• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.370-377
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• 2018

Currently, Korea is ahead of the forest sector such as forest management, forest investigation and forest management, which is not insufficient compared with the forest advanced countries (Germany, Japan, Austria). However, there is a lack of systematic and advanced forest management plan and related research, and it is not enough to construct GIS for practical and complex analysis. Therefore, in order to perform forest analysis effectively, this study maps forest basic statistics (2010, 2015) based on GIS to map forest information. As a result, the forest area, growing stock, average growing stock, and forest rate could be produced with the maximized visual effect by detailed administrative districts, and systematic analysis of the time series changes was also possible. Forest area increased only in Goseong, Sejong, Cheolwon, Yeoncheon, Daejeon, and Seoul Guro-gu, and decreased in all other areas, while growing stock increased in most areas, Uljin, Ulleung, Seoul Nowon-gu, and Seoul Gangdong-gu. The average growing stock was found to increase in most areas excluding the four administrative districts and the forest rate was higher in 10 regions (Goseong, Yeoncheon, Gongju, Busan Dong-gu, Daegu Seo-gu, etc.) but it decreased in most regions excluding 10 regions. Based on this research, we plan to produce and analyze forest information maps for smaller administrative districts and more.

• Journal of the Korean Wood Science and Technology
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• v.32 no.6
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• pp.7-13
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• 2004

In this study, the Korean pine wood (Pinus densiflora Sieb. et Zucc) and Italian poplar wood (Populus euramericana Guinier) was treated with a mixture of monoammonium phosphate (MAP) and boric acid. Their usability as fire retardant and as decay-resistant construction and interior materials were evaluated by testing of chemicals, corrosion rate and absorption rate, weight loss and chemical contents. An experiment was performed to compare treated pine wood and Italian poplar wood. According to the results, Italian poplar wood had higher specific gravity and retention of chemicals than pine wood, and treated wood showed higher decay-resistance than untreated one. Weight loss was less in treated wood than untreated one because the degree of decay was lower in the former than the latter. Corrosion rate and absorption rate met the KS standard for wood preservative performance. The chemical contents analysis was carried out to determine the degree of decay and it was found that the preservative effect of chemical treatment was lower in Italian poplar wood than in pine wood.

• The Korean Journal of Ecology
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• v.19 no.5
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• pp.403-416
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• 1996

Global natural vegetation mapping (GNVM) system was developed for estimating potential forest area of the globe. With input of monthly mean temperature and monthly precipitation observed at weather stations, the system spherically interpolates them into 1°×1°grid points on a blobe, converts them into vegetation types, and produces a potential vegetation map and a potenital vegetation area. The spherical interpolation was based on negative exponential function fed from the constant radius stations with oval weighing method which is latitudinally elongated weighing in temperature and longitudinally elongated weighing in precipitation. The temperature values were corrected for altitude by applying a linear lapse-rate (0.65℃ / 100m) with reference to a built-in digital terrain map of the globe. The vegetation classification was based upon Koppen’s sKDICe. The potential forest area is estimated for 6.96 Gha (46.24％) of the global land area (15.05 Gha).

• Korean Journal of Ecology and Environment
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• v.48 no.1
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• pp.61-67
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• 2015

This study, which was conducted from Apr. 2013 to Jan. 2014, was carried out as part of a project of making a more detailed ecological zoning map with 1/5,000 scale. The necessity of electronic vegetation map with large scale has arisen in order to make the best use of basic research findings on resource monitoring of National Parks and to enhance efficiency in National Park management. In order to improve accuracy and speed of vegetation research process, the data base for vegetation research was categorized into five groups, namely broad-leaved forest, coniferous forest, mixed forest, rock vegetation and miscellaneous one. And then a vegetation map for vegetation research was created for the research on the site. What is in the database for vegetation research and the vegetation map reflecting findings from vegetation research showed similar distribution rate for broad-leaved forest with 71.965% and 71.184%, respectively. The distribution rate of coniferous forest (16.010%, 15.747%), mixed forest (10.619%, 12.085%), and rock vegetation (0.015%, 0.002%) did not have much difference. In a detailed vegetation map reflecting vegetation research findings, the broad-leaved mountain forest was the most widely distributed with 60.096% based on the physiognomy classification. It was followed by mountain coniferous forest (16.332%), mountain valley forest (15.887%), and plantation forest (3.558%) As for vegetation conservation classification evaluated in the national park, grade I and grade II areas took up 200.44 km2, 61.80% and 108.80 km2, 33.55% respectively. The combined area of these two amounts to 95.35%, making this area the first grade area in ecological nature status. This means that this area is highly worth preserving its vegetation. The high rate of grade I area such as climax forests, unique vegetation, and subalpine vegetation seems to be attributable to diverse innate characteristics of Odaesan National Park, high altitude, low level of artificial disturbance, the subalpine zone formed on the ridge of the mountain top, and their vegetation formation, which reflects climatic and geological characteristics, despite continuous disturbance by mountain climbing.

