• Korean Journal of Agricultural and Forest Meteorology
• /
• v.9 no.4
• /
• pp.247-259
• /
• 2007

In this study, we examined the flux of carbon and water using an eco-hydrological model, Regional Hydro-Ecologic Simulation System (RHESSys). Our purposes were to develop a set of parameters optimized for a well-designed experimental watershed (Gwangneung Research Watershed, GN) and then, to test suitability of the parameters for predicting carbon and water fluxes of other watershed with different regimes of climate, topography, and vegetation structure (i.e Gangseonry Watershed in Mt. Jumbong, GS). Field datasets of stream flow, soil water content (SWC), and wood biomass product (WBP) were utilized for model parameterization and validation. After laborious parameterization processes, RHESSys was validated with the field observations from the GN watershed. The parameter set identified at the GN watershed was then applied to the GS watershed in Mt. Jumbong, which resulted in good agreement for SWC but poor predictability for WBP. Our study showed that RHESSys simulated reliable SWC at the GS by adjusting site-specific porosity only. In contrast, vegetation productivity would require more rigorous site-specific parameterization and hence, further study is necessary to identify primary field ecophysiological variables for enhancing model parameterization and application to multiple watersheds.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.14 no.2
• /
• pp.89-97
• /
• 2012

This study was conducted to estimate forest site productivity and productive areas of Quercus acutissima and Quercus mongolica using environmental factors including climatic variables. Using the data set from digital forest site map and forest climatic map, a total of 42 environmental variables were regressed on site index for developing the best site index equations for Quercus acutissima and Quercus mongolica. Five to six environmental factors by species were selected as independent variables in the best site index equations. For the site index equations, three evaluation statistics (i.e., mean difference, standard deviation of difference, and standard error of difference) were applied to the test data set for the validation of the results, The site index equations fitted well to the test data set with relatively low bias and variation. As a result, it was concluded that the site index equations by species were well capable of estimating site quality. Finally, based on the site index equations, the productive areas by species were estimated by applying GIS technique to the digital forest maps. In addition, the distribution of productive areas by species was illustrated.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1047-1056
• /
• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.3
• /
• pp.83-98
• /
• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Korean Journal of Environment and Ecology
• /
• v.30 no.2
• /
• pp.173-184
• /
• 2016

Knowledge of the relationships among interspecific competition, spatial distributions and ecological niches plays an important role in understanding biogeographical distribution patterns of species. In this study, the distributional characteristics and ecological niches of the three Viperidae species (Gloydius ussuriensis, G. brevicaudus, and G. saxatilis) in South Korea were determined based on observation data and species distribution model. The effects of interspecific competition on geographical distribution and the division of the ecological niches of the vipers were also examined based on the models of predicted species distribution. The results showed that altitude was the most important environmental variable for their distribution, and the altitudes at which these snakes were distributed correlated with the climate of that region. Although interspecific ecological niches are quite overlapped, their predicted distribution patternsvary by the Taebaek Mountains. When overlaying the distribution models, most of the overlapping habitats were forest areas, which were relatively less overlapped than were the entire research areas. Thus, a parapatric distribution pattern was expected. The abundance of species occurring sympatrically was positively correlated with each other, indicating the lack of serious interspecies competition in this region. In conclusion, although the three Viperidae species in South Korea occupy similar ecological niches, these snakes exhibit parapatric distribution patterns without direct competition. Further research on various geographic variables (e.g., altitude, microhabitat characteristics) using relatively fine grid sizes, as well as further detailed ecological and behavioral research, is needed to determine the causative factors for the parapatric distribution pattern.