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In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an
Sung-Hwan Park; Hyung-Sup Jung; Sunmin Lee; Heon-Seok Yoo; Nam-Wook Cho; Moung-Jin Lee. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences 2021, 11, 7133 .
AMA StyleSung-Hwan Park, Hyung-Sup Jung, Sunmin Lee, Heon-Seok Yoo, Nam-Wook Cho, Moung-Jin Lee. A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network. Applied Sciences. 2021; 11 (15):7133.
Chicago/Turabian StyleSung-Hwan Park; Hyung-Sup Jung; Sunmin Lee; Heon-Seok Yoo; Nam-Wook Cho; Moung-Jin Lee. 2021. "A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network." Applied Sciences 11, no. 15: 7133.
Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.
Seong-Hyeok Lee; Kuk-Jin Han; Kwon Lee; Kwang-Jae Lee; Kwan-Young Oh; Moung-Jin Lee. Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sensing 2020, 12, 3372 .
AMA StyleSeong-Hyeok Lee, Kuk-Jin Han, Kwon Lee, Kwang-Jae Lee, Kwan-Young Oh, Moung-Jin Lee. Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sensing. 2020; 12 (20):3372.
Chicago/Turabian StyleSeong-Hyeok Lee; Kuk-Jin Han; Kwon Lee; Kwang-Jae Lee; Kwan-Young Oh; Moung-Jin Lee. 2020. "Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques." Remote Sensing 12, no. 20: 3372.
Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.
Azam Kadirhodjaev; Fatemeh Rezaie; Moung-Jin Lee; Saro Lee. Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model. ISPRS International Journal of Geo-Information 2020, 9, 566 .
AMA StyleAzam Kadirhodjaev, Fatemeh Rezaie, Moung-Jin Lee, Saro Lee. Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model. ISPRS International Journal of Geo-Information. 2020; 9 (10):566.
Chicago/Turabian StyleAzam Kadirhodjaev; Fatemeh Rezaie; Moung-Jin Lee; Saro Lee. 2020. "Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model." ISPRS International Journal of Geo-Information 9, no. 10: 566.
Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.
Sunmin Lee; Jung-Yoon Jang; Yunjee Kim; Namwook Cho; Moung-Jin Lee. Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method. Remote Sensing 2020, 12, 2663 .
AMA StyleSunmin Lee, Jung-Yoon Jang, Yunjee Kim, Namwook Cho, Moung-Jin Lee. Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method. Remote Sensing. 2020; 12 (16):2663.
Chicago/Turabian StyleSunmin Lee; Jung-Yoon Jang; Yunjee Kim; Namwook Cho; Moung-Jin Lee. 2020. "Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method." Remote Sensing 12, no. 16: 2663.
The social and economic harm to North Korea caused by water-related disasters is increasing with the increase in the disasters worldwide. Despite the improvement of inter-Korean relations in recent years, the issue of water-related disasters, which can directly affect the lives of people, has not been discussed. With consideration of inter-Korean relations, a government-wide technical plan should be established to reduce the damage caused by water-related disasters. Therefore, the purpose of this study was to identify remote sensing and GIS techniques that could be useful in reducing the damage caused by water-related disasters while considering inter-Korean relations and the disasters that occur in North Korea. To this end, based on the definitions of disasters in South and North Korea, water-related disasters that occurred during a 17-year period from 2001 to 2017 in North Korea were first summarized and reclassified into six types: typhoons, downpours, floods, landslides, heavy snowfalls, and droughts. In addition, remote sensing- and GIS-based techniques in South Korea that could be applied to water-related disasters in North Korea were investigated and reclassified according to applicability to the six disaster types. The results showed that remote sensing and other monitoring techniques using spatial information, GIS-based database construction, and integrated water-related disaster management have high priorities. Especially, the use of radar images, such as C band images, has proven essential. Moreover, case studies were analyzed within remote sensing- and GIS-based techniques that could be applicable to the water-related disasters that occur frequently in North Korea. Water disaster satellites with high-resolution C band synthetic aperture radar are scheduled to be launched by South Korea. These results provide basic data to support techniques and establish countermeasures to reduce the damage from water-related disasters in North Korea in the medium to long term.
Sunmin Lee; Sung-Hwan Park; Moung-Jin Lee; Taejung Song. Priority Analysis of Remote Sensing and Geospatial Information Techniques to Water-Related Disaster Damage Reduction for Inter-Korean Cooperation. Journal of Sensors 2020, 2020, 1 -10.
