Terrain Analysis: Principles and Applications - Hardcover

Hardcover

ISBN 10: 0471321885 ISBN 13: 9780471321880

Publisher: Wiley, 2000

Synopsis

The only reference on the use of GIS and related technologies in terrain analysis
In this landmark publication, reflecting the collaborative effort of thirteen research groups based in four countries, leading experts detail how GIS and related technologies, such as GPS and remote sensing, are now being used, with the aid of computer modeling, in terrain analysis. Continuing the innovative work of Professor Ian Moore, a visionary who saw terrain analysis as a robust method for modeling the large areas and complex spatial patterns of environmental systems, Terrain Analysis puts into action TAPES, or Terrain Analysis Programs for Environmental Sciences, Dr. Moore's innovative tool for terrain analysis. The book's contributors describe how TAPES are applied to specific geomorphologic problems, explain the algorithms used in current terrain analysis software, and examine the interpretation and use of terrain attributes in predictive models.
With expert coverage of terrain analysis in the digital age, Terrain Analysis will be welcomed by ecologists, environmental engineers, geographers, and hydrologists who increasingly depend on GIS, GPS, and remote sensing.

About this title

About the Author

JOHN P. WILSON is a professor in the Department of Geography, University of Southern California, Los Angeles.

JOHN C. GALLANT is a research scientist with CSIRO Land and Water, Canberra, Australia.

Excerpt. © Reprinted by permission. All rights reserved.

Terrain Analysis

Principles and Applications

John Wiley & Sons, Inc.

Copyright © 2000 John P. Wilson and John C. Gallant
All right reserved.
ISBN: 978-0-471-32188-0

Chapter One

Digital Terrain Analysis

John P. Wilson and John C. Gallant

1.1 PRINCIPLES AND APPLICATIONS

The development and application of the TAPES: Terrain Analysis Programs for the Environmental Sciences software tools described in this book was motivated by our view of the world as a stage on which a series of hierarchically scaled biophysical processes are played out (Figure 1.1). This approach is useful because it can handle the complexity of individual landscape processes and patterns as well as some of the difficulties that are encountered in delineating the appropriate spatial and temporal scales (O'Neill et al. 1986, Mackey 1996, Malanson and Armstrong 1997). Many of the important biophysical processes operating at or near the earth's surface are influenced by both past events and contemporary controls, interactions, and thresholds (Dietrich et al. 1992, Grayson et al. 1993, Montgomery and Dietrich 1995). These interrelationships are complicated and may be best understood using a dynamic systems modeling approach (Kirkby et al. 1996). The boundaries separating different spatial and temporal scales are not very clear and they may vary with individual processes and/or landscapes (cf. Sivapalan and Wood 1986, Mackey 1996, Malanson and Armstrong 1997).

This state of affairs suggests that additional work is required to identify the important spatial and temporal scales and the factors that influence or control the processes and patterns operating at particular scales. The potential benefits may be substantial. Schaffer (1981), working with interacting systems of populations in community ecology, and Phillips (1986), working on examples in fluvial geomorphology, have demonstrated that the key processes operating over different timescales can be considered independently of each other. Phillips (1988) has also shown how the key processes operating at different spatial scales and affecting the hydraulic gradient of a desert stream in Arizona can be considered independently of each other. Band et al. (1991) generated landscape units with low internal variance and high between-unit variance for the important parameters in a nonlinear, deterministic model designed to simulate carbon, water, and nitrogen cycles in a forest ecosystem using a series of hillslope and watershed templates. However, this result may not be universally applicable. Phillips (1988) warned that the key differences in spatial scales cannot be related to fundamental landscape units in numerous instances. Grayson et al. (1993) argued that we should avoid implementing at one scale models developed at a different scale because the simplifying assumptions will often undermine the validity of the original models. Kirkby et al. (1996) concluded that different processes and interactions are likely to emerge as dominant as we move from the plot scale to catchment and regional scales in soil erosion modeling applications. This state of affairs is true of other hydrological, geomorphological, and biological settings as well.

