• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.465-475
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• 2001

Based on the community structural attributes, such as species composition, diameter and height distribution, topographic position, and species diversity in the natural deciduous forest of Mt. Gari area, this study suggested desirable species and composition ratio to achieve ecological management of forests so as to maintain forest stability and enhance economical values. The results are as follows : 1. Twenty-five tree species were growing in the study forest. Of these Quercus mongolica, Pinus densiflora, Juglans mandshurica, Quercus serrata, Cornus controversa, Acer mono, Fraxinus rhynchophylla, and Tilia mandshurica were selected for desirable species through the evaluation of dominant and dominant potential. Kalopanax pictus, considered to be highly valuable species, was also included. 2. Taking account of different species composition pattern by topographic positions, we select as desirable species of J. mandshurica, C. controversa, Q. mongolica, A. mono, T. mandshurica, and F. rhynchophylla in the valley area, Q. mongolica, Q. serrata, A. mono, T. mandshurica, F. rhynchophylla, and K. pictus in the mid-slope area, and Q. mongolica, P. densiflora, Q. serrata, and Fraxinus rhynchophylla in the ridge area. 3. Based on the estimation of species diversity index for the overstory components, the reasonable forest stability levels of the indices were estimated at 1.96, 1.68, 1.94, and 1.27 for whole forest, valley, midslope, and ridge, respectively. 4. The recommended species composition ratios in the study forest were suggested Q. mongolica to be 30%, A. mono, F. rhynchophylla, Q. serrata, and T. mandshurica to be 10%~15%, J. mandshurica, P. densiflora, and C. controversa to be 5%~10%, and K. pictus to be 5%.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.2
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• pp.87-92
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• 2002

Soil erosion is detrimental to sustain soil productivity in north Korea, since agriculture of this country depends largely upon the slope land in mountainous area. Taking any measure for protection from erosion should be based on prediction of soil loss. Estimation of rainfall factor, R, in north Korea for the Universal Soil Loss Equation was attempted. The monthly precipitation data of the twenty six locations provided by the Korean Meteorological Adminstration were used. From the relationship between II_30 and the July-August precipitation concentration percents, the regional adjustment factor was obtained. The rainfall factor was calculated with the monthly precipitation data and the regional adjustment factor. The annual precipitation in north Korea ranged from 606 to 1,520mm, and the July-August precipitation concentration percents were 34.4 to 53.8. The regional adjustment factor ranged from 0.53 to 1.33 showing lower value in the highland and east coastal region than in the mid mountainous inland and west region. The R-factor value estimated from the monthly precipitation and the regional adjustment factor ranged from 107 to 483, which was lower than average value in south Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.3
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• pp.195-202
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• 2007

The normalized difference in incident solar energy between a target surface and a level surface (overheating index, OHI) is useful in eliminating estimation error of site-specific maximum temperature in complex terrain. Due to the complexity in its calculation, however, an empirical proxy variable called northern exposure index (NEI) which combines slope and aspect has been used to estimate OHI based on empirical relationships between the two. An experiment with real-world landscape and temperature data was carried out to evaluate performance of the NEI - derived OHI (N-OHI) in reduction of spatial interpolation error for daily maximum temperature compared with that by the original OHI. We collected daily maximum temperature data from 7 sites in a mountainous watershed with a $149 km^2$ area and a 795m elevation range ($651{\sim}1,445m$) in Pyongchang, Kangwon province. Northern exposure index was calculated for the entire 166,050 grid cells constituting the watershed based on a 30-m digital elevation model. Daily OHI was calculated for the same watershed ana regressed to the variation of NEI. The regression equations were used to estimate N-OHI for 15th of each month. Deviations in daily maximum temperature at 7 sites from those measured at the nearby synoptic station were calculated from June 2006 to February 2007 and regressed to the N-OHI. The same procedure was repeated with the original OHI values. The ratio sum of square errors contributable by the N-OHI were 0.46 (winter), 0.24 (fall), and 0.01 (summer), while those by the original OHI were 0.52, 0.37 and 0.15, respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.48-55
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• 2011

Solar radiation components reflected by the sea surface ($R_{ss}\uparrow$) are additional energy sources comprising the solar radiation regime. Previous studies, based on observational approaches, indicated that $R_{ss}\uparrow$ is an available climatological resource. However, an estimation process for $R_{ss}\uparrow$ has not been established. In this case study over Jeju Island in South Korea, we applied a new estimation process to solar radiation modeling and discussed the spatial distribution of $R_{ss}\uparrow$ and its seasonal variation. Our results showed that the illuminated area and the intensity of $R_{ss}\uparrow$ became greatest at the winter solstice and least at the summer solstice. We estimated the illuminated area of $R_{ss}\uparrow$ as it expanded over the southern slope of Jeju Island. At the winter solstice, on a daily basis, the area and intensity of illumination by $R_{ss}\uparrow$ were $182.3km^2$ and $0.41\;MJ\;m^{-2}\;day\;{-1}$, respectively. Comparing the daily accumulative and instantaneous values of $R_{ss}\uparrow$ intensity, the difference was about 20 times greater in daily cases than in instantaneous cases. On the other hand, for instantaneous values, the $R_{ss}\uparrow$ intensity accounted for up to 33% of the three components, i.e., direct, diffuse and reflected radiation in winter solstice. In addition, it was estimated that the sea surface reflectance depended on the wind speed. Therefore, in a practical use of this revised model, wind conditions should be considered as a critical factor in estimating $R_{ss}\uparrow$.

