegetation stress. In this study, multi-temporal vegetation index is compared with the most representative meteorological drought indices like PSDI, SPI. Based on the results from analyses, usability of vegetation index as a tool of drought analysis is proposed.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.983-989
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• 2018

The real-time monitoring of surface vegetation is essential for the management of droughts, vegetation growth, and water resources. The availability of land cover maps based on remotely collected data makes the monitoring of surface vegetation easier. The vegetation index in an area is likely to be proportional to meteorological elements there such as air temperature and precipitation. This study investigated relationship between vegetation index based on Moderate Resolution Image Spectroradiometer (MODIS) and ground-measured meteorological elements at the Yongdam catchment station. To do this, 16-day averaged data were used. It was found that the vegetation index is well correlated to air temperature but poorly correlated to precipitation. The study provides some intuition and guidelines for the study of the droughts and ecologies in the future.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.582-584
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• 2003

This paper explores two such indexes----the Aerosol Free Vegetation Index (AFRI) and the Atmospherically Resistant Vegetation Index (ARVI). Comparisons were made with the NDVI (normalized vegetation index) to see if they indeed performed better. In general, the results showed that the AFRI and ARVI (with gamma=1) did indeed perform better than their NDVI counterpart study with the related channels were employed.