- Apply for Authority
- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
Abstract
The objective of this study was to provide basic data for the conservation of wetland ecosystems in the Civilian Control Zone and the management of Yongyangbo wetlands in South Korea. Yongyangbo wetlands have been designated as protected areas. A field survey was conducted across five sessions between April 2019 and August of 2019. A total of 248 taxa were identified during the survey, including 72 families, 163 genera, 230 species, 4 subspecies, and 14 varieties. Their life-forms were Th (therophytes) - R5 (non-clonal form) - D4 (clitochores) - e (erect form), with a disturbance index of 33.8%. Three taxa of rare plants were detected: Silene capitata Kom. and Polygonatum stenophyllum Maxim. known to be endangered species, and Aristolochia contorta Bunge, a least-concern species. S. capitata is a legally protected species designated as a Class II endangered species in South Korea. A total of 26 taxa of naturalized plants were observed, with a naturalization index of 10.5%. There was one endemic plant taxon (Salix koriyanagi Kimura ex Goerz). In terms of floristic target species, there was one taxon in class V, one taxon in Class IV, three taxa in Class III, five taxa in Class II, and seven taxa in Class I. Three invasive alien species (Ambrosia trifida L., Ambrosia artemisiifolia L., and Humulus japonicus Siebold & Zucc) were observed. For continuous conservation of Yongyangbo Wetlands, it is necessary to remove invasive alien plants and block the inflow of non-point pollutants.
Abstract
Sanankerto is one of pilot projects for tourism villages in Indonesia due to its natural tourism potential with a 24-ha bamboo forest located in Boon Pring Andeman area. However, the distribution of existing bamboo has never been identified or mapped. Thus, the management is facing difficulty in planning and developing tourism potential as well as spatial management in the area. Therefore, the objectives of this study were to identify and analyze the structure of bamboo vegetation in the Boon Pring Tourism village and to perform vegetation mapping. The type of research was descriptive exploratory with a cluster sampling technique (i.e., a two-stage cluster) covering an area of ± 10 ha. Bamboo vegetation analysis was performed by calculating diversity index (H’), evenness index (E), and Species Richness index (R). Data were collected through observation and interviews with local people and the manager to determine zonation division. Mapping of bamboo vegetation based on zoning was processed into thematic maps using ArcGis 10.3. Micro climatic factors were measured with three replications for each sub-cluster. Data were analyzed descriptively and quantitatively. Nine species of bamboo identified. Diversity, evenness, and species richness indices differed at each location. Activities of local communities, tourists, and manager determined the presence, number, and distribution of bamboo species. These bamboo distribution maps in three zoning (utilization, buffer, and core) can be used by manager for planning and developing natural tourism potential.
Abstract
The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.
