Transcript Fundamentals of GIS - University of Vermont

------Using GIS-Fundamentals of GIS
Lecture 5:
Introduction to Raster Spatial
Analysis
By Austin Troy and Weiqi Zhou, University of Vermont
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Raster data-A Refresher
Raster Elements
–Extent
–# rows
–# columns
–Coordinates
–Origin
–Orientation
–Resolution
–Grid cell
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of 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.
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of 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.
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Fundamentals of 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
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Fundamentals of 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.
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Fundamentals of 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
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Fundamentals of 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
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Fundamentals of 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.
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Raster Data Structuring
• Quad tree:
Homogeneous
(all one value)
Not homogeneous: more
than one value within
quadrant
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of 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
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of 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
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Raster Data Structuring
• Quad tree:
One value (leaf node)
Mixed values (non-leaf)
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------Using GIS-Fundamentals of GIS
Converting vector to raster
Slide by Weiqi Zhou
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------Using GIS-Fundamentals of GIS
Converting vector to raster
Slide by Weiqi Zhou
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------Using GIS-Fundamentals of GIS
Raster Overlay Queries
•The raster data model performs overlay operations more
efficiently than the vector model Raster cells have a oneto-one relationship between layers
•Raster overlay queries involve the combining of
two or more separate thematic layers to identify
relationships between them such as:
–Areas that are common to all layers
–Areas that meet criteria from each layer
Query example:
[elevation > 2500] AND [Slope>20]
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------Using GIS-Fundamentals of GIS
Overlay Calculations
•Map algebra can be performed to identify
relationships between layers, or to derive
indices that describe phenomena
•Map calculations create a new layer
Calculation example:
(Soil_depth_1990) – (Soil_depth_2000)=loss in soil
between 1990 and 2000
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------Using GIS-Fundamentals of GIS
Source: ESRI
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------Using GIS-Fundamentals of GIS
Map Query Examples
Single layer numeric example: elevation > 2000 ft
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Fundamentals of GIS
Map Query Examples
Results in a binary True/False layer
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------Using GIS-Fundamentals of GIS
Map Query Examples
Multi-criteria, single layer, categorical map query: looking for all
developed land use types, using attribute codes (11, 12, 13) and OR
Vertical
lines mean
OR
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------Using GIS-Fundamentals of GIS
Map Query Examples
Results in a 1/0 binary layer, showing urbanized areas
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------Using GIS-Fundamentals of GIS
Map Query Examples
One can then convert this to a vector shapefile or feature class
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Map Query: 2 layer Examples
Multi-layer queries are use criteria across two or more layers; in
this case we’ll query land use (categorical), elevation (number)
and slope (number)
Let’s say we want to
find identify potential
habitat for a rare plant
that grows at higher
elevation, on steeper
slopes and in
coniferous forest
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------Using GIS-Fundamentals of GIS
Map Query Examples
First we would generate a slope map from out Digital Elevation
Model by going to Surface>>Derive Slope
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------Using GIS-Fundamentals of GIS
Map Query Examples
Let’s say our criteria are elevation >800, slope >20% and land use
category= coniferous forest (42)
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------Using GIS-Fundamentals of GIS
Map Query Examples
Again we end up with a 1/0 binomial query layer
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Map Calculation
We can also run calculations between layers: here we’ll multiply the
k factor (soil erodability factor) by slope; let’s just imagine this will
yield a more accurate and spatially explicit index of erodability that
factors in slope at each pixel
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------Using GIS-Fundamentals of GIS
Map Calculation
Now we simply type in the equation and a new grid is created that
solves that equation
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------Using GIS-Fundamentals of GIS
Map Calculation
The darker areas are those with both steep slope and erodable soils.
This has the advantage over map query in that we now have a
continuous index of values, rather than just a “true” “false”
dichotomy
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------Using GIS-Fundamentals of GIS
Map Calculation and Query
We could then, for instance, run a map query to find areas that have
high erodability factors and urban land use.
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------Using GIS-Fundamentals of GIS
Zonal Statistics
Now, say we had a proposed subdivision map (this one is made up).
We could overlay it on our new index layer and figure out which
proposed subdivisions are problematic
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------Using GIS-Fundamentals of GIS
Zonal Statistics
Using Zonal Statistics we could summarize the mean, max or sum
of the soil index for each of those units, even though one is grid and
one is polygon. Here we summarize by mean the subdivision zones
by the soil erodability calculation layer.
