• Journal of the Korean Geographical Society
• /
• v.48 no.4
• /
• pp.497-508
• /
• 2013

The objective of this study is to determine the spatiotemporal influences of climatic factors and atmospheric aerosol on phenological cycles of the Korea Peninsular on a regional scale. High temporal-resolution satellite data can overcome limitations of ground-based phenological studies with reasonable spatial resolution. Study results showed that phenological characteristics were similar among evergreen forest, deciduous forest, and grassland, while the inter-annual vegetation index amplitude of mixed forest was differentiated from the other forest types. Forest types with high VI amplitude reached their maximum VI values earlier, but this relationship was not observed within the same forest type. The phase of VI, or the peak time of greenness, was significantly influenced by air temperature. Aerosol optical thickness (AOT) time-series showed strong seasonal and inter-annual variations. Generally, aerosol concentrations were peaked during late spring and early summer. However, inter-annual AOT variations did not have significant relationships with those of VIs. Weak relationships between AOT amplitude and EVI amplitude only indicates that there would be potential impacts of aerosols on vegetation growth in the long run.

• Journal of Korea Water Resources Association
• /
• v.43 no.11
• /
• pp.995-1009
• /
• 2010

Accelerated soil erosion due to extreme climate change, such as increased rainfall intensity, and human-induced environmental changes, is a widely recognized problem. Existing soil erosion models are generally based on the gross erosion concept to compute annual upland soil loss in tons per acre per year. However, such models are not suitable for event-based simulations of erosion and deposition in time and space. Recent advances in computer geographic information system (GIS) technologies have allowed hydrologists to develop physically based models, and the trend in erosion prediction is towards process-based models, instead of conceptually lumped models. This study aims to propose an effective and robust distributed rainfall-sediment yield-runoff model consisting of basic element modules: a rainfall-runoff module based on the kinematic wave method for subsurface and surface flow, and a runoff-sediment yield-runoff model based on the unit stream power method. The model was tested on the Cheoncheon catchment, upstream of the Yongdam dam using hydrological data for three extreme flood events due to typhoons. The model provided acceptable simulation results with respect to both discharge and sediment discharge even though the simulated sedigraphs were underestimated, compared to observations. The spatial distribution of erosion and deposition demonstrated that eroded sediment loads were deposited in the cells along the channel network, which have a short overland flow length and a gentle local slope while the erosion rate increased as rainfall became larger. Additionally, spatially heterogeneous rainfall intensity, dependant on Thiessen polygons, led to spatially-distinct erosion and deposition patterns.

• The KIPS Transactions:PartD
• /
• v.16D no.4
• /
• pp.487-496
• /
• 2009

This paper addresses an indexing scheme capable of efficiently processing range queries in a large-scale trajectory database. After discussing the drawbacks of previous indexing schemes, we propose a new scheme that divides the temporal dimension into multiple time intervals and then, by this interval, builds an index for the line segments. Additionally, a supplementary index is built for the line segments within each time interval. This scheme can make a dramatic improvement in the performance of insert and search operations using a main memory index, particularly for the time interval consisting of the segments taken by those objects which are currently moving or have just completed their movements, as contrast to the previous schemes that store the index totally on the disk. Each time interval index is built as follows: First, the extent of the spatial dimension is divided onto multiple spatial cells to which the line segments are assigned evenly. We use a 2D-tree to maintain information on those cells. Then, for each cell, an additional 3D $R^*$-tree is created on the spatio-temporal space (x, y, t). Such a multi-level indexing strategy can cure the shortcomings of the legacy schemes. Performance results obtained from intensive experiments show that our scheme enhances the performance of retrieve operations by 3$\sim$10 times, with much less storage space.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.627-636
• /
• 2021

Although many studies on ship detection using synthetic aperture radar (SAR) satellite images are being conducted around the world, there are still very few employing SAR microsatellites, as most of the microsatellites are optical satellites. Recently, the ICEYE and Capella Space have embarked on the development of microsatellites with SAR sensor, and similar projects are being initiated globally in line with the flow of the new space era [e.g., for the ICEYE: 18 satellites (~2021); Capella Space: 36 satellites (~2023); and the Coast Guard SAR: 32 satellites in the early development stage]. In preparation for these new systems, it is important to review the SAR microsatellite system and the recent advances in this technology. Accordingly, in this paper, the current status and characteristics of optical and SAR microsatellite constellation operation are described, and studies using them are investigated. In addition, based on the status and characteristics of the representative SAR microsatellites, specifically the ICEYE and Capella systems, methods for using SAR microsatellite data for ship detection applications are described. Our results confirm that the SAR microsatellites operate as a constellation and have the advantages of short revisit cycles and quick provision of high-resolution images. With this technology, we expect SAR microsatellites to contribute greatly to the monitoring a wide-area target vessel, in which the spatiotemporal resolution of the imagery is especially important.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.1
• /
• pp.67-74
• /
• 2022

In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.

• Korean Journal of Mineralogy and Petrology
• /
• v.35 no.3
• /
• pp.199-214
• /
• 2022

The Yeongnam Massif is one of representative basement provinces in the Korean Peninsula, which has experienced high-temperature, low-pressure (HTLP) regional metamorphism and partial melting. Here we reviewed recent developments in Paleoproterozoic (1.87-1.84 Ga) hot orogenesis of the Yeongnam Massif, typified by the granulite-facies metamorphism and partial melting recorded in the HTLP rocks. In particular, spatiotemporal linkage between the metamorphic and magmatic activities, including the Sancheong-Hadong anorthositic magma as a heat source, provides a key to understand the widespread HTLP metamorphism and partial melting in the Yeongnam Massif. Crustal anatexis, resulting from the fluid-present melting and muscovite/biotite dehydration melting, has yielded various types of leucosomes and leucogranites. Zircon and monazite petrochronology, using in-situ U(-Th)-Pb data from the secondary ion mass spectrometry, indicates that the HTLP metamorphism and anatexis lasted over a period of ~15 Ma at ca. 1870-1854 Ma. In addition, a fluid influx event at ca. 1840 Ma was locally recognized by the occurrence of incipient charnockite. Taken together, the Yeongnam Massif preserves a prolonged evolutionary record of the HTLP metamorphism, partial melting, and fluid influx diagnostic for a hot orogen. Such an orogen is linked to the Paleoproterozoic orogeny widespread in the North China Craton, and most likely represents the final phase of crustal evolution in the Columbia/Nuna supercontinent.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4B
• /
• pp.389-398
• /
• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.747-763
• /
• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Journal of Cadastre & Land InformatiX
• /
• v.48 no.2
• /
• pp.79-98
• /
• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1031-1042
• /
• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.