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Landscape Proposes, DEM Discloses

Swati Grover
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Our planet earth has been blessed with the most beautiful and exotic landscapes like the Himalayas, the Grand Canyon, the Alps, Cairngorms and many more. These landscapes have been subjected to various surface processes over the years thus hanging the landscape. In order to understand these changing landforms our science and Technology has not left any stones unturned. For the last few decades, the application of GIS technologies has provided geosciences research with a series of new possibilities for quantitative analysis. The automated derivation of landforms has become the necessity for quantitative analysis in geosciences. Furthermore the application of GIS and Remote Sensing technologies, particularly the Digital Elevation Models (DEM) has become an important tool for data management and numerical data analysis for the purposes of geological and geomorphological mapping. A Digital Elevation Model (DEM) is an ordered array of number that represents the spatial distribution of elevations above some arbitrary datum in a landscape. Sampling a regular array of elevation values derived from topographic maps, either aerial photographs or the satellite images, normally generates a DEM. It can be used to derive a wealth of information about the morphology of land surface. Raster processing systems of these digital data use algorithms based on neighbourhood operations to calculate the various terrain attributes like slope, aspect and shaded relief.

DEM and digital datasets
Digital Elevation and Remote Sensing data sets contain different, yet complementary information relating to the geological and geomorphological features. Digital Elevation/Terrain Models (DEMs)/(DTM) represent the topography and landforms whereas remote sensing records the reflectance/emittance, or spectral characteristics of surfaces. The topographic surface can be generated with the three spatial coordinates (as mentioned above) and can be represented as digital formats in the form of gridded matrices of elevations, series of parallel profiles, digitised contours or triangulated irregular networks (TINS). Description of the terrain surface through digital elevation models (DEMs) strongly depends on data collection methods and DEM data structures. DEM/DTM have been useful tools in recognising geological and geomorphological features. Further combining the other data sets by draping one over the other is a powerful way to examine the relationships between the two. The most common use of this technique combines surface and subsurface geologic information with digital elevation model (DEM). For example, a bedrock geologic map draped over the topography will show the relationships between lithology and landforms.

Processing of digital datasets
Information extracted from a dense grid digital elevation model can be used in identifying different attributes for geomorphology and geology that can be used in a terrain classification. This can be further combined with other quantitative data such that derived from the remote sensing systems. This information extracted from the DEM and Remote Sensing data needs to be processed and analysed using computer softwares. In particular, satellite imagery contributes to the interpretation of geomorphological and geological signatures, specifically for large and remote areas. Topography is not represented explicitly in the satellite imageries, which has been a problem in using remote sensing data for mapping geology and geomorphology. Therefore, to overcome this problem, digital elevation data can be integrated along with remote sensing data in order to extract topographic information.

Surface characterisation and Digital Elevation Model
The term “Terrain” differs in meaning over the term “Elevation”. Elevation is preferred when only relief is represented whereas terrain is implied to attributes of landscape other than the altitude of that landscape. The functional significance of using DTM could therefore imply a digital model of any single value surface. The ideal structure of DEM may be different if it is used as structure for landform analysis than if it is used to determine the topographic attributes of the landscape. Description of the terrain surface through DEMs strongly depends on the data collection methods and DEM data structures. Structuring network of elevation data for its acquisition and analysis can be done in three principle ways: (a) Square Grid network, (b) Triangular Irregular network (TIN), (c) Contour based network.

Scales and Accuracy of DEM have significant trends in representation of terrain for modelling. The DEM scale accompanied by spatial resolution has been appreciated by the development of a broad scale remote sensing instruments, the compilation of global coverages of terrain data and earth surface data commensurate with this resolution. The finest scale of DEM with spatial resolution generally used is from 5 to 50 metres. Accuracy of DEM and its derived products are of crucial importance because errors in the base data will propagate through spatial analyses. This is true particularly in classification where elevation, slope and aspect are derived from the DEMs and used with other spatial data, the errors depicted often cause a wrong interpretation of the topography

The topographic surface is a surface that is visible and shows little change over a period. Surfaces are features that are generally referred to as containing height values called Z values distributed throughout an area defined by sets of X and Y coordinate values. Digital Terrain/Elevation Model has been the generic term used to refer the digital representation of the topographic surface. Digital representations of the topography may be in the form of gridded matrices of elevations, series of parallel profiles, digitised contours, or triangulated irregular networks (TINS). Gridded matrices of elevation may give adequate representations of the coarse surface form in the areas of high relief but often do a poor job of defining the details of the land surface in areas of low relief. The construction of mathematical model of the earth surface as DTM/DEM has been the powerful method for representing the relief. This mathematical model can be used to derive information on height, aspect, slope angle, watersheds, radiation incidence, hill shadows and cut – and – fill estimates which may be essential as components of management plan or inputs to a process model. The review of past studies done on understanding the surface topography has argued that there are two basics of mathematically continuous surfaces one which is visible and the other is a mathematical surface but not visible. The visible topographic surfaces that we see for example hiking over topographic surfaces and walking along the contours, patterns that stream carve into the topographic surfaces. On the other hand, non-visible surfaces, were we choose to map with contours with the help of sampling, that also include the water table, topographic surfaces of buried stratigraphy or of relict landscapes and monthly temperature map.

