Vectorbased models used for terrain, including contours and TIN

1
Terrain
Introduction to GIS

• Vector-based models used for terrain, including
  contours and TIN
• Problem creates distinct terrain entities that
  distort reality terraces and triangular facets
• Raster based grids are more commonly used
• They are optimal for showing spatial
  micro-variation in elevation although still have
  the problem of being like miniature steps
• Lattices deal with this through interpolation

2
Weather
Introduction to GIS

• Weather station data Vector, coded with points
• Average precipitation surface Raster
  interpolation of points
• Average precipitation contours vector lines
• Both are interpolations, but one may be more
  accurate in a given situation
• Downside of contours terrace effect, fewer
  intervals, more categorical

3
Metropolitan Areas
Introduction to GIS

• No official administrative boundary for this
• Where does one metro area begin and another end?
  Look at the New York New Jersey area.
• For a precise bounding, say for administrative
  purposes, use vector
• Can also include fuzzy boundaries
• To represent a gradual change from one urban area
  to another, use raster

4
Introduction to GIS
Types of Vector Topology

• Arc-node and node topology the way that line
  features connect to point features
• Polygon topology the way that neighboring
  polygons connect and share borders
• Route topology the way that a line feature of
  one type (e.g. commuter rail line) shares
  segments with line features of another type (e.g.
  Amtrack rail line)
• Regions topology the way that polygons overlap
  (e.g. GIS layers with a time component) or when
  spatially separate polygons are part of the same
  feature

5
Reclassification with Grids
------Using GIS--
Introduction to GIS
Here we reclass to 3 classes, based on natural
breaks
6
Reclassification with Grids
------Using GIS--
Introduction to GIS
7
Reclassification with Grids
------Using GIS--
Introduction to GIS
8
Reclassification with Grids
------Using GIS--
Introduction to GIS
9
Raster Data Structuring
Introduction to GIS

• Methods for storing raster data in a more
  computationally and memory efficient way.
• Where a raster layer is random noise, this does
  not work.
• Requires repetitive patterns or areas of
  homogeneity.
• The fewer z values, the easier to compress.
• Simplest method is cell-by-cell encoding where
  cell values are stored by row and column number
  This is essentially uncompressed.
• DEMs and satellite images generally use this
  structure because there is typically so much
  variation.

10
Raster Data Structuring
Introduction to GIS

• Run-length encoding (RLE)
• Compression method that records cell values in
  groups called runs.
• It records the starting and ending pixel for a
  run with the same value for a given row, so
  hundreds of pixels could be recorded with only
  two values, if they all have the same value and
  are adjacent.
• However, because it measures runs along rows, it
  is not efficient for two dimensional areas of
  homogeneity.
• RLE can reduce file size by 101, depending on
  data.

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Raster Data Structuring
Introduction to GIS

• Runs
• Row 2 3,4
• Row 3 2, 8
• Row 4 4,7
• Row 5 5,7
• Row 6 2,6

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Raster Data Structuring
Introduction to GIS

• Chain code
• This is a more efficient method for dealing with
  two-dimensional compression
• This defines a homogeneous two-dimensional area
  using cardinal directions and units movements to
  define bounding perimeter in relative terms from
  a known point
• For instance, go 2 N, 1 W, 1N, 3 W, 1S.etc.

13
Raster Data Structuring
Introduction to GIS

• Here, starting from the lower left, the computer
  would define that coordinate then code 1N, 3E,
  1N, 1W, 1N, 2W, 1N, 1E, 1N, 2E etc..
• This would define the perimeter of a homogeneous
  area.
• All must have exactly the same value

14
Raster Data Structuring
Introduction to GIS

• Block code
• A method that uses square blocks to represent
  areas of homogeneous values
• Each block is encoded only with location of one
  corner cell and the dimensions since they are
  square, only one dimension needs to be given
• Uses medial axis transformation technique

15
Raster Data Structuring
Introduction to GIS

• Quad tree
• Divides a grid into hierarchy of quadrants
• Starts with four quadrants any quadrant that has
  totally homogeneous cells will not be subdivided
  further, but is stored as a lead node which is
  coded only with that value and the id of the
  quadrant.
• Any quadrants with more than one value are
  subdivided again into four more quadrants and
  again the computer checks for homogeneity.
• It keeps on doing this until it has generated all
  its leaf node or until it gets down to the pixel
  level
• This is known as recursive decomposition
• This is good where one part of a grid is very
  uniform and the rest is heterogeneous.

