Topographic position index r

It only takes a minute to sign up. Does anyone know how to calculate Topographic Ruggedness Index a. The process essentially calculates the difference in elevation values from a center cell and the eight cells immediately surrounding it. Then it squares each of the eight elevation difference values to make them all positive and averages the squares.

These are focal statistics for the original grid and for its square. This consists of 9 grid operations in totoall of which are fast. They are readily carried out in the raster calculator ArcGIS 9.

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BTW, this is not an "average elevation change" because elevation changes can be positive and negative : it is a root mean square elevation change. The Riley et al. This is very close to unscaled variance. If you want an implementation of Riley's TRI then please follow the methodology outlined by whuber the methodology provided by user generalized the metric to the maximum in the window and does not represent the cell by cell variation.

I find this more flexible and justifiable. There are also some other surface configuration metrics including rugosity and dissection.

Please see the post by whuber explaining the correct process Step 2: Use the Raster Calculator to perform the following functions on the 2 raster datasets you just created.

This sounds very much like the Topographic Position Index, a process I used recently for one of my projects. Sign up to join this community. The best answers are voted up and rise to the top. Ask Question. Asked 10 years, 1 month ago. Active 4 months ago. Viewed 22k times. Improve this question.

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topographic position index r

Active Oldest Votes. Improve this answer. This is a matter of principle, not antagonism towards ArcGIS in particular. One should avoid being locked in to a single software solution: not only is it professionally risky, it's intellectually stifling. I know I asked for an arcgis specific solution, but I'm accepting this one because of it's directness. GDAL Utilities are easy to acquire and install, universally acknowledged as best in class, and the command to generate this particular product is the definition of simplicity.

Let's do a little just a little algebra. The workflow therefore is: Given a DEM. Thank you Bill. I appreciate seeing the specifics of how a tool or operation works. From this anyone with a suitable investment of time and intellectual energy can build a new apparatus to carry this job out using the tools they have at hand.Compute slope, aspect and other terrain characteristics from a raster with elevation data.

The elevation data should be in map units typically meter for projected planar raster data. RasterLayer object with elevation values. Only relevant for slope and aspect. If 'tangent' is selected that is used for slope, but for aspect 'degrees' is used as 'tangent' has no meaning for aspect. Indicating how many neighboring cells to use to compute slope for any cell.

Either 8 queen case or 4 rook case. Only used for slope and aspect, see Details. The Horn algorithm may be best for rough surfaces, and the Fleming and Hoffer algorithm may be better for smoother surfaces Jones, ; Burrough and McDonnell, They are encoded as powers of 2 0 to 7. The cell to the right of the focal cell 'x' is 1, the one below that is 2, and so on:. If two cells have the same drop in elevation, a random cell is picked. That is not ideal as it may prevent the creation of connected flow networks.

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The terrain indices are according to Wilson et al. TRI Terrain Ruggedness Index is the mean of the absolute differences between the value of a cell and the value of its 8 surrounding cells.

TPI Topographic Position Index is the difference between the value of a cell and the mean value of its 8 surrounding cells.

Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. Such measures can also be computed with the focal function:. Burrough, P. McDonnell, Principles of Geographical Information Systems.

Oxford University Press. Fleming, M. Greenlee, D.

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Raster and vector processing for scanned linework. Photogrammetric Engineering and Remote Sensing Jones, K. A comparison of algorithms used to compute hill slope as a property of the DEM. Ritter, P. A vector-based slope and aspect generation algorithm. Wilson, M. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope.

Marine Geodesy It only takes a minute to sign up. I have a shapefile of US counties and high-resolution elevation data that spans the entire contiguous United States. My goal is to calculate a terrain ruggedness index for each county. The functions that I've been able to find, e. Based on mdsummer's excellent answer and given a boundary shapefile and a raster layer of elevation data, it's easy to calculate zonal statistics:.

I'm having difficulty seeing how to apply a function that takes a raster layer to each county individually. If I run the following code:. How do I apply a function like tri that takes a raster layer to the raster that's contained within each county individually? Once I have that, it's easy enough to calculate the mean TRI across all cells within the county, for example, using the same zonal statistics approach described above?

Just because something is published does not mean that it is necessarly correct.

