Topographic Position and Landforms Analysis. Weiss, A. D. In ESRI International User Conference.
Topographic Position and Landforms Analysis [link]Paper  abstract   bibtex   
[Excerpt: Introduction] Many physical and biological processes acting on the landscape are highly correlated with topographic position: a hilltop, valley bottom, exposed ridge, flat plain, upper or lower slope, and so on. Examples of these processes include soil erosion and deposition; hydrological balance and response; wind exposure; and cold air drainage. These biophysical attributes in turn are key predictors of habitat suitability, community composition, and species distribution and abundance. This poster presents an algorithm, implemented in GRID, for generating a multi-scale Topographic Position Index, classifying this index into slope position and landform types, and using the Topographic Position Index to characterize watersheds. This work was done as a contractor with U.S. EPA Region 10, working on the Environmental Monitoring and Asssessment Program Western Landscape Project (Jones et al 2000). [] [...] [Basic Algorithm] The Topographic Position Index (TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell (Fig. 2a). In this example an annulus neighborhood is used, but continuous circles or other shapes could be used. Since the only input required is a digital elevation model, TPI can be readily generated almost anywhere. Positive TPI values represent locations that are higher than the average of their surroundings, as defined by the neighborhood (ridges). Negative TPI values represent locations that are lower than their surroundings (valleys). TPI values near zero are either flat areas (where the slope is near zero) or areas of constant slope (where the slope of the point is significantly greater than zero). Topographic position is an inherently scale-dependent phenomenon. As an example, consider a location in a meadow in Yosemite valley. At a fine scale of 100m, the topographic position would be a flat plain. This may be an appropriate scale for looking at soil transport or site water balance. At a scale of several kilometers, this same point is at the bottom of a 1500m deep canyon, which may be more significant for overall hydrology, mesoclimate, wind exposure, or cold air drainage. The ecological characteristics of a site may be affected by TPI at several scales. In a study of vegetation distributions in the Spring Mountains of southern Nevada (Guisan, Weiss, and Weiss 1999) species distribution models show significant relationships to TPI at scales of 300m, 1000m, and 2000m. TPI was generally second most important predictive variable after elevation. [] [...]
@inproceedings{weissTopographicPositionLandforms2001,
  title = {Topographic Position and Landforms Analysis},
  booktitle = {{{ESRI International User Conference}}},
  author = {Weiss, Andrew D.},
  date = {2001},
  url = {http://mfkp.org/INRMM/article/13930813},
  abstract = {[Excerpt: Introduction]

Many physical and biological processes acting on the landscape are highly correlated with topographic position: a hilltop, valley bottom, exposed ridge, flat plain, upper or lower slope, and so on. Examples of these processes include soil erosion and deposition; hydrological balance and response; wind exposure; and cold air drainage. These biophysical attributes in turn are key predictors of habitat suitability, community composition, and species distribution and abundance. This poster presents an algorithm, implemented in GRID, for generating a multi-scale Topographic Position Index, classifying this index into slope position and landform types, and using the Topographic Position Index to characterize watersheds. This work was done as a contractor with U.S. EPA Region 10, working on the Environmental Monitoring and Asssessment Program Western Landscape Project (Jones et al 2000).

[] [...] [Basic Algorithm] The Topographic Position Index (TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell (Fig. 2a). In this example an annulus neighborhood is used, but continuous circles or other shapes could be used. Since the only input required is a digital elevation model, TPI can be readily generated almost anywhere. Positive TPI values represent locations that are higher than the average of their surroundings, as defined by the neighborhood (ridges). Negative TPI values represent locations that are lower than their surroundings (valleys). TPI values near zero are either flat areas (where the slope is near zero) or areas of constant slope (where the slope of the point is significantly greater than zero). Topographic position is an inherently scale-dependent phenomenon. As an example, consider a location in a meadow in Yosemite valley. At a fine scale of 100m, the topographic position would be a flat plain. This may be an appropriate scale for looking at soil transport or site water balance. At a scale of several kilometers, this same point is at the bottom of a 1500m deep canyon, which may be more significant for overall hydrology, mesoclimate, wind exposure, or cold air drainage. The ecological characteristics of a site may be affected by TPI at several scales. In a study of vegetation distributions in the Spring Mountains of southern Nevada (Guisan, Weiss, and Weiss 1999) species distribution models show significant relationships to TPI at scales of 300m, 1000m, and 2000m. TPI was generally second most important predictive variable after elevation.

[] [...]},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13930813,classification,data-transformation-modelling,geospatial,landform,landscape-modelling,topographic-position-index}
}
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