• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.461-472
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• 2022

Natural and anthropogenic factors cause forest types to continuously change. Since the ratio of forest area by forest type is important information for identifying the characteristics of national forest resources, an accurate understanding of the prospect of forest type change is required. The study aim was to use National Forest Inventory (NFI) time-series data to understand the characteristics of forest type change and to estimate future prospects of nationwide forest type change. We used forest type change information from the fifth and seventh NFI datasets, climate, topography, forest stand, and disturbance variables related to forest type change to analyze trends and characteristics of forest type change. The results showed that the forests in Korea are changing in the direction of decreasing coniferous forests and increasing mixed and broadleaf forests. The forest sites that were changing from coniferous to mixed forests or from mixed to broadleaf forests were mainly located in wet topographic environments and climatic conditions. The forest type changes occurred more frequently in sites with high disturbance potential (high temperature, young or sparse forest stands, and non-forest areas). We used a climate change scenario (RCP 8.5) to establish a forest type change model (SVM) to predict future changes. During the 40-year period from 2015 to 2055, the SVM predicted that coniferous forests will decrease from 38.1% to 28.5%, broadleaf forests will increase from 34.2% to 38.8%, and mixed forests will increase from 27.7% to 32.7%. These results can be used as basic data for establishing future forest management strategies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.175-185
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• 2020

Quantitative and qualitative surveys were conducted during scuba diving activity between May 2017 and June 2017 in order to analyze spring marine algal community structure at Gijang-gun, Busan. A total of 121 species including 13 (10.7 %) Chlorophyta, 18 (14.9 %) Phaeophyta, and 90 (74.4 %) Rhodophyta were identified. The species count in the subtidal zone (110 species) was twice as high as in the intertidal zone (56 species). In terms of regions, 88,76, and 75 species were found in Mundong, Gongsu, and Daebyeon, respectively. Mean biomass for the whole study area was 1,501.5 g·m^-2, higher for the subtidal zone (1,869.4 g·m^-2) than for the intertidal zone (1,133.5 g·m^-2). Mean biomass by region values were 2,234.0 g·m^-2, 1,228.1 g·m^-2, and 1,044.4 g·m^-2 for Mundong, Gongsu, and Daebyeon, respectively. Ulva australis, Sargassum thunbergii, and Corallina pilulifera had high biomass in the intertidal zone, while Sargassum macrocarpum, Undaria pinnatifida, and Phycodrys fimbriata had high biomass in the subtidal zone. The composition ratio of marine algal functional forms suggests that the entire study area had 'Low' status, and all sites fell within 'Low' to 'Moderate' status. Results indicate that opportunistic species are widely distributed throughout the Gijang-gun coast, hence potential effects of disturbance and environmental pollution should be considered. Seaweed farming in particular - a major activity along the Gijang-gun coast -, together with effects of other anthropogenic activities such as national fishing port development and the coastal improvement project, could have long-term effects on marine algal communities. Therefore, long-term monitoring and management plans for marine algal communities will be required.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.215-231
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• 2020

Forest landscape models (FLMs) can be used to investigate the complex interactions of various ecological processes and patterns, which makes them useful tools to evaluate how environmental and anthropogenic variables can influence forest ecosystems. However, due to the large spatio-temporal scales in FLMs studies, parameterization and validation can be extremely challenging when applying to new study areas. To address this issue, we focused on the parameterization and application of a spatially explicit forest landscape model, LANDIS-II, to Mt. Gyebang, South Korea, with the use of the National Forest Inventory (NFI) and long-term ecological research (LTER) site data. In this study, we present the followings for the biomass succession extension of LANDIS-II: 1) species-specific and spatial parameters estimation for the biomass succession extension of LANDIS-II, 2) calibration, and 3) application and validation for Mt. Gyebang. For the biomass succession extension, we selected 14 tree species, and parameterized ecoregion map, initial community map, species growth characteristics. We produced ecoregion map using elevation, aspect, and topographic wetness index based on digital elevation model. Initial community map was produced based on NFI and sub-alpine survey data. Tree species growth parameters, such as aboveground net primary production and maximum aboveground biomass, were estimated from PnET-II model based on species physiological factors and environmental variables. Literature data were used to estimate species physiological factors, such as FolN, SLWmax, HalfSat, growing temperature, and shade tolerance. For calibration and validation purposes, we compared species-specific aboveground biomass of model outputs and NFI and sub-alpine survey data and calculated coefficient of determination (R²) and root mean square error (RMSE). The final model performed very well, with 0. 98 R² and 8. 9 RMSE. This study can serve as a foundation for the use of FLMs to other applications such as comparing alternative forest management scenarios and natural disturbance effects.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.100-114
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• 2018

Nationally designated Cultural Properties 'Scenic site No.12 Maisan Mountain, Jinan' designated areas and some protected areas, and taking into account the dynamics of naturalized plants causing problems, we will restore the original vegetation scenery of Mt. Maisan. The results of this study are as follows. A total of 76 families, 192 genera, 286 taxa, and inland and inhabited areas, 76 and 138 genera and 163 taxa were identified in the areas of Ammaibong. The total number of naturalized plants identified in this study area is 28 taxa total, which corresponds to 7.1% naturalization rate(NR) among the vascular plants of all 395 taxa, and the urbanization index(UI) corresponds to 8.4% of the 333 taxa of Korean naturalized plants. Ecosystem disturbance plants identified in the survey area were Ambrosia artemisiifolia 1 taxa. The naturalized plants controlled and managed by separate anthropogenic vegetation management within the designation and protection area of Maisan scenic place are three species of herbaceous Rumex acetosella, A. artemisiifolia and Festuca arundinacea. It was identified as a breed. Indigofera bungeana and F. arundinacea communities around the stairway and Amorpha fruticosa, I. bungeana, A. artemisiifolia and Amaranthus patulus of the top of Am-Maibong were selected as the first priority sites for exclusion of exotic species in Maisan area and target naturalized plants species to the Ammaibong peak. In addition, R. acetosella community around the temple was suggested to be removed first to preserve endemic species. For the restoration of vegetation, we suggest that Stephanandra incisa, Spiraea blumei, Weigela subsessilis, etc. should be planted after removal of I. bungeana, and F. arundinacea, C. lanceolata, Carex callitrichos var. nana.