Jong-Kuk Choi

Landslide susceptibility mapping using GIS and the weight-of-evidence model

The weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial database containing information such as topography, soil, forest, geology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factors rating for the Boun area in Korea, which had suffered substantial landslide damage following heavy rain in 1998. In the topographic database, the factors were slope, aspect and curvature; in the soil database, they were soil texture, soil material, soil drainage, soil effective thickness and topographic type; in the forest map, they were forest type, timber diameter, timber age and forest density; lithology was derived from the geological database; land-use information came from Landsat TM satellite imagery; and lineament data from IRS satellite imagery. Tests of conditional independence were performed for the selection of factors, allowing 43 combinations of factors to be analysed. For the analysis of mapping landslide susceptibility, the contrast values, W + and W -, of each factors rating were overlaid spatially. The results of the analysis were validated using the previous landslide locations. The combination of slope, curvature, topography, timber diameter, geology and lineament showed the best results. The results can be used for hazard prevention and land-use planning.

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor’s relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, “distance from fault” had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

Initial validation of GOCI water products against in situ data collected around Korean peninsula for 2010–2011

This paper provides initial validation results for GOCI-derived water products using match-ups between the satellite and ship-borne in situ data for the period of 2010–2011, with a focus on remote-sensing reflectance (Rrs). Match-up data were constructed through systematic quality control of both in situ and GOCI data, and a manual inspection of associated GOCI images to identify pixels contaminated by cloud, land and inter-slot radiometric discrepancy. Efforts were made to process and quality check the in situ Rrs data. This selection process yielded 32 optimal match-ups for the Rrs spectra, chlorophyll a concentration (Chl_a) and colored dissolved organic matter (CDOM), and with 20 match-ups for suspended particulate matter concentration (SPM). Most of the match-ups are located close to shore and thus the validation should be interpreted limiting to near-shore coastal waters. The Rrs match-ups showed the mean relative errors of 18–33% for the visible bands with the lowest 18–19% for the 490 nm and 555 nm bands and 33% for the 412 nm band. Correlation for the Rrs match-ups was high in the 490–865 nm bands (R2=0.72–0.84) and lower in the 412 nm band (R2=0.43) and 443 nm band (R2=0.66). The match-ups for Chl_a showed a low correlation (<0.41) although the mean absolute percentage error was 35% for the GOCI standard Chl_a. The CDOM match-ups showed an even worse comparison with R2<0.2. These match-up comparison for Chl_a and CDOM would imply the difficulty to estimate Chl_a and CDOM in near-shore waters where the variability in SPM would dominate the variability in Rrs. Clearly, the match-up statistics for SPM was better with R2=0.73 and 0.87 for two evaluated algorithms, although GOCI-derived SPM overestimated low concentration and underestimated high concentration. Based on this initial match-up analysis, we made several recommendations -1) to collect more offshore under-water measurements of the Rrs data, 2) to include quality flags in level-2 products, 3) to introduce an ISRD correction in the GOCI processing chain, 4) to investigate other types of in-water algorithms such as semianalytical ones, and 5) to investigate vicarious calibration for GOCI data and to maintain accurate and consistent calibration of field radiometric instruments.

Application of the Geostationary Ocean Color Imager (GOCI) to estimates of ocean surface currents

The Geostationary Ocean Color Imager (GOCI) can be utilized efficiently to observe subtle changes in oceanic environments under cloud-free conditions because it receives ocean color images around the Korean Peninsula hourly, for 8 h a day. Here we investigated the applicability of the GOCI for estimating hourly variations in ocean surface currents, which provide significant information on seawater circulation for fisheries, shipping controls, and more. Ocean surface currents were deduced from eight images of GOCI-derived total suspended matter (TSM) from highly turbid coastal waters and images of chlorophyll concentration (CHL) for relatively clear waters. The results showed that GOCI TSM-derived ocean surface currents can effectively estimate and represent fast tidal currents, as well as flood and ebb tides on the west coast of Korea, in comparison with in situ measurements. GOCI-derived CHL scenes successfully illustrated currents moving along boundaries where warm and cold seawaters mix, in addition to mesoscale currents such as the East Korea Warm Current (EKWC) in the East Sea of Korea. Satellite-based sea surface temperature and sea surface height images supported the reliability of GOCI-derived ocean surface currents in the East Sea.

A Study of Decadal Sedimentation Trend Changes by Waterline Comparisons within the Ganghwa Tidal Flats Initiated by Human Activities

