Low resolution (LR) Grid Digital Elevation Models (DEMs) are the inputs of multi frame super resolution (MFSR) algorithm to obtain high resolution (HR) grid DEM. In digital image MFSR, non-redundant information carrying LR image pairs are a necessity. By using the analogy between digital image and grid DEMs, it is proven that, although the LR grid DEMs have a single source, they carry non-redundant information and they can be inputs of MFSR. Quality of grid DEM can be increased by using MFSR techniques. The level of spatial enhancement is directly related to the amount of non-redundant information that the LR grid DEM pairs carry. It is seen that super resolution techniques have potential to increase the accuracy of grid DEMs from a limited sampling.

学位授与大学：京都大学 ; 取得学位: 博士(工学) ; 学位授与年月日: 2007-09-25 ; 学位の種類: 新制・課程博士 ; 学位記番号: 工博第2849号 ; 請求記号: 新制/工/1419 ; 整理番号: 25534 / Understanding river water quantity and quality variation is one of the fundamental requirements for the integrated watershed management. Monitoring is usually preferred to examine and understand the river water quantity and quality, especially focusing on pre-specified objectives. Although monitoring is invaluable in many instances, it is of less use to forecast the foreseeable changes, especially, for the long-term prediction that is usually required by the decision-makers. Therefore, for the decision-making, modeling is widely practiced. Due to the limited understanding of hydrological processes inside a watershed, models often fail to estimate properly, which in worst case could often mislead the targeted plans. Among several aspects, spatial variability such as land cover, topography, soil, geology is believed to affect the overall performance of the model. Such thought lead to the concept of distributed models that were supposed to represent spatial variability through modeling specific variations inside the watershed by using several representative units or grids. In that meaning, distributed models required to identify and assign the values of its parameters to represent the physical processes defined by the governing equations for each grid. Due to the unavailability of required spatial information at appropriate grid sizes, even physically based and conceptually sound distributed models fail to estimate properly thereby offsetting the credibility of distributed models. Therefore, in this study, we set a major objective to develop a new distributed water quantity and water quality model to address some of the stated issues. Major emphasis was given to conceptually sound but simple structure of the model. In addition to that, model aimed to utilize the potential of recent advances in spatial information, such as remote sensing and GIS, to generate and process the spatial data, and to determine the values of its essential parameters. The approach was expected to provide an example that the complexity of the model should be preferred only if the defined processes could be ascertained within some reasonable limit. At the initial stage, several spatial data were collected from different sources and they were processed into raster format, which was one of the essential requirements for the distributed model. Analysis of spatial database indicated that the watershed was characterized by forested parts in the hills, and densely populated urban areas in plains. Rainfall occurred quite frequently but they were of short duration. Besides constructing spatial database, several water quantity and quality surveys were also conducted at different spatial and temporal conditions from 2000 to 2006. The data were mainly used to understand variation patterns of water quantity and quality at both spatial and temporal conditions. Later on, some of the data were also used for the verification of model in study area. 28 water quality indices (WQIs) were observed for each observation, which were mainly utilized to understand the overall variation pattern of river water quality. Initial analysis of flow rate condition of the river showed that the rainfall-runoff responses were quite rapid after the rainfall but such effect appear for very short duration (< 2 days). Then, analysis of variance (ANOVA) and two multivariate analysis techniques (MVA), namely, principle component analysis (PCA) and cluster analysis (CA) were used to explore effectively the river water quality datasets. Analysis showed that the observed covariation among majority of WQIs could be due to the inter-linkages among rainfall pattern, atmospheric deposition of acidic ions, soil and geology of dominant forest areas, topography, and climatic conditions. The identified pattern indicated that there could be close relationship between the biogeochemical processes in the forest areas with both river water quantity and quality variation. A new distributed water quantity and quality model was developed especially focusing on the biophysical characteristics of the watershed. Basic structure of the model was similar to the concept of lumped tank model, which was often credited for its simple and sound conceptual structure. Two storey tanks were conceptualized for each grid, but model also took into consideration of drainage channels in urban areas and natural river channels as rapidly conveying structures. Besides, the model considered all