No-Wook Park

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.

Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets

The potential of using maximum entropy modeling for landslide susceptibility mapping is investigated in this paper. Although the maximum entropy model has been applied widely to species distribution modeling in ecology, its applicability to other kinds of predictive modeling such as landslide susceptibility mapping has not yet been investigated fully. In the present case study of Boeun in Korea, multiple environmental factors including continuous and categorical data were used as inputs for maximum entropy modeling. From the optimal setting test based on cross-validation, the effective feature type for continuous data representation was found to be a hinge feature and its combination with categorical data showed the best predictive performance. Factor contribution analysis indicated that distances from lineaments and slope layers were the most influential factors. From interpretations on a response curve, steeply sloping and weathered areas that consisted of excessively drained granite residuum soils were very susceptible to landslides. Predictive performance of maximum entropy modeling was slightly better than that of a logistic regression model which has been used widely to assess landslide susceptibility. Therefore, maximum entropy modeling is shown to be an effective prediction model for landslide susceptibility mapping.

Accounting for temporal contextual information in land-cover classification with multi-sensor SAR data

This paper investigates the potential of accounting for temporal contextual information in order to improve the accuracy of land-cover classification in summer with Synthetic Aperture Radar (SAR) data. Bi-temporal multi-sensor datasets collected in the Nonsan area of Korea were used to illustrate this approach. Multi-sensor data, including Japanese Earth Resources Satellite (JERS)-1 Optical Sensor (OPS) data acquired in April, and three different SAR sensor datasets from European Resource Satellite (ERS)-2, JERS-1, and Radarsat-1 obtained in the following July, were used for supervised classification in July. By comparing the classification result in April with a training set in July, transition probabilities between land-cover classes in the April–July period were empirically estimated and regarded as the temporal contextual information. A tau model is applied as a main integration methodology to combine multiple SAR data and the temporal contextual information. From the evaluation of the classification results in terms of accuracy statistics, using multiple SAR sensor data showed an increase of about 29% in overall accuracy compared with the case of single SAR sensor data. The incorporation of temporal contextual information into scattering information greatly contributed to a significant improvement of about 25% in overall accuracy over multiple SAR sensor integration only, and showed the best discrimination capability.

Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data

A geostatistical contextual classifier for land-cover mapping using remote sensing data is presented. To integrate spatial information with spectral information derived from remote sensing data, a geostatistical indicator approach is adopted to determine the probability of a certain land-cover class occuring at an unsampled location given that any other land-cover classes occur at neighboring locations. The geostatistical indicator algorithm applied here is simple indicator kriging with local means. This approach can directly integrate both spatial information of ground data (hard data) and spectral information of remote sensing data (soft data) within an indicator kriging framework. This algorithm is applied to the classification of multi-sensor remote sensing data for land-cover mapping. This classification result is compared with a result obtained from a conventional spectral information based classification method.

Integration of IKONOS imagery for geostatistical mapping of sediment grain size at Baramarae beach, Korea

We describe the potential of high-resolution remote sensing imagery in the geostatistical mapping of sediment grain size distribution in order to supplement sparsely sampled ground observations. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsampled locations. Multiple regression and generalized additive models are applied to compute local mean values. From a case study of Baramarae beach, Korea, all imagery bands showed a reasonable linear relationship with grain size values in phi units, having a correlation coefficient of more than –0.80. Accounting for the IKONOS imagery via simple kriging with local means could reflect detailed surface characteristics with less smoothing effects. Cross validation results showed that the mean square errors from simple kriging with local means via the generalized additive model provided a relative improvement of about 60% over univariate multi-Gaussian kriging and a superior predictive capability when compared with simple kriging with local means via the traditional multiple regression model.

Identification of landslide area using remote sensing data and quantitative assessment of landslide hazard

This paper discusses the application of remote sensing techniques and GIS-based prediction modeling for landslide hazard mapping. First, we aim at identifying the past landslide locations by comparing various remote sensing data (KOMPSAT EOC, IRS, SPOT). Using the detected landslides locations and spatial database, probabilistic prediction model based on likelihood ratio function is constructed to generate a prediction map for landslide hazard mapping. After generating the prediction results, to provide the measures of significance of prediction results, a validation of the prediction results is carried out. Through the validation procedure, the prediction results can provide meaningful interpretation with respect to the future landslides. A case study from Jangheung area in Korea is carried out to illustrate above schemes.

Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges.

Time-series Mapping and Uncertainty Modeling of Environmental Variables: A Case Study of PM 10 Concentration Mapping

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 () concentration mapping in Incheon Metropolitan city using monthly concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

Application of Indicator Geostatistics for Probabilistic Uncertainty and Risk Analyses of Geochemical Data

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

Multi-sensor data fusion for supervised land-cover classification using Bayesian and geostatistical techniques

We propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. The classification based only on the traditional spectral approach cannot preserve the accurate spatial information and can result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is incorporated into the Bayesian framework. This new approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data was carried out. Analysis of the results indicates that the proposed method considerably improves the classification accuracy, compared to the methods based on the spectral information alone.