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------Using GIS-Fundamentals of GIS
Zonal Statistics
This will create a DBF table that summarizes the pixel values by
mean, median, max, min, etc., of all the pixels falling within a given
polygon. Each row represent a polygon and each column represents
a different summary statistic
Polygon layer with zones
Unique ID for polygons
This joins the DBF table to
the polygon layer
Statistic by which your
data will be charted
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------Using GIS-Fundamentals of GIS
Zonal Statistics
It gives us a DBF table with values of mean, max, min, stdv, etc. in
the table, plus a chart summarizing the means;
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------Using GIS-Fundamentals of GIS
Zonal Statistics
Now we can plot out the subdivision boundaries (zones) by a soil
erosion statistic. In this case, clearly the most meaningful one is the
mean of the soil erosion statistic. This represent the mean value, by
polygon, of all the soil erosion pixels underlaying that polygon
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------Using GIS-Fundamentals of GIS
Reclassification with Grids
Here we reclass to
3 classes, based
on natural breaks
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------Using GIS-Fundamentals of GIS
Reclassification with Grids
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------Using GIS-Fundamentals of GIS
Reclassification with Grids
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------Using GIS-Fundamentals of GIS
Reclassification with Grids
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Fundamentals of GIS
Neighborhood Statistics
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Fundamentals of GIS
Low Pass filtering
• Functionality: averaging filter
– Emphasize overall, general trends at the expense of local
variability and detail.
– Smooth the data and remove statistical “noise” or extreme
values.
• Summarizing a neighborhood by mean
– The larger the neighborhood, the more you smooth, but the
more processing power it requires.
– A circular neighborhood: rounding the edges of features.
– Resolution of cells stays the same.
– Using median instead of mean, but the concept is similar.
©2007 Austin Troy
Fundamentals of GIS
High Pass Filter
• Functionality: edge enhancement filter
– Emphasize and highlight areas of tonal roughness, or
locations where values change abruptly from cell to cell.
– Emphasize local detail at the expense of regional,
generalized trends.
• Perform a high pass filter
– Subtracting a low pass filtered layer from the original.
– Summarizing a neighborhood by standard deviation
– Using weighted kernel neighborhood
©2007 Austin Troy
Fundamentals of GIS
Neighborhood Statistics
• Min, max, mean,
standard deviation,
range, sum, variety
• Window size/shape
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Fundamentals of GIS
Low pass filter with bathymetry
• Low pass filtering: filtering out anomalies
Bathymetry mass points:
sunken structures
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Fundamentals of GIS
• After turning into raster grid
We see sudden
anomaly in grid
Say we wanted to “average”
that anomaly out
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Fundamentals of GIS
• Try a low-pass filter of 5 cells
We can still see those anomalies but
they look more “natural” now
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Fundamentals of GIS
• Try a low-pass filter of 25 cells
The anomalies have been “smoothed
out” but at a cost
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Fundamentals of GIS
• We can also do a local filter in that one area
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Fundamentals of GIS
What about high pass filters?
• Say we wanted to isolate where the wreck was
All areas of sudden change, including
our wrecks, have been isolated
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Fundamentals of GIS
Low pass filter for elevation
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Fundamentals of GIS
A low pass filter of the DEM done by taking the mean values for a
3x3 cell neighborhood: notice it’s hardly different
DEM
Low pass
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Fundamentals of GIS
10 unit square neighborhood
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Fundamentals of GIS
20 unit square neighborhood
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Fundamentals of GIS
In this high-pass filter the mean is subtracted from the original
It represents
all the local
variance that
is left over
after taking
the means for
a 3 meter
square
neighborhood
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Fundamentals of GIS
We do this using the map calculator
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Fundamentals of GIS
If we do a high-pass filter by subtracting from the original
the means of a
20x 20 cell
neighborhood,
it looks
different
because more
local variance
was “thrown
away” when
Dark areas represent
taking a mean
things like cliffs and
with a larger
steep canyons
neighborhood
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Using standard deviation is a form of high-pass filter because it is
looking at
local variation,
rather than
regional
trends. Here
we use 3x3
square
neighborhood
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Fundamentals of GIS
• Note how similar it looks to a slope map because it is showing
standard deviation, or normalized variance, in spot heights,
which is similar to a rate of change.
