• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.1
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• pp.26-33
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• 2019

It is necessary to establish a UAV pilot license and training system because the number of UAV-related accidents has rapidly risen. Most of accidents are caused by the human factors such as the lack of control skill and aviation knowledge. In this paper, we investigate licensing policy of small UAV pilots and examine the level of UAV licensing system and classification criteria based on comparative analysis of national cases such as USA, UK and China. Recently, the Ministry of Land, Infrastructure and Transport Affairs is planning to improve the safety regulation by taking into account the risk level of the licensing system, which has been classified according to the existing weight and commercial purpose. From the comparative analysis, we suggested a improvement policy for UAV licensing system in the view of pilot license segmentation, beyond Visual Line-of-sight flight and high risk UAV for non-commercial.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.639-645
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• 2009

The objective of this research was to introduce the TPI approach for interpreting land-forms of mountain forests in South Korea. We develop an objective procedure to decide the scale factor as a basic analytical unit in land-form classification of rugged mountain areas using TPI. In order to determine the scale factor associated with the pattern of slope profiles, the gradient variance curve was derived from a revised hypsometric curve developed using the relief energy of topographic profiles. Using the gradient variance curve, found was the grid size with which the change in relief energy got the peak point. The grid size at the peak point was determined as the scale factor for the study area. In order to investigate the performance of the procedure based on the gradient variance curve, it was applied to determination of the site-specific scale factors of 3 different terrain conditions; highly-rugged, moderately-rugged and relatively less-rugged. The TPI associated with the corresponding scale factors by study site was, then, determined and used in classifying the land-forms. According to the results of this study, the scale factor gets shorter with more rugged terrain conditions. It was also found that the numbers of valleys and ridges estimated with TPI show almost the same trends as those of the observed and the scale factors tends to approach to the mean distance of ridges.

• Korean Journal of Environmental Biology
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• v.38 no.1
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• pp.189-196
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• 2020

Mosquitos serve as vectors for diseases, causing inconvenience as well as a threat to human life and health. Concern about mosquitos introducing and spreading new diseases has been intensifying. We observed a variety of mosquito habitats based on land cover classification from Korea's Ministry of Environment, and the mosquito species that could appear were classified according to the each habitat type. Finally, we suggested the best control methods for each type of habitat considering habitat characteristics and the ecological traits of mosquitos. Urban areas harbor various habitats for pests, contributing significantly to mosquito habitats. Control must be performed regarding larva and adults because various sources for habitats exist. Public mosquito control programs such as educational training, as well as information brochures can be effective in managing mosquito populations and public health. Agricultural areas show high densities of mosquito larva to lentic zones such as reservoirs, wetlands, paddy fields. So, biological control using natural predators may be effective in controlling mosquito populations. Forests are major habitats for Aedes albopctus, so physical controls should be deployed for residents living nearby, and excessive deforestation should be minimized. Other areas including aquatic ecosystems should be adopted regarding biological control using Bti (Bacillus thuringiensis var. israelensis) and chemical control for eradicating mosquitos. We classified habitats into four types of land cover patterns considering ecological traits and habitat preference, and suggest adequate control methods for each habitat type. Our suggestion can be used to positively contribute toward effective managing mosquito's density and reducing the damage to public health.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.457-466
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• 2011

The objectives of this study are to produce level 3 type LULC map and analysis of phenological features of North Korea, ISODATA clustering of the 88scenes of MVC of MODIS NDVI in 2008 and 8scenes in 2009 was carried out. Analysis of phenological phases based mapping method was conducted, In level 2 type map, the confusion matrix was summarized and Kappa coefficient was calculated. Total of 27 typical habitat types that represent the dominant species or vegetation density that cover land surface of North Korea in 2008 were made. The total of 27 classes includes the 17 forest biotopes, 7 different croplands, 2 built up types and one water body. Dormancy phase of winter (${\sigma}^2$ = 0.348) and green up phase in spring (${\sigma}^2$ = 0.347) displays phenological dynamics when much vegetation growth changes take place. Overall accuracy is (851/955) 85.85% and Kappa coefficient is 0.84. Phenological phase based mapping method was possible to minimize classification error when analyzing the inaccessible land of North Korea.