AMA StyleSunmin Lee, Sung-Hwan Park, Moung-Jin Lee, Taejung Song. Priority Analysis of Remote Sensing and Geospatial Information Techniques to Water-Related Disaster Damage Reduction for Inter-Korean Cooperation. Journal of Sensors. 2020; 2020 ():1-10.
Chicago/Turabian StyleSunmin Lee; Sung-Hwan Park; Moung-Jin Lee; Taejung Song. 2020. "Priority Analysis of Remote Sensing and Geospatial Information Techniques to Water-Related Disaster Damage Reduction for Inter-Korean Cooperation." Journal of Sensors 2020, no. : 1-10.
Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and flooded area caused by the collapse of the Xe-Pian Xe-Namnoy Dam in Laos on 23 July 2018, using Sentinel-1 ground range detected (GRD) images. The collapse of this dam gained worldwide attention and led to a large number of casualties at least 98 people, as well as enormous economic losses. Thus, it is worth noting that this study quantitatively analyzed changes in both the Hinlat area, which was flooded, and the Xe-Namnoy reservoir. This study aims to suggest a practical method of change detection which is to simply compute flood extent and water volume in rapidly analysis. At first, a α -stable distribution was fitted to intensity histogram for removing the non-water-affected pixels. This fitting differs from other typical histogram fitting methods, which is applicable to histograms with two peaks, as it can be applied to histograms with not only two peaks but also one peak. Next, another type of threshold based on digital elevation model (DEM) data was used to correct for residual noise, such as speckle noise. The results revealed that about 2.2 × 108 m3 water overflowed from the Xe-Namnoy reservoir, and a flooded area of about 28.1 km3 was detected in the Hinlat area shortly after the dam collapse. Furthermore, the water quantity and flooded area decreased in both study areas over time. Because only SAR GRD images were used in this study for rapid change detection, it is possible that more accurate results could be obtained using other available data, such as optical images with high spatial resolution like KOMPSAT-3, and in-situ data collected at the same time.
Yunjee Kim; Moung-Jin Lee. Rapid Change Detection of Flood Affected Area after Collapse of the Laos Xe-Pian Xe-Namnoy Dam Using Sentinel-1 GRD Data. Remote Sensing 2020, 12, 1 .
AMA StyleYunjee Kim, Moung-Jin Lee. Rapid Change Detection of Flood Affected Area after Collapse of the Laos Xe-Pian Xe-Namnoy Dam Using Sentinel-1 GRD Data. Remote Sensing. 2020; 12 (12):1.
Chicago/Turabian StyleYunjee Kim; Moung-Jin Lee. 2020. "Rapid Change Detection of Flood Affected Area after Collapse of the Laos Xe-Pian Xe-Namnoy Dam Using Sentinel-1 GRD Data." Remote Sensing 12, no. 12: 1.
Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.
Sunmin Lee; Yunjung Hyun; Saro Lee; Moung-Jin Lee. Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques. Remote Sensing 2020, 12, 1200 .
AMA StyleSunmin Lee, Yunjung Hyun, Saro Lee, Moung-Jin Lee. Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques. Remote Sensing. 2020; 12 (7):1200.
Chicago/Turabian StyleSunmin Lee; Yunjung Hyun; Saro Lee; Moung-Jin Lee. 2020. "Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques." Remote Sensing 12, no. 7: 1200.
Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated by field surveys that are labor intensive. Recently, remote sensing techniques have been actively used to explore large and inaccessible areas. In addition, machine learning techniques that could classify and analyze large amounts of data are being used in various fields. Thus, this study aims to analyze the forest vertical structure (number of tree layers) to estimate forest aboveground biomass in Jeju Island from optical and radar satellite images using artificial neural networks (ANN). For this purpose, the eight input neurons of the forest related layers, based on remote sensing data, were prepared: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), NDVI texture, NDWI texture, average canopy height, standard deviation canopy height and two types of coherence maps were created using the Kompsat-3 optical image, L-band ALOS PALSAR-1 radar images, digital surface model (DSM), and digital terrain model (DTM). The forest vertical structure data, based on field surveys, was divided into the training/validation and test data and the hyper-parameters of ANN were trained using the training/validation data. The forest vertical classification result from ANN was evaluated by comparison to the test data. It showed about a 65.7% overall accuracy based on the error matrix. This result shows that the forest vertical structure map can be effectively generated from optical and radar satellite images and existing DEM and DTM using the ANN approach, especially for national scale mapping.
