• Journal of Korean Society of Forest Science
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• v.1 no.1
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• pp.22-29
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• 1962

(1) The objects of this study are to observe the possibility of regenerating a young merchantable pitch pine stand by sprouts and to compare the growth trend of sprouts with that of seedlings of same age grown under the almost same circumstances. (2) A plot of 20 year old pitch pine plantation, i.e. 200 trees on 0.1 ha of average D.B.H. 14 cm was clearcut at 20 cm above ground in April, 1945. By the late spring of that year sixty per cent of the cut stumps had sprouted. (3) Fourty to eighty sprouts were found on each stump (maximum:412 sprouts) at the first, but many of them had gradually died out leaving only four to five sprouts per stump by the time of three years after cutting. At that time only one vigorous sprout was left per stump by eliminating the weaker ones. (4) The sprouts, as they grew, started to cover the old stumps with new tissues developed from lower part of sprouts;consequently forming new root systems from the base of new tissues, and they appeared to be seedlings. When the age of sprouts was thirteen years old, the old stumps were completely decayed away and the reproduced stand from sprouts was averaged at 9.7 cm in D.B.H. and at 5.5m in height. (5) When the age of sprouts was thirteen years old, the sprouts exceeded the seedlings in both of total present growth and mean annual increment in height, volume, D.B.H. and basal area, but the seedlings began to exceed the sprouts in current annual increment of height, volume, D.B.H. and basal area at about ten years of age. The rates of increment of the seedling in height, volume, ect. were larger than those of sprouts except when they were one to four years old. From above facts, the following may be concluded: (1) In regenerating a pitch pine stand by sprouts, the lower the stump height, the better the result. (2) If no light limit exists, regenerating a pitch pine stand by sprouts is well possible even at the age of 20 year. (3) Pitch pine reproduction started from sprouts exceeds the seedlings of same age in growth under the almost same circumstances until they get ten years of age.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.4
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• pp.85-97
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• 2010

This study analyzed the distribution, structure and environmental condition of the vegetation of the Chionanthus retusus Lindly et Paxton community at the Wansanchielbong in the Jeonju city to offer basic data for sustainable conservation and ecological management system. And the results are as follows; 1. The average pH of soil at the community was pH 5.69 and it was slightly higher than the average of forest soil pH of Korea. But if the degree of pH will be down, it will be needed some more fertilization of Calcium. 2. The total average for contents of organism was 4.98%. And the nitrate - nitrogen content(mg/kg) of A, B, C, D quadrat was 20.29%, 28.87%, 7.65%, and 23.3% respectively. And there were good condition except quadrat C which was contaminated by amount of earth and sand. 3. The flora of the Chionanthus retusus Lindly et Paxton community was listed as 60 taxa; 37 families, 50 genera, 47 species, 10 varieties and 3 forms. The average appearance species of each Quadrat were A sector 30, B sector 26, C sector 19 and D 19 taxa respectively. 4. Surveyed woody plants in the community were as follows : Chionanthus retusus, Zelkova serrata, Quercus variabilis, Cornus walteri, Robinia pseudo-acacia and those were mixed status. And Chionanthus retusus, Zelkova serrata, Robinia pseudo-acacia, Albizzia julibrisin, Cudrania tricuspidata, Symplocos chinensis for. pilosa were mixed in mid layer trees. Herbaceous plants were founded such as Chionanthus retusus, Zelkova serrata, Robinia pseudo-acacia, Grewia parviflora, Rosa multiflora, Trachelospermum asiaticum was dominant with 35~64% in the ground cover, and Commelina communis, Calamagrostis arundinacea, Dryopteris bissetiana, Lilium lancifolium were founded also. 5. The importance values of Chionanthus retusus was 40.2% in the quadrat A1, 50.2% at quadrat A, 50.0% B1, 45.2% B2, 22.4% C1, 73.6% C2, 33.2% D1 and the total average of I.V. was 44.9%. 6. The average height of surveyed Chionanthus retusus was 5.7m and the average DBH was 12.4cm. The number of trees higher than 2m were 107 and the number of trees lower than 2m were 63. The total numbers of Chionanthus retusus were 170. 7. The age of surveyed Chionanthus retusus were analyzed 42 thru 87 years old and that of Zelkova serrata were 42, Quercus variabilis were 60, Quercus aliena were 48, Robinia pseudo-acacia were 40. 8. The number of trees with DBH 40 through 50cm were 6, and that of 30~39cm were 3, and that of 20~29cm were 16, so the total number that was over 20cm was 25. And there were 70 trees under 10cm of DBH and 63 seedlings. It will be very important data to conserve the habitat that the structure and environmental condition of the Chionanthus retusus Lindly et Paxton community at the Wansanchielbong was stable, and sustainable monitoring will be needed. Now that community is nurse forest of Jeonju City but more positive preservation plan will be needed and assigning monument of city or province also be necessary.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.162-173
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• 2009

