1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Editorial: Terrestrial Sediment and Heat Fluxes 111
numerous ways to map topography (e.g. photogrammetry, interferometry and LiDAR) and
thus derive maps of slope and other topographic factors that control water erosion, while
vegetation indices can be used to estimate vegetation cover, the most important parameter
controlling resistance to erosion. Initially these parameters were combined in qualitative
ways using GIS overlays to produce maps of erosion risk. However, they were soon used
to implement spatial models of erosion.
Though these developments in remote sensing have enabled the estimation of numerous
parameters of importance to soil erosion modelling, there is a gap between what the models
require and what it is possible to obtain. As a result, in nearly all cases, some parameters
need to be measured in the field and interpolated in a GIS for effective implementation of
the model. It is often costly and time-consuming to frequently monitor such parameters
in detail over large areas. Thus, there is a need for parsimonious models that utilize any
parameters available from remote sensing.
The first chapter in this Part (chapter 6) is by Lane et al. and demonstrates how error
can be managed in the application of digital photogrammetry to the quantification of river
topography of large, braided, gravel-bed rivers. The chapter reviews the traditional treatment
of error in digital elevation models (DEM), and then considers how the error can be
identified, explained and corrected in this study in the context of a specific example. This
is an important topic because many land erosion models rely on the parameterization of
mass transfer processes using a DEM. The second two chapters of this Part illustrate the
implementation of water and wind erosion models at coarse scales. In Chapter 7, Okin
and Gilette outline the need for regional modelling of sand and dust emission, transport
and deposition. They then implement a regional scale wind erosion model developed from
Bagnold’s equations and compare the results to ground measured observations in the Jornada
Basin, New Mexico. They find that the model under-estimates wind erosion and dust
flux throughout much of the basin because the parameterization using soil and landuse
maps failed to capture the surface variability in the landscape. They conclude that there is
a need to develop remote techniques to map the fine scale heterogeneity in the landscape
that exerts a large control on wind erosion and dust flux.
In Chapter 8 Drake et al. implement a water erosion model for the catchment of Lake
Tanganyika with the aim of quantifying the source areas within the catchment, the transfer
of sediment to the lake and the sediment dispersal within the catchment. The model is
applied using remote sensing to estimate spatial and temporal variations in vegetation
cover, rainfall and the near surface sediment distribution within the lake. The erosion model
results have been shown to be highly sensitive to scale, with increasingly coarser spatial
resolutions causing a gradual reduction in predicted erosion rates. Scaling techniques have
been employed in an effort to overcome these problems. It is concluded that validation
of the model is needed but that this is problematic when applying coarse resolution data
over large areas. These two chapters thus highlight the numerous problems that can be
encountered when applying models to coarse resolution imagery but also provide methods
that point towards solutions to certain aspects of the problem.
The final process that is considered in this section is fire. Fire affects humans, atmosphere,
vegetation and soils, and successful fire modelling has the potential to further our
understanding, prediction and management of this phenomenon. The first model of fire considered
its spread and was developed by Fons (1946). Since then a large number of models
have been developed to consider not only diffusion, but also fire properties and physical