• Atmosphere
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• v.19 no.2
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• pp.169-182
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• 2009

To improve the performance of climate and numerical models, concerns on the land-atmosphere schemes are steadily increased in recent years. For the realistic calculation of land-atmosphere interaction, a land surface information of high quality is strongly required. In this study, a new land cover map over the Korean peninsula was developed using MODIS (MODerate resolution Imaging Spectroradiometer) data. The seven phenological data set (maximum, minimum, amplitude, average, growing period, growing and shedding rate) derived from 15-day normalized difference vegetation index (NDVI) were used as a basic input data. The ISOData (Iterative Self-Organizing Data Analysis), a kind of unsupervised non-hierarchical clustering method, was applied to the seven phenological data set. After the clustering, assignment of land cover type to the each cluster was performed according to the phenological characteristics of each land cover defined by USGS (US. Geological Survey). Most of the Korean peninsula are occupied by deciduous broadleaf forest (46.5%), mixed forest (15.6%), and dryland crop (13%). Whereas, the dominant land cover types are very diverse in South-Korea: evergreen needleleaf forest (29.9%), mixed forest (26.6%), deciduous broadleaf forest (16.2%), irrigated crop (12.6%), and dryland crop (10.7%). The 38 in-situ observation data-base over South-Korea, Environment Geographic Information System and Google-earth are used in the validation of the new land cover map. In general, the new land cover map over the Korean peninsula seems to be better classified compared to the USGS land cover map, especially for the Savanna in the USGS land cover map.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of Forest and Environmental Science
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• v.35 no.3
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• pp.181-188
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• 2019

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.105-112
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• 2014

The main purpose of this study is to measure spatio-temporal variation of forest tree volume based on the RCP(Representative Concentration Pathway) 8.5 scenario, targeting on Pinus densiflora forests which is the main tree species in South Korea. To estimate nationwide scale, $5^{th}$ forest type map and National Forest Inventory data were used. Also, to reflect the impact of change in place and climate on growth of forest trees, growth model reflecting the climate and topography features were applied. The result of the model validation, which compared the result of the model with the forest statistics of different cities and provinces, showed a high suitability. Considering the continuous climate change, volume of Pinus densiflora forest is predicted to increase from $131m^3/ha$ at present to $212.42m^3/ha$ in the year of 2050. If the climate maintains as the present, volume is predicted to increase to $221.92m^3/ha$. With the climate change, it is predicted that most of the region, except for some of the alpine region, will have a decrease in growth rate of Pinus densiflora forest. The growth rate of Pinus densiflora forest will have a greater decline, especially in the coastal area and the southern area. With the result of this study, it will be possible to quantify the effect of climate change on the growth of Pinus densiflora forest according to spatio-temporal is possible. The result of the study can be useful in establishing the forest management practices, considering the adaptation of climate change.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.19-27
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• 2011

The objective of this study was to suggest technical approaches for preparation and down scaling of climate data used for predicting the potential forest distribution. To predict the forest distribution, we employed a Korean-specific forest distribution model, so-called the TAG(Thermal Analogy Group), and defined the PFT(Plant Functional Types) based on the HyTAG(Hydrological and Thermal Analogy Group). The climate data with 20km spatial resolution were interpolated to fit on the input data format with 1km spatial resolution. Two potential forest distribution maps were estimated using climate data constructed by kriging, one of the interpolation and down-scaling approaches, with and without lapse rate considered. Through the verification process by comparing two potential maps with the actual vegetation map, the forest distribution using the lapse rate was proven to be 38% more accurate.