The species composition and recruitment of apogonids in the coastal waters off southwestern Taiwan were investigated, in order to understand their spatiotemporal variation and the biology of the dominant species, Apogon fasciatus. From June 2000 to August 2005, samples were collected by a beam trawl, operating at seven stations, including Jiading, Zouying, Jhongjhou, Linyuan, Dapeng, Linbian and Fangliao. In total, 33 cruises with 411 nets were included. A total of 4,540 specimens of Apogonidae were collected, belonging to 2 genus and 15 species. There were 6 species, including Apogon fraenatus, Apogon moluccensis, Apogon nigripinnis, Apogon nigrifasciatus, Apogon notatus and Apogon semilineatus, which were first recorded in the sandy or muddy habitats. The number of species was highest at Fangliao (11 species) and lowest at Dapeng and Jiading (5 spp.). Abundance and biomass were highest at Zouying (15.3 ind./net ) and Jhongjhou (60.0 g/net), respectively, whereas, the lowest abundance and biomass were both at Jiading (2.1 ind./net; 11.1 g/net). The most abundant species was Apogon fasciatus (86.6%), which also was the dominant species at each station, following by Apogon pleuron (4.5%), Apogon striatus (2.7%), Archamia bleekeri (2.7%) and Apogon niger (1.2%), comprising 97.7% of the apogonids. Although A. fasciatus and A. pleuron resemble similarly in shape and decorative pattern, the length-weight relationships are significantly different (ANOCA, p<0.001). The former is BW = 1.7 ¡Ñ (10^-6)(TL^3.488), the later is 3.4¡Ñ(10^-6)(TL^3.312) . The parameters, K, L¡Û, t0, of von Bertalanffy growth function of A. fasciatus were 1.88 yr^-1, 105.5 mm TL and -0.04. Showing that A. fasciatus growths much quicker before Age 1 than thereafter. The minimum size of mature female A. fasciatus was found 46.39 mm TL, and the size at maturity (L50) is estimated 75.39 mm TL, approximately 7 month-old. The frequency distribution of egg diameter indicated it is a multiple spawner. In May and October which were transit period between dry and rainy seasons, the abundance of A. fasciatus was normally higher than rest months reflecting the annual recruitment of this species. The GSI variation showed that the major breeding season was form on March to September. However, the small A. fasciatus (< 30 mm TL, about 1 month-old) were found throughout the year that implied A. fasciatus may reproduce annually. Moreover, coastal southward waters of Linyuan was the mainly habitat of A. fasciatus in southwestern coastal waters off Taiwan.

Accurate information on the spatial-temporal distributions of air pollution at a regional scale is crucial for effective air quality control, as well as to impact studies on local climate and public health. The current practice of mapping air quality relies heavily on data from monitoring stations, which are often quite sparse and irregularly spaced. The research presented in this dissertation seeks to advance the methodologies involved in spatiotemporal analysis of air quality that integrates remotely-sensed data and in situ measurement. Aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is analyzed to estimate fine particulate matter (PM2.5) concentrations as the target air pollutant. The spatial-temporal distribution of columnar aerosol loading is investigated through mapping MODIS AOD in southern Ontario, Canada throughout 2004. Clear distribution patterns and strong seasonality are found for the study area. There is a detectable relationship between an AOD level and underlying land use structure and topography on the ground. MODIS AOD was correlated with the ground-level PM2.5 concentration (GL-[PM2.5]) at various wavelengths. The AOD-PM2.5 correlation is found to be sensitive to spatial-temporal scale changes. Further, a semi-empirical model has been developed for a more accurate prediction of GL-[PM2.5]. The model employs MODIS AOD data, assimilated meteorological fields, and ground-based meteorological measurements and is able to explain 65% of the variability in GL-[PM2.5]. To achieve a more accurate and informative spatiotemporal modelling of GL-[PM2.5], a method is proposed that integrates the model-predictions and in situ measurements in the framework of Bayesian Maximum Entropy (BME) analysis. A case study of southern Ontario demonstrates the procedures of the method and support for its advantages by comparison with conventional geostatistical approaches. The BME estimation, coupled with BME posterior variance, can be used to depict GL-[PM2.5] distribution in a stochastic context. The methodologies covered in this work are expected to be applicable to the modelling or analysis of other types of air pollutant concentrations. / Thesis (Ph.D, Geography) -- Queen's University, 2009-01-08 14:43:49.333

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Recent research has enabled reconstruction of a scene from multiple images. These algorithms rely on assumptions that the scene does not change drastically from one photo to the next. Construction photographs, in particular, pose challenges for the existing algorithms. I propose a novel image-based reconstruction algorithm that overcomes these limitations and reconstructs a 4D indoor Manhattan model using construction photographs. Finally, I present a novel user interface that combines image-based rendering and intuitive 4D navigation controls to observe and explore the resulting reconstruction.

Recent research has enabled reconstruction of a scene from multiple images. These algorithms rely on assumptions that the scene does not change drastically from one photo to the next. Construction photographs, in particular, pose challenges for the existing algorithms. I propose a novel image-based reconstruction algorithm that overcomes these limitations and reconstructs a 4D indoor Manhattan model using construction photographs. Finally, I present a novel user interface that combines image-based rendering and intuitive 4D navigation controls to observe and explore the resulting reconstruction.

