• The Korean Journal of Ecology
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• v.24 no.5
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• pp.259-265
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• 2001

This paper describes the species composition of the remnant forest vegetation (Natural Monument No. 65) in the Mok-do island of Ulsan city and its relation to ecological long-term monitoring. Syntaxonomical classification and actual vegetation map were depicted in very fine scale 1:800 for better understanding spatial distribution and vitality of individual trees and communities. A total of 111 species and 13 plant communities occurred on the 19,166 ㎡ area. Evergreen broad-leaved forest of Machilus thunbergii is a representative vegetation type, which covers 37.4% of the island area. Evergreen coniferous forest of Pinus thunbergii covers 18.6% of the island. These two forests occurred at different parts of the island, i.e., the former at the rearward and the later at the frontward of island against marine. 95.7% of trees analysed was determined as critically and absolutely monitored individuals. From a conservation perspective the Mok-do vegetation is extremely vulnerable, which must be long-termly monitored using an assessment of tree vitality and a fine scale map of vegetation.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.196-202
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• 2020

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91~0.92, GNDVI were 0.92~0.94, NGRDI were 0.71~0.85 and NDREI were 0.84~0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.195-197
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• 2003

Soil moisture is a crucial variable in research works of hydrology, meteorology and plant sciences. Adequate soil moisture is essential for plant growth; excesses and deficits of soil moisture must be considered in agricultural practices. There are already several remote sensing methods used for monitoring soil moisture, such as thermal inertia, vegetation water-supplying index, crop water stress index and multi-factor regression. In this paper, an improved method has been discussed which is based on the thermal inertia. We analyzed the problems of monitoring soil moisture using satellites at first, and then put forward an simplified method which directly uses land surface temperature differences to measure soil moisture. Also we have taken the influence of vegetation into account, and import NDVI into the model. The method was used in the study of soil moisture in Heilongjiang Province, China, and we draw the conclusion by the experiments that the model can evidently increase the precision of monitoring soil moisture.

• 한국지형공간정보학회:학술대회논문집
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• 2005.08a
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• pp.101-106
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• 2005

Vegetation shows unique spectrum characteristics compared with other materials. If such characteristics are used, land change pattern can be determined. Thus, vegetation has an absorption belt and a reflective belt in visible and near infrared, and reflectance is very high. Then, various methods of monitoring vegetation paying attention to the absorption wavelength region and reflective region of vegetation are proposed. However, there are various problems in grasping change of vegetation by NDVI, PVI, etc. It is very difficult especially to remove various noise ingredients in the received satellite data. Until now, it is difficult to compensate for shadow effect when NDVI is used in vegetation analysis. The results is, if the shadow is about 60% the pixel will be wrongly classified as may be vegetation or not.

• ALGAE
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• v.22 no.1
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• pp.37-44
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• 2007

In order to understand the impact of the heavy-oil pollution by the 1997 Nakhodka oil spill on the intertidal macroalgal vegetation, we have been monitoring succession in the intertidal flora since 1997 at Oh-ura, Takno, and Imago-Ura Cove, Kasumi in Hyogo Prefecture, northwestern coast of Honshu, Japan. We employed two different monitoring methods: 1) The percent cover of macro-algae (seaweeds) in 1 x 1 m quadrats along 450 m intertidal transects parallel to the shoreline were assessed and recorded by photographic imaging until 2002, and for 30-40 m transects of the most heavily polluted areas in 2004 and 2006; 2) The percent cover of macro-algae in 0.5 x 0.5 m quadrats along a transect line perpendicular to the shore were recorded and all macrophytes within the quadrat were completely removed to record the wet weight of each taxon (1997-2006). Based on the monitoring data, we conclude that the high intertidal zone at Imago-ura, where a large part of the stranded oil accumulated, suffered the heaviest damage and experienced the slowest recovery. In addition, although the original status of macroalgal vegetation before the impact was not well-documented, it appeared that recovery from the damage caused by the oil pollution required four to five years.