Information on the expected geographical shift of suitable zones for growing crops under future climate is a starting point of adaptation planning in agriculture and is attracting much concern from policy makers as well as researchers. Few practical schemes have been developed, however, because of the difficulty in implementing the site-selection concept at an analytical level. In this study, we suggest site-selection criteria for quality Fuji apple production and integrate geospatial data and information available in public domains (e.g., digital elevation model, digital soil maps, digital climate maps, and predictive models for agroclimate and fruit quality) to implement this concept on a GIS platform. Primary criterion for selecting sites suitable for Fuji apple production includes land cover, topography, and soil texture. When the primary criterion is satisfied, climatic conditions such as the length of frost free season, freezing risk during the overwintering period, and the late frost risk in spring are tested as the secondary criterion. Finally, the third criterion checks for fruit quality such as color and shape. Land attributes related to these factors in each criterion were implemented in ArcGIS environment as relevant raster layers for spatial analysis, and retrieval procedures were automated by writing programs compatible with ArcGIS. This scheme was applied to the A1B projected climates for South Korea in the future normal years (2011-2040, 2041-2070, and 2071-2100) as well as the current climate condition observed in 1971-2000 for selecting the sites suitable for quality Fuji apple production in each period. Results showed that this scheme can figure out the geographical shift of suitable zones at landscape scales as well as the latitudinal shift of northern limit for cultivation at national or regional scales.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.1-24
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• 2014

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis. Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world. We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively. The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change. Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.3
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• pp.502-509
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• 2011

It is important to determine optimum soil covering depths for tree survival and growth because soil covering depths for establishing tree planting bases in coastal reclaimed lands are related to the costs for soil collection, transportation and land reclamation. The objectives of this study were carried out to determine optimum soil covering depths for the normal growth of planted trees in a coastal reclaimed land. The study sites were located in Asan National Industrial Complex in Pyeongtaek City, Gyeonggi-do. Four tree species (Pinus thunbergii, Chamaecyparis pisifera, Zelkova serrata, Quercus acutissima) with one hundred eighty trees of each species were planted in various depths of soil covering (no soil covering, 0.5 m, 1.5 m, 2.0 m soil covering treatments) on April 1998, and the tree growth patterns were measured on September 2000. The change of soil properties, tree mortality rate, root collar diameter and height growth were measured from each soil covering depth treatment on September 2000. Soil pH, EC, exchangeable cations ($K^+$, $Na^+$, $Ca^{2+}$, $Mg^{2+}$), anion $Cl^-$, and base saturation increased with decreased soil covering depths. The mortality rates of tree species showed decreased with increased soil covering depths. The height growth of tree species increased with increased soil covering depths. Height growth of Pinus thunbergii was significantly different between the soil covering depth below 0.5m and other three covering depths, while the growth of other species (C. pisifera, Z. serrata, Q. acutissima) was significantly higher in soil covering depths below 1.5 m than in other soil covering depth treatments. The root collar diameter growth of all tree species showed increasing trends with increased soil covering depths. It is recommended to cover the soil depths above 1.5 m to decrease mortality and to stimulate the tree growth of C. pisifera, Z. serrata and Q. acutissima, while P. thunbergii which is a salt tolerate species could be planted in the 1.0 m soil covering depth.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.338-349
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• 2022