Abstract This study presents results of a human impact investigation of large-scale construction projects on sedimentation trend and morphologic changes in the Ganghwa tidal flats over 10 years using time-series waterlines from Landsat thematic mapper/enhanced thematic mapper. Waterlines were extracted from remotely sensed images. These images are useful for studying changes in coastlines and tidal flat topography. A reference digital elevation model (DEM) was constructed; then actual waterlines from satellite images were compared with modeled waterlines from the reference DEM. Systematic comparison with respect to horizontal differences between the two waterlines provided information on local conditions of sediment trends within the study area. Seaward or landward migration of waterlines was a clear indication of the change in sedimentation pattern. Deposition has been dominant at the main channel between the southern Ganghwa and the Yeongjong tidal flats, whereas erosion has been dominant at the eastern lower tidal flat. These sedimentation patterns complied with field observations along two survey lines and with the result from depth-sounding data. Deposition narrowed the channel and created a slightly more meandering shape. A hydrodynamic model anticipated a significant change of ocean current as a result of the coastal construction projects, and the modeling result matched well in terms of current conditions with the present sedimentation pattern analyzed by waterline comparison. A series of coastal construction projects in this area clearly affected the local hydrodynamics of tide and currents to a large extent and resulted in the changes of sedimentation trends during a decade, and it is necessary to keep the monitoring area to understand long-term impacts initiated by human activities within a relatively short period. Although it was not possible to estimate the total volume of sediment transportation with this method, trends of sedimentation processes within tidal flats can be effectively deduced.

Quantitative estimation of suspended sediment movements in coastal region using GOCI

ABSTRACT Choi, J. K., Hyun Yang, H. J. Han, Ryu, J. H. and Park, Y.J., 2013. Quantitative estimation of the suspended sediment movements in the coastal region using GOCI The Geostationary Ocean Color Imager (GOCI), the worlds first geostationary ocean color observation satellite, is useful for monitoring the temporal dynamics of coastal water turbidity because it can obtain satellite images every hour during the daytime. Temporal variation in turbidity is the key to understanding sediment dynamics in coastal regions. For example, a certain patch of suspended sediment in surface water can be traced every hour by generating a GOCI- derived map of suspended sediment concentration (SSC). By calculating the variations in the position of the patch every hour quantitatively, we can obtain information on the current movement in the region quantitatively. Here, we investigate the applicability of GOCI data to monitoring of the temporal movement of suspended sediment in coastal areas and to the development of algorithms for calculating the current speed and direction. Our study was performed in areas near Gyeonggi Bay on the mid-west coast of the Korean Peninsula. Field work was performed to obtain in situ measurements of SSC and optical properties of the water surface. These data could then be combined to derive an SSC algorithm based on the relationship between the SSC and remote sensing reflectance (Rrs) values. We calculated the suspended sediment movement from hourly SSC images. Current velocity and direction were also measured in the field to validate and identify the calculated movement. GOCI images acquired on the same day as the samples were used to generate a map of turbidity and to estimate the differences in SSC displayed in each image. We found that GOCI could be effectively used to monitor the temporal dynamics of the turbidity of coastal waters, i.e., sediment movements driven by currents along the west coast of the Korean Peninsula. Sediment movements can be applied to develop GOCI-based algorithms that calculate current velocities and generate maps of current vectors in this coastal area.

Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat. The control factors were compiled as a spatial database from remotely sensed data combined with GIS analysis. The relative weight of each factor was integrated as a species potential index (SPI), which produced habitat potential maps. The maps were compared with the surveyed habitat locations, revealing a strong correlation between the potential maps and species locations. The combination of a GIS-based weights-of-evidence model and remote sensing techniques is an effective method in determining areas of macrobenthos habitat potential in a tidal flat setting.

Integration of a subsidence model and SAR interferometry for a coal mine subsidence hazard map in Taebaek, Korea

Coal mine subsidence hazard can be effectively evaluated by geographic information system (GIS) analysis if sufficient data is provided. It is, however, difficult to obtain ground-based data, especially in remote and less populated mining areas. In this study, we construct and validate a coal mine subsidence hazard map in Taebaek, Korea, by integration of space-borne L-band synthetic aperture radar (SAR) measurements and a fuzzy-based subsidence model. There is an approximately 15-year time interval between the radar measurements used for the subsidence hazard model and those used to validate the subsidence. A subsidence hazard map was constructed using Japanese Earth Resources Satellite (JERS-1) SAR data from the early 1990s and the subsidence model. For the coal mine subsidence hazard mapping, a certainty factor analysis was used to estimate the relative weights of four control factors influencing coal mine subsidence, and the relative weight of each factor was then integrated to produce a subsidence hazard index by a fuzzy combination operator. The predicted hazard areas were then investigated and validated by comparison with subsidence occurrences observed by Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) interferometry in 2007–2008. The results showed a good agreement between the predicted locations vulnerable to subsidence and the actual subsidence occurrences with a prediction accuracy of about 73% and a root mean square error of about 0.35. These results demonstrate that a map produced by integration of a subsidence model and SAR interferometry can be used to predict and monitor coal mine subsidence hazards, especially in remote regions.

Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

Landslide susceptibility analysis using weight of evidence

Bayesian probability model, using the weight-of-evidence method, have applied to the task of evaluating landslide susceptibility using GIS data. The location chosen for the study was the Janghung area in Korea, which suffered substantial landslide damage following heavy rain in 1998. Using the location of the landslides, and topographic factors such as soil, forest and land use, the weight-of-evidence method was used to calculate each factors rating. Tests of conditional independence were performed for the selection of the factors, allowing the large number of combinations of factors to be analyzed. For each factors rating, the contrast value, W/sup +/ and W/sup -/, was overlaid for landslide susceptibility mapping. The results of the analysis were verified using the observed landslide locations, and among the combinations, the slope, aspect, curvature, soil material and timber types show the best results.