• Hence it is emphasizing local variability over regional trends.
• The resolution of the slope is quite high because it is sampling
only every nine cells.
• When we go to a larger neighborhood, by definition, the
resulting map is much less detailed because the standard
deviation of a large neighborhood changes little from cell to cell,
since so many of the same cells are shared in the neighborhood
of cell x,y and cell x,y+1.
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Fundamentals of GIS
• Here is the same function with 8x8 cell neighborhood.
Here, the
coarser
resolution due
to the larger
neighborhood
makes it so
that slope rates
seem to vary
more gradually
over space
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Later on we’ll look at filters and remote sensing imagery, but here is
a brief example
of a low-pass
filter on an image
that has been
converted to a
grid. This can
help in
classifying land
use types
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Fundamentals of GIS
Raster terrain functions in Arc GIS
Arc GIS allows you to take a digital elevation
model and derive:
•Hillshade
•Aspect
•Slope
•Contours
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------Using GIS-Fundamentals of GIS
Raster terrain functions in AV
DEM
+
Hillshade = Hillshaded DEM
+
=
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------Using GIS-Fundamentals of GIS
Raster terrain functions in AV
This is done by making a hillshade using Spatial
analyst, putting the hillshade “under” the DEM in
the TOC and making the DEM transparent
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------Using GIS-Fundamentals of GIS
Raster terrain functions in Arc GIS
Slope:
Contours:
Aspect:
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Fundamentals of GIS
Viewshed analysis
This is a multi-layer function that analyzes visibility based on
terrain.
It requires a grid terrain layer and a point layer and produces a
visibility grid layer that tells you where the feature can be seen
from, or alternately, what areas someone standing at that feature
could see (remember, line of sight is two way).
If there are more than one point feature, then each grid cell tells you
how many of the point features can be seen from a given point.
However in that case, you lose information about the other
direction; You don’t know which features can see a particular
grid cell.
Viewshed analysis can use “offsets” to define the height of the
viewer or of the object being viewed; designated using a new
field in the input layer’s attribute table.
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Fundamentals of GIS
Viewshed analysis
Let’s say we’re local planners who are considering putting
in a new waste treatment facility in valley where the
vacation homes of five rich and powerful Hollywood
executives are located.
We want it in a place that won’t ruin anyone’s views,
since they comprise 95% of the local tax base.
This generates a grid with three values, representing how
many houses can see a given pixel
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Fundamentals of GIS
Viewshed analysis
This is done in ArcGIS 8, but can also be done in ArcView.
Red represents areas that can be seen by 1 house, blue by 2 or more
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Fundamentals of GIS
Viewshed analysis
In order to compare the viewability of several facilities, separate
viewshed analyses need to be done for each feature.
In the next example we will look at three candidate sites for a
communications tower.
Each will produce a viewability grid.
This grid can then be superimposed on a layer showing residential
areas.
Since each grid will belong to a different tower, we can tell which
tower will be most viewable from the residential areas through
simple overlay analysis.
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Fundamentals of GIS
Viewshed analysis
In this case, red is for tower 1, blue for 2 and green for 3
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------Using GIS-Fundamentals
Introduction
toof
GIS
GIS
Proximity
Can use raster distance functions to create zones based on proximity
to features; here, each zone is defined by the highway that is closest
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------Using GIS-Fundamentals of GIS
Distance Measurement
Can create
distance
grids from
any feature
theme
(point, line,
or polygon)
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------Using GIS-Fundamentals of GIS
Distance Measurement
Can also
weight distance
based on
friction factors,
like slope
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Combining distance and zonal stats
• Can also
summarize
distances by
vector
geography
using zonal
stats
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Combining distance and zonal stats
• Here we
summarize by
the mean
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Fundamentals of GIS
Density Functions
•We can also use sample points to map out density raster surfaces. This need to
require a z value in each, it can simply be based on the abundance and distribution
of points.
•Pixel value gives the number of points within the designated neighborhood of
each output raster cell, divided by the area of the neighborhood
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Fundamentals of GIS
Density Functions
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Fundamentals of GIS
Density Functions
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