Über die Autorin bzw. den Autor

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.

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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.

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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.

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Terrain Analysis

John Wiley & Sons, Inc.

Chapter One

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...