major aspects affecting the estimation of water quantity, such as interception of the rainfall, evapotranspiration loss, surface runoff, sub-surface runoff, and ground water runoff. Compared with the original tank model, major emphasis was given to assign the values major parameters, such as coefficients and storage heights of the outlets, by relating them with the hilly topography of the study area and the variation in land cover, soil, and geology. The model was further integrated with water quality component, which was based on two fundamental assumptions of build-up and wash-off of the WQIs in the environment. Build-up was based on the land cover type and population, while wash off was based on the estimated runoff volume. Remote sensing and GIS techniques were used to assist in the modeling process. At first, remote sensing was mainly focused in the classification of land cover by utilizing seasonal Landsat ETM+ images. In addition to urban and vegetated urban categories, four major forest categories (shaded, deciduous, mixed, and evergreen) were identified. Then leaf area index (Lai) was determined for each vegetation category. Lai was mainly used to determine the rainfall interception by the canopy in the forest areas. In this study, forest areas showed the capacity to intercept as high as 1.2 mm of rainfall, which could be quite important during smaller rainfall events. Remote sensing was further used to determine the transpiration coefficient of the vegetations, which was a major requirement for the estimation of evapotranspiration (Et) loss by the FAO Penman- Monteith method used in the model simulation. Et was estimated even reached more than 4 mm/d in summer months, but it was relatively lower (< 2 mm/d) in the winter months. These facts suggested that consideration of both interception and Et loss in a forested watershed could have significant influence on the estimation of flow rates by the model. At the final stage, model was applied in the study area. Mainly three approaches were considered to assess the estimation by the model. First was conventional approach in which comparison between the observed and estimated data were done considering different spatial and temporal contexts. Assigned values of the parameters gave satisfactory prediction for both water quantity and quality for the selected grid size of 50 m in which the relative error was usually less than 1. The second approach evaluated the model by considering different scale of the grids ranging from 100m to 500m. It was observed that grid resizing usually affected the basin attributed such as slope, outlet height, drainage characteristics following nearly proportionate pattern than other categorical variables such as land cover or geology. Usually same parameter values gave very different prediction level for both magnitude and shape of the hydrographs (or pollutographs), in which increasing grid size was accompanied by the increasing peak event estimation or overall error. The effects were further assessed by changing the values of key parameters for each grid size targeting the minimum differences between the observed and estimated values. Interestingly, the parameters also showed some identifiable (increasing or decreasing) trend with the change in grid size. Particularly, due to the direct effect of predicted runoff on the reference WQIs, its showed more complex variation pattern at different grid sizes. Overall assessment of the distributed model indicated that the model was quite sensitive to the selection of key parameters for different grid sizes. It indicated that the values of calibrated parameters might not give stable result if the scale of input data were changed. It could further indicate that the choice of grid size should be assessed before the actual application of the model considering the spatial variability of the watershed. In the third approach, model was utilized to estimate at different scenarios, namely, rainfall variation and land cover changes. The differences in the estimated results could indicate that the model could be available for the watershed management at different runoff and land cover scenarios in future. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第13378号 / 工博第2849号 / 新制||工||1419(附属図書館) / 25534 / UT51-2007-Q779 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 田中 宏明, 教授 藤井 滋穂, 教授 清水 芳久 / 学位規則第4条第1項該当

This thesis presents the development of a new multiscale stochastic fracture mechanics modelling framework informed by in-situ X-ray Computed Tomography (X-ray CT) tests, which can be used to enhance the quality of new designs and prognosis practices for fibre reinforced composites. To reduce the empiricism and conservatism of existing methods, this PhD research systematically has tackled several challenging tasks including: (i) extension of the cohesive interface crack model to multi-phase composites in both 2D and 3D, (ii) development of a new in-house loading rig to support in-situ X-ray CT tests, (iii) reconstruction of low phase-contrast X-ray CT datasets of carbon fibre composites, (iv) integration of X-ray CT image-based models into detailed crack propagation FE modelling and (v) validation of a partially informed multiscale stochastic modelling method by direct comparison with in-situ X-ray CT tensile test results.