• Spatial Information Research
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• v.10 no.4
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• pp.533-550
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• 2002

Soil organic matter (SOM) content and other physical soil properties were extracted from a digital soil database, the Soil Survey Geographic (SSURGO) database, to map the amount of SOM and determine its relationship with topographic positions in floodplain areas along a river basin in Douglas County, Kansas. In the floodplains, results showed that slope and SOM content had a significant negative relationship. Soils near river channels were deep and nearly level, and they had the greatest SOM content in the floodplain areas. For the whole county, SOM content was influenced primarily by soil depth and percent SOM by weight. Among different slope areas, soils on mid-range slopes (10-15%) and ridgetops had the highest SOM content because they had relatively high percent SOM content by weight and very deep soils, respectively. SOM content was also significantly variable among different land cover types. Forest/woodland had significantly higher SOM content than others, followed by cropland, grassland, and urban areas.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

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

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.405-416
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• 2019

Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of internet and geospatial information technology. In the field of geospatial information, machine learning is also being applied to classify or recognize objects using images and point clouds. In this study, the problem of manually constructing training data using existing digital map version 1.0 was improved, and the technique of classifying roads, buildings and vegetation using image and point clouds were proposed. Through experiments, it was possible to classify roads, buildings, and vegetation that could clearly distinguish colors when using true ortho-image with only RGB (Red, Green, Blue) bands. However, if the colors of the objects to be classified are similar, it was possible to identify the limitations of poor classification of the objects. To improve the limitations, random forest and support vector machine techniques were applied after band fusion of true ortho-image and normalized digital surface model, and roads, buildings, and vegetation were classified with more than 85% accuracy.

• Journal of agriculture & life science
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• v.50 no.5
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• pp.111-120
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• 2016

The objective of this study was to provide basic information for selecting the right timing and the right place of erosion control of stream on Gyeongsangnam-do. In order to achieve this objective, a total of 526 erosion control dams and 230 mountains stream conservation facilities on the constructed places and construction planned places for the erosion control were investigated on site, forest physiognomy, and hydrologic conditions. The erosion control dams and mountain stream conservation facilities were mostly constructed in the area, which has the sedimentary rock, 200-400m of altitude, a slope of 21~30°, and II of landslide hazard map. Among the forest environmental factors, it was only similar to the construction frequency in the areas that have small diameter class, III age class. Also, we investigated the hydrological environmental factors that determine the size and numbers of erosion control dam. The places constructed to the highest frequency were below 50ha in the area, 2.1~4.0km/㎢ of drainage density, longitudinal water system, 61~90mm of maximum precipitation per hour, and 201~300mm of day maximum precipitation. As the results, the sites and floodgate conditions between the constructed places and stream conservation facilities for the erosion control showed to be very similar. Therefore, these results indicate that the erosion control of the stream of the areas, which have the disruption of mountain peaks and the high erosion risk areas, should be used on both the erosion control dam and stream conservation facilities.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1119-1130
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• 2021

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.158-166
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• 2019

Implementing proper land management techniques, such as selecting the best crops and applying the best cultivation techniques at the farm level, is an effective way for farmers to adapt to climate change. Also it will be helpful if the farmer can get the information of agro-weather and the growth status of cultivating crops in real time and the simulated results of applying optional technologies. To test this, a system (web site) was developed to produce agro-weather data and crop growth information of farms by combining agricultural climate maps and crop growth modeling techniques to highland area for summer-season Chinese cabbage production. The system has been shown to be a viable tool for producing farm-level information and providing it directly to farmers. Further improvements will be required in the speed of information access, the microclimate models for some meteorological factors, and the crop growth models to test different options.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.120-134
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• 2015

InVEST Water Yield model developed by Natural Capital Project was applied for South Korea to assess domestic forest ecosystem's water provisioning services. The InVEST Water Yield model required 8 input dataset, including six spatial map data and two derived by coefficients. By running the model with relatively easy acquired and modified data, the result of domestic forest ecosystem's water provisioning services was 9,409,622,083 ton using the standard of the year 2011. The result showed similar patterns and distribution of rainfall in 2011, but showed difference when compared with existing researches spatially driven in nationwide statistical analysis results. This difference is assumed to occur with different model mechanism in spatial implementation and statistical analysis. So given that the model is currently still developing, applications should be taken on qualitative perspectives rather than on quantitative perspectives. Additionally, for advancing the application of InVEST water yield model, quantification of suitable input data and comparison using multi-modeling is required.