Most of the hydrological, geomorphological, and ecological research of the past century has been conducted at the global and nano- or microscales identified in Figure 1.1 (Mackey 1996). The meso- and toposcales have received much less attention, and yet these scales are important because many of the solutions to environmental problems, such as accelerated soil erosion and non-point-source pollution, will require changes in management strategies at these landscape scales (Moore and Hutchinson 1991). The influence of geologic substrate on soil chemistry (e.g., Likens et al. 1977) and impact of prevailing weather systems and elevation-driven lapse rates on long-term average monthly climate (e.g., Daly et al. 1994, Hutchinson 1995) exemplify some of the controls operating at the mesoscale. The influence of surface morphology on catchment hydrology and the impact of slope, aspect, and horizon shading on insolation probably represent the most important controls operating at toposcales. Numerous studies have shown how the shape of the land surface can affect the lateral migration and accumulation of water, sediments, and other constituents (e.g., Moore et al. 1988a). These variables, in turn, influence soil development (e.g., Kreznor et al. 1989) and exert a strong influence on the spatial and temporal distributions of the light, heat, water, and mineral nutrients required by photosynthesizing plants (Mackey 1996). The increased popularity of work at these two intermediate scales during the past decade has capitalized on the increasing availability of high-resolution, continuous, digital elevation data and the development of new computerized terrain-analysis tools (Wilson 1996, Burrough and McDonnell 1998, Wilson and Burrough 1999).

1.1.1 Digital Elevation Data Sources and Structures

Most of the currently available digital elevation data sets are the product of photogrammetric data capture (I. D. Moore et al. 1991). These sources rely on the stereoscopic interpretation of aerial photographs or satellite imagery using manual or automatic stereoplotters (Carter 1988, Weibel and Heller 1991). Additional elevation data sets can be acquired by digitizing the contour lines on topographic maps and conducting ground surveys. The advent and widespread use of Global Positioning Systems (GPS) in agriculture and other settings provides many new and affordable opportunities for the collection of large numbers of special-purpose, one-of-a-kind elevation data sets (Fix and Burt 1995, Twigg 1998, Wilson 1999a).

These digital elevation data are usually organized into one of three data structures-(1) regular grids, (2) triangulated irregular networks, and (3) contours-depending on the source and/or preferred method of analysis (Figure 1.2). Square-grid digital elevation models (DEMs) have emerged as the most widely used data structure during the past decade because of their simplicity (i.e., simple elevation matrices that record topological relations between data points implicitly) and ease of computer implementation (I. D. Moore et al. 1991, 1993f, Wise 1998). These advantages offset at least three disadvantages. First, the size of the grid mesh will often affect the storage requirements, computational efficiency, and the quality of the results (Collins and Moon 1981, I. D. Moore et al. 1991). Second, square grids cannot handle abrupt changes in elevation easily and they will often skip important details of the land surface in flat areas (Carter 1988). However, it is worth noting that many of the problems in flat areas occur because the U.S. Geological Survey (USGS) and others persist in recording elevations in whole meters. Third, the computed upslope flow paths will tend to zigzag across the landscape and increase the difficulty of calculating specific catchment areas accurately (Zevenbergen and Thorne 1987, I. D. Moore et al. 1991). Several of these obstacles have been overcome in recent years. For example, there is no generic reason why regular DEMs cannot represent shape well in flat areas, so long as the terrain attributes are calculated by a method that respects surface drainage. ANUDEM (Hutchinson 1988, 1989b) is one such method and is described in more detail in Chapter 2. Similarly, the advent of several new compression techniques have reduced the storage requirements and improved computational efficiency in recent years (e.g., Kidner and Smith 1992, Smith and Lewis 1994). DEMs with grid sizes of 500, 100, 30, 10, and even 1 m are increasingly available for different parts of the globe (see U.S. Geological Survey 1993, Ordinance Survey 1993, and Hutchinson et al. 1996 for examples).

