Transcript Lecture 14 - University of Vermont

------Using GIS-Fundamentals of GIS
Lecture 14:
More Raster and Surface Analysis in
Spatial Analyst
By Austin Troy and Weiqi Zhou, University of Vermont
Lecture Materials by Austin Troy except where noted © 2008
------Using GIS-Fundamentals of GIS
Reclassifying Raster Data
Why? …. Here we reclass into 5 groups
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
------Using GIS-Fundamentals of GIS
Reclassification with Grids
Lecture Materials by Austin Troy except where noted © 2008
------Using GIS-Fundamentals of GIS
Reclassification with Grids
Lecture Materials by Austin Troy except where noted © 2008
------Using GIS-Fundamentals of GIS
Reclassification with Grids
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Reclassify: Soil moisture example
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Reclassify: Soil moisture example
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Neighborhood Statistics (Focal)
• A method of summarizing raster data within a neighborhood by a
statistical measure, like mean, std dev.
– Neighborhood shape
– Neighborhood settings
• Window size
• Units
– Statistic types
Fundamentals of GIS
Neighborhood Statistics
• Statistic type: Mean
• 3x3 cell squared neighborhood.
Neighborhood
Processing cell
Fundamentals of GIS
Neighborhood Statistics
• Neighborhood statistics creates a new grid
layer with the neighborhood values
• This can be used to:
–
–
–
–
Simplify or “filter down” the features represented
Emphasize areas of sudden change in values
Look at rates of change
Look at these at different spatial scales
Fundamentals of GIS
Neighborhood Filters
• Improve the quality of raster grids by
eliminating spurious data or enhancing
features.
• Filter types
– Low pass filters
– High pass filters
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
Low pass filter -- bathymetry
• Why? …. filtering out anomalies
Bathymetry mass points:
sunken structures
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
• After turning into raster grid
We see sudden
anomaly in grid
Say we wanted to “average”
that anomaly out
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
• Try a low-pass filter of 5 cells
We can still see those anomalies but
they look more “natural” now
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
• Try a low-pass filter of 25 cells
The anomalies have been “smoothed
out” but at a cost
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
• We can also do a local filter in that one area
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Low pass filter for elevation
Lecture Materials by Austin Troy except where noted © 2008
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
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
10 unit square neighborhood
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
20 unit square neighborhood
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
What about high pass filters?
• Say we wanted to find the wrecks
All areas of sudden change, including
our wrecks, have been isolated
Lecture Materials by Austin Troy except where noted © 2008
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
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
We do this using the raster calculator
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
… or Math >> Minus
Lecture Materials by Austin Troy except where noted © 2008
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
Lecture Materials by Austin Troy except where noted © 2008
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 -- emphasizing local variability over
regional trends.
• The resolution of 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.
Lecture Materials by Austin Troy except where noted © 2008
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
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Changing Cell Size (Focal)
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Changing Cell Size
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Change cell size – may cause data loss
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
“Hidden” effect of Focal Functions on cell values
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
Fundamentals of GIS
Change cell size WARNING
Slide courtesy of Leslie Morrissey
Lecture Materials by Austin Troy except where noted © 2008
------Using GIS-Fundamentals of GIS
Distance Analysis
• Used to answer questions related to distance
– Proximity
– Straight Line Distance Measurement
– Cost Weighted Distance Measurement
– Shortest Path
------Using GIS-Fundamentals of GIS
Proximity
• Can use raster distance functions to create zones based on
proximity to features; here, each zone is defined by the
closest stream segment
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
------Using GIS-Fundamentals of GIS
Distance Measurement
• Can create
distance grids
from any feature
theme (point, line,
or polygon)
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
------Using GIS-Fundamentals of GIS
Distance Measurement
• Can also weight
distance based on
friction factors,
like slope
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
------Using GIS-Fundamentals of GIS
Combining Distance and Zonal Stats
• Can also
summarize
distances by
vector
geography
using zonal
stats
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
------Using GIS-Fundamentals of GIS
Combining Distance and Zonal Stats
• Here we
summarize by
the mean
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
Fundamentals of GIS
Density Functions
• We can also use sample points to map out density raster surfaces. For pixels with
no underlying sample point, the z value 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
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
Fundamentals of GIS
Density Functions
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
Fundamentals of GIS
Density Functions
Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011
Fundamentals of GIS
Spatial Interpolation
Lecture Materials by Austin Troy except where noted © 2008
Slide courtesy of Leslie Morrissey
Fundamentals of GIS
Spatial Interpolation
Lecture Materials by Austin Troy except where noted © 2008
Slide courtesy of Leslie Morrissey
Fundamentals of GIS
Pitfalls of Spatial Interpolation
Lecture Materials by Austin Troy except where noted © 2008
Slide courtesy of Leslie Morrissey
Fundamentals of GIS
Pitfalls of Spatial Interpolation
Lecture Materials by Austin Troy except where noted © 2008
Slide courtesy of Leslie Morrissey
Fundamentals of GIS
Spatial Interpolation in ArcMap
Lecture Materials by Austin Troy except where noted © 2008