• Journal of Soil and Groundwater Environment
• /
• v.9 no.3
• /
• pp.56-61
• /
• 2004

The topographic variables, which influence the precipitation phenomena, are classified by elevation (ELEV), slope (SLOPE), distance to sea (SEA), obstruction (OBST), barrier (BAR), roughness (SHIELD), extracted and analysed according to resolutions. This study is performed through 100 m, 200 m, 400 m, 600 m, 800 m and 1,000 m based on 50 m DTM using TOVA (Topographic Variables Extraction Program). The result of a case study on Jeju weather station says that the variance according to resolution is generally less than that according to cardinal direction, but particularly SHIELD values and some cases for 600m resolution have a significant results.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2004.09a
• /
• pp.147-150
• /
• 2004

Firstly, the precipitation data have to be interpolated for the estimation of water resources. For this purpose, the correlative analysis is made between the topographic variables, which, influence the precipitation phenomena, are classified by elevation(ELEV), slope(SLOPE), distance to the sea(SEA), obstacle (OBST), barrier(BAR), and roughness index(SHIELD), using TOVA(Topographic Variables Extraction Program) and events precipitation during the periods from january the 1st 2000 to December 31 2002. The coefficients of determination show that each event has different topographic influence and ELEV, SLOPE and OBST to the South-West, and SHIELD of every direction have close relationship with the precipitation. The multiple regression model explains 96% of the spatial variation of precipitation.

• Atmosphere
• /
• v.27 no.4
• /
• pp.385-398
• /
• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Journal of The Geomorphological Association of Korea
• /
• v.17 no.1
• /
• pp.73-84
• /
• 2010

This paper is to analyze effectiveness and efficiency of topographic features in visibility analysis. For this research aim, this paper compares the analysis results of topographic features and relationships between topographic features and their visibility analysis on surfaces. This paper employs peak, pass, pit, ridge and valley features from the topographic features for which five areas including mountain and plain areas in Britain are selected and their DEM data are generated. The summaries of the analysis results are as follows: Firstly, it is clear that relationship between high elevation points and their visibility is not highly correlated. This means that highly elevated points are not necessarily better visible areas and they are not suitable for searching for large visible areas. Secondly, the positions that can see large visible areas are highly correlated with their elevation and are distributed within a certain range which has small deviation of their correlation between visibility and elevation. This means that to search for large visible areas, it is necessary to employ the positions located at relatively high elevation area. Thirdly, for all of the five areas, the visibility results of the topographic features are compared with maximal visibility resulted from a while surface areas, and it is identified that topographic features show similar visibility performances of that maximal visibility. From the results stated above, it can be inferred that topographic features and its topographic characteristics are enable to be a research motivation to the visibility analysis topics. Furthermore, the results of this paper can be contributed to explore suitable variables and factors for solving multiple viewshed problems.

• Journal of Forest and Environmental Science
• /
• v.31 no.3
• /
• pp.177-191
• /
• 2015

The effect of environmental variables on the presence and abundance of tree species in a tropical lowland undisturbed limestone and non-limestone forest in Ben En National Park, Vietnam was investigated. The relationships between 13 environmental variables and 29 tree species with a DBH ${\geq}10cm$, as well as between six 6 physical variables with 26 species of seedling and sapling communities were assessed by canonical correspondence analysis (CCA). Data concerning all tree species ${\geq}10cm$ DBH were collected from eighteen $400m^2$ sample plots, while the abundance of regeneration (all individuals ${\leq}5cm$ DBH) was counted in fifty $2{\times}20m$ strip-plots. The significance of species-environments correlations were tested by distribution-free Monte Carlo tests. The CCA of the 29 examined tree species and 13 environmental variables indicated that the presence and abundance of the tree species were closely related to topographic factors. We may confirm that soil properties including pH, soil moisture content, and soil textures, were the most crucial factor in tree species composition and their distribution. Several species including Pometia pinnata, Amesiodendron chinense, Gironniera cuspidate, Cinnamomum mairei, and Caryodaphnopsis tonkinensis were not controlled by soil properties and topographic variables. The CCA also indicated that the abundance of regeneration tree species at all sites had positive and significant correlations with soil depth, while the occurrence of several other tree species (such as Koilodepas longifolium and Aglaia dasyclada) was positively correlated with a higher slope and rocky outcrop.

• Korean Journal of Ecology and Environment
• /
• v.46 no.1
• /
• pp.53-59
• /
• 2013

The purpose of this study was to analyze the topographic effect of the LAI (Leaf Area Index), which has been widely used as an index that quantifies the structure of forest vegetation in Deogyusan National Park. With this aim, the study was conducted through a regression analysis which took as explanation the following variables: the elevation, slope, aspect, and soil moisture conditions. The LAI was taken as the response variable. Overall, the correlation between the Field-LAI and topographic factors was less than 0.5, which was relatively low. Except for topographic altitude, there was no statistical significance regarding the correlation with other factors. Meanwhile, regarding the orientation of the correlation, the higher the attitude, the steeper slope, the lower the soil moist, the lower the LAI value. The topographic altitude was found as a statistically significant explanation variable. The TWI (Topographic Wetness Index), which was used in this study to explain the soil moisture conditions, was not significantly related to the LAI distribution. The results of this study are expected to be utilized as basic data in more accurate forecasting the LAI distribution using remote sensing data.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.9
• /
• pp.849-854
• /
• 2001

GTM(Generative Topographic Mapping) model is a probabilistic version of the SOM(Self Organizing Maps) which was proposed by T. Kohonen. The GTM is modelled by latent or hidden variables of probability distribution of data. It is a unique characteristic not implemented in SOM model, and, therefore, it is possible with GTM to analyze data accurately, thereby overcoming the limits of SOM. In the present investigation we proposed a BGTM(Bayesian GTM) combined with Bayesian learning and GTM model that has a small mis-classification ratio. By combining fast calculation ability and probabilistic distribution of data of GTM with correct reasoning based on Bayesian model, the BGTM model provided improved results, compared with existing models.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.504-507
• /
• 2006

Airborn Lidar technology has been applied to diverse applications with the advantages of accurate 3D information. Further, Lidar intensity, backscattered signal power, can provid us additional information regarding target's characteristics. Lidar intensity varies by the target reflectance, moisture condition, range, and viewing geometry. This study purposes to generate normalized airborne LiDAR intensity image considering those influential factors such as reflectance, range and geometric/topographic factors (scan angle, ground height, aspect, slope, local incidence angle: LIA). Laser points from one flight line were extracted to simplify the geometric conditions. Laser intensities of sample plots, selected by using a set of reference data and ground survey, werethen statistically analyzed with independent variables. Target reflectance, range between sensor and target, and surface slope were main factors to influence the laser intensity. Intensity of laser points was initially normalized by removing range effect only. However, microsite topographic factor, such as slope angle, was not normalized due to difficulty of automatic calculation.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.435-440
• /
• 2002

The objective of this study is to analyze the relationship between polarimetric radar backscatters and stand characteristics over the mountainous forest area. L- and P-band full polarimetric airborne SAR data obtained in September 2000 were processed to compare with forest stand maps and ground collected stand variables. After the geometric registration of SAR image, mean radar backscatters were extracted for those ground plots where the stand parameters, such as tree height, DBH, and basal area, were measured during and after the SAR data acquisition. Preliminary analysis was focused on the topographic influence of radar backscattering under the homogeneous forest stand condition. Topographic effects, assessed by the local incidence angles, were different obvious in L-band data while it was not clear with P-band data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.142-142
• /
• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.