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- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
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 study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer’s habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.
Abstract
During our observations of changes in halophyte distribution in Hampyeong Bay over a period of five years, we found that the distribution area showed a maintenance for Phragmites communis community, a tendency of gradual increase for Zoysia sinica community, gradual decrease for Suaeda maritima community, and disappearance for Limonium tetragonum community during the studied period. The Phragmites communis community stably settled in areas adjacent to land and appeared not to be significantly affected by physical factors (such as tides and waves) or disturbances caused by biological factors (such as interspecific competition). Among studied species, germination time was shown to be the fastest for Suaeda maritima. In addition, this species showed certain characteristics that allowed it to settle primarily in new habitats formed by sand deposition as its growth was not halted under conditions with high amounts of sand and high organic matter content. However, in areas where Zoysia sinica and Suaeda maritima resided together, the area inhabited by Suaeda maritima gradually decreased due to interspecific competition between the two species. This was believed to be the result of a sharp decrease in the germination of Suaeda maritima since May, while the germination of Zoysia sinica was continuously maintained, indicating that the latter had an advantage in terms of seedling competition. In the case of the Limonium tetragonum community, its habitat was found to have been completely destroyed because it was covered by sand. The study area was confirmed to have undergone a large change in topography as tides and waves resulted in sand deposition onto these lands. Hampyeong Bay is considered to have experienced changes in halophyte distribution related to certain complex factors, such as changes in physical habitats and changes in biological factors such as interspecific competition.
Abstract
This study aimed to determine the applicability of drones and air quality sensors in environmental monitoring of air pollutant emissions by developing and testing two new methods. The first method used orthoimagery for precise monitoring of pollutant-emitting facilities. The second method used atmospheric sensors for monitoring air pollutants in emissions. Results showed that ground sample distance could be established within 5 cm during the creation of orthoimagery for monitoring emissions, which allowed for detailed examination of facilities with naked eyes. For air quality monitoring, drones were flown on a fixed course and measured the air quality in point units, thus enabling mapping of air quality through spatial analysis. Sensors that could measure various substances were used during this process. Data on particulate matter were compared with data from the National Air Pollution Measurement Network to determine its future potential to leverage. However, technical development and applications for environmental monitoring of pollution-emitting facilities are still in their early stages. They could be limited by meteorological conditions and sensitivity of the sensor technology. This research is expected to provide guidelines for environmental monitoring of pollutant-emitting facilities using drones.