This study aims to investigate whether modular community planting, which entailed planting a variety of species of seedlings at high density, was more effective in restoring natural forests than the existing mature tree planting. We also investigated whether the planting density of the modular community planting facilitates growth or improves the tree layer coverage. We conducted outdoor experiments in which the samples were divided into a mature tree planting plot (control plot), where mature trees were planted at wide intervals, and a modular community planting (MCP) plot (treatment plot), where multiple seedlings were planted in high density. The MCP plot was further divided into the plot in which 3 seedlings were planted per m² and the plot of 1 seedling per m². We measured the specimens' survival rate, growth rate (tree height, crown width, and root collar diameter), and cover rate for 26 months from May 2019 and the predicted future tree height growth using the measured tree height. The survival rate and relative growth rate of the MCP were higher than those of the mature tree planting plot. The vertical coverage rate of the tree crown in the MCP exhibited complete coverage of the ground before 23 months, while the coverage rate of the mature tree planting decreased due to transplantation stress. The seedlings in the MCP, which were planted at high density, grew well and were predicted to grow higher than the mature trees in the large tree planting plot within 5 to 6.5 years after planting. It was due to multiple species, seedlings, high-density planting, and planting foundation improvements, such as soil enhancement and mulching. In other words, the seedlings planted in the MCP had a higher survival rate as their environmental adaptation after planting was better, and their early growth was also larger than the trees in the mature planting plot. The high-density mixed planting of various native species not only mitigated the inter-complementary environmental pressures but also facilitated growth by inducing competition between species. Moreover, the planting foundation improvement effectively increased the seedlings' viability and growth rate. A reduction in follow-up management costs is expected as the tree layer coverage sharply increases due to the higher planting density. In the MCP (3 seedlings per m² and 1 seedling per m²), the tree height growth was promoted with the higher planting density, and the crown width and root collar diameter tended to be larger with the lower planting density, but these differences were not statistically significant.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Korean Society of Forest Science
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• v.13 no.1
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• pp.67-78
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• 1971

The measures of contour-terracing with sod has been executed as a major measures for hillside erosion control works for a long time in Korea. It is, however, recognized that pair terracings make a new slope-face having the more steeper degree of slope between the upper and the lower terraces on hillsides and it also does not contribute for establishing the natural vegetation-cover by penetration of pioneer seeds on the slope faces or cut-faces of hillsides. The study was therefore conducted in connection with the above problems on the cut-face having slope of $40^{\circ}$ and 1.6 meter in slope length with clay soils. Plot allocation for the experiment consists of 3 kinds of 3 replica plots having each $1.6m^2$ of slope area, i. e., the control plot with direct seeding on slopes only ($T_1$), the covering plot with the straw-mats after seeding on slopes ($T_2$) and the seeding plot after covering with the straw-mats. ($T_3$). The main results obtained may be summarized as follows : 1. Effects of the straw-mat mulchings on surface soil loss control:-The total amount of soil losses from each treatments are measured as 4,651 gr from $T_1$, 163 gr. from $T_2$ and 2,891 gr. from $T_3$ treatment respectively. (Refer to table No. 2, 3 and 4). In short, it is recognized that effect of $T_2$ treatment is compared as 28.5 times than that of $T_1$ treatment and 17.7 times than that of $T_3$ treatment respectively. Effect of $T_3$ treatment compared with $T_1$ treatment is also such recognizable as 1.6 times in control of surface soil losses on a slope face. 2. Effect of the straw-mat mulchings on soil moisture content on slopes; -Average per cent of surface soil moisture content by treatments show as 21.60 at the $T_1$, 23.04 at the $T_2$ and 22.21 at the $T_3$ treatment respectively and that of subsurface soil moisture content by treatment show as 23.81 at the $T_1$, 26.16 at the $T_2$ and 24.81 at the $T_3$ treatment respectively. The variance of soil moisture content by treatments was highly significant (Refer table No. 7, 8 and 9). 3. Effect of the straw-mat mulchings on vegetation establishment;-Average numbers of germination by treatments are counted as 237 Nos. at the $T_1$, 246 Nos. at the $T_2$ and 262 Nos. at the $T_3$ treatment plots and the vegetation coverage on ground was almost same as about 90% of covers in all treatments. This effect is more or less lower than that of surface soil erosion control. 4. Regarding the effect on surface soil erosion control, the straw-mat mulchings would be effective as a new measures for control of soil erosion on erosion susceptible lands such slope-faced bare-lands as cut-fill faces, mass-movement faces and bare hillsides.