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Raster Data Structuring
Introduction to GIS

• Quad tree

Homogeneous (all one value)
Not homogeneous more than one value within
quadrant
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Raster Data Structuring
Introduction to GIS

• Quad tree now we break down those quadrants with
  non-homogeneous values into four sub quadrants

Not homogeneous more than one value within
quadrant
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Raster Data Structuring
Introduction to GIS

• Quad tree and we keep doing this until weve
  come down to the point where there are only
  homogeneous quadrants, even if those are
  one cell in dimension

Not homogeneous more than one value within
quadrant
19
Raster Data Structuring
Introduction to GIS

• Quad tree

One value (leaf node) Mixed values (non-leaf)
20
Vector Compression
Introduction to GIS

• Vector data take up a lot of memory, so
  compression techniques are needed.
• These are automated techniques for simplifying
  line segments by removing points, while still
  preserving geometric accuracy
• Simplest form is elimination of repetitive
  characters, like the first character, or
  coordinate value, of all coordinates along a
  particular horizontal axis
• Another is to keep every nth point on a line
• Yet another is to remove points and estimate
  functions Spline function can estimate
  polynomials

21
Vector Compression
Introduction to GIS

• One of the most common methods is the
  Douglas-Peucker method
• Draw a straight line between first and last
  points in a curved line segment and calculate
  orthogonal distance from each point to line
  those that fall within certain defined distance
  are removed
• The new end point of the straight line is then
  moved to the point with the greatest orthogonal
  distance and process starts again.

22
Vector Compression
Introduction to GIS

• Douglas-Peucker method

23
PLSS
Introduction to GIS

• Public Land Survey System is used for
  partitioning of land
• Land is US West and Midwest are divided up into
  nested hierarchy
• 6x6 mile townships
• 36 mile square parcels called sections

24
PLSS
Introduction to GIS

• Note the nested system

25
PLSS
Introduction to GIS

• Here are the townships for Washington

26
PLSS
Introduction to GIS

• BLM is currently developing a Geographic
  Coordinate Database of PLSS in the west
• The database contains lat/long coordinates and
  descriptive information for section corners and
  monuments recorded in the PLSS
• This is important, because many peoples land
  ownership in the west is based on this system

27
PLSS
Introduction to GIS
How its been done in the past survey markers or
benchmarks are key
28
IDW-How it works
Introduction to GIS

• Zij Zxy /Dp
• Z value at location ij is f of Z value at known
  point xy times the inverse distance raised to a
  power P.
• Z value field numeric attribute to be
  interpolated
• Power determines relationship of weighting and
  distance where p 0, no decrease in influence
  with distance as p increases distant points
  becoming less influential in interpolating Z
  value at a given pixel

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IDW-How it works
Introduction to GIS

• What is the best P to use?
• It is the P where the Root Mean Squared
  Prediction Error (RMSPE) is lowest, as in the
  graph on right
• To determine this, we would need a test, or
  validation data set, showing Z values in x,y
  locations that are not included in prediction
  data and then look for discrepancies between
  actual and predicted values. We keep changing the
  P value until we get the minimum level of error.
  Without this, we just guess.

30
IDW-How it works
Introduction to GIS

• This can be done in ArcGIS using the
  Geostatistical Wizard
• You can look for an optimal P by testing your
  sample point data against a validation data set
• This validation set can be another point layer or
  a raster layer
• Example we have elevation data points and we
  generate a DTM. We then validate our newly
  created DTM against an existing DTM, or against
  another existing elevation points data set. The
  computer determine what the optimum P is to
  minimize our error

31
IDW-How it works
Introduction to GIS
32
IDW-How it works
Introduction to GIS

• There are two IDW method options Variable and
  fixed radius
• 1. Variable (or nearest neighbor) User defines
  how many neighbor points are going to be used to
  define value for each cell
• 2. Fixed Radius User defines a radius within
  which every point will be used to define the
  value for each cell

33
IDW-How it works
Introduction to GIS

• Can also define Barriers User chooses
  whether to limit certain points from being used
  in the calculation of a new value for a cell,
  even if the point is near. E.g. wouldn't use an
  elevation point on one side of a ridge to create
  an elevation value on the other side of the
  ridge. User chooses a line theme to represent
  the barrier

34
Spline Method
Introduction to GIS

• SPLINE method
• Can also control
• Weight this controls the tautness of the curves.
  High weight value with the Regularized Type, will
  result in an increasingly smooth output surface.
  Under the Tension Type, increases in the Weight
  will cause the surface to become stiffer,
  eventually conforming closely to the input
  points.
• Number of points around a cell that will be used
  to fit the curve

35
Kriging Method
Introduction to GIS

• Like IDW interpolation, Kriging forms weights
  from surrounding measured values to predict
  values at unmeasured locations. As with IDW
  interpolation, the closest measured values
  usually have the most influence. However, the
  kriging weights for the surrounding measured
  points are more sophisticated than those of IDW.
  IDW uses a simple algorithm based on distance,
  but kriging weights come from a semivariogram
  that was developed by looking at the spatial
  structure of the data. To create a continuous
  surface or map of the phenomenon, predictions are
  made for locations in the study area based on the
  semivariogram and the spatial arrangement of
  measured values that are nearby.
• --from ESRI Help

36
USGS Transfer FormatsOptional
Introduction to GIS

• Optional Old DLG format
• This lab will use files in this format
• The Optional format is based on an 80-byte
  logical record length with a ground planimetric
  coordinate system and topological linkages
  contained in node, line, and area elements.
• The DLG files in optional format do NOT contain
  record delimiters (e.g. commas). Use the chop
  utility with the following DOS command to deal
  with this problem
• chop 80 infilename outfilename
• Files in an Optional format carry an opt.gz
  extension, and files in the SDTS format carry a
  tar.gz extension

