• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.170-178
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• 2012

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Korean Journal of Medicinal Crop Science
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• v.28 no.5
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• pp.354-359
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• 2020

Background: The recent, decline in Black-berry Magnolia Vine (Schisandra repanda; BMV) native to Jeju Island, Korea, has raised concerns about the causes of this decline. We investigated the native environment of S. repanda and evaluated its propagation through cuttings to provide preliminary data for its restoration in Jeju Island. Methods and Results: The native environment of the BMV in the Hallasan National Park was surveyed and the climatic variables (temperature and humidity) were analyzed. The effects of the sex of the donor plant (male/female) type of cutting (softwood/hardwood), and treatment with a plant growth regulator (indole-3-butylic acid/rootone) on the rooting of BMV were investigated. Additinallly, the rooting rate, root count, and root length, as well as temperature and humidity were measured in the vinyl moist chambers. BMV was observed in 63 plants distributed from 567 m to 1,364 m above sea level of the Hallasan National Park. In the cutting experiment, the rooting rate was 71.9%, and it was higer in female plants (75.0%) than in male plants (68.8%). Conclusions: The mass propagation of BMV through cuttings valuable for its restoration as without such safeguard measures, the population could face extinction within a few decades.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.86-92
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• 1999

Evapotranspiration is one of important variables affecting ecosystem processes such as vegetation distribution and growth. It acts as a limiting factor for natural water resource management. The transpiration of vegetation is mainly determined by climatic factors. The lower slope of the study area was densely forested with Pinus densiflora S. et Z. of 8 m height, and the upper slope was covered with poorly grown Pinus densiflora S. et Z. and Quercus trees. The amount of evapotranspiration was estimated to 590.3 mm/yr by annual water budget method. The canopy resistance of Penman-Monteith model was determined as 99 s/m. Seasonal evapotranspiration can be estimated with the calculated evaporation and the canopy resistance. The amount of evapotranspiration peaked in May. That is considered from both the direct evaporation of intercepted rainfall and the transpiration of vegetation during the dry spring season.

• IE interfaces
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• v.18 no.2
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• pp.158-166
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• 2005

Human-error and mental stress caused by psychophysiological dissonance between people and artificial environments have become a social problem. And it is a common knowledge that comfort environment reduces human-error and mental stress. Comfort sensibility is related to complex interactions between fabric, climatic, physiological and psychological variables. Currently, comfort sensibility has been evaluated by many sensory tests. However, it is difficult to evaluate comfort sensibility because a concrete concept of comfort sensibility is hard to define. In this paper, we propose a model to evaluate the comfort sensibility using Fuzzy-weighted score on an individual's subjective state for the stimulus. To represent the degree of comfort sensibility level for the stimulus, we represent comfort sensibility using 2 dimensional sensibility vector model. And we use the fuzzy-weighted score that is a fuzzy version of the weighted checklist technique computerized for evaluating the subjects. As an example, this model is applied to 1/f fluctuation sound evaluation. The results show that this model can be effectively used to the quantitative evaluation of comfort sensibility for the stimulus.

• The Korean Journal of Quaternary Research
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• v.17 no.1
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• pp.17-26
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• 2003

A hierarchy is defined as a system wherein its parts are interconnected with one another and the lower levels are constrained by the higher levels to various degrees, depending on the time constraints of the behavior. We illustrate that the concept of hierarchy is identified in the Baekdoodaegan frame, and that the Korean national area was hierarchically divided into many watersheds in many old maps. The Baekdoodaegan and Jeongmack were consistent with the hierarchy of climatic as well as spatial variables. It is evident from an old map that the concept was coined as early as in 1402. A typical configuration of Korean landscape, which was developed in the arrangement of village and surrounding landscape elements situated in a watershed, may be associated with the hierarchical approach to land and culture. Although current practices based on the concept are meaningful, further diverse studies are needed to improve ideas.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.1-11
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• 2004

Based on surface energy balance climatology, surface temperatures should respond to drying conditions well before plant response. To test this hypothesis, land surface temperatures (LST) derived from MODIS data were analyzed to determine how the data were correlated with climatic water balance variables and NDVI anomalies during a growing season in Western and Central Kansas. Daily MODIS data were integrated into weekly composites so that each composite data set included the maximum temperature recorded at each pixel during each composite period. Time-integrated, or cumulative values of the LST deviation standardized with mean air temperatures had significantly high correlation coefficients with SM, AE/PE, and MD/PE, ranging from 0.65 to 0.89. The Standardized Thermal Index (STI) is proposed in this study to accomplish the objective. The STI, based on surface temperatures standardized with observed mean air temperatures, had significant temporal relationships with the hydroclimatological factors. STI classes in all the composite periods also had a strong correlation with NDVI declines during a drought episode. Results showed that, based on LST, air temperature observations, and water budget analysis, NDVI declines below normal could be predicted as early as 8 weeks in advance in this study area.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.167-167
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• 2023

