• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.113-125
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• 2018

Subalpine zone is geographically vulnerable to climate change. Forest vegetation in this zone is one of the important basic indicator to observe the influence of climate change. This study was conducting phytosociological community classification and endemic-rare plants investigation based on vulnerable species to climate change at the subalpine zone, Mt. Jiri. Vegetation data were collected by 37 quadrate plots from March to October, 2015. In order to understand the species composition of plant sociological vegetation types and the ecological impacts of species, we analyzed the layer structure of vegetation type using important values. Vegetation type was classified into eight species groups and five vegetation units. The vegetation types can be suggested as an indicator on the change of species composition according to the future climate change. There were 9 taxa endemic plants and 17 taxa rare plants designated by KFS(Korea Forest Service) where 41.2% of them were the northern plant. Endemic-rare plants increased as the altitude of vegetation unit increase. Importance value analysis showed that the mean importance value of Abies koreana was highest of all vegetation units. Based on analysis of each layer, all units except vegetation unit 1 were considered to be in competition with the species such as Quercus mongolica and Acer pseudosieboldianum. The results of this study can be a basic data to understand the new patterns caused by climate change. In addition, it can be a basic indicator of long-term monitoring through vegetation science approach.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.1
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• pp.65-73
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• 2013

FDB(Feature DataBase) attributes have been produced by using the resource data prior to the year 2002. Due to this reason, the attributes need to be updated to the up-to-date ones. In this regards, this study focuses on the way of finding areas whose attributes need to be updated. Forest and crop classes were chosen as target classes among FDB features. MODIS Landcover data and FDB are, first, compared to detect the changed forest and crop areas from 2001 to 2008. Then, vegetation vitality changes are analyzed using MODIS annual NDVI data. Based on the change detection and the vegetation vitality analysis, the index of area selection for updating FDB attributes is proposed in this study.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.570-572
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• 2003

In this paper, a method of vegetation spectrum measurement using multi spectrum camera was studied. Each pixel in taken images using multi spectrum camera have spectrum data, the relationship between spectrum data and distribution, structure, etc. are directly turned out. In other words, detailed spectrum data information of object including spatial distribution can be obtained from those images. However, the camera has some problems for applying field measurement and data analysis. In this study, those problems are solved.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Korean Journal of Environmental Biology
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• v.40 no.1
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• pp.99-111
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• 2022

Vegetation is the basis for biodiversity conservation and sustainable development. In the Environmental Impact Assessment (EIA), which is the most direct and efficient policy measure to prevent degradation of nature, vegetation-related assessment has limitations as it is not based on quantitative and scientific methods. In addition, it focuses on the presence of protected species; hence, it does not take into account the role of vegetation as a habitat on a wide-area scale. As a way to overcome these limitations, this study aims to contribute to the quantification and advancement of future EIA on vegetation. Through the review of previous studies, core areas, connectivity, and vegetation condition were derived as the items to be dealt within the macroscopic aspect of vegetation impact assessment. Each item was spatially constructed using land cover maps and satellite imageries, and time series change analysis was performed. As a result, it was found that vegetation has been continuously deteriorating due to development in all aspects, and in particular, development adversely affects not only the inside of the project site but also the surrounding area. Although this study suggested the direction for improvement of the EIA in the vegetation sector based on data analysis, a more specific methodology needs to be established in order to apply it to the actual EIA process. By actively utilizing various environmental spatial data, the impact of the development on the natural ecosystem can be minimized.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.3
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• pp.35-51
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• 2014

A phytosociological vegetation survey was conducted in July to September 2011 in order to examine the vegetation community structure in Mt. Ohseo area. It was aimed to provide basic data for the effective vegetation conservation by analyzing the importance, species diversity and community similarity of the forest community in Mt. Ohseo for each layer, followed by the classification of the actual forest vegetation. According to the cluster analysis, the community type of Mt. Ohseo was classified into a total of 4 vegetation communities: Pinus densiflora community, Cornus controversa-Quercus serrata community, Miscanthus sinensis community, and Quercus mongolica community; the vegetation type 4 showed the lowest species diversity index of 0.5236, and vegetation type-2 showed the highest species diversity index of 0.6606. The community similarity between Quercus mongolica community and Pinus densiflora community showed the highest 0.679, and the community similarity between Quercus serrata community and Pinus densiflora community and between Quercus serrata community and Quercus mongolica community showed the levels of 0.5, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Korean Journal of Environment and Ecology
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• v.29 no.1
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• pp.46-61
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• 2015

The purpose of this study is to investigate a vegetation structure around Tongdosa temple forest and provincial park and to provide preliminary data. In order to look over the vegetation status, an actual vegetation map was drawn around study area. Vegetation structure survey was carried out for 6 representative communities of actual vegetation which were Quercus variavilis community, Carpinus tschonoskii community, Pinus densiflora community, P. densiflora-Broadleaf deciduous Forest community, Q. mongolica community and Broadleaf deciduous Forest community. The area of the Tongdosa district measured $29,202,262m^2$. Actual vegetation type were divided into 35 types, and the ratio of Q. variavilis community was 32.35 % ($9,447,932m^2$). To investigate the structure of 6 representative communities, 58 plots were set up and unit area plots measured $100m^2$. The estimated age of the forest is 50~100-years-old and the oldest tree P. densiflora is 113-years-old.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.1-9
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• 2015

The development of drought index that provides detailed-spatial-resolution drought information is essential for improving drought planning and preparedness. The objective of this study was to develop the concept of using satellite-based hybrid drought index called the Vegetation Drought Response Index in South Korea (VegDRI-SKorea) that could improve spatial resolution for monitoring local and regional drought. The VegDRI-SKorea was developed using the Classification And Regression Trees (CART) algorithm based on remote sensing data such as Normalized Difference Vegetation Index (NDVI) from MODIS satellite images, climate drought indices such as Self Calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Index (SPI), and the biophysical data such as land cover, eco region, and soil available water capacity. A case study has been done for the 2012 drought to evaluate the VegDRI-SKorea model for South Korea. The VegDRI-SKorea represented the drought areas from the end of May and to the severe drought at the end of June. Results show that the integration of satellite imageries and various associated data allows us to get improved both spatially and temporally drought information using a data mining technique and get better understanding of drought condition. In addition, VegDRI-SKorea is expected to contribute to monitor the current drought condition for evaluating local and regional drought risk assessment and assisting drought-related decision making.