• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.387-402
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• 2005

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.1-12
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• 2014

Crown fire, the main propagation type of large forest fire, has caused extreme damage with the fast spread rate and the high flame intensity. In this paper, we developed the probability equation to predict the crown fires using the spatial features of topography, fuel and weather in damaged area by crown fire. Eighteen variables were collected and then classified by burn severity utilizing geographic information system and remote sensing. Crown fire ratio and logistic regression model were used to select related variables and to estimate the weights for the classes of each variables. As a results, elevation, forest type, elevation relief ratio, folded aspect, plan curvature and solar insolation were related to the crown fire propagation. The crown fire propagation probability equation may can be applied to the priority setting of fuel treatment and suppression resources allocation for forest fire.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.87-92
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• 2011

In order to better understand the role of litter layer on hydrological cycle in forest, we estimated the interception storage capacity of the litter layer at Gwangneung deciduous forest. We first made a thickness map of the litter layer at the study site based on field survey and then collected representative litter samples for the laboratory experiment. We constructed a measurement device consisting of sample tray, drain collector, tipping bucket, and a data logger. Using this device, we examined the relationship between the interception storage capacity ($C_i$) and the thickness (d) of the litter layer. For the range of d from 25 to 100 mm, there was a simple linear relationship between $C_i$ and d, which changed with the intensity of the simulated rain. The results were extrapolated to d smaller than 25 mm by considering that no interception occurs without litter layer. Overall, $C_i$ increased rapidly when d was low (< 25 mm) but the rate of increase decreased as d increased due to clumping. With an average thickness of 59 mm, the estimated $C_i$ at the site was 0.94 (${\pm}0.39$) mm. Such an interception storage capacity of the litter layer is comparable to that of the forest canopy, suggesting that the litter layer can play an important role in the forest water cycle.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.7
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• pp.3-12
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• 2004

The need to predict the rate of soil erosion, both under existing conditions and those expected to occur following soil conservation practice, has been led to the development of various models. In this study Morgan model especially developed for field-sized areas on hill slopes was applied to assess the rate of soil erosion using RS/GIS environment in the Dukchun river basin, one of two tributaries flowing into Jinyang lake. In order to run the model, land cover mapping was made by the supervised classification method with Landsat TM satellite image data, the digital soil map was generated from scanning and screen digitizing from the hard copy of soil maps, digital elevation map (DEM) in order to generate the slope map was made by the digital map (DM) produced by National Geographic Information Institute (NGII). Almost all model parameters were generated to the multiple raster data layers, and the map calculation was made by the raster based GIS software, IL WIS which was developed by ITC, the Netherlands. Model results show that the annual soil loss rates are 5.2, 18.4, 30.3, 58.2 and 60.2 ton/ha/year in forest, paddy fields, built-up area, bare soil, and upland fields respectively. The estimated rates seemed to be high under the normal climatic conditions because of exaggerated land slopes due to DEM generation using 100 m contour interval. However, the results were worthwhile to estimate soil loss in hilly areas and the more precise result could be expected when the more accurate slope data is available.

• Journal of Environmental Science International
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• v.13 no.12
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• pp.1033-1039
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• 2004

The methodology developed by Soil Conservation Service for determination of runoff value from precipitation is applied to estimate the precipitation recharge in the Pyungchang river basin. Two small areas of the basin are selected for this study. The CN values are determined by considering the type of soil, soil cover and land use with the digital map of 1:25,000. Forest covers more than $94{\%}$ of the study area.. The CN values for the study area vary between 47 in the forest area and 94 in the bare soil under AMC 2 condition. The precipitation recharge rate is calculated for the year when the precipitation data is available since 1990. To obtain the infiltration rate, the index of CN and five day antecedent moisture conditions are applied to each precipitation event during the study period. As a result of estimation, the value of precipitation recharge ratio in the study area vary between $15.2{\%}\;and\;35.7{\%}$ for the total precipitation of the year. The average annual precipitation recharge rate is $26.4{\%}\;and\;26.8{\%}$, meaning 377.9mm/year and 397.5mm/year in each basin.

• The Korean Journal of Ecology
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• v.21 no.4
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• pp.389-399
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• 1998

this study was focused on the effects of fire on spatial change of vegetation landscape in rural region. Fire types recognized as erown fire, severe surface fire and light surface fire in order of increasing intensity were described in a fire map. GIS was introduced to understand the relationship between fire types and topographic conditions or vegetation types. We also investigated land-use type and regeneration strategies after burning. Fire intensity depended on topographic conditions and vegetation types. Special land-use type in this area was collection of edible mushroom (Tricholoma matsutake). Mushrooms had been obtained from Pinus densiflora forests existing as edaphic climax or managed artificially. Regeneration strategy in burned areas was to make sprouts from burned oak stumps. A higher density and growth rate of sprouts, as compared to those on unburned areas, facilitated vegetation succession from P. densiflora forest to oak forest and consequently led to change of landscape pattern.

• Journal of The Geomorphological Association of Korea
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• v.28 no.1
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• pp.83-99
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• 2021

Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors 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 the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.