1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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Editorial: Terrestrial Sediment and Heat Fluxes Nick Drake Remote sensing has been used to either parameterize or validate a wide diversity of terrestrial environmental models. In this Part we consider the integration of remote sensing with models of river sediment and soil erosion and fire heat fluxes. These applications illustrate some of the diversity of modelling approaches that can be employed, the different ways by which models can be linked to remote sensing, the variety of scales at which they can be applied and the scaling problems that can be encountered when applying models at coarse scales. Beginning with soil erosion, natural soil erosion rates are generally low, however, anthropogenic practices tend to increase erosion through factors such as overgrazing, agricultural intensification and the implementation of poor agricultural practices. Accelerated soil erosion leads to higher nutrient losses, a reduction in soil depth and thus over time a lower soil water holding capacity. These factors eventually lead to reduced biomass yields and can ultimately result in desertification. Erosion also has important off-site effects. For example, accelerated water erosion rates lead to increased sedimentation rates elsewhere that can adversely affect the ecology and biodiversity of aquatic systems. There is, therefore, a need to model soil erosion in order to predict the consequences of these actions. Models of soil erosion by water were first developed in the 1940s by analysing the results of erosion plot studies (Zingg, 1940). This research led to the development of the universal soil loss equation (USLE) (Wischmeier and Smith, 1958), an empirical model that proved extremely popular with both managers and researchers and dominated the field for some time, particularly in America where it was developed. However, the problems of applying the model outside America, and the fact that it can only be used to estimate average annual erosion led to the development of alternatives. By the 1980s it was recognized that physically 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.
110 Spatial Modelling of the Terrestrial Environment based modes have the potential to overcome the limitations of empirical models and since then numerous physically based models have been developed and tested. However, the diverse array of factors that affect erosion means that the models are becoming increasingly complex and have thus, in turn, become more demanding on the number of parameters that they require. Furthermore, there is a lack of understanding of some of the key processes, such as soil crusting and rill initiation, that affect erosion dynamics and these processes are not usually parameterized in models. If they are, the equations used to represent them are derived from field or laboratory experiments, and are usually somewhat empirical. Consequently, many physically based models have empirical elements and, over time, a spectrum of models with varying amounts of theoretical rigour and empiricism has emerged. Wind erosion modelling started with Bagnold (1941) who developed a theoretical model to explain the saltation of particles induced by wind. This model forms the basis of many of the physically based models used today. The development of empirical models such as the wind erosion equation (WEQ) (Woodruff and Siddoway, 1965) was attractive for resource managers as they could be readily implemented to provide an estimate of on-site soil loss. However, as was the case with water erosion models, now much effort is being spent developing physically based process models in order to overcome the limitations of their empirical counterparts. Thus, the physically based wind erosion prediction system (WEPS) has recently been developed as a replacement for the WEQ. Early wind and water erosion modelling efforts were applied at the plot scale although it had long been recognized that the majority of eroded soil appeared to come from a limited number of sources and that spatial models were required to understand erosion at the regional scale. Remote sensing was an attractive solution in this regard but there was a large gulf between the accuracy of information that was required and what remote sensing could supply. Remote sensing was first used to study erosion by employing aerial photographs to interpret and map erosional and depositional landforms. Initially aerial photos simply enabled terrain mapping to be conducted over larger areas than was possible with localized field techniques. The next logical step was to apply these methods to monitor landform changes (for example, interpreting changes in coastal landforms in order to monitor coastal erosion). This spatial monitoring provided new insights. For example, Gay (1962) monitored the movement of Barchan Dunes by interpreting aerial photographs acquired in different years. He found that not only was the rate of movement proportional to the dune height, as Bagnold’s (1941) model predicts, but the movement was also proportional to the dune width, and noted that this means that all Berhcan dunes, regardless of size, sweep equal areas at equal rates. In the 1970s the advent of satellite remote sensing enabled the development of new applications. Sensors such as the coastal zone colour scanner were developed with wavelengths on them that can penetrate into the water column and allow monitoring of eroded sediments in near surface waters. Though this development provided new insights into near surface sediment transport and dispersal, it supplied little information on hillslope and river channel erosion. This was because the fine scale of hillslope processes is unresolved by the coarse spatial resolution of the sensors. Additionally, water erosion in these regions tends to occur when it is raining and, therefore, cloud obscures the view from optical sensors. Developments in image processing methods and the ever-increasing diversity of sensors in space meant that by the 1980s it had become possible to derive many of the parameters that control erosion from remotely sensed imagery. For example, remote sensing provides
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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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Page 118 and 119: Editorial: Terrestrial Sediment and
Page 120 and 121: 6 Remotely Sensed Topographic Data
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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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200 Spatial Modelling of the Terres
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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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