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#1
Dimaras

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Hello

i'm a student in geomatics and i have a problem to my project. My project is related with designing contour lines..
As you can see to the map i have contour lines shapefile that are like polygon, in spite of being more curvy. I would like to find methods and advices how could i change and generalize the shape of these lines.. i would like to make them more curvy.

I use the Arcgis software.. The map is in this picture.

Attached File  _____________________.jpg   259.72KB   106 downloads

I need help.

Thank you!

#2
Hans van der Maarel

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What's your DEM resolution and what's your output contour interval? At first glance I would say your DEM isn't nearly accurate enough for the contour interval you've chosen.
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#3
Sv_BG

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I think you have to use the Focal statistics tool (Arctoolbox --> Spatial analyst tools --> Neighbourhood) in order to smooth the DEM and after that to create good looking contour line set from it.

I recommend this ESRI's article - http://www.esri.com/...oolglaciers.pdf

So, have a nice reading and good luck :)

#4
James Hines

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Okay from the looks of it at first glance I would say that you tried to produce a DEM from points & the result is very bad. I can't remember from an ArcMap point of view but in Manifold I have found that the best way of generating a DEM from points is to use the Kriging or Gravity method.

Attached File  DEST.jpg   39.27KB   40 downloads Attached File  Gravity.jpg   36.86KB   39 downloads

The data came in as points from a SQL database I'm currently producing in Web Developer & Management Studio. Though I created the isothermal maps from a DEM displaying income levels the results are display the different methodologies; first map similar to the same problem that Dimaras is faced with, & the second map shows a much better result. Though the process is the opposite where I'm creating contours from the DEM the problem is likely similar to the method of creating the contours. In the next example you can see that the DEST method only uses the extent of the points where in Gravity the DEM is a rectangle going beyond the extent of where the points are. Plus notice that the first sample is more traingular then the second method.

Attached File  DESTDEM.jpg   93.43KB   36 downloads Attached File  GravityDEM.jpg   69.21KB   36 downloads

Anyways I hope that gives some idea of what's going on.

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#5
Charles Syrett

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I agree with Hans -- you're trying to create too many contours from the grid. If you specify a larger contour interval, you'll see a lot less of that checkerboard pattern that's a tell-tale sign of poor interval selection. Then you'll be able to use whatever smoothing tools Arc has to improve the curves on the contours.

The theory behind this is that you don't try to create contours with an interval that's less than the resolution of the grid. Not only does it look bad, it also creates a false sense of the accuracy of the contours.

James ("Hasdrubal") also made an interesting point about the different methods of creating the DEM, or grid, from which the contours are generated. The Krige method was developed for statistical, or abstract, surfaces, and generally does a better job of generating a grid that will produce rounded contours. However, it will also introduce artifacts, and is generally not the first choice for creating grids of actual terrain. Actual terrain requires a more rigorous approach to grid creation, though sometimes at the expense of the appearance of the resulting contours. The DEST method that James mentions was developed specifically for gridding terrain.

In my opinion, nothing matches the pre-digital method of contouring using analog photogrammetric processes. No gridding was involved -- just human observation of the three-dimensional model. Ask anyone who hikes in mountainous terrain, and uses different contour maps generated using pre-digital and post-digital methods. Details of the terrain that"slip" through the gridding and breakline-generating processes will be faithfully shown on the older contour maps.

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#6
Sv_BG

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Btw, when I wrote about the Focal statistics tool, I forgot to mention that after using it you shouldn't use the elevation model for any analysis. And don't forget - the more you smooth - the faker contour lines you'll produce (but they will be good looking :D).

In fact, I think there is a difference in the results - between the contours which are produced by an already smoothed DEM and the contours which you smooth after you produce them from a rough DEM. I prefer to smooth the DEM first and after that to produce contour lines. Of course this depends on the quality of the DEM. If the elevation model is made by interpolation of hand digitized contours from topographic maps, there is no need to smooth it in order to produce contour lines.

Hasdrubal, it is basically the same with the basic interpolation methods in ArcGIS - IDW, Spline, Natural Neighbour and the Kriging method. I'm not into geostatistics, so I'll paste a part of a helpful article which explains everything well:

DEM modelling with common interpolation algorithms

We were tested some most common interpolation algorithms based on inverse distance weighted (IDW), kriging and spline using the same data sources. The IDW methods apply the idea that influence decreases with increasing the distance from particular points. The method could be good for interpolation of geomorphologically smooth areas. Kriging methods take into consideration autocorrelation structures of elevations in order to define optimal weights for different distances from a point and then automatically evaluate the results. The method requires a skilled user with geostatistical knowledge. Spline-based methods fit a minimum-curvature surface through the input points. The interpolation ensures continuous and differentiable (smooth) surface. Rapid changes in gradient or slope may occur in vicinity of the data points.

We employed all of three described algorithms using contour lines data sources on the study area, which is geomorphologically variable (see Fig 1). All of the algorithms were used on standardised way and with default parameters. First of all we decided to asses the results with visual approach, which is suitable for general overview of consequences of the interpolation methods.

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Fig 1: Contours with interval of 10 m and lake of Bled in the western Slovenia (a). DEM is produced with IDW - smooth (B), kriging - more details © and spline based method - smooth but with recognisable characteristic features (d) (area of 5000 by 5000 m).

For general purpose it is difficult to decide which algorithm produces the best DEM form contour lines. IDW algorithm is optimal if we need results produced in a short time and if the real terrain is smooth. Kriging method is useful in this case but some problems occur mainly on the areas with low density of data sources. Spline-based algorithm produces smooth surface and fortunately without many of badly interpolated areas. If we would like decide to use only one of three basic methods of the contour lines interpolation, then we can think on following way:

  • to get optimal result without much effort: use spline algorithm
  • to get the best general result for more advanced analyses and visualisation: use kriging algorithm
  • to get the fastest result: use IDW.
We can stress that there are no bad DEM interpolation algorithms. Some of them have simply more advantages in certain circumstances. The algorithms are actually the most flexible part of the whole modelling process. It is because usually nobody has opportunity to use the ideal data and one can therefore only select the algorithm that is the most suitable for the used data sources and application.

If operator or user knows a purpose of the DEM's application, then he can decide about importance of particular quality parameters. Generally, the optimal is the DEM that requires good results after evaluation of many geomorphologic and statistical quality parameters. Let's propose to allow combination of the three proposed basic algorithms. Then the best DEM from contour lines would be produced as combination of kriging and spline. The kriging would be applied for the areas around the characteristical features like peaks, sinks, valleys, ridges, edges, etc., but the spline algorithm would be preferred on the other areas.


You can read the full article here - http://www.gisdevelo...gy/tm/tm003.htm


Svetoslav




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