Yong-Suk Lee; Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Sung-Hwan Park; Moung-Jin Lee. Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sensing 2020, 12, 797 .
AMA StyleYong-Suk Lee, Sunmin Lee, Won-Kyung Baek, Hyung-Sup Jung, Sung-Hwan Park, Moung-Jin Lee. Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network. Remote Sensing. 2020; 12 (5):797.
Chicago/Turabian StyleYong-Suk Lee; Sunmin Lee; Won-Kyung Baek; Hyung-Sup Jung; Sung-Hwan Park; Moung-Jin Lee. 2020. "Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network." Remote Sensing 12, no. 5: 797.
Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.
Sung-Hwan Park; Hyung-Sup Jung; Moung-Jin Lee. Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network. Remote Sensing 2020, 12, 253 .
AMA StyleSung-Hwan Park, Hyung-Sup Jung, Moung-Jin Lee. Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network. Remote Sensing. 2020; 12 (2):253.
Chicago/Turabian StyleSung-Hwan Park; Hyung-Sup Jung; Moung-Jin Lee. 2020. "Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network." Remote Sensing 12, no. 2: 253.
In recent years, machine learning techniques have been increasingly applied to assessment of various natural disasters, including landslides and floods. Machine learning techniques can be used to make predictions based on the relationships among events and their influencing factors. In this study, a machine learning approach were applied based on landslide location data in a geographic information system environment. Topographic maps were used to determine the topographical factors. Additional soil and forest parameters were examined using information obtained from soil and forest maps. A total of 17 factors affecting landslide occurrence were selected and a spatial database was constructed. Naïve Bayes and Bayesian network models were applied to predict landslides based on selected risk factors. The two models showed accuracies of 78.3% and 79.8%, respectively. The results of this study provide a useful foundation for effective strategies to prevent and manage landslides in urban areas.
Sunmin Lee; Moung-Jin Lee; Hyung-Sup Jung; Saro Lee. Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea. Geocarto International 2019, 35, 1665 -1679.
AMA StyleSunmin Lee, Moung-Jin Lee, Hyung-Sup Jung, Saro Lee. Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea. Geocarto International. 2019; 35 (15):1665-1679.
Chicago/Turabian StyleSunmin Lee; Moung-Jin Lee; Hyung-Sup Jung; Saro Lee. 2019. "Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea." Geocarto International 35, no. 15: 1665-1679.
Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future.
Sunmin Lee; Yunjung Hyun; Moung-Jin Lee. Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea. Sustainability 2019, 11, 1678 .
AMA StyleSunmin Lee, Yunjung Hyun, Moung-Jin Lee. Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea. Sustainability. 2019; 11 (6):1678.
Chicago/Turabian StyleSunmin Lee; Yunjung Hyun; Moung-Jin Lee. 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea." Sustainability 11, no. 6: 1678.
We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.
Sung-Jae Park; Chang-Wook Lee; Saro Lee; Moung-Jin Lee. Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sensing 2018, 10, 1545 .
AMA StyleSung-Jae Park, Chang-Wook Lee, Saro Lee, Moung-Jin Lee. Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea. Remote Sensing. 2018; 10 (10):1545.
Chicago/Turabian StyleSung-Jae Park; Chang-Wook Lee; Saro Lee; Moung-Jin Lee. 2018. "Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea." Remote Sensing 10, no. 10: 1545.
As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.
Sunmin Lee; Moung-Jin Lee; Saro Lee. Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environmental Earth Sciences 2018, 77, 656 .
AMA StyleSunmin Lee, Moung-Jin Lee, Saro Lee. Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environmental Earth Sciences. 2018; 77 (18):656.
Chicago/Turabian StyleSunmin Lee; Moung-Jin Lee; Saro Lee. 2018. "Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees." Environmental Earth Sciences 77, no. 18: 656.
Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability.
Sunmin Lee; Saro Lee; Moung-Jin Lee; Hyung-Sup Jung. Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools. Sustainability 2018, 10, 648 .
AMA StyleSunmin Lee, Saro Lee, Moung-Jin Lee, Hyung-Sup Jung. Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools. Sustainability. 2018; 10 (3):648.
