• Journal of Korean Society of Forest Science
• /
• v.94 no.5 s.162
• /
• pp.313-320
• /
• 2005

This study was performed in a 38-year-old Mongolian pine (Pinus sylvestris L. var. mongolica Litvin) plantation in northeast China. Data were collected from 5 sample trees with different canopy position ranging in DBH from 14.6 cm to 23.8 cm. Sawn speciments that included the biggest knot were taken from the stem below the living crown. Number and distribution of knots per whorl below the living crown were studied by relative height below living crown (RHBC). A linear model expressed as function of whorl age (AGE), whorl height ($H_k$) and the stem diameter at which the whorl was located ($D_k$) was developed to predict the knot diameter and angle. The number of annual rings in four periods and the width of respective zone alone stem were used as dependant variables to analyze the knot develop phases. In average, the number of years from branch birth to ceased forming rings was 7.8, the branches remained alive for 4.2 years without forming annual rings, and branches were occluded 14.4 years after their death. These results can provide abundance branch and knot information so as to describe current and past tree growth dynamic of Mongolian pine plantation.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.5 no.1
• /
• pp.11-18
• /
• 1993

A key element of a successful search and rescue is the correct prediction of the target location. In this paper, new computer models for drift prediction are suggested from the analysis of several methods currently used in other countries. Depending on the availability of the environmental data, users may select a model between the modified versions of U.S. Coast Guard CASP and FLENUMWEACEN SAR. Targets include boats, life rafts and person in water. Life rafts and boats are further classifed. New models are tested and compared with the limited number of field experimental results.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.4
• /
• pp.70-79
• /
• 2013

These day cities experience serious climatic changes due to environmental load caused by disturbance in the circulation systems of water resources and energy. As technological improvement to respond to various climatic changes and disasters are also requested in the field of construction, inter-disciplinary studies linked to the establishment of sustainable environmental control and energy systems is required in a consilient perspective. This study aims to infer correlations in the impact of environmental changes caused by rooftop greening system on the photovoltaic power generation efficiency through computer simulation in an integrated perspective. By doing so, it seeks to provide basic study for developing a photovoltaic system integrated with building revegetation that is sustainable in environmental and resource aspects. A simulation showed that, in the case of sunshine hours in June, the green surface indicated temperature lowering effects of $9.19^{\circ}C$ on average compared to the non-green surface and temperature was $9.81^{\circ}C$ lower. Due to such greening effects, at the highest sunlight timepoint in June, Pmpp improved 119W and heat loss rate dropped 7.8%.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.2
• /
• pp.228-232
• /
• 2018

Process-based crop models have been used to assess the impact of climate change on crop production. These models are implemented in procedural or object oriented computer programming languages including FORTRAN, C++, Delphi, Java, which have a stiff learning curve. The requirement for a high level of computer programming is one of barriers for efforts to develop and improve crop models based on biophysical process. In this study, we attempted to develop a Chinese cabbage model using Microsoft Excel with Visual Basic for Application (VBA), which would be easy enough for most agricultural scientists to develop a simple model for crop growth simulation. Results from Soil-Plant-Atmosphere-Research (SPAR) experiments under six temperature conditions were used to determine parameters of the Chinese cabbage model. During a plant growing season in SPAR chambers, numbers of leaves, leaf areas, growth rate of plants were measured six times. Leaf photosynthesis was also measured using LI-6400 Potable Photosynthesis System. Farquhar, von Caemmerer, and Berry (FvCB) model was used to simulate a leaf-level photosynthesis process. A sun/shade model was used to scale up to canopy-level photosynthesis. An Excel add-in, which is a small VBA program to assist crop modeling, was used to implement a Chinese cabbage model under the environment of Excel organizing all of equations into a single set of crop model. The model was able to simulate hourly changes in photosynthesis, growth rate, and other physiological variables using meteorological input data. Estimates and measurements of dry weight obtained from six SPAR chambers were linearly related ($R^2=0.985$). This result indicated that the Excel/VBA can be widely used for many crop scientists to develop crop models.

• Korean Journal of Environment and Ecology
• /
• v.26 no.5
• /
• pp.801-811
• /
• 2012

The purpose of this study is to find out how land cover and planting of an urban park influence temperature. Field research on the land cover and planting status was conducted for Bundang Central Park in Sungnam-si. 30 study plots in the site were selected to closely analyze land cover type and planting structure. The temperature was measured 10 times for each plot. Land coverage type, planting type, planting layer structure and green space area (the ratio of green coverage, GVZ) were chosen as factors impacting temperature and statistics were analyzed for the actual temperature measured. Analysis on how the land coverage type influences temperature showed that planting site had a low temperature and that grassland and paved land had a high temperature. When it comes to planting type, the temperature at the land planted with conifers and broad-leaved trees was low, while the temperature at grassland and paved land was high. With regard to planting layer structure, canopy and canopy-underplanting type showed low temperature, while grassland and paved land showed high temperature. An analysis on the relation between green space area and temperature found out that both ratio of green coverage and GVZ had a high level of negative correlation with the temperature measured. According to regression model of green space area and the temperature measured, for every 1% increase in the ratio of green coverage, temperature is expected to lower by $0.002^{\circ}C$. Also, for every $1m^3/m^2$ increase in GVZ, temperature is expected to go down by $0.122^{\circ}C$.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_4
• /
• pp.1327-1339
• /
• 2019