Soil organic carbon (SOC) is a critical component of soil health and is crucial in mitigating climate change by sequestering carbon from the atmosphere. Accurate estimation of SOC storage is essential for understanding SOC dynamics and developing effective soil management strategies. This study aimed to investigate the factors influencing the spatial distribution of SOC storage in South Korea, using bulk density (BD) prediction to estimate SOC stock. The study utilized data from 393 soil series collected from various land uses across South Korea established by Korea Rural Development Administration from 1968-1999. The samples were analyzed for soil properties such as soil texture, pH, and BD, and SOC stock was estimated using a predictive model based on BD. The average SOC stock in South Korea at 30 cm topsoil was 49.1 Mg/ha. The study results revealed that soil texture and land use were the most significant factors influencing the spatial distribution of SOC storage in South Korea. Forested areas had significantly higher SOC storage than other land use types. Climate variables such as temperature and precipitation had a relative influence on SOC storage. The findings of this study provide valuable insights into the factors influencing the spatial distribution of SOC storage in South Korea.

• Journal of Forest and Environmental Science
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• v.40 no.2
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• pp.111-117
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• 2024

An evaluation was carried out on variability in morphology of fruits and seeds (number and weight) of Tetrapleura tetraptera (Schumach. and Thonn.) Taub. from different populations across its distribution range in Nigeria. Bulk fruit samples were collected and examined for variations in morphological characters. Differences in morphological character of fruits and seeds among the populations were determined using analysis of variance at 5% level of probability. The relationships among morphological characters were determined using Pearson correlation coefficient (r). Significant variations (p<0.05) existed among T. tetraptera populations for all the evaluated characters: fruit length, fruit width, number of seeds per fruit and seed weight. A positive significant strong correlation (r=0.96) was found between seed weight and number of seeds per fruit, while no correlation existed between fruit length, width and number of seeds. Seed weight was positively correlated with minimum altitude (r=0.97) and maximum altitude (r=0.99) of seed populations. Number of seeds was also significantly correlated with maximum altitude (r=0.965). There was no significant correlation between geo-climatic variables and fruit dimensions (length and width). Observed variations in morphological traits within and across populations of T. tetraptera may be used as proxy to estimate genetic diversity and selection of superior trees for improved productivity.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.2
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• pp.142-149
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• 1995

This study was conducted to investigate the relationships between yearly variation of climatic components and yearly variations of productivity in monoculture cotton. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components from the four varieties(Kinggus, Yongdang local. 113-4, 380) were collected from 1978 to 1992 in Mokpo area. The meteorological data gathered at the Mokpo Weather Station for the same period were used to find out the relationships between climatic components and productivity. Yearly variation of the amount of precipitation and number of stormy days in July are large with coefficients of the variations(C.V)84.89 and 97.05%, respectively, while yearly variation, of the average temperature, maximum temperature, minimum temperature from May to Sep. are relatively small. Seed cotton yield before frost in Sep. and Oct. very greatly with C.V. of 68.77, 78.52%, respectively. Number of boll bearing branches and lint percentage show more or less small in C.V. with 11.77 and 19.13%, respectively and flowering date and boll opening date show still less variation. Correlation coefficients between precipitation in May and number of boll bearing branches, duration of sunshine in July and number of bolls per plant, maximum temperature in July and total seed cotton before the frost in Sep., Oct., and Nov. evaporation in Aug. are positively sig-nificant at the 1% level. There are highly significantly positive correlated relationships among yield(total seed cotton) and yield components. Total seed cotton yield(Y) can be predicted by multiple regression equation with independent variables of climatic factors in July such as monthly averages of average temperature($X_1$), maximum temperature($X_2$) and minimum temperature($X_3$), monthly amount of precipitation ($X_4$), evaporation($X_5$), monthly average of relative humidity($X_6$), monthly hours with sunshine($X_7$) and number of rainy days($X_8$). The equation is estimatedas Y =-1080.8515 + 144.7133$X_1$+15.8722$X_2$ + 164.9367$X_3$ + 0.0802$X_4$ + 0.5932$X_5$ + 11.3373$X_6$ + 3.4683$X_7$- 9.0846$X_8$. Also, total seed cotton yield(Y) can be predicted by the same method with climatic components in Aug., Y =2835.2497 + 57.9134$X_1$ - 46.9055$X_2$ - 41.5886X$_3$ + 1.2559$X_5$ - 21.9687$X_6$ - 3.3763$X_7$- 4.1080$X_8$- 17.5586$X_9$. And the error between observed and theoretical yield were less with approached linear regression.