Chicago/Turabian StyleSunmin Lee; Saro Lee; Moung-Jin Lee; Hyung-Sup Jung. 2018. "Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools." Sustainability 10, no. 3: 648.
This study developed habitat potential maps for the marten (Martes flavigula) and leopard cat (Prionailurus bengalensis) in South Korea. Both species are registered on the Red List of the International Union for Conservation of Nature, which means that they need to be managed properly. Various factors influencing the habitat distributions of the marten and leopard were identified to create habitat potential maps, including elevation, slope, timber type and age, land cover, and distances from a forest stand, road, or drainage. A spatial database for each species was constructed by preprocessing Geographic Information System (GIS) data, and the spatial relationship between the distribution of leopard cats and environmental factors was analyzed using an artificial neural network (ANN) model. This process used half of the existing habitat location data for the marten and leopard cat for training. Habitat potential maps were then created considering the relationships. Using the remaining half of the habitat location data for each species, the model was validated. The results of the model were relatively successful, predicting approximately 85% for the marten and approximately 87% for the leopard cat. Therefore, the habitat potential maps can be used for monitoring the habitats of both species and managing these habitats effectively.
Saro Lee; Sunmin Lee; Wonkyong Song; Moung-Jin Lee. Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning. Applied Sciences 2017, 7, 912 .
AMA StyleSaro Lee, Sunmin Lee, Wonkyong Song, Moung-Jin Lee. Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning. Applied Sciences. 2017; 7 (9):912.
Chicago/Turabian StyleSaro Lee; Sunmin Lee; Wonkyong Song; Moung-Jin Lee. 2017. "Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning." Applied Sciences 7, no. 9: 912.
The purpose of this study was to analyze geospatial information (GI) research trends using text-mining techniques. Data were collected from 869 papers found in the Korea Citation Index (KCI) database (DB). Keywords extracted from these papers were classified into 13 GI domains and 13 research domains. We conducted basic statistical analyses (e.g., frequency and time series analyses) and network analyses, using such measures as frequency, degree, closeness centrality, and betweenness centrality, on the resulting domains. We subdivided the most frequent GI domain for more detailed analysis. Such processes permit an analysis of the relationships between the Research Fields associated with each GI. Our time series analysis found that the Climate and Satellite Image domain frequencies continuously increased. Satellite Image, General-Purpose Map, and Natural Disaster Map in the GI domain and Climate and Natural Disaster in the Research Field domain appeared in the center of the GI-Research Field network. We subdivided the Satellite Image domain for detailed analysis. As a result, LANDSAT, KOMPSAT, and MODIS displayed high scores on the frequency, degree, closeness centrality, and betweenness centrality indices. These results will be useful in GI-based interdisciplinary research and the selection of new research themes.
Kwan-Young Oh; Moung-Jin Lee. Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology. Journal of Sensors 2017, 2017, 1 -15.
AMA StyleKwan-Young Oh, Moung-Jin Lee. Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology. Journal of Sensors. 2017; 2017 ():1-15.
Chicago/Turabian StyleKwan-Young Oh; Moung-Jin Lee. 2017. "Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology." Journal of Sensors 2017, no. : 1-15.
The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.
Sunmin Lee; Moung-Jin Lee; Hyung-Sup Jung. Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea. Applied Sciences 2017, 7, 683 .
AMA StyleSunmin Lee, Moung-Jin Lee, Hyung-Sup Jung. Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea. Applied Sciences. 2017; 7 (7):683.
Chicago/Turabian StyleSunmin Lee; Moung-Jin Lee; Hyung-Sup Jung. 2017. "Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea." Applied Sciences 7, no. 7: 683.
