• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.63-66
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• 2003

This study applied a hydrogeological field survey and isotope investigation to identify source locations and delineate pathways of groundwater contamination by nitrogen compounds. The infiltration and recharge processes were analyzed with groundwater-level fluctuation data and oxygen-hydrogen stable isotope data. The groundwater flow pattern was investigated through groundwater flow modeling and spatial and temporal variation of oxygen isotope data. Based on the flow analysis and nitrogen isotope data, source types of nitrate contamination in groundwater are identified. Groundwater recharge largely occurs in spring and summer due to precipitation or irrigation water in rice fields. Based on oxygen isotope data and cross-correlation between precipitation and groundwater level changes, groundwater recharge was found to be mainly caused by irrigation in spring and by precipitation at other times. The groundwater flow velocity calculated by a time series of spatial correlations, 231 m/yr, is in good accordance with the linear velocity estimated from hydrogeologic data. Nitrate contamination sources are natural and fertilized soils as non-point sources, and septic and animal wastes as point sources. Seasonal loading and spatial distribution of nitrate sources are estimated by using oxygen and nitrogen isotopic data.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.87-92
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• 2010

Large pressure on the growing population has increased rapid change in the LULC (land use/land cover) patterns in the watershed area. Spatial distribution of LULC information and its changes are desirable for any effective planning, managing and monitoring activities. The aim of the study is to produce the 1,50,000 scaled LULC change map for the sub-watershed, Western Moyar, India using the multi-temporal satellite image dataset of IRS LISS III images for the year 1989, 1999, and 2002. About 9 classes are extracted using onscreen visual interpretation techniques for all the three years. The change detection analysis was performed using matrix method for period I (1989-1999) and period II (1999-2002). The study reveals that the changes noticed in period II (1999-2002) is comparatively more than period I (1989-1999), which is dynamic information to protect the sub-watershed area from the deterioration and paves the way to for the sustainable development.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.2
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• pp.139-149
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• 2014

A new technique, namely the combination of satellite and trajectory analysis (CSTA), for exploring the spatio-temporal distribution information of volcanic ash plume (VAP) from volcanic eruption. CSTA uses the satellite derived ash property data and a matching forward-trajectories, which can generate airmass history pattern for specific VAP. In detail, VAP properties such as ash mask, aerosol optical thickness at 11 ${\mu}m$ ($AOT_{11}$), ash layer height, and effective radius from the Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite were retrieved, and used to estimate the possibility of the ash forecasting in local atmosphere near volcano. The use of CSTA for Iceland's Eyjafjallaj$\ddot{o}$kull volcano erupted in May 2010 reveals remarkable spatial coherence for some VAP source-transport pattern. The CSTA forecasted points of VAP are consistent with the area of MODIS retrieved VAP. The success rate of the 24 hour VAP forecast result was about 77.8% in this study. Finally, the use of CSTA could provide promising results for VAP monitoring and forecasting by satellite observation data and verification with long term measurement dataset.

• Korean journal of applied entomology
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• v.52 no.3
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• pp.243-248
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• 2013

This study was conducted to understand the settlement process of Ascotis selenaria larvae into citrus orchards with respect to oviposition site and analysis of the spatial distribution pattern of the larvae. A. selenaria eggs were not found on citrus trees in field and green house, but not on citrus trees in the field. A. selenaria larvae showed a significant clump distribution in the greenhouse. In the open citrus field, the index of dispersion was around 1.0 in most cases, with a weak clumping degree. However, the d-statistic was between -1.96 and 1.96, indicating a statistically significant random distribution. In addition, the Green's index (a clumping index) was very low in all cases, even though the clump distribution was accepted. for most samples, the probability distribution of larval frequency in the field satisfied the probability distribution functions of Poisson (random pattern) and the negative binomial (clump pattern) distribution. In addition, the temporal distribution of the larvae in the open field showed a pattern which was formed by colonizers from outside oviposition sites. Further, the difference in larval spatial distribution between field and greenhouse orchards was discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.138-150
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• 2012