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In this case aggregating the TRI to a county is certainly incorrect. The distributional qualities of the metric, in relation to inference, become meaningless. Given the linked journal, bad dogs! You are functionally taking the mean of a derivative metric that represents localized mean deviation. I would highly recommend reading up on MAUP, perhaps starting with Cressie's "Change of support and the modifiable areal unit problem" and ecological fallacy in spatial data by reading Wakefield's "Spatial Aggregation and the Ecological Fallacy".

Since the basic idea here is to identify topographic variability within an experimental unit to indicate "ruggedness", one could address the underlying distributions directly. Since highly relieved areas would also be expected to exhibit highly skewed, standard Gaussian moments may not be adequate.

First, let's calculate the pixel-level TRI and calculate the mean for each county. You can see that the variability is not correctly represented, at least not visually. We can also write a global approximation of TRI using the median and the deviation value. This actually looks fairly reasonable and is comparable to MAD. Although, it did pick up Frenso county as very high ruggedness which spans the southern Sierra's whereas MAD did not. Edit and disclaimer : Please see Jeffrey Evan's comment above.

Below you will find a computational solution to OP's question, but users should be aware of the consequences of deciding to aggregate something like TRI across broad spatial scales. This isn't super efficient, but it'll get the job done. If x is your raster and y is your county shape:.

topographic position index r

Edit: Here's a way to quickly apply the function to a state-level shapefile that has county information.The function allows to perform landform classification on the basis of the Topographic Position Index calculated from an input Digital Terrain Model 'RasterLayer' class.

Size in terms of cells per side of the neighborhood moving window to be used; it must be an odd integer. If the class classification is selected, this paramenter sets the s mall n eighborhood to be used.

If the class classification is selected, this paramenter sets the l arge n eighborhood to be used. The TPI is the difference between the elevation of a given cell and the average elevation of the surrounding cells in a user defined moving window. For landform classification, the TPI is first standardized and then thresholded; to isolate certain classes, a slope raster which is internally worked out is also needed.

Two methods are available: -the first devised by Weiss produces a 6-class landform classification comprising: -- valley -- lower slope -- flat slope -- middle slope -- upper slope -- ridge -the second devised by Jennes produces a class classification comprising: -- canyons, deeply incised streams -- midslope drainages, shallow valleys -- upland drainages, headwaters -- u-shaped valleys -- plains -- open slopes -- upper slopes, mesas -- local ridges, hills in valleys -- midslope ridges, small hills -- mountain tops, high ridges.

The second classification is based on two TPI that make use of two neighborhoods moving windows of different size: a s mall n eighborhood and a l arge n eighborhooddefined by the parameters sn and ln. Besides rasters representing the different landform classes, the function optionally returns the TPI raster, either un- or standarized.

The output rasters are plotted on the R graphic console and returned by the function as objects of 'RasterLayer' class within a list. Created by DataCamp.

topographic position index r

R function for landform classification on the basis od Topographic Position Index The function allows to perform landform classification on the basis of the Topographic Position Index calculated from an input Digital Terrain Model 'RasterLayer' class.

Community examples Looks like there are no examples yet. Post a new example: Submit your example. API documentation. Put your R skills to the test Start Now.Topographic position index TPI is a method of terrain classification where the altitude of each data point is evaluated against its neighbours.

If a point is higher than its surroundings, the index will be positive, as for example on ridges and hilltops, while the figure will be negative for sunken features such as valleys.

Topographic position index: a numpy based implementation

Note that the size of the examined neighbourhood matters; what may globally be a large valley can locally become flat terrain or even elevated relief. Weiss has proposed a complex terrain classification on the basis of the amount of difference hilltop, incised valley, shallow valley etc.

Topographic position index depends on neighbourhood size Salinas-Melgoza et al. Technically, TPI functions as simple average filter or, more precisely, a kernel filter where each cell is assigned the average value of its neighbourhood see on wikipedia. The neighbourhood size and shape is typically fixed, it does not vary from cell to cell. This average value is then subtracted from the actual height value to evaluate the difference negative values will, then, represent concave surfaces and positive ones convexities.

Terrain position index concavities are in blue and convexities in red. These are animal tracks and some human paths on a Lidar derived DEM. The answer is in the simplicity of the method: it can easily be customised and experimented with in order to obtain that original result which will distinguish your work form other push-button solutions. We can play with window size or specific weight factors, for instance when window periphery should count less than the window centre.