• Korean Journal of Environment and Ecology
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• v.25 no.4
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• pp.590-600
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• 2011

The research was carried out for prediction of the potential habitats of warm-temperate evergreen broad-leaved trees under the current climate(1961~1990) and three climate change scenario(2081~2100) (CCCMA-A2, CSIRO-A2 and HADCM3-A2) using classification tree(CT) model. Presence/absence records of warm-temperate evergreen broad-leaved trees were extracted from actual distribution data as response variables, and four climatic variables (warmth index, WI; minimum temperature of the coldest month, TMC; summer precipitation, PRS; and winter precipitation, PRW) were used as predictor variables. Potential habitats(PH) was predicted 28,230$km^2$ under the current climate and 77,140~89,285$km^2$ under the three climate change scenarios. The PH masked by land use(PHLU) was predicted 8,274$km^2$ and the proportion of PHLU within PH was 29.3% under the current climate. The PH masked by land use(PHLU) was predicted 35,177~45,170$km^2$ and increased 26.9~36.9% under the three climate change scenarios. The expansion of warm-temperate evergreen broad-leaved trees by climate change progressed habitat fragmentation by restriction of land use. The habitats increase of warm-temperate evergreen broad-leaved trees had been expected competitive with warm-temperate deciduous broadleaf forest and suggested the expand and northward shift of warm-temperate evergreen broad-leaved forest zone.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.661-678
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• 2006

Products developed in this research is a software which can transfer the type of shape(.shp) into the type of ascii using the land cover data and the topography data in the metropolitan area of Seoul. In addition, it can calculate the $CO_2$ flux according to distribution of plants within the land cover data. The $CO_2$ flux is calculated by the experimental equation which is compose of the meteorological parameters such as the solar radiation and the air temperature. The net flux was shown in about $-19ton/km^2$ by removing $CO_2$ through the photosynthesis during daytime, and in 2 ton/km2 by producing it through the respiration during nighttime on 10 August 2004, the maximum day of air temperature during the period of 3yr(2001 to 2004), in the metropolitan area of Seoul. Spatial distribution of the air temperature and the wind field is simulated by substituting the middle classification of the land cover map data, upgraded by the Korean Ministry of Environment(KME), for the land-use data of the United States Geological Survey(USGS) within the Meteorological Mesoscale Model Version 5(MM5) on 10 August 2006 in the metropolitan area of Seoul. Difference of the air temperature between both data was shown in the maximum range of $-2^{\circ}C\;to\;2.9^{\circ}C$, and the air temperature due to the land use data of KME was higher than that of USGS in average $0.4^{\circ}C$. Also, those of wind vectors were meanly lower than that of USGS in daytime and nighttime. Furthermore, the hourly time series of Volatile Organic Components(VOCs) is calculated by using the Biosphere Emission and Interaction System Version 2(BEIS2) including the new land cover data and the meteorological parameters such as the air temperature and so]ar insolation. It is possible to calculate the concentration of ozone due to the biogenic emission of VOCs.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.493-503
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• 2014

A hierarchical classification scheme, which can reduce the spectral ambiguity and also reflect crop cultivation patterns from past land-cover maps, is presented for the purpose of the early production of crop classification maps in large-scale crop areas. Specifically, the effects of mixed pixels are minimized not only by applying a hierarchical classification approach based on different spectral characteristics from crop growth cycles, but also by considering temporal contextual information derived from past crop cultivation patterns. The applicability of the presented classification scheme was evaluated by a case study of Iowa State in USA with time-series MODIS 250 m Normalized Difference Vegetation Index(NDVI) data sets and past Cropland Data Layers(CDLs). Corn and soybean, which are major crop types in the study area and also display spectral similarity, could be properly classified by applying different classification stages and accounting for past crop cultivation patterns. The classification result by the presented scheme showed increases of minimum 7.68%p and maximum 20.96%p in overall accuracy, compared with one based on purely spectral information. In addition, the combination of temporal contextual information during classification was less affected by the number of NDVI data sets and the best overall accuracy of 86.63% was achieved. Thus, it is expected that this classification scheme can be effectively used for the early production of large-area crop classification maps in major feed-grain importing countries.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.