• Ecology and Resilient Infrastructure
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• v.4 no.1
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• pp.3-11
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• 2017

In this study, change in vegetation on bars was analyzed using the data on hydrology and river morphology with on-site photographic monitoring data for the sites of interest of the Naeseong Stream during the period from March 2013 to July 2016 when the impoundment of Yeongju Dam began. The effect of flow condition on burial and removal of vegetation covered on bar surfaces was elucidated by comparison of on-site photographic monitoring data with continuous water level plotted with on the cross-section profile. In 2014 burial happened due to late flood, while July flood caused burial and removal in 2016. On the contrary vegetation increased in 2015 due to low flow without flood. Results of this study showing natural changes in vegetation will be reference to changes which is expected to be caused by dam impoundment.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• The Korean Journal of Ecology
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• v.20 no.4
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• pp.265-274
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• 1997

All landscapes are mosaics of habitat patches of different types. Therefore, there are always edged between habitat patches in a landscape. Forest animal has an important role in vegetation development and maintenance by seed dispersal around forest. Movement of animals depends on the spatially heterogeneous structure and pattern of vegetation landscapes because each animal has special habitats in a landscape. Especially, forast edge with high permeability and prey density is one of the important habitats to the animals. Therefore, understanding the ecological characteristics of the forest edges as a corridor connecting mosaic vegetation patches is necessa교 새 establish the strategies for the nature conservation and sustainable vegetation management. Under this idea, we examined the animal influenced on pine seeds as one of the method of monitoring the animal activity in mosaic vegetation. Man-made mosaic vegetations including open, edge and inner forests were carefully selected in the rural landscape. We carried out predation test on pine seeds during one year. A result was that damages on seed was more significant at forest edge than inner and open forest. Pine seed on seedbeds was mainly attacked by squirrels and mice than birds. Pine seed was damaged by squirrels in different types of vegetation by seasons. Rate of seed predation at forest edge was, in special, higher than that of other sites. According to this results, it is suggested that the relationship between animal behavior and spatial vegetation structure relating to human impact such as the distance from settlement to vegetation appeares to be in the rural vegetation landscape.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.33-43
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• 2022

This study was conducted to monitor the vegetation recovery in the areas damaged by the forest fires on the east coast that occurred in April 2000. The study site was a forest fire-damaged area in Samcheok-si, Gangwon-do, and 21 monitoring areas (12 natural restoration sites, 9 artificial restoration sites) were selected to analyze the vegetation recovery trend since 1998. The vegetation recovery trend was compared by calculating the values according to the year using the difference Normalized Burn Ratio (dNBR) and Normalized Difference Vegetation Index (NDVI) based on satellite images (Landsat TM/ETM+ and Sentinel-2A). As the result of this study, all 21 sites, vegetation was recovered, and both groups showed the greatest recovery in summer. In the case of the dNBR, the artificial restored sites showed higher values than the natural restored sites, and in the case of the NDVI, the natural restored sites were higher than the artificially restored sites in summer and autumn. However, the difference between the two groups of natural and artificial restoration sites was not significant. Therefore, the direction of forest restoration after forest fire damage can be effectively restored if properly implemented for the purpose of restoration of the target site.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.6
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• pp.99-109
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• 2007

The main objectives of this comparative study were 1) to compare the floristic similarity of species composition between the extant vegetation and seedlings from soil seed bank and 2) to quantify the potential contribution of soil seed bank to revegetation of forest in a constructed area, which is called "ecological impact mitigation" in conjunction with the power plant extension. Forest topsoil of seven plots was collected from the surface soil after measurements were taken on the ground vegetation in each plot. A greenhouse experiment was conducted and monitored to analyze the germination potential of soil seed bank. The forest topsoil was spread on plastic trays ($0.7m^2{\times}7$) filled with a 5cm layer of sterilized potting mix. The results of monitoring for 2 years in a greenhouse were as follows : 1) seedlings of soil seed bank per 4.9$m^2$ were 1,269 with 36 species (1st year) and 2,615 with 25 species (2nd year). 2) 38${\pm}$8% of the flora species were germinated from soil seed bank. It can be concluded that the use of soil seed bank would be effective to promote establishment of diverse species and vegetation. However, it behooves to continue monitoring on succession of vegetation and pursue revegetation with other methods for ecological restoration. Finally, adequate topsoil deposit and gathering methods should be studied properly.