Title: Vectorbased models used for terrain, including contours and TIN
1Terrain
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
2Weather
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
3Metropolitan 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
4Introduction 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
5Reclassification with Grids
------Using GIS--
Introduction to GIS
Here we reclass to 3 classes, based on natural
breaks
6Reclassification with Grids
------Using GIS--
Introduction to GIS
7Reclassification with Grids
------Using GIS--
Introduction to GIS
8Reclassification with Grids
------Using GIS--
Introduction to GIS
9Raster 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.
10Raster 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.
11Raster 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
-
12Raster 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.
13Raster 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
14Raster 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
15Raster 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.
16Raster Data Structuring
Introduction to GIS
Homogeneous (all one value)
Not homogeneous more than one value within
quadrant
17Raster 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
18Raster 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
19Raster Data Structuring
Introduction to GIS
One value (leaf node) Mixed values (non-leaf)
20Vector 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
21Vector 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.
22Vector Compression
Introduction to GIS
23PLSS
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
24PLSS
Introduction to GIS
25PLSS
Introduction to GIS
- Here are the townships for Washington
26PLSS
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
27PLSS
Introduction to GIS
How its been done in the past survey markers or
benchmarks are key
28IDW-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
29IDW-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.
30IDW-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
31IDW-How it works
Introduction to GIS
32IDW-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 -
33IDW-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
34Spline 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
35Kriging 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
36USGS 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
37USGS 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
38Importing 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
39Importing 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
40The 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
41The 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
42LANDSAT 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
43IKONOS 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
44Geometric 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
45Geometric 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
46Noise 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.
47Multi-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
48Spectral 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