1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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Near Real-Time Modelling of Regional Scale Soil Erosion 161 is a strong relationship between v and NDVI, however, this relationship varies, with field measurements using radiometers and spectroradiometers providing higher NDVI values than those obtained from ground measurements correlated to AVHRR imagery. This is due to atmospheric effects that affect the satellite-based measurements but not the spectroradiometer data. Because no atmospheric correction was applied to the AVHRR imagery a regression relationship was calculated using only the AVHRR-derived NDVI and the vegetation cover information: V c = 1.333 + 131.877 NDVI (3) Fit statistics: r 2 = 0.73, F = 241.63,p = 4.26 × 10 −27 . It is important to note that the regions of persistent cloud cover could introduce errors in vegetation cover estimation and thus potentially introduce uncertainties for soil erosion estimation. In cloud-covered areas it is likely to be raining for much of the time, and these regions will tend to suffer high-overland flow and erosion. However, they are also the areas where the actual vegetation cover is at its most uncertain and errors in vegetation cover can produce a considerable change in predicted erosion as it is the most sensitive parameter in the model (Drake et al., 1999). Scale also poses a problem when using coarse resolution vegetation cover estimates to parameterize regional soil erosion models. A reduction in predicted erosion occurs as the spatial resolution of the vegetation cover is reduced (Drake et al., 1999). This is caused by a loss of variance and is a result of the way erosion is parameterized in the model. The negative exponential relationship between cover and erosion (equation (1)) means that erosion is very high in bare areas but very low once cover exceeds around 40%. If we consider an image of vegetation patches (i.e., fields) with 100% cover, surrounded by bare fields, erosion is very high in bare areas and non-existent in the vegetated fields. When the spatial resolution of the image is reduced to a point where it exceeds the field size, predicted erosion is reduced because some of the vegetation cover of the vegetated field is assigned to the bare areas. To overcome this scaling problem we have implemented the Polya function method developed by Zhang et al. (2002). This technique represents spatial variation of vegetation cover in a pixel as a histogram of cover and uses eight neighbouring pixels to estimate the sub-pixel histogram at a specified spatial resolution (we use 30 m). Thus the output of the Poyla function is an image with a degraded spatial resolution (8 km) that contains a histogram of vegetation cover variation within each pixel. The erosion model is then run for each column of the histogram and the results summed to obtain erosion for the decad in question. Soil erodibility is computed from maps of soil properties. No detailed soil maps of the region are available and thus it was estimated from data contained within the 8 km resolution UNEP/GRID Digital FAO Soil Map of the World. We employed the table of Mitchel and Bubenzer (1980) (Table 8.1) and Boolean algebra to convert maps of texture and organic matter data into spatially distributed K values. Because the soils are clay rich, soil erodibility within the catchment is generally quite low. The SCS model (Soil Conservation Service, 1972) has been used to estimate overland flow. The model was selected not only because it is a catchment scale model and thus should not suffer spatial scaling problems, but also because it has similar parameter requirements to the soil erosion model. The only additional parameters needed are landuse and rainfall.
162 Spatial Modelling of the Terrestrial Environment Table 8.1 The relationship between soil texture, organic matter and soil erodibility 0.875 < 0.875 ≤ OM 1.625 ≤ 2.5 ≤ Texture Class %OM < 1.625 OM < 2.5 OM < 3.5 OM ≥ 3.5 Sand 0.05 0.04 0.03 0.025 0.02 Loamy sand 0.12 0.11 0.1 0.09 0.08 Sandy loam 0.27 0.255 0.24 0.215 0.19 Loam 0.38 0.36 0.34 0.315 0.29 Silt loam 0.48 0.45 0.42 0.375 0.33 Silt 0.6 0.56 0.52 0.47 0.42 Sandy clay loam 0.27 0.26 0.25 0.23 0.21 Clay loam 0.28 0.265 0.25 0.23 0.21 Sandy clay 0.14 0.135 0.13 0.125 0.12 Silty clay 0.25 0.24 0.23 0.21 0.19 Clay 0.13 0.17 0.21 0.25 0.29 The model is defined as: OFp = (r i − Ia) 2 (4) (r i + 0.8S) where OFp is the overland flow and r i is a rainfall event in the catchment, Ia is the initial abstraction and is equal to 0.2S where S is the the potential retention of water in the catchments and is calculated by: S = 25400 − 254 (mm) (5) CN where CN is the runoff curve number. Curve numbers can be estimated from published tables (e.g. Rawls et al., 1993) and describe the ability of the pixel to produce runoff, with high curve numbers providing ideal conditions for runoff generation. We have used these tables to estimate curve numbers with information on soil texture derived from the FAO Soil Map of the World, vegetation cover derived from NDVI, and landcover information from the International Geosphere and Biosphere Program (IGBP) Global 1 km Landuse Map. These curve number maps are dynamic on a decadal basis since they change according to variations in vegetation cover. Famine Early Warning System (FEWS) precipitation estimates were used to derive overland flow from the curve number maps (equation (4)). They are based upon a combination of METEOSAT Cold Cloud Duration estimates and Global Telecommunications System (GTS) raingauge data (Herman et al., 1997). First, a preliminary estimate of accumulated precipitation is made using the GOES Precipitation Index (GPI) (Arkin and Meisner, 1987). The GPI uses the hourly duration of cold cloud tops during a decad for the determination of accumulated rainfall, 3 mm of precipitation being allocated to each hour that cloud top temperatures are less than 235 K. The GPI estimate is corrected using a bias field that is calculated by incorporating the GTS observational data and fitting the biases to a grid using optimal interpolation producing an estimate of convective rainfall. Over regions in which precipitation is due to orographic lifting and the clouds are relatively warm, an additional procedure is used which incorporates the local terrain features with numerical
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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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Page 120 and 121: 6 Remotely Sensed Topographic Data
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Page 204 and 205: PART III SPATIAL MODELLING OF URBAN
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11 Modelling the Impact of Traffic
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Modelling the Impact of Traffic Emi
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PART IV CURRENT CHALLENGES AND FUTU
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268 Index Bi-spectral InfraRed Dete
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270 Index floodplain maps, UK 92 fl
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272 Index Long-Term Ecological Rese
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274 Index snow depth measurement ne
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276 Index urban land use in remotel
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1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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