Chiang Wei

Locating landslides using multi-temporal satellite images

Landuse/landcover change detection using remotely sensed images has been widely investigated. Most applications of this type involve either image differencing or image classification using multi-temporal images. If multi-temporal images are to be used for quantitative analysis based on their radiometric information, as in the case of change detection or landuse classification, geometric rectification and radiometric correction must be performed priori to subsequent image analyses. In particular, geometric rectification has significant effect on the accuracy of landuse change detection in areas of rugged terrain. Remote sensing image rectification is commonly done by applying a polynomial trend mapping (PTM) model to image coordinates and map coordinates of ground-control-points. A major drawback of the PTM model is that it does not capture the random characteristics of terrain elevation. In this study an ordinary kriging approach is applied for image-to-image registration. The approach considers residuals of the PTM model as anisotropic random fields and employs the ordinary kriging method for spatial interpolation of the residual random fields. Band-ratioing technique was also employed for relative radiometric normalization. From the grey-level histograms of pre- and post-event band-ratio images, we determined the percentage of landuse changes in the study area. Image differencing was then performed using the pre- and post-event band-ratio image pair. Finally, a grey-level threshold of the band-ratio difference image is determined as the value whose exceeding probability equals the areal percentage of landuse change. DTM data of the study area were also used to further restrict landslide areas to steep slope areas.

Entropy and kriging approach to rainfall network design

Hydrological data are the basic ingredients for planning, constructing, and operating of hydraulic structures. A well-designed rainfall network can accurately provide and reflect the information of rainfall in a catchment. However, in past studies, the required number and optimal location of rain gauge stations have yet to produce a satisfactory result. A more accurate design is required. Hence, in this study, a proposed model composed of kriging and entropy with probability distribution function is introduced to relocate the rainfall network and to obtain the optimal design with the minimum number of rain gauges. The ordinary kriging is used to generate rainfall data of potential locations where rain gauge stations may be installed. The information entropy based on probability is used to measure the uncertainty of rainfall distribution. The probability distribution function will be introduced to fit the statistical characteristics of data of the rain gauges. By calculating the joint entropy and the transferable information, the relocated rain gauges are prioritized and the minimum number and location of the rain gauges in the catchment can be obtained to construct the optimal rainfall network to replace the existing rainfall network.

Estimation of River Pollution Index in a Tidal Stream Using Kriging Analysis

Tidal streams are complex watercourses that represent a transitional zone between riverine and marine systems; they occur where fresh and marine waters converge. Because tidal circulation processes cause substantial turbulence in these highly dynamic zones, tidal streams are the most productive of water bodies. Their rich biological diversity, combined with the convenience of land and water transports, provide sites for concentrated populations that evolve into large cities. Domestic wastewater is generally discharged directly into tidal streams in Taiwan, necessitating regular evaluation of the water quality of these streams. Given the complex flow dynamics of tidal streams, only a few models can effectively evaluate and identify pollution levels. This study evaluates the river pollution index (RPI) in tidal streams by using kriging analysis. This is a geostatistical method for interpolating random spatial variation to estimate linear grid points in two or three dimensions. A kriging-based method is developed to evaluate RPI in tidal streams, which is typically considered as 1D in hydraulic engineering. The proposed method efficiently evaluates RPI in tidal streams with the minimum amount of water quality data. Data of the Tanshui River downstream reach available from an estuarine area validate the accuracy and reliability of the proposed method. Results of this study demonstrate that this simple yet reliable method can effectively estimate RPI in tidal streams.

A Feature-Space Indicator Kriging Approach for Remote Sensing Image Classification

An indicator kriging (IK) approach for remote sensing image classification is proposed. By introducing indicator variables for categorical data, the work of image classification is transformed into estimation of class-dependent probabilities in feature space using ordinary kriging. Individual pixels are then assigned to the class with maximum class probability. The approach is distribution free and yields perfect classification accuracies for training data provided that collocated data in feature space do not exist. Technical considerations regarding implementation of IK such as indicator semivariogram modeling and handling of collocated data in feature space are also described. The IK, Gaussian-based maximum likelihood, nearest neighbor, and support vector machine (SVM) classifiers were applied to study areas within the Shimen reservoir watershed (case A: FORMOSAT-2) and Taipei city (case B: SPOT 4). The results show that the overall accuracies of the proposed IK classifier and SVM can achieve higher than 97% for training data and 81% for testing data. (The overall accuracies of IK are a little higher than those of SVM.) IK and SVM are found to be superior to the other two classifiers in terms of overall accuracies for both training and testing data. The proposed IK classifier has the following advantages: 1) It can deal with anisotropic problem in feature space; 2) it is a nonparametric method and needs not to know the type of probability distribution; and 3) it yields 100% classification accuracy for the training data provided that collocated data in feature space do not exist.

Cooling Effect of Rivers on Metropolitan Taipei Using Remote Sensing

This study applied remote sensing technology to analyze how rivers in the urban environment affect the surface temperature of their ambient areas. While surface meteorological stations can supply accurate data points in the city, remote sensing can provide such data in a two-dimensional (2-D) manner. The goal of this paper is to apply the remote sensing technique to further our understanding of the relationship between the surface temperature and rivers in urban areas. The 2-D surface temperature data was retrieved from Landsat-7 thermal infrared images, while data collected by Formosat-2 was used to categorize the land uses in the urban area. The land surface temperature distribution is simulated by a sigmoid function with nonlinear regression analysis. Combining the aforementioned data, the range of effect on the surface temperature from rivers can be derived. With the remote sensing data collected for the Taipei Metropolitan area, factors affecting the surface temperature were explored. It indicated that the effect on the developed area was less significant than on the ambient nature zone; moreover, the size of the buffer zone between the river and city, such as the wetlands or flood plain, was found to correlate with the affected distance of the river surface temperature.

Rainfall Network Optimization Using Radar and Entropy

In this study, a method combining radar and entropy was proposed to design a rainfall network. Owing to the shortage of rain gauges in mountain areas, weather radars are used to measure rainfall over catchments. The major advantage of radar is that it is possible to observe rainfall widely in a short time. However, the rainfall data obtained by radar do not necessarily correspond to that observed by ground-based rain gauges. The in-situ rainfall data from telemetering rain gauges were used to calibrate a radar system. Therefore, the rainfall intensity; as well as its distribution over the catchment can be obtained using radar. Once the rainfall data of past years at the desired locations over the catchment were generated, the entropy based on probability was applied to optimize the rainfall network. This method is applicable in remote and mountain areas. Its most important utility is to construct an optimal rainfall network in an ungauged catchment. The design of a rainfall network in the catchment of the Feitsui Reservoir was used to illustrate the various steps as well as the reliability of the method.

A new approach to selecting a regionalized design hyetograph by principal component analysis and analytic hierarchy process

Designing storm hyetographs is the essential element of hydrologic modeling analysis and storm water drainage design. In order to reasonably use storm hyetograph design in an un-gauged area, a regional representative hyetograph from an alternative and uniform area must be found. A new approach is proposed in this study to select a regional design storm hyetograph using principal component analysis (PCA) and analytic hierarchy process (AHP). The proposed approach combines both PCA and cluster analysis techniques. Furthermore, the AHP method is also used to establish the regional design hyetograph. A case study applied in the area of northern Taiwan shows that our method can successfully categorize the area into three homogenous zones. A representative regional hyetograph can be obtained by selecting the largest priority vector or by the weighted average of rain gauges in each zone.