Surface reflectance obtained by absolute atmospheric correction from satellite images is useful for scientific land applications and analysis ready data (ARD). For Landsat and Sentinel-2 images, many types of radiometric processing methods have been developed, and these images are supported by most commercial and open-source software. However, in the case of KOMPSAT 3/3A images, there are currently no tools or open source resources for obtaining the reflectance at the top-of-atmosphere (TOA) and top-of-canopy (TOC). In this study, the atmospheric correction module of KOMPSAT 3/3A images is newly implemented to the optical calibration algorithm supported in the Orfeo ToolBox (OTB), a remote sensing open-source tool. This module contains the sensor model and spectral response data of KOMPSAT 3A. Aerosol measurement properties, such as AERONET data, can be used to generate TOC reflectance image. Using this module, an experiment was conducted, and the reflection products for TOA and TOC with and without AERONET data were obtained. This approach can be used for building the ARD database for surface reflection by absolute atmospheric correction derived from KOMPSAT 3/3A satellite images.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.15 no.2
• /
• pp.77-84
• /
• 2012

This study suggested the planting model of Zelkova serrata communities in the areas with the warmth index of both 80~100 and $100{\sim}120^{\circ}C{\cdot}month$. Warmth index was calculated with 449 weather points using inverse distance weighted interpolation method. The planting species were selected by correlation analysis between Z. serrata and each species of four or more frequency among the 36 relev$\acute{e}$ surveyed for this study. The result of this study is summarized as follows : Warmth index of Z. serrata communities was among $74{\sim}118^{\circ}C{\cdot}month$. Results of the correlation analysis between Z. serrata and each species observed that the Z. serrata belongs to the tree layer with warmth index of 80~100 and $100{\sim}120^{\circ}C{\cdot}month$. On the other hand, the species of Carpinus laxiflora, Quercus serrata, Prunus sargentii and Platycarya strobilacea appeared only in the tree layer with warmth index of $80{\sim}100^{\circ}C{\cdot}month$. Z. serrata and Styrax japonica appeared in the subtree layer with the warmth index of 80~100 and $100{\sim}120^{\circ}C{\cdot}month$, while Acer pseudosieboldianum, Lindera erythrocarpa, Acer mono, Quercus serrata, etc. appeared in the subtree layer with the warmth index of $80{\sim}100^{\circ}C{\cdot}month$. Z. serrata, Ligustrum obtusifolium, Lindera obtusiloba, Callicarpa japonica and Zanthoxylum schinifolium all appeared in the shrub layer with the warmth index of 80~100 and $100{\sim}120^{\circ}C{\cdot}month$. Lindera erythrocarpa, Orixa japonica, Staphylea bumalda, Akebia quinata and Sorbus alnifolia appeared in the shrub layer with the warmth index of $80{\sim}100^{\circ}C{\cdot}month$ and Styrax japonica and Stephanandra incisa appeared in the shrub layer with the warmth index of $100{\sim}120^{\circ}C{\cdot}month$, The numbers of each species planted in a $100m^2$ area of the Z. serrata community were suggested as follows : five in tree layer, five in subtree layer and nine in shrub layer. The average area of canopy are suggested to be about $86m^2$ for tree layer, $34m^2$ for subtree layer and $34m^2$ for shrub layer.

• Journal of Bio-Environment Control
• /
• v.25 no.3
• /
• pp.146-152
• /
• 2016

Due to complicate canopy structures of Irwin mangoes grown in greenhouses, it is difficult to determine their growth parameters accurately. Leaf area, leaf fresh weight, and leaf dry weight are widely used as indicators to diagnose the tree growth. Therefore, it is necessary to establish models that can non-destructively estimate these growth indicators. The objective of this study was to establish regression models to estimate leaf area, leaf fresh weight, and leaf dry weight of Irwin mangoes (Mangifera indica L. cv. Irwin) by using leaf length, leaf width, petiole length, and SPAD value. The input values of leaf length, leaf width, petiole length, and SPAD value of 6-year old Irwin mangoes were measured, and the corresponding output values of leaf area, leaf fresh weight, and leaf dry weight were also measured. After 14 models were selected among the existing models, coefficients of the models were estimated by regression analysis. Three models with higher $R^2$ and lower RMSE values selected. In validation the $R^2$ values for the selected models were 0.967, 0.743, and 0.567 in the leaf area, leaf fresh weight, and leaf dry weight models, respectively. It is concluded that this models will be helpful to conveniently diagnose the growth of the Irwin mango.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.459-469
• /
• 2010

This study is to develop a grid-based daily runoff model considering seasonal vegetation canopy condition. The model simulates the temporal and spatial variation of runoff components (surface, interflow, and baseflow), evapotranspiration (ET) and soil moisture contents of each grid element. The model is composed of three main modules of runoff, ET, and soil moisture. The total runoff was simulated by using soil water storage capacity of the day, and was allocated by introducing recession curves of each runoff component. The ET was calculated by Penman-Monteith method considering MODIS leaf area index (LAI). The daily soil moisture was routed by soil water balance equation. The model was evaluated for 930 $km^2$ Yongdam watershed. The model uses 1 km spatial data on landuse, soil, boundary, MODIS LAI. The daily weather data was built using IDW method (2000-2008). Model calibration was carried out to compare with the observed streamflow at the watershed outlet. The Nash-Sutcliffe model efficiency was 0.78~0.93. The watershed soil moisture was sensitive to precipitation and soil texture, consequently affected the streamflow, and the evapotranspiration responded to landuse type.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.138-147
• /
• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.