Transcript Document 7458534

Introduction to GIS
Terrain
• 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 microvariation in elevation although still have the
problem of being like miniature “steps”
– Lattices deal with this through interpolation
Introduction to GIS
Weather
• 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
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.
• 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
“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 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:
Homogeneous
(all one value)
Not homogeneous: more
than one value within
quadrant
Introduction to GIS
Raster Data Structuring
• Quad tree: now we break down those quadrants
with non-homogeneous values into four sub
quadrants
Not homogeneous: more
than one value within
quadrant
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
quadrant
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 DouglasPeucker 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
PLSS
•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
Introduction to GIS
PLSS
•Note the nested system
Introduction to GIS
PLSS
•Here are
the
townships
for
Washington
Introduction to GIS
PLSS
•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
PLSS
How it’s been done in
the past; survey
markers or benchmarks
are key
Introduction to GIS
p
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
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
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
cell
•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
SDTS2DEM.avx
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
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
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:
resampling
•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