Triangulated irregular networks (TINs) have also found widespread use (e.g., Tajchman 1981, Jones et al. 1990, Yu et al. 1997). TINs are based on triangular elements (facets) with vertices at the sample points (I. D. Moore et al. 1991). These facets consist of planes joining the three adjacent points in the network and are usually constructed using Delauney triangulation (Weibel and Heller 1991). Lee (1991) compared several methods for building TINs from gridded DEMs. However, the best TINs sample surface-specific points, such as peaks, ridges, and breaks in slope, and form an irregular network of points stored as a set of x, y, and z values together with pointers to their neighbors in the net (I. D. Moore et al. 1991). TINs can easily incorporate discontinuities and may constitute efficient data structures because the density of the triangles can be varied to match the roughness of the terrain (I. D. Moore et al. 1991). This arrangement may cancel out the additional storage that is incurred when the topological relations are computed and recorded explicitly (Kumler 1994).

The third structure incorporates the stream tube concept first proposed by Onstad and Brakensiek (1968) and divides landscapes into small, irregularly shaped polygons (elements) based on contour lines and their orthogonals (Figure 1.2) (O'Loughlin 1986, I. D. Moore et al. 1988a). This structure is used most frequently in hydrological applications because it can reduce complex three-dimensional flow equations into a series of coupled one-dimensional equations in areas of complex terrain (e.g., Moore and Foster 1990, Moore and Grayson 1991, Grayson et al. 1994). Excellent reviews of digital elevation data sources and data structures are presented by Carter (1988), Weibel and Heller (1991), and I. D. Moore et al. (1991). The proliferation of digital elevation sources and preprocessing tools means that the initial choice of data structure is not as critical as it once was (Kemp 1997a, b). Numerous methods have been proposed to convert digital elevation data from one structure to another, although care must be exercised with each of these methods to minimize unwanted artifacts (e.g., Krajewski and Gibbs 1994). In addition, larger quantities of data do not necessarily produce better results: Eklundh and Martensson (1995), for example, used ANUDEM (Hutchinson 1988, 1989b) to derive square grids from contours and demonstrated that point sampling produces faster and more accurate square-grid DEMs than the digitizing of contours. Similarly, Wilson et al. (1998) used ANUDEM to derive square grids from irregular point samples and showed that many of the x, y, z data points acquired with a truck-mounted GPS were not required to produce satisfactory square-grid DEMs. ANUDEM calculates ridge and streamlines from points of maximum local curvature on contour lines and incorporates a drainage enforcement algorithm that automatically removes spurious sinks or pits in the fitted elevation surface (Hutchinson 1988, 1989b). ANUDEM is one of several programs of this type and an early version has been implemented in the ARC/INFO (Environmental Systems Research Institute, Redlands, CA) geographical information system (GIS) with the TOPOGRID command. Qian et al. (1990) describe an alternative approach that utilizes local operators and global reasoning to automatically extract drainage networks and ridge lines from digital elevation data. Similarly, Smith et al. (1990) proposed a two-step, knowledge-based procedure for extracting channel networks from noisy DEM data. Kumler (1994) described the method used by the U.S. Geological Survey to generate square-grid DEMs from digital contour lines.

Carrara et al. (1997) compared several methods for generating DEMs from contour lines; however, the range of terrain types, sample structures, and modeling routines is so great that attempts to make generalizations about "best" models is tremendously difficult (Burrough and McDonnell 1998, Dixon et al. 1998, Wilson 1999b). In addition, some of the interpolation methods that have been proposed are difficult to use and Eklundh and Martensson (1995) recommended that less experienced users focus on the quality of the input data instead of learning sophisticated interpolation methods. Simpler interpolation methods will give satisfactory results so long as the input data are well sampled and sophisticated algorithms are likely to produce unsatisfactory results if applied to poor data (e.g., Wilson et al. 1998).