It is very important to notice here that the topographic surfaces stand apart from all the other mathematically derived smooth surfaces in terms of how we collect the data and how it is represented about these surfaces. However, many computer softwares have been developed over the years to visualise these topographic surfaces by interpolating the elevation, which is the base of these surfaces. Interpolation of these arrays of z-values by different methods represents these topographic surfaces to produce digital terrain models from a very large array of critical points subjectively selected from the field or contours or satellite images or aerial photographs. Today there are programmes of this discontinuities. Techniques have also been developed to define topographic structures such as ridges, channels, watersheds and other hydrologic features from DEMs.

DEM detecting morphometric parameters
Landscape units are a unique combination of topography, vegetation, soils, landforms and lithology. Digital Elevation Model of surface topography directly represents features in the landscape at a range of scales. Topographic analysis is the quantitative analysis of topographic surfaces with the aim of studying surface and near-surface processes. A number of topographic attributes such as slope, aspect and plan curvature (contours) can be calculated from elevation models, which form the basis for many of the geomorphological landforms. Digital elevation data and analytical techniques have been developed as computer capabilities have made it efficient to process large volume of digital topographic data. The potentially large volume of raw, digital data can be used rapidly to calculate values of numerous geomorphometric parameters over large areas. The different parameters involved in the geomorphometric analysis include elevation, slope, aspect, relief and convexity, which are also used in parametric landscape analysis alone.

Remote sensing data for attribute extraction
Remote sensing system along with digital elevation data provides a lot of important information about the characteristics of the earth surface. The remote sensing devices record the electromagnetic reflectance (or emittance) properties of the visible surfaces. Rocks are assemblages of minerals, so their spectra are composites of those for each of their constituents. Spectral signatures classification, textural analysis and segmentation have been used in many instances in studying geology. These techniques are generally not reliable yet universally applicable when coupled along with the digital processing of the DTM/DEM for pattern recognition for geological and geomorphological mapping. For many landscapes in temperate climates, the visible surface is generally vegetation, which masks the underlying soil or rock material. A strong link, however, often exists between vegetation and the underlying geomorphological conditions. Reflectance information from remote sensing data is used as a surrogate measure of the surface characteristics, in turn related to geomorphological materials. Spatial information contained in remotely sensed data is also utilised in generation of DEM from a stereoscopic pair of satellite images such as SPOT.

Identification of geology has been a difficult task in a vegetation-covered terrain. The presence of even small amounts of vegetation tends to mask the spectral features of the low albedo geological materials. Geological remote sensing of mountainous terrain anyhow has to rely on the relationship between rocks, soil and vegetation. In general, geology (or more strictly, soil parent material) is linked, via soil development to vegetation. Although there are well-documented cases of specific distinct vegetation assemblages associated with certain rock types, generally the influence of geological substrate is more subtle. Pioneering studies on the same lines in the early 70s used this method of integration of digital elevation and remotely sensed data that gave great potential of classified accuracy in topographic information.

Mapping Geomorphology and Geology
After the geomorphic attributes are calculated as the different derivatives of elevation data they are needed to be represented in a classified manner in order to display them on a map. The calculation for derivatives is done for each cell in the altitude matrix of the DEM. The classification process of the derivatives is achieved by means of a look up table in which the appropriate classes and their colour combination or grey scale is defined. Several maps are produced showing geomorphological and geological features with the help of DEM.

Aspect Maps are generally classified as nine classes based on main compass directions and one for the flat terrain. Some maps are produced by calibrating the mean and standard deviation of the frequency distribution of the gradient values. Gradient generally differs in different regions and is restricted to the standard classification. The number of classes can be self-defined at the mean, the mean ± 0.6 standard deviation and the mean ± 1.2 standard deviations. This map can be either be generated by altitude matrix or either TIN DEMs. Shaded Relief Maps are prepared on the principle of automation where the terrain might look like it was made of an ideal material, illuminated at from a given position. The resultant map does not display terrain cover but only the digitised land surface. The light source is at 45° above the horizon in the north-west. These maps are useful for presenting a single image of terrain in which the three-dimensional aspects are accurately portrayed. A cross reference check may be required to confirm the correlation between the terrain attributes, landforms and lithology.

Conclusion
The integrated study of Digital Elevation Models and satellite reflectance imagery in GIS environment has permitted the interpretation of various landforms and the underlying bedrock configuration, the construction of maps and the measurement of different variables in X, Y, Z terrain coordinates in areas where accessibility is limited. This integrated system of using GIS and remotely sensed data helps in interpreting the different geologic and geomorphic attributes in a much less tedious way. Generating exhaustive databases which are easy to manage in Geographic Information Systems carrying out different spatial analyses and displaying the surface data in 3-dimensional system are easy with the available tools in both GIS and Remote sensing system. Thus GIS is designed to bring together spatial data structures and representing them spatially, all of them are co-registered to the same geographic coordinates.