37
USGS Transfer Formats SDTS
Introduction to GIS

• Spatial Data Transfer Standard
• Newer Standard for USGS data
• Large scale DLGs only available in this format
• The Federal Geographic Data Committee has
  mandated that all federal digital geographic data
  go to this standard
• The Standard allows the exchange of digital
  spatial data between different computer systems.
  It provides a solution to the problem of spatial
  data transfer from the conceptual level to the
  details of physical file encoding.
• Several software tools have been developed for
  the importing SDTS data, but each data product
  requires a different software tool

38
Importing SDTS
Introduction to GIS

• There are several SDTS import functions in Arc
  Toolbox but they dont support all conversions
• Often youll have to use Arc View scripts, like
  DLG20A.AVE which, used in conjunction with a DOS
  utility called CHOP, allows use of 1100,000 DLGs
• 124,000 SDTS DEMs can be imported as grids in AV
  using a freely available extension called SDTS
  grid import, or SDTS2DEM.avx

39
Importing SDTS
Introduction to GIS

• Several good SDTS resource pages
• http//mcmcweb.er.usgs.gov/sdts/
• http//data.geocomm.com/sdts/demmap.pdf
• http//data.geocomm.com/sdts/
• http//data.geocomm.com/sdts/sdts_tutorial.txt

40
The Physics of RS
Introduction to GIS

• The geometry of reflectance is largely a function
  of surface characteristics, such as roughness
• Specular reflectors are like mirrors, where
  angle of reflection equals angle of incidence
• Diffuse (Lambertian) reflectors are rough
  surfaces that reflect uniformly in all directions
• Real world objects are in between

41
The Physics of RS
Introduction to GIS

• Diffuse reflections contain spectral info on the
  color of the reflecting surface
• Specular reflections do not
• Still water and ice trend towards specular
  reflections
• In RS we mainly care about that portion of the
  incident energy that is reflected

42
LANDSAT TM
Introduction to GIS

• TM uses 16 detectors per band, except thermal,
  which uses four 100 detectors, versus 16 for MSS
• At any instant all 100 detectors view a different
  area on the ground due to spatial separation of
  detectors.
• Therefore, accurate band to band data
  registration (correct overlaying) requires
  knowledge of the relative projection of the
  detectors as an fn of time this requires knowing
  relative position of each detector array with
  respect to the optical axis

43
IKONOS data
Introduction to GIS

• The high resolution data sets are broken into
  several products, based on the processing steps.
  The more steps, the more expensive. Each has
  different level of error. Lowest error is the
  precision plus line of products
• All IKONOS data are available as a single pan BW
  image, as multispectral layers or as
  pan-sharpened multispectral imagery
• Pan sharpening process adds pixel color to 1 m
  pan data by combining the pan and multispectral
  data. Ground control is used for precision
  products.
• Regular multi-spectral comes without pan
  sharpening

44
Geometric Correction
Introduction to GIS

• Raw digital images contain two types of geometric
  distortions systematic and random
• Systematic sources are understood and can be
  corrected by applying formulas
• Random distortions, or residual unknown
  systematic distortions are corrected using
  multiple regression of ground control points that
  are visible from the image

45
Geometric Correction resampling
Introduction to GIS

• Random distortion correction regresses
  difference between image position and ground
  position as a function of where a pixel is in the
  x and y directions.
• Define a grid of empty undistorted map cells
• Overlay the randomly distorted image and guess at
  what image cell value corresponds with what empty
  undistorted cells using the transformation
  equation from the regression

46
Noise Removal
Introduction to GIS

• RS data tend to have random radiometric noise
  from periodic drift, detector malfunction,
  interface problems, hiccups in data
  transmission
• A common method for this is destripping
  procedures, in which histograms for the lines
  produced from a given detector are compared to
  each other and problems in a given detector can
  be isolated and compensated for with a gray scale
  adjustment factor.

47
Multi-image Manipulation
Introduction to GIS

• Principal Components is a statistical method of
  cluster analysis that can be used to enhance and
  help interpret multi-spectral image data
• Problem pixel values in different layers tend to
  be highly correlated, meaning that slight
  differences between bands are hard to perceive,
  so it may be hard to differentiate different
  features
• PCs are a way of separating out redundant info
  from info that is unique to each band and each
  layer is uncorrelated
• In a simple 2 band case, first image shows
  average of two (that which is common) and second
  shows difference (that which is not common) , but
  as add more bands, create additional components,
  although first one explain the most
• Good example at http//www.cira.colostate.edu/ramm
  /cal_val/PCI.htm

48
Spectral Classification
Introduction to GIS

• Other classification techniques, besides
  supervised and unsupervised classification,
  include
• Hybrid classification for instance, using
  unsupervised training areas to help analyst id
  numerous spectral classes that need to be defined
  in order to adequately represent the land cover
  information classes to be differentiated in a
  supervised classification.
• Spectral Mixture analysis and fuzzy
  classification both for classification of mixed
  pixels