Terrain by ewghwehws


									     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
     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
    Introduction to GIS

            Metropolitan Areas
• 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
      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
                                  ------Using GIS--
      Introduction to GIS

    Reclassification with Grids

Here we reclass to
3 classes, based
on natural breaks
                              ------Using GIS--
Introduction to GIS

Reclassification with Grids
                              ------Using GIS--
Introduction to GIS

Reclassification with Grids
                              ------Using GIS--
Introduction to GIS

Reclassification with Grids
       Introduction to GIS

          Raster Data Structuring
• Methods for storing raster data in a more
  computationally and memory efficient way.
• Where a raster layer is random noise, this does not
• 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.
• DEM’s and satellite images generally use this structure
  because there is typically so much variation.
       Introduction to GIS

          Raster Data Structuring
• Run-length encoding (RLE):
  – Compression method that records cell values in groups called
  – 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 10:1, depending on data.
     Introduction to GIS

        Raster Data Structuring

• Runs:
   – Row 2: 3,4
   – Row 3: 2, 8
   – Row 4: 4,7
   – Row 5: 5,7
   – Row 6: 2,6
      Introduction to GIS

         Raster Data Structuring
• 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.
        Introduction to GIS

           Raster Data Structuring
•  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
      Introduction to GIS

         Raster Data Structuring
• 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
       Introduction to GIS

           Raster Data Structuring
• 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.
         Introduction to GIS

             Raster Data Structuring
• Quad tree:

      (all one value)

Not homogeneous: more
than one value within
         Introduction to GIS

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

Not homogeneous: more
than one value within
         Introduction to GIS

            Raster Data Structuring
• Quad tree: and we keep doing this until we’ve 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
      Introduction to GIS

         Raster Data Structuring
• Quad tree:                One value (leaf node)
                            Mixed values (non-leaf)
       Introduction to GIS

             Vector Compression
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
     Introduction to GIS

           Vector Compression
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.
    Introduction to GIS

          Vector Compression
Douglas-Peucker method
     Introduction to GIS

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

•Note the nested system
       Introduction to GIS

•Here are
     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 people’s land
ownership in the west is based on this system
       Introduction to GIS

How it’s been done in
the past; survey
markers or benchmarks
are key
           Introduction to GIS

                  IDW-How it works
•Zij= Zxy /D
•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
•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
          Introduction to GIS

                 IDW-How it works
•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.
          Introduction to GIS

                 IDW-How it works
•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
Introduction to GIS

       IDW-How it works
         Introduction to GIS

                IDW-How it works
•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
   •2. Fixed Radius: User defines a radius within which every
   point will be used to define the value for each cell
      Introduction to GIS

             IDW-How it works
•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
       Introduction to GIS

                   Spline Method
•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
          Introduction to GIS

                     Kriging Method
•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
        Introduction to GIS

        USGS Transfer Formats:Optional
• 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
       Introduction to GIS

      USGS Transfer Formats: SDTS
• 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
      Introduction to GIS

                 Importing SDTS
• There are several SDTS import
  functions in Arc Toolbox but they
  don’t support all conversions
• Often you’ll have to use Arc View
  scripts, like DLG20A.AVE which,
  used in conjunction with a DOS
  utility called CHOP, allows use of
  1:100,000 DLGs
• 1:24,000 SDTS DEMs can be
  imported as grids in AV using a
  freely available extension called
  SDTS grid import, or
       Introduction to GIS

                   Importing SDTS
• 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
     Introduction to GIS

             The Physics of RS
•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
     Introduction to GIS

             The Physics of RS
•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
       Introduction to GIS

                  LANDSAT TM
•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
        Introduction to GIS

                     IKONOS data
•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
•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
     Introduction to GIS

          Geometric Correction
•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
      Introduction to GIS

          Geometric Correction:
•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
     Introduction to GIS

                Noise Removal
•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.
          Introduction to GIS

           Multi-image Manipulation
•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
        Introduction to GIS

             Spectral Classification
•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

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