The purpose of this study was to develop a Korean climate change vulnerability assessment tool, the Vulnerability Assessment Tool to build Climate Change Adaptation Plan (VESTAP). Based on Intergovernmental Panel on Climate Change methodology, VESTAP can be used to evaluate Korea’s vulnerability to major climate impacts (including 32 conditions in 8 categories). VESTAP is based on RCP 4.5/8.5 scenarios and can provide evaluation results in 10-year intervals from the 2010s to 2040s. In addition, this paper presents the results of a case study using VESTAP for targeted assessment of health vulnerability to heat waves under the RCP 8.5 scenario for the 2040s. Through vulnerability assessment at the province level in South Korea, Daegu Metropolitan City was identified as the most vulnerable region. The municipality and submunicipality levels of Daegu were also assessed in separate stages. The results indicated that Pyeongni 3-Dong in Seo-Gu was most vulnerable. Through comprehensive analysis of the results, the climate exposure index was identified as the greatest contributor to health vulnerability in Korea. Regional differences in climate exposure can be moderated by social investment in improving sensitivity and adaptive capacity. This study is significant in presenting a quantitative assessment of vulnerability to climate change by the administrative unit in South Korea. The results of this study are expected to contribute to the efficient development and implementation of climate change adaptation policies in South Korea.
Kwan-Young Oh; Moung-Jin Lee; Seong-Woo Jeon. Development of the Korean Climate Change Vulnerability Assessment Tool (VESTAP)—Centered on Health Vulnerability to Heat Waves. Sustainability 2017, 9, 1103 .
AMA StyleKwan-Young Oh, Moung-Jin Lee, Seong-Woo Jeon. Development of the Korean Climate Change Vulnerability Assessment Tool (VESTAP)—Centered on Health Vulnerability to Heat Waves. Sustainability. 2017; 9 (7):1103.
Chicago/Turabian StyleKwan-Young Oh; Moung-Jin Lee; Seong-Woo Jeon. 2017. "Development of the Korean Climate Change Vulnerability Assessment Tool (VESTAP)—Centered on Health Vulnerability to Heat Waves." Sustainability 9, no. 7: 1103.
Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.
Sunmin Lee; Jeong-Cheol Kim; Hyung-Sup Jung; Moung Jin Lee; Saro Lee. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk 2017, 8, 1185 -1203.
AMA StyleSunmin Lee, Jeong-Cheol Kim, Hyung-Sup Jung, Moung Jin Lee, Saro Lee. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk. 2017; 8 (2):1185-1203.
Chicago/Turabian StyleSunmin Lee; Jeong-Cheol Kim; Hyung-Sup Jung; Moung Jin Lee; Saro Lee. 2017. "Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea." Geomatics, Natural Hazards and Risk 8, no. 2: 1185-1203.
The Landsat TM and ETM+ images have been acquired for the long period from the 1980s until the present with the temporal resolution of a 16-day repeat cycle from the visible, near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands. The Landsat multi-temporal images have been successfully used to monitor variations of the Earth surface during 27 years. In this paper, we observe the variations of (1) the snow cover area, (2) the snowline height and (3) the land surface temperature (LST) lapse rate at Mt. Kilimanjaro using a total number of 15 Landsat-5 TM and Landsat-7 ETM+ images from June 1984–July 2011. Segmentation of normalized difference snow index (NDSI) images with a threshold of 0.6 is used to extract snow cover. Snowline altitude is then determined by combining the snow cover classification maps with a digital elevation model (DEM). And the LST lapse rate is also calculated from the TIR band in the forest area. The results from this study show that (1) the snow cover area largely decreases from 10.1 km2 to 2.3 km2 during about 27 years, which corresponds to a 77.2% reduction, (2) the snowline height rose from 4760 m to 5020 m by about 260 m, and (3) the LST lapse rate shifted from −5.2 °C/km to −2.7 °C/km. This study demonstrates that multi-temporal Landsat images can be successfully used for the spatiotemporal analysis of long-term snow cover changes.
Sung-Hwan Park; Moung-Jin Lee; Hyung-Sup Jung. Spatiotemporal analysis of snow cover variations at Mt. Kilimanjaro using multi-temporal Landsat images during 27 years. Journal of Atmospheric and Solar-Terrestrial Physics 2016, 143-144, 37 -46.
AMA StyleSung-Hwan Park, Moung-Jin Lee, Hyung-Sup Jung. Spatiotemporal analysis of snow cover variations at Mt. Kilimanjaro using multi-temporal Landsat images during 27 years. Journal of Atmospheric and Solar-Terrestrial Physics. 2016; 143-144 ():37-46.
Chicago/Turabian StyleSung-Hwan Park; Moung-Jin Lee; Hyung-Sup Jung. 2016. "Spatiotemporal analysis of snow cover variations at Mt. Kilimanjaro using multi-temporal Landsat images during 27 years." Journal of Atmospheric and Solar-Terrestrial Physics 143-144, no. : 37-46.