Lecture 16 Terrain modelling: the basics - PowerPoint PPT Presentation

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Lecture 16 Terrain modelling: the basics

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Title: Lecture 16 Terrain modelling: the basics


1
Lecture 16Terrain modellingthe basics
  • Outline
  • introduction
  • DEMs and DTMs
  • derived variables
  • example applications

2
Adding the third dimension
  • In high relief areas variables such as altitude,
    aspect and slope strongly influence both human
    and physical environments
  • a 3D data model is therefore essential
  • use a Digital Terrain Model (DTM)
  • derive information on
  • height (altitude), aspect and slope (gradient)
  • watersheds (catchments)
  • solar radiation and hill shading
  • cut and fill calculations
  • etc.

3
DEMs and DTMs
  • Some definitions
  • DEM (Digital Elevation Model)
  • set of regularly or irregularly spaced height
    values
  • no other information
  • DTM (Digital Terrain Model)
  • set of regularly or irregularly spaced height
    values
  • but, with other information about terrain surface
  • ridge lines, spot heights, troughs, coast/shore
    lines, drainage lines, faults, peaks, pits,
    passes, etc.

4
UK DEM data sources
  • Ordnance Survey
  • Landform Panorama
  • source scale 150,000
  • resolution 50m
  • vertical accuracy 3m
  • Landform Profile
  • source scale 110,000
  • resolution 10m
  • vertical accuracy 0.3m

5
Comparison
Landform Panorama
Landform Profile
6
LIDAR data (LIght Detection And Ranging)
Horizontal resolution 2m Vertical accuracy 2cm
7
Modelling building and topological structures
  • Two main approaches
  • Digital Elevation Models (DEMs) based on data
    sampled on a regular grid (lattice)
  • Triangular Irregular Networks (TINs) based on
    irregular sampled data and Delaunay triangulation

8
DEMs and TINs
DEM with sample points
TIN based on same sample points
9
Advantages/disadvantages
  • DEMs
  • accept data direct from digital altitude matrices
  • must be resampled if irregular data used
  • may miss complex topographic features
  • may include redundant data in low relief areas
  • less complex and CPU intensive
  • TINs
  • accept randomly sampled data without resampling
  • accept linear features such as contours and
    breaklines (ridges and troughs)
  • accept point features (spot heights and peaks)
  • vary density of sample points according to
    terrain complexity

10
Task
  • Make you own TIN from a piece of paper

11
Derived variables
  • Primary use of DTMs is calculation of three main
    terrain variables
  • height
  • altitude above datum
  • aspect
  • direction area of terrain is facing
  • slope
  • gradient or angle of terrain

12
Question
  • What might slope and aspect maps be used for?

13
Calculating slope
  • Inclination of the land surface measured in
    degrees or percent
  • 3 x 3 cell filter
  • find best fit tilted plane that minimises squared
    difference in height for each cell
  • determine slope of centre (target) cell

z a bx cy
14
Calculating aspect
  • Direction the land surface is facing measured in
    degrees or nominal classes (N, S, E, W, NE, SE,
    NW, SW, etc.)
  • use 3 x 3 filter and best fit tilted plane
  • determine aspect for target cell

15
Other derived variables
  • Many other variables describing terrain
    features/characteristics
  • hillshading
  • profile and plan curvature
  • feature extraction
  • etc.

16
Examples
height
slope
aspect
hillshading
plan curvature
Feature extraction
17
Question
  • What other important variables can be derived
    from DEMs?

18
Problems with DEMs
  • Issues worth considering when creating/using DTMs
  • quality of data used to generate DEM
  • interpolation technique
  • give rise to errors in surface such as
  • sloping lakes and rivers flowing uphill
  • local minima
  • stepped appearance
  • etc.

19
Example applications
  • Visualisation
  • terrain and other 3D surfaces
  • Visibility analysis
  • intervisibility matrices and viewsheds
  • Hydrological modelling
  • catchment modelling and flow models
  • Engineering
  • cut fill, profiles, etc.

20
Terrain visualisation
  • Analytical hillshading
  • Orthographic views
  • any azimuth, altitude, view distance/point
  • surface drapes (point, line and area data)
  • Animated fly-through
  • What if? modelling
  • photorealism
  • photomontage
  • CAD

21
Examples of hillshading and orthographic
projection
Hillshading
Orthographic projection
DEM
22
Example surface drape
Rainfall
Draped image
DEM
23
Example animated fly-through
24
Photorealism
25
Photo-realism what if? visualisation
Visualisation 1 before felling
Visualisation 3 strip felling
Visualisation 2 clear-cut
26
Wind farm photomontage
before
after
wire-frame model
27
Conclusions
  • Need for third dimensional GIS
  • especially in environmental applications
  • new data models/structures
  • new opportunities for analysis
  • Basic uses and derived variables
  • Application areas
  • visualisation
  • visibility analysis
  • etc.

28
Practical
  • Using DEMs for hillslope geomorphology
  • Task Derive key variables from DEM and relate to
    slope profiles
  • Data The following datasets are provided for the
    Hohe Tauern Alps, Austria
  • 25m resolution DEM
  • 10m interval contour data (derived from 25m
    resolution DEM)

29
Practical
  • Steps
  • Display DEM in ArcMap or GRID
  • Derive slope and aspect variables using slope and
    aspect functions in GRID
  • Derive valley cross and long profiles using the
    identity tool in ArcMap
  • Plot altitude, slope and aspect against distance
    along profile in Excel
  • Relate to physical form

30
Learning outcomes
  • Familiarity with TIN/DEM construction in Arc/Info
  • Experience with deriving surface variables
  • Experience with displaying surfaces in Arcplot

31
Useful web links
  • View global DEMs
  • http//www.ngdc.noaa.gov/mgg/image/images.htmlrel
    ief
  • DEM derived operations
  • http//www.powerdata.com.au/derive.htm

32
After reading week
  • Terrain modelling applications
  • Access modelling
  • Landscape evaluation
  • Hazard mapping
  • Practical Visibility assessment
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