• The Korean Journal of Ecology
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• v.27 no.2
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• pp.99-106
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• 2004

We developed the estimation model for the vegetation developmental processes on the severely burned slope areas after forest fire in the east coastal region, Korea. And we calculated the vegetation indices as a useful parameter for the development of land management technique in the burned area and suggested the changes of the vegetation indices after forest fire. In order to estimate the woody standing biomass in the burned area, allometric equations of the 17 woody species regenerated by sprouter were investigated. According to the our results, twenty year after forest fire need for the development to the normal forest formed by 4 stratum structure, tree, sub-tree, shrub and herb layer. The height of top vegetation layer, basal area and standing biomass of woody species show a tendency to increase linearly, and the ground vegetation coverage and litter layer show a tendency to increase logarithmically after forest fire. Among vegetation indices, Ive and Ivcd show a tendency to increase logarithmically, and Hcl and Hcdl show a tendency to increase linearly after forest fire. The spatial variation of the most vegetation factors was observed in the developmental stages less than the first 5 years which were estimated secondary disaster by soil erosion after forest fire. Among vegetation indices, Ivc and Ivcd were the good indices for the representation of the spatial heterogeneity in the earlier developmental stages, and Hcl and Hcdl were the useful indices for the long-term estimation of the vegetation development after forest fire.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1039-1049
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• 2015

The inflow estimation at large multipurpose dam reservoir is carried out by considering the water balance among the discharge, the storage change during unit time interval obtained from the observed water level near dam structure and area-volume curve. This method can be ideal for level pool reservoir but include potential errors when the inflow is influenced by the water level slope due to backwater effects from upstream flood inflows and strong wind induced by typhoon. In addition, the other uncertainties arisen from the storage reduction due to sedimentation after the dam construction and water level noise due to mechanical vibration transmitted from the electric power generator. These uncertainties impedes the accurate hydraulic inflow measurement requiring exquisite hydrometric data arrangement for reservoir waterbody. In this study, the distributed hydrologic model using UBC-3P boundary setting was applied and its feasibility was evaluated. Finally, the modeling performance has been verified since the calculated determination coefficient has been in between 0.96 to 0.99 after comparing with observed peak inflow and total inflow at Namgang dam reservoir.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.93-103
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• 2020

In this study, we estimated soil moisture values using the Deep Neural Network(DNN) scheme at the mountainous regions. In order to test the sensitive analysis of DNN scheme, we collected the measured(at the soil depths of 10 cm and 30 cm) soil moisture and DNN input(weather and land surface) data at the Pyeongchang-gun(relatively flat) and Geochang-gun(steep slope) sites. Our findings indicated that the soil moisture estimates were sensitive to the weather variables(5 days-averaged rainfall, 5 days precedent rainfall, accumlated rainfall) and DEM. These findings showed that the DEM and weather variables play the key role in the processes of soil water flow at the mountainous regions. We estimated the soil moisture values at the soil depths of 10 cm and 30 cm using DNN at two study sites under different climate-landsurface conditions. The estimated soil moisture(R: 0.890 and RMSE: 0.041) values at the soil depth of 10 cm were comparable with the measured data in Pyeongchang-gun site while the soil moisture estimates(R: 0.843 and RMSE: 0.048) at the soil depth of 30 cm were relatively biased. The DNN-based soil moisture values(R: 0.997/0.995 and RMSE: 0.014/0.006) at the soil depth of 10 cm/30 cm matched well with the measured data in Geochang-gun site. Although uncertainties exist in the results, our findings indicated that the DNN-based soil moisture estimation scheme demonstrated the good performance in estimating soil moisture values using weather and land surface information at the monitoring sites. Our proposed scheme can be useful for efficient land surface management in various areas such as agriculture, forest hydrology, etc.

• Survey Research
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• v.9 no.2
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• pp.1-27
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• 2008

Problems of incomplete data are pervasive in statistical analysis. In particular, incomplete data have been an important challenge in repeated measures studies. The objective of this study is to give a brief introduction to missing data mechanisms and conventional/recent missing data methods and to assess the performance of various missing data methods under ignorable and non-ignorable missingness mechanisms. Given the inadequate attention to longitudinal studies with missing data, this study applied recent advances in missing data methods to repeated measures models and investigated the performance of various missing data methods, such as FIML (Full Information Maximum Likelihood Estimation) and MICE(Multivariate Imputation by Chained Equations), under MCAR, MAR, and MNAR mechanisms. Overall, the results showed that listwise deletion and mean imputation performed poorly compared to other recommended missing data procedures. The better performance of EM, FIML, and MICE was more noticeable under MAR compared to MCAR. With the non-ignorable missing data, this study showed that missing data methods did not perform well. In particular, this problem was noticeable in slope-related estimates. Therefore, this study suggests that if missing data are suspected to be non-ignorable, developmental research may underestimate true rates of change over the life course. This study also suggests that bias from non-ignorable missing data can be substantially reduced by considering rich information from variables related to missingness.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.