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Fine Spatial Resolution (fine + spatial_resolution)
Selected AbstractsGeostatistical Prediction and Simulation of Point Values from Areal DataGEOGRAPHICAL ANALYSIS, Issue 2 2005Phaedon C. Kyriakidis The spatial prediction and simulation of point values from areal data are addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement or unknown value). It is shown that the geostatistical framework (i) can explicitly and consistently account for the support differences between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, alternative point-support simulated realizations of a spatial attribute reproduce (i) a point-support histogram (Gaussian in this work), (ii) a point-support semivariogram model (possibly including anisotropic nested structures), and (iii) when upscaled, the available areal data. Such point-support-simulated realizations can be used in a Monte Carlo framework to assess the uncertainty in spatially distributed model outputs operating at a fine spatial resolution because of uncertain input parameters inferred from coarser spatial resolution data. Alternatively, such simulated realizations can be used in a model-based hypothesis-testing context to approximate the sampling distribution of, say, the correlation coefficient between two spatial data sets, when one is available at a point support and the other at an areal support. A case study using synthetic data illustrates the application of the proposed methodology in a remote sensing context, whereby areal data are available on a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions and realizations are consistent with the available information at the coarser (pixel-level) spatial resolution. [source] Evaluation of model complexity and space,time resolution on the prediction of long-term soil salinity dynamics, western San Joaquin Valley, CaliforniaHYDROLOGICAL PROCESSES, Issue 13 2006G. Schoups Abstract The numerical simulation of long-term large-scale (field to regional) variably saturated subsurface flow and transport remains a computational challenge, even with today's computing power. Therefore, it is appropriate to develop and use simplified models that focus on the main processes operating at the pertinent time and space scales, as long as the error introduced by the simpler model is small relative to the uncertainties associated with the spatial and temporal variation of boundary conditions and parameter values. This study investigates the effects of various model simplifications on the prediction of long-term soil salinity and salt transport in irrigated soils. Average root-zone salinity and cumulative annual drainage salt load were predicted for a 10-year period using a one-dimensional numerical flow and transport model (i.e. UNSATCHEM) that accounts for solute advection, dispersion and diffusion, and complex salt chemistry. The model uses daily values for rainfall, irrigation, and potential evapotranspiration rates. Model simulations consist of benchmark scenarios for different hypothetical cases that include shallow and deep water tables, different leaching fractions and soil gypsum content, and shallow groundwater salinity, with and without soil chemical reactions. These hypothetical benchmark simulations are compared with the results of various model simplifications that considered (i) annual average boundary conditions, (ii) coarser spatial discretization, and (iii) reducing the complexity of the salt-soil reaction system. Based on the 10-year simulation results, we conclude that salt transport modelling does not require daily boundary conditions, a fine spatial resolution, or complex salt chemistry. Instead, if the focus is on long-term salinity, then a simplified modelling approach can be used, using annually averaged boundary conditions, a coarse spatial discretization, and inclusion of soil chemistry that only accounts for cation exchange and gypsum dissolution,precipitation. We also demonstrate that prediction errors due to these model simplifications may be small, when compared with effects of parameter uncertainty on model predictions. The proposed model simplifications lead to larger time steps and reduced computer simulation times by a factor of 1000. Copyright © 2006 John Wiley & Sons, Ltd. [source] Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effectsJOURNAL OF BIOGEOGRAPHY, Issue 11 2004Nicolas Titeux Abstract Aim, To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location, The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods, Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ,pure' environmental variation, (b) spatially-structured environmental variation, (c) ,pure' spatial variation and (d) unexplained, non-spatial variation. Results, The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ,pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions, The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ,noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of ,false correlation') and to bring out the ,pure' environmental variation for ecological interpretation. [source] Optical remote mapping of rivers at sub-meter resolutions and watershed extentsEARTH SURFACE PROCESSES AND LANDFORMS, Issue 1 2008W. Andrew Marcus Abstract At watershed extents, our understanding of river form, process and function is largely based on locally intensive mapping of river reaches, or on spatially extensive but low density data scattered throughout a watershed (e.g. cross sections). The net effect has been to characterize streams as discontinuous systems. Recent advances in optical remote sensing of rivers indicate that it should now be possible to generate accurate and continuous maps of in-stream habitats, depths, algae, wood, stream power and other features at sub-meter resolutions across entire watersheds so long as the water is clear and the aerial view is unobstructed. Such maps would transform river science and management by providing improved data, better models and explanation, and enhanced applications. Obstacles to achieving this vision include variations in optics associated with shadows, water clarity, variable substrates and target,sun angle geometry. Logistical obstacles are primarily due to the reliance of existing ground validation procedures on time-of-flight field measurements, which are impossible to accomplish at watershed extents, particularly in large and difficult to access river basins. Philosophical issues must also be addressed that relate to the expectations around accuracy assessment, the need for and utility of physically based models to evaluate remote sensing results and the ethics of revealing information about river resources at fine spatial resolutions. Despite these obstacles and issues, catchment extent remote river mapping is now feasible, as is demonstrated by a proof-of-concept example for the Nueces River, Texas, and examples of how different image types (radar, lidar, thermal) could be merged with optical imagery. The greatest obstacle to development and implementation of more remote sensing, catchment scale ,river observatories' is the absence of broadly based funding initiatives to support collaborative research by multiple investigators in different river settings. Copyright © 2007 John Wiley & Sons, Ltd. [source] | ||||||||||