Such freedom can be very useful when fine tuning Lidar visualisations, for example. In what follows, I will only provide the basic architecture for a TPI filter, based on the sliding window routine developed for numpy. Window size and weights can be fine-tuned in relation to specific problems see below.

The script is using both, window geometry square, circle etc. Geometry is modelled by setting unwanted window parts to zero, while weights should be non-zero values. For Gaussian window weights: see numpy documentation. However, I find more useful and intuitive to hard-code window definitions.

This is very handy when comparing results on different datasets — there can be no error when the window is specified cell by cell. We can also keep a diary with window definitions that work best, like:.

topographic position index r

Danger in this approach is in the assumption that the central data point is in the geometric centre of the kernel window. For this to be true, dimensions of the window should be in odd numbers. For instance, in 3x3 window, the central pixel is at 1,1 position.

Therefore, be sure to count your pixels. Larger windows may become unwildely; in that case you can always use the on-the-fly method, as above. Kernel processing on Wikipedia. Salinas-Melgoza, M.

Skutsch, and J. Lovett : Predicting aboveground forest biomass with topographic variables in human-impacted tropical dry forest landscapes. Ecosphere 9 1 :e Toggle navigation LandscapeArchaeology. Running the script in QGIS Python console """ Topographic position index for elevation models, a mock script to be tuned according to you needs. All comments are welcome at LandscapeArchaeology.

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SetProjection dem. GetProjection ds. SetGeoTransform dem. GetGeoTransform ds.The central purpose of this toolbox is to provide ArcGIS users a convenient way to calculate hillslope position from elevation grids.

However, the Relief Analysis Toolbox also includes some other ArcGIS models that may be of interest to anyone working with landscape and landform segmentation.

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The main features of this toolbox are:. Download the Relief Analysis toolbox Includes instructions and a suggested color scheme for displaying hillslope position. Classification of hillslope position has a long history in soil geomorphology. The base maps developed by the model can also help identify areas of possible mismapping, especially where soil boundaries cross topographic breaks. This information can enable the mapper to redefine many existing soil map unit boundaries, placing them more correctly at locations where defendable landscape breaks exist.

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Development of the hillslope position classification tool is documented in the following publications and dissertation, and should be used for citation as appropriate:. Miller, B. Schaetzl, Digital classification of hillslope position. Semantic calibration of digital terrain analysis. Cartography and Geographic Information Science Journal Incorporating tacit knowledge of soil-landscape relationships for digital soil and landscape mapping applications.

Analysis Tools ArcGIS toolbox digital terrain analysis hillslope position landform class relief spatial analysis. More about the Digital Classification of Hillslope Position Classification of hillslope position has a long history in soil geomorphology. References Development of the hillslope position classification tool is documented in the following publications and dissertation, and should be used for citation as appropriate: Miller, B.

Spread the word. Leave a Reply Cancel reply.To be useful for silvicultural and forest management practices, the models of Site Index SI should be based on accessible predictor variables.

In this study, we used spatially explicit data obtained from digital elevation models and climate data to develop SI prediction models with high local precision. Predicting tree growth and yield is a key component to sustainable forest management and depends on accurate measures of site quality. The aim of this study was to develop both empirical models to predict site index SI from biophysical variables and a dynamic model of top height growth for plantations of Pinus elliottii Engelm.

Site productivity described by SI was related to environmental characteristics, including topographic and climatic variables. Separate models were created from only topographic data and the combination of topographic and climate data. The key factors affecting site productivity were the landscape position and the mean precipitation of the last 5 years before the reference age, both related to the amount of plant-available water in the soils.

The models developed here are likely to have considerable application in forestry, since they are based on accessible predictor variables, which make them useful for silvicultural and forest management practices, particularly for non-forest areas and for the young or uneven-aged stands.

Relief Analysis Toolbox

This is a preview of subscription content, access via your institution. Please try refreshing the page. If that doesn't work, please contact support so we can address the problem. The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. For Ecol Manag — Ann For Sci Bontemps JD, Bouriaud O Predictive approaches to forest site productivity: recent trends, challenges and future perspectives. Forestry — Forests — OmniaScience Monographs, pp Can J For Res — Google Scholar.

Hydrol Sci J — Geomorphology — Fang Z, Bailey RL Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments.

For Sci —