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Spatiotemporal heterogeneity in predator activity can generate and influence the availability of refugia to prey. In eastern forests, white-footed mice (Peromyscus leucopus) and eastern chipmunks (Tamias striatus) are abundant generalist rodents, and large-scale removal experiments have confirmed they are important predators of gypsy moth (Lymantria dispar) pupae and songbird nests and eggs. Models predict the extinction of gypsy moth populations when confronted with abundant mouse populations, but small-scale (10s of m) heterogeneity in rodent activity may allow for the persistence of moth populations. I quantified the magnitude, variability, temporal persistence, and spatial structure of white-footed mouse and eastern chipmunk activity, and evaluated the effects of small-scale (30 x 30 m "spots") rodent removal, on 3 pairs of oak-dominated plots for 3, 2-week periods in summers 2008 and 2009 at the Cary Institute of Ecosystem Studies, Millbrook, NY, USA. Small-mammal track activity (1/check) was best fit by a beta-binomial distribution, and the mean and CV ranges of mouse and chipmunk track activity were similar between years. Disattenuated correlations of mouse and chipmunk activity were similar between sampling periods, as well as between years. I found little evidence of spatial structure in rodent activity at the scales sampled (15-250 m). Mean local track activity counterintuitively increased in removal spots compared to control spots for mice in 2008 and chipmunks in 2009. Local, between-year track activity was more strongly correlated and of greater magnitude in persistent removal spots than in non-persistent removal spots for both mice and chipmunks Environmental factors like abundant alternative food sources can influence predator foraging behavior by concentrating predator space use and altering predation rates on incidental prey items. However, the spatial scale of this aggregative effect, and impact on consumption rates on incidental prey items, are not well understood. In spring 2010, I conducted live-trapping, measured local rodent track activity, and quantified consumption rates on two incidental prey items (almonds [Prunus dulcis] and maple [Acer saccharum] seeds) on 6 plots provided with 3 supplemental food treatments (control, corn, and sunflower seeds) at Touch of Nature Environmental Center, Carbondale, IL, USA. A half-normal, cosine detectability function best fit our live-trapping data in both pre- and post-experiment trapping sessions, but considerable support remained for other models. Overall mean track activity was greater in control treatments than in sunflower and corn treatments. I found a significant interaction effect of treatment and distance, and significantly increased activity in control treatments at distances of 0, 10, and 40 m. Overall mean almond and maple seed consumption was greater in control treatments than in sunflower and corn treatments, but was greater in corn than sunflower treatments and increased from period 1 to period 3 at all distances. Mean almond consumption by mouse only and mouse + unknown predator groups was greater in control treatments than in sunflower and corn treatments. Mean maple seed consumption by mouse only and mouse + unknown predator groups was greater in control treatments than in sunflower and corn treatments.

Positron emission tomography (PET) is a widely used molecular imaging modality,which offers quantitative information about many biochemical processes in vivo. In particular, the dynamic PET data provide physiologically meaningful parametricimages after the estimation of the parameters of a model that best describes thekinetic behaviour of the injected radiotracer. Spatiotemporal 4D image reconstructionalgorithms estimate these physiological parameters directly from the raw sinogramdata, where the noise distribution can be more accurately modelled and thus leading tostatistically more reliable parameter estimates. In this thesis a novel direct parametricimage reconstruction algorithm is introduced, which is based on the expectationmaximisation (EM) framework and is applicable to any spatiotemporal model. Themethod is evaluated for the spectral analysis model, which is a linear temporal modeland a two-tissue compartment model, which is a nonlinear temporal model. Inaddition, the method is evaluated for a linear spatial model and in particular the modelthat is normally used to describe the blurring components in image-based resolutionmodelling. Finally, the performance of gradient-based 3D reconstruction algorithmswas also assessed as an alternative to the well-established EM-based algorithms.

Scientific datasets from global-scale scientific models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. These data are frequently gridded measurements spanning two or three spatial dimensions, the time dimension, and often several data dimensions which vary by the specific dataset. These data are useful in many modeling and analysis applications across the geosciences. Unlike vector spatial datasets, raster spatial datasets lack widely adopted conventions in file formats, data organization, and dissemination mechanisms. Raster datasets are often saved using the Network Common Data Format (NetCDF), Gridded Binary (GRIB), Hierarchical Data Format (HDF), or Geographic Tagged Image File Format (GeoTIFF) file formats. Several of these are entirely or partially incompatible with common GIS software which introduces additional complexity in extracting values from these datasets. We present a method and companion Python package as a general-purpose tool for extracting time series subsets from these files using various spatial geometries. This method and tool enable efficient access to multidimensional data regardless of the format of the data. This research builds on existing file formats and software rather than suggesting new alternatives. We also present an analysis of optimizations and performance.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第17967号 / 情博第511号 / 新制||情||91(附属図書館) / 30797 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 松山 隆司, 教授 乾 敏郎, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM