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Digital land surface parameters, also known
as topographic attributes or geomorphometric
parameters are commonly derived from the
digital elevation model (DEM) using some
geomorphometric method. There has been an
increasing interest in the use of relief
data in the last decade accompanied by a
growing availability of DEMs. This page
gives practical instructions on how to derive
a set of land surface parameters in ILWIS.
You can also download a simple (Baranja
Hill) dataset to tests scripts and modify
them according to your needs. These scripts
can also be used to derive DEM parameters
for your own study area. For more information
about the methods to improve plausibility
of DEMs and for reduction of errors in DEM
parameters, see: Hengl, T., Gruber, S. and
Shrestha, D.P., 2004. Reduction
of errors in digital terrain parameters
used in soil-landscape modelling.
International Journal of Applied Earth Observation
and Geoinformation (JAG), 5:97-112.
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ILWIS
scripts for DEM parameterization
in ILWIS (20 KB).
Download and unzip to "C:\Program Files\ILWIS 3.3 Academic\Scripts\" |
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Baranja hill case study - a small case study to test the scripts: contour lines, spot heights, water bodies (17 KB). |
DO-IT-YOURSELF
STEP 0: Get the most recent version of ILWIS and install it on your PC. Detailed explanation of GIS operations and ILWIS commands can be found in the ILWIS help documentation or user's guides (both available on-line).
STEP 1: Download the and unzip the
ILWIS
scripts for DEM parameterization,
best in in the default directory of scripts
(C:\Program Files\ILWIS 3.3 Academic\Scripts\).
If you need more details on how to create
and run a script, we advise you to read
the ILWIS
3.0 Academic user's guide chapter 12.
The ILWIS script consists of set of commands
that can be used with up to nine script
parameters. These can be either spatial
objects, values or textual strings. A script,
in principle, consists of two parts: definition
of script parameters and list of commands.
Sign "//" is used to exclude to insert comments
and explanation of formulas.
STEP 2: Import your contour lines file (or raster DEM) in ILWIS directory (e.g. d:\ilwis_maps\) and set coordinate system of the map.
STEP 3: Run a script from the to left menu (operations list) or from the main menu -> Operations -> Scripts. Use the help button to find more information about the algorithm.
DEM_interpolation
Purpose of this script is to interpolate sampled elevations, then filter out padi terraces and incorporate water bodies. Input parameters are: %1 - contour lines (segments), %2 - sampled heights (points), %3 - water mask (raster, the same grid), %4 - grid definition (georeference), %5 - RMSE(z), %6 - arbitrary value representing the ratio of change of elevation with distance, %7 - number of iterations. Assumptions: linear interpolation, exact values measured at sampled locations; water mask needs to have the same grid definition. See example.
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Filter_outliers
Purpose of this script is to automatically detect and filter local outliers using normal probability. Input parameters are: %1 - DEM (raster), %2 - filter matrix (filter), %3 - estimated RMSE(z) (value or map). Assumptions: constant model of spatial auto-correlation. You can estimate weights for your own dataset using this MS Excel table. Enter your variogram model, then edit the default filter by putting your own weights. Note: it is possible that also a small number of real features such as small lakes and depressions that can occur naturally will be corrected away with this script.
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TP_morphometric
This script will derive seven (local) geomorphometric parameters: slope in % (SLOPE), aspect (ASPECT), profile curvature (PROFC), planar curvature (PLANC), mean curvature (MEANC), slope-adjusted norhtness (NORTH) and solar insolation for given angles (SOLINS). Input parameters: %1.mpr. - DEM, %2 - smoothing parameter s[0,1] (default - 0.5), %3 - RMSE(z) (value), %4 - Solar angle, %5 - Azimuth. All filters work in 3x3 window environment. The algorithm follows the formulas of Evens-Young method, explained in detail by Shary et al. (2002; Geoderma).
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Flow_indices
This script will derive catchment area (CATCH), wetness index (WTI), stream power index (SPI) and sediment transport index (STI) using the mulitple flow directions algorithm by Quinn et al. (1991). Input parameters are: %1 - DEM (raster), %2 - filter matrix (filter), %3 - estimated RMSE(z) (value or map). Input parameters: %1 - DEM (preferably depressionless), %2 - slope map (raster), %3 - number of iterations for calculation of Af, %4 - number of iterations for filtering (10). The derivation of the catchment area consists of four steps:
- Generate the slope-lengths for each diagonal and cardinal direction (8 maps) and their sum;
- Generate the drainage fraction out of cell for each direction;
- Generate drainage fraction into each cell for each direction as a fraction of the contributing cell;
- Propagate the total number ofcontributing cells using n iterations with start map consisting of 1's.
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Anisotropic_CV
This script will estimate the Anisotropic
Coefficient of Variation (ACV) of
topographic surface by comparing derivatives
of elevation in 4 directions (in ILWIS
derived as DFDX, DFDY, DFDDN, DFDUP
filters). ACV describes general geometry
of shapes (polygons) and can be used
to distinguish complex from simple
(oval) shapes of topography. It is
derived as the standard deviation
of absolute derivatives, standardized
by the average value for derivatives.
Input parameters: %1.mpr. - DEM.
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G_landforms
This script will extract six generic landforms (stream, ridge, slope, plain and pit) using the
fuzzy k-means classification of three input DEM parameters: SLOPE, PLANC and ACV. The generic landforms are:
- Stream channel (valley bottom) - Locations of water accumulation and transition; concave shapes;
- Ridge - Locations of water run-off; convex shapes;
- Peak - Conical convex shapes;
- Slope - Sloping part with generally higher shape complexity;
- Plain (Terrace) - Flat areas of low relief and low shape complexity;
- Pit - Conical concave shapes;
Input parameters are: %1 - table with central values has to have same domain as the class map, %2 standard fuzziness factor (1.5), %3 - domain, %4 - SLOPE, %5 - PLANC and %6 - ACV. The output are membership values (0-1) to each generic landform. You also need to download the LF_class table where the class centers are defined. You can modify these as you see them fit.
If you change values in the table, make sure that none of the standard deviations in not equal zero, otherwise the algorithm will not be able to derive memberships. IMPORTANT: the definition of classes and class centres is arbitrary and might require modifications for different study areas.
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