• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2003.04a
• /
• pp.64-64
• /
• 2003

The aim of this study is to apply and verify of logistic regression at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database.13${\times}$1ure, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the logistic regression coefficient were overlaid for landslide susceptibility mapping. Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2003.04a
• /
• pp.63-63
• /
• 2003

The aim of this study is to apply and verify of Bayesian probability model, the likelihood ratio and statistical model, at Janghung, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of IRS satellite images, field surveys, and maps of the topography, soil type, forest cover, geology and land use were constructed to spatial database. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter and density of forest were extracted from the forest database. Land use was classified from the Landsat TM image satellite image. As each factor's ratings, the likelihood ratio coefficient were overlaid for landslide susceptibility mapping, Then the landslide susceptibility map was verified and compared using the existing landslide location. The results can be used to reduce hazards associated with landslides management and to plan land use and construction.

• Journal of Korean Society for Geospatial Information Science
• /
• v.16 no.3
• /
• pp.59-64
• /
• 2008

Soil erosion is a major problem in the case of forests in hilly terrains. Soil erosion removes the fertile topsoil, making unsuitable for growth and establishment of vegetation. In the present study, erosion prone areas in a forest region situated in the Moyar sub-watershed of Western ghats was identified using GIS with data collected from India. The thematic layers such as forest cover, slope and drainage density were used for analysis. In the erosion prone map, majority of area (48%) was under medium category, and about 35% of area was under high erosion prone category. Very high erosion prone category occupied 7% of the forest area. This erosion prone map would be an ideal spatial data to take up necessary management actions at appropriate places in this watershed to prevent erosion.

• Korean Journal of Plant Resources
• /
• v.25 no.4
• /
• pp.504-506
• /
• 2012

Hosta 'Yellow Tiara' was induced using methyle methane sulfonate (MMS) to obtain mutagens. Among the induced leaf-variegated hosta plants, some with yellow-green leaves were selected. They were cultivated via vegetative propagation. Assessment of the botanical characteristics was conducted for three years since 2004. The major characteristic of these cultivars is that they maintain their yellow-green leaves uniformly all year round. These cultivars are useful as materials for pot plants or as ground cover.

• Economic and Environmental Geology
• /
• v.56 no.1
• /
• pp.65-73
• /
• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Journal of Korean Society of Forest Science
• /
• v.105 no.3
• /
• pp.284-293
• /
• 2016

In order to assess th changes in vegetation characteristics over time in the forest-lands which is isolated by urbanization, vegetation surveys based on the Braun-Blanquet phytosociological method was carried out in 1996 and 2015 on Mt. Hwangyeong located in the center of Busan metropolitan city, South Korea. The number of vegetation types based on floristic composition showed no significant changes, the vegetation units under the community levels was more or less represents the difference. the average total vegetation cover and average number of species per unit area ($100m^2$) was increased 16% and 2 species, respectively. The relative importance value (RIV) for each tree species, Quercus spp. like as Q. mongolica and Q. serrata was decreased, while the warmth-tolerant trees, evergreen broad-leaved trees, and mesophyte like Lindera erythrocarpa, Styrax japonica, Osmanthus heterophyllus, and Stephanandra incisa was increased significantly. Changes of the life form spectrums of vascular plants did not substantially, hemicryptophyte(H) and therophyte (Th) was decreased, while nanophanerophyte(N) was increased significantly. Also through the creating of large-scale vegetation map (based on physiognomic vegetation types) was compared the spatial distribution characteristics of each vegetation types.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.10 no.3
• /
• pp.102-106
• /
• 2008

This study was carried out to evaluate the relationships between stand attributes and litterfall amounts in a 28-year old Quercus acutissima stand. Eighteen sampling plots of $20m{\times}20m$ were chosen and litterfall was collected from May 2005 to December 2006. There was no correlation between stand attributes(tree density, mean diameter at breast height, mean height, basal area) and litterfall amounts except for flower and miscellaneous litter for the study period. There were no significant relationships between leaf litter and basal area(r=0.02, 0.05; P=0.93, 0.83) and between leaf litter and tree density(r=-0.10, 0.05; P=0.85, 0.69). Also, leaf litter was affected neither by mean diameter at breast height(r=-0.08, 0.30; P=0.73, 0.22) nor by mean height(r=-0.24, 0.09; P=0.34, 0.70). Other litter amounts such as bark, branch, and acorn showed no relationships with the stand attributes (P>0.05). The lack of significant relationships between litterfall amounts and stand attributes could be due to the closed canopy with a complete crown cover in this mature oak stand.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.1
• /
• pp.105-113
• /
• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Spatial Information Research
• /
• v.11 no.2
• /
• pp.101-117
• /
• 2003

High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.2
• /
• pp.171-183
• /
• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.