1.1.2 Calculation and Use of Topographic Attributes in Hydrological, Geomorphological, and Biological Applications

Many of the most popular topographic attributes, such as slope, specific catchment area, aspect, and plan and profile curvature, can be derived from all three types of elevation data for each and every element as a function of its surroundings (I. D. Moore et al. 1991, 1993f). Individual terrain-analysis tools have been classified in various ways based on the characteristics of the computed attributes and/or their spatial extent. Some authors distinguish tools that perform operations on local neighborhoods (i.e., 3 by 3 moving windows) from those that perform operations on extended neighborhoods (calculation of upslope drainage areas, viewsheds, etc.) (e.g., Burrough and McDonnell 1998). We usually distinguish primary attributes that are computed directly from the DEM and secondary or compound attributes that involve combinations of primary attributes and constitute physically based or empirically derived indices that can characterize the spatial variability of specific processes occurring in the landscape (I. D. Moore et al. 1991, 1993f). This same logic is adopted here.

Primary attributes include slope, aspect, plan and profile curvature, flow-path length, and upslope contributing area (see Table 1.1 for a more complete list). Most of these topographic attributes are calculated from the directional derivatives of a topographic surface. They can be computed directly with a second-order finite difference scheme or by fitting a bivariate interpolation function z = f(x, y) to the DEM and then calculating the derivatives of the function (Moore et al. 1993d, Mitasova et al. 1996, Florinsky 1998).We may or may not want to calculate a depressionless DEM first and we must specify one or more rules to determine drainage directions and the connectivity of individual elements in order to calculate flow-path lengths and upslope contributing areas (e.g., Jenson and Domingue 1988, Martz and De Jong 1988). The overall aim is to be able to use the computed attributes to describe the morphometry, catchment position, and surface attributes of hillslopes and stream channels comprising drainage basins (e.g., Speight 1974, 1980, Band 1986, 1993a, b, Jenson and Domingue 1988, Montgomery and Foufoula-Georgiou 1993, Moore et al. 1993a). Dikau (1989), Dymond et al. (1995), Brabyn (1997), Giles (1998), and Burrough et al. (2000a, b) have all used computed topographic attributes to generate formal landform classifications.

The secondary attributes that are computed from two or more primary attributes are important because they offer an opportunity to describe pattern as a function of process (Table 1.2). Those attributes that quantify the role played by topography in redistributing water in the landscape and in modifying the amount of solar radiation received at the surface have important hydrological, geomorphological, and ecological consequences in many landscapes. These attributes may affect soil characteristics (because the pedogenesis of the soil catena is affected by the way water moves through the environment in many landscapes), distribution and abundance of soil water, susceptibility of landscapes to erosion by water, and the distribution and abundance of flora and fauna. Three sets of compound topographic indices are discussed below to illustrate how these attributes are constructed and used in hydrological, geomorphological, and ecological applications.

Two topographic wetness indices have been used extensively to describe the effects of topography on the location and size of saturated source areas of runoff generation as follows:

[W.sub.T] = ln ([A.sub.s]/T tan [beta]) (1.1)

W = ln ([A.sub.s]/tan [beta]) (1.2)

where [A.sub.s] is the specific catchment area ([m.sup.2][m.sup.-1]), T is the soil transmissivity when the soil profile is saturated, and [beta] is the slope gradient (in degrees) (I. D. Moore et al. 1991, 1993d). The second equation contains one less term because it assumes uniform soil properties (i.e., that the soil transmissivity is constant throughout the landscape). Wood et al. (1990) have shown that the variation in the topographic component is often far greater than the local variability in soil transmissivity and that Equation 1.2 can be used in place of Equation 1.1 in many landscapes. Both of these indices predict that points lower in the catchment, and particularly those points near the outlets of the main channels, are the wettest points in the catchment, and the soil-water content decreases as the flow lines are retraced upslope to the catchment divide (Wilson and Gallant 1998).

(Continues...)

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Edition details

• Publisher: Wiley
• Publication date: 2000
• Language: English
• ISBN 10: 0471321885
• ISBN 13: 9780471321880
• Binding: Hardcover
• Edition number: 1
• Number of pages: 516
• Editor: Wilson John P., Gallant John C.