1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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266 Spatial Modelling of the Terrestrial Environment of snow depth observed from space and point measurements on the ground are compared. While there are similarities evident in the computed variograms, we are still uncertain of the spatial variation characteristics of snow depth beyond those that can be measured with a sparse network of points on the ground or from coarse resolution satellite observations. As Burke et al. point out, downscaling methods using statistical techniques or land surface modelling techniques can address this scaling issue but they are not straightforward. An example of a science community attempting to improve our understanding of the spatial variability of environmental parameters is the Cold Lands Processes Experiment (Cline et al., 2001). This was a two-year field experiment in Colorado, USA, conducted during the winters of 2002 and 2003 and designed to improve our fundamental understanding of the hydrology, meteorology, and ecology of the terrestrial cryosphere. It is a process-oriented experiment that seeks to address several key questions relating to mass and energy dynamics in the cryosphere and the spatial variability of these dynamics. The ultimate goal of the project is to provide a comprehensive benchmark data set that can help refine models of cryospheric processes and define the nature and scale of observations required from remote sensing instruments that could be used effectively to quantify key cryospheric processes (snow cover, frozen ground, etc.). The emphasis is firmly on spatial variability of processes and how processes scale up or down. This information should then be used to inform modellers and technologists alike to which spatial scales their models and instruments should be calibrated. 13.3 Outlook The future of spatial modelling of the terrestrial environment, therefore, appears to look very promising. The spatial resolution issue is related to our understanding of the scaling of processes and different research communities have shown great coherence in addressing this issue (e.g. the cryosphere, soil moisture, rainfall and vegetation monitoring communities). The production of increasingly accurate DEMs (both in the vertical and horizontal dimensions) is ongoing and the global characterization of vegetation from enhanced satellite instruments is constantly being improved. These advances are the result of coherence of the research communities at the international research community level and will help pave the way for the important enhancement of improved land surface models, which will enable us to better parameterize and constrain land surface conditions for global environmental models. If we are to be able to predict future environmental trends in our world with increased accuracy, spatial models of land surface processes (in both natural and human environments) are a vital part of that effort. References Casti, J.L., 1997, Would-be Worlds: How Computer Simulation Is Changing the Frontiers of Science (Chichester: John Wiley and Sons). Cline, D., Armstrong, R., Davis, R.E., Elder, K. and Liston, G., 2001, NASA Cold Land Processes Field Experiment plan 2002–2004, NASA Earth Science Enterprise: Land Surface Hydrology Program, NASA: Washington, DC.
Index Table in Bold, figure in Italic. ablation, estimates of mass loss by 31 ablation rates 20 Advanced Microwave Scanning Radiometer EOS (AMSR-E) 43, 44, 54, 253, 259 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 185, 186 Advanced Very High Resolution Radiometer (AVHRR) 158, 266 cloud-free images, Lake Tanganyika 164–5, 168 air pollution, fire-related 177 air quality modelling 233 air quality monitoring 228–9 airborne laser altimetry (LiDAR) see LiDAR airborne stereo-photogrammetry 86 Airborne Visible/IR Imaging Spectrometer (AVIRIS) 186 albedo-ice positive feedback 14 Along Track Scanning Radiometer (ATSR-2) 158, 164–5, 168 Antarctic Ice Sheet 14, 20–1 balance velocities of grounded portion 26 DEM(s) satellite-derived 31 for whole ice sheet 21, 23 east Antarctica, ice rheology 30 iceberg fluxes 25–6 mass budget 13 anthropogenic disturbance 138 assimilation techniques 70 Assimilation Value Index 69 ASTER see Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) atmospheric dust 137, 138–9 atmospheric forcing data 251–2 European Centre for Medium-Range Weather Forecasts (ECMWF) products 252 Global Data Assimilation System (GDAS) weather forecast model 251 NASA/Goddard Earth Observing System (GEOS) 251–2 automated data correction methods 133–4 automated data generation 114 automatic mesh generation 95, 96 automation, in error identification 121 AVHRR see Advanced Very High Resolution Radiometer (AVHRR) balance velocities 20–1, 26 Antarctica and Greenland 26, 27, 28 basal sliding 20 best estimates 11 Bi-spectral InfraRed Detection (BIRD) satellite, Hot Spot Recognition System (HSRS) 187 comparison with MODIS 188, 189–91 Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr. C○ 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.
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Spatial Modelling of the Terrestria
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Copyright C○ 2004 John Wiley & So
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Contents List of Contributors Prefa
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Contributors Jonathan L. Bamber Sch
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List of Contributors xi George Perr
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xiv Preface in theory and applicati
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2 Spatial Modelling of the Terrestr
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Editorial: Spatial Modelling in Hyd
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Editorial: Spatial Modelling in Hyd
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2 Modelling Ice Sheet Dynamics with
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Modelling Ice Sheet Dynamics by Sat
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36 Spatial Modelling of the Terrest
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4 Using Coupled Land Surface and Mi
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Coupled Land Surface and Microwave
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100 Spatial Modelling of the Terres
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Editorial: Terrestrial Sediment and
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Editorial: Terrestrial Sediment and
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6 Remotely Sensed Topographic Data
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Remotely Sensed Topographic Data fo
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7 Modelling Wind Erosion and Dust E
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Modelling Wind Erosion and Dust Emi
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8 Near Real-Time Modelling of Regio
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Near Real-Time Modelling of Regiona
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High Low TSM Concentration Figure 8
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9 Estimation of Energy Emissions, F
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Fireline Intensity and Biomass Cons
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Figure 9.4 The upper row shows EOS
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PART III SPATIAL MODELLING OF URBAN
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Page 232 and 233: 11 Modelling the Impact of Traffic
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Page 248 and 249: PART IV CURRENT CHALLENGES AND FUTU
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