The purpose of this study was to examine the regional disparity of ambulatory health care utilization considering spatio-temporal variation in South Korea during 1996-2008(precisely, in 1996, 1999, 2002, 2005, and 2008) using bayesian hierarchial spatio-temporal model. The spatial pattern uses an intrinsic gaussian conditional autoregressive (CAR) error component. Ornstein-Uhlenbeck method was applied to detect the temporal patterns. The results showed that substantial temporal-geographical variation depending on diseases exists in Korea. On the Contrary to the pattern of total outpatient utilizations, for example, the areas that chronic diseases distributed relatively high were most in rural where the proportion of elderly population was higher than in the urban. Chungcheongnam-do, Junlabuk-do, and Kyeongsangbuk-do had higher risks in hypertension, whereas arthritis was higher risk in the Kyeonggi-do, Chungcheongbuk-do, Junlanam-do, and Junlabuk-do. The results of this study suggested that the effective health intervention programmes needed to alleviate the regional variation of health care utilization. These outcomes also provided the foundation for further investigation of risk factors and interventions in these high-risk areas.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.3-6
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• 2005

In this paper, the real scenario of water situation (e.g. water management, water availability and flooding) in an irrigated rice cultivation area in Suphanburi Province, Central-West Thailand is discussed together with the NDVI time series data. The result shown is derived by our classifier named 'Peak Detector Algorithm (PDA)'. The method discriminated 5 classes in terms of irrigation activities and cropping intensities, namely, Non-irrigated, Poorly irrigated - 1 crop/year, Irrigated - 2 crops/year, Irrigated - 3 crops/year and Others (no cultivation happens in a year or other land covers). The overall accuracy of all classified results (1999-2001) is around $77\%$ against independent ground truth data (general activities or function of an area). In the classified results, spatial and temporal inconsistency appeared significantly in the Western and Southern areas of Suphanburi. The inconsistency resulted mainly by anomaly of rainfall pattern in 1999 and their temporal irrigation activity. The algorithm however, was proved that it could detect actual change of irrigation status in a year.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.469-487
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• 2007

Grain-Size Trend Analysis is the methodology to identify net sediment transport pathways, based on the assumption that the movement of sediment from the source to deposit leaves the identifiable spatial pattern of mean, sorting, and skewness of grain size. It can easily be implemented with low cost, so it has great potentials to contribute to geomorphological research, whereas it can also be used inadequately without recognition of its limitations. This research aims to compare three established methods of grain-size trend analysis to search for the adequate way of application, and also suggest the research tasks needed in improving this methodology 1D pathway method can corporate the field experience into analyzing the pathway, provide the useful information of depositional environments through X-distribution, and identify the long-term trend effectively. However, it has disadvantage of the dependence on subjective interpretation, and a relatively coarse temporal scale. Gao-Collins's 2D transport vector method has the objective procedure, has the capability to visualize the transport pattern in 2D format, and to identify the pattern at a finer temporal scale, whereas characteristic distance and semiquantitative filtering are controversial. Le Roux's alternative 2D transport vector method has two improvement of Gao-Collins's in that it expands the empirical rules, considers the gradient of each parameters as well as the order, and has the ability to identify the pattern at a finer temporal scale, while the basic concepts are arbitrary and complicated. The application of grain sire trend analysis requires the selection of adequate method and the design of proper sampling scheme, based on the field knowledge of researcher, the temporal scale of sediment transport pattern targeted, and information needed. Besides, the relationship between the depth of sample and representative temporal scale should be systematically investigated in improving this methodology.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.340-354
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• 2021

The aim of this study was to investigate the spatial and temporal distribution of phytoplankton biomass and community composition in Jinhae Bay on the southern coast of Korea. Phytoplankton pigment analysis was conducted using ultra performance liquid chromatography (UPLC) were conducted from April to December 2019 at seven stations. Temperature, salinity, and dissolved oxygen (DO) and inorganic nutrients (dissolved nitrogen, dissolved phosphorus, and orthosilicic acid) were measured to investigate the environmental factors associated with the structure of phytoplankton community. Phytoplankton biomass (Chl-a) was the highest in July (mean 15.4±4.3 ㎍/L) and the lowest in December (mean 3.5±0.6 ㎍/L). Fucoxanthin was the most abundant carotenoid and showed a similar variation pattern to Chl-a, peridinin, and Chl-b. Phytoplankton community composition analysis showed that diatoms were a predominant group with an average abundance of 70 % whereas chlorophytes, cryptophytes, and dinoflagellates often appeared with lower averages. Further, the dominance of diatoms was closely correlated with water temperature and N:P ratio, which might be influenced by high temperatures in the summer and nutrient loading from the land. Additionally, freshwater and nutrient input by rainfall was estimated to be the most important environmental factor. Hence, the spatial and temporal variations in the composition of phytoplankton pigments and phytoplankton community were correlated with physicochemical and environmental parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3315-3337
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• 2012

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.