• Journal of Environmental Science International
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• v.20 no.6
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• pp.701-710
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• 2011

The purpose of this study is to investigate the vegetation structure and ecological restoration of disturbed forest due to artificial plant. To this end, 12 plots were set up and surveyed. The result analyzed considering mean importance percentage(M.I.P) showed that the types were divided into three groups which are artificial planted forest type(three plots), natural forest-artificial planted forest type(four plots), natural forest type(five plots). Dominant proportion of artificial planted species were as follows: artificial planted forest type was over 60%, natural forest-artificial planted forest types were 14~49%. The range of Shannon's index of all associations was from 0.7131 to 0.7771(natural forest-artificial planted forest > natural forest > artificial planted forest). Also we suggested restoration method of vegetation for ecological value as follow: Control of density considering step and Remove of Pinus koraiensis seedlings of understory layer and shurb layer.

• The Korean Journal of Ecology
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• v.27 no.4
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• pp.217-224
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• 2004

The purpose of this study is to clarify the effects of the conversion of the forest management type from a natural deciduous broad-leaved forest to an artificial evergreen coniferous forest based on organic matter dynamics. We investigated the amounts and carbon contents of the forest floor and the litterfall, soil chemical characteristics and cellulose decomposition rates in the natural deciduous broad-leaved forest and adjacent artificial evergreen coniferous forest. In the artificial evergreen coniferous forest were planted Japanese cypress (Chamaecyparis obtusa) on the upper slope and Japanese cedar (Cryptomeria japonica) on the lower slope. The soil carbon and nitrogen contents, CEC and microbial activity had decreased due to the conversion of the forest management type from a natural deciduous broad-leaved forest to an artificial Japanese cypress forest, and were almost the same for the conversion to a Japanese cedar forest. Under the same conditions, it is considered that the soil fertility was different by planting specific tree species because the organic matter dynamics were changed by them.

• Journal of Korean Society of Forest Science
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• v.94 no.5 s.162
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• pp.338-341
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• 2005

This study was conducted to describe the differences in artificial nest boxes use of tits between deciduous and coniferous forests at 2nd campus of Chung-Ang University ($37^{\circ}00^{\prime}04^{{\prime}{\prime}}N$, $127^{\circ}13^{\prime}96^{{\prime}{\prime}}E$), Ansung, Korea from January to August 2005. Tree species richness, tree species diversity index (H') and total basal areas were higher in deciduous forest than in coniferous forest. High, middle, low and understory canopy layers were more developed in deciduous forest, except the coverage of bush-ground layer. Varied tit Parus varius, marsh tit P. palustris and great tit P. major used the artificial nest boxes in this study. Number of breeding pairs of tits used artificial nest boxes, clutch size, and weight and size of eggs were higher in deciduous forest than in coniferous forest. The differences in habitat structure between study sites are very likely to have influenced how breeding birds used the available habitat. Artificial nest boxes could be used as management and conservation tool for birds, particularly in areas, where the availability of natural cavities and coverage of higher layer are limited.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.4
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• pp.220-225
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• 2007

The succession of marine benthic algae and the restoration of an artificial seaweed forest on a porous type (shaped half saw teeth) artificial reef at Jeju island, Korea was studied. Young thalli of Sargassum horneri and Ecklonia cava were attached to different artificial substrates. In general, the succession on the artificial reefs led from filamentous algae to perennial algae and involved more than 25 species that are useful fishery resources, including E. cava. Coralline algae were dominant on the artificial reefs at the Kangjung site. The maximum algal biomass on the artificial reef in October 2005 was $1,990g/m^2$ at Biyang. In conclusion, a climax community and seaweed forest can be attained one year after the substrate is constructed.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.555-567
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• 2023

This study was conducted to compare the level of vegetation recovery based on the forest restoration techniques (natural restoration and artificial restoration) determined using the satellite imagery that targeted forest fire damaged areas in Goseong-gun, Gangwon-do. The study site included the area affected by the Goseong forest fire (1996) and the East Coast forest fire (2000). We conducted a time-series analysis of satellite imagery on the natural restoration sites (19 sites) and artificial restoration sites (12 sites) that were created after the forest fire in 1996. In the analysis of satellite imagery, the difference normalized burn ratio (dNBR) and normalized difference vegetation index (NDVI) were calculated to compare the level of vegetation recovery between the two groups. We discovered that vegetation was restored at all of the study sites (31 locations). The satellite image-based analysis showed that the artificial restoration sites were relatively better than the natural restoration sites, but there was no statistically significant difference between the two groups (p > 0.05). Therefore, it is necessary to select a restoration technique that can achieve the goal of forest restoration, taking the topography and environment of the target site into account. We also believe that in the future, accurate diagnosis and analysis of the vegetation will be necessary through a field survey of the forest fire-damaged sites.

• 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 Forest and Environmental Science
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• v.35 no.1
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• pp.25-30
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• 2019

The composition of artificial soil in a containerized seedling production plays an important role in seedling quality as well as environmental issues. We investigated the effects of different types of biomaterials and mixed ratio with artificial soil on the growth of Fraxinus rhynchophylla seedlings. Soil medium was supplemented with 3 levels (0%, 10%, 20%) of pine bark, mushroom sawdust and rice husk. Root collar diameter (RCD), height growth, and biomass have significantly increased when rice husk was applied. Compared with the control, RCD and height growth showed highest in 20% rice husk treatment with an increase of 5.7% and 17.6%, respectively. In contrast, the treatments of pine bark and mushroom sawdust showed lower results in growth parameters (RCD, height growth, and total biomass) than control. Seedling quality index was also highest at the 20% rice husk treatment, but there was not statistically different among treatments. Our results suggested rice husk can be substituted up to 20% of substrates for containerized F. rhynchophylla seedling production system.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.43-57
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• 2022

This study aims to classify forest fire-affected areas, identify forest types by the intensity of forest fire damage using multi-time Landsat-satellite images before and after forest fires and to analyze the effects of artificial restoration sites and natural restoration sites. The difference in the values of the Normalized Burned Ratio(NBR) before and after forest fire damage not only maximized the identification of forest fire affected and unaffected areas, but also quantified the intensity of forest fire damage. The index was also used to confirm that the higher the intensity of forest fire damage in all forest fire-affected areas, the higher the proportion of coniferous forests, relatively. Monitoring was conducted after forest fires through Normalized Difference Vegetation Index(NDVI), an index suitable for the analysis of effects by restoration type and the NDVI values for artificial restoration sites were found to no longer be higher after recovering the average NDVI prior to the forest fire. On the other hand, the natural restoration site witnessed that the average NDVI value gradually became higher than before the forest fires. The study result confirms the natural resilience of forests and these results can serve as a basis for decision-making for future restoration plans for the forest fire affected areas. Further analysis with various conditions is required to improve accuracy and utilization for the policies, in particular, spatial analysis through forest maps as well as review through site checks before and immediately after forest fires. More precise analysis on the effects of restoration will be available based on a long term monitoring.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1965-1974
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• 2021

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.