Geographic Information Processing
Introduction to
Geographic
Information
Processing
GPS Satellite - NASA
3/14/2016
© 2009 Raymond P. Jefferis III
Lect 01 - 1
Basic Observations
• Every object on the Earth has a geographic
location as one of its properties. Satellites
can provide this location to GPS receivers.
• Every geographic location has attributes of
possible interest with respect to other
locations or objects.
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© 2009 Raymond P. Jefferis III
Lect 01 - 2
Geographic Information Systems
• Geographically referenced information
– Spatial information (geography)
– Attribute information (sensing)
– Relational information (topology)
• Computer storage and retrieval
• Computations to extract data features
• User display of data features
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© 2009 Raymond P. Jefferis III
Lect 01 - 3
Examples
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Navigation systems
Position tracking systems
Mapping systems
Precision farming
Flood plain management
Land use management
Radio wave propagation
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© 2009 Raymond P. Jefferis III
Lect 01 - 4
Emergency Vehicle Tracking
Resource
Tracking 1/21/2007
Red line
shows track
to fire
location
and
returning.
DR-6755 (ARC)
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© 2009 Raymond P. Jefferis III
Lect 01 - 5
Reasons
• Resource tracking (NIMS compliance)
• Exposure monitoring (radiation, HazMat)
• Legal record of time, place, velocity, height,
and direction of motion
• Emergency support if needed
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© 2009 Raymond P. Jefferis III
Lect 01 - 6
Locating Pumping Stations
Photo: Trans-Alaska Pipeline System (TAPS)
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© 2009 Raymond P. Jefferis III
Lect 01 - 7
Reasons
• Correlation of maintenance with
– weather
– earthquakes
• Asset mapping
• Planning
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© 2009 Raymond P. Jefferis III
Lect 01 - 8
Mapping Mahogany Trees
Map: Carlos D. Rodriguezon: U.S. Forestry Service
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© 2009 Raymond P. Jefferis III
Lect 01 - 9
Reasons
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Asset mapping and management
Correlation of growth with location
Fertilizer application
Law enforcement
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© 2009 Raymond P. Jefferis III
Lect 01 - 10
PA, NJ, DE Tornado Locations
Tornado Touchdown Points, 2000 - 2004, The National Atlas of the United States of Americaｨ
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Lect 01 - 11
Reasons
• Allocation of resources
– monitoring instruments
– emergency response services
• Insurance
– risk assessment
– claims
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© 2009 Raymond P. Jefferis III
Lect 01 - 12
Defining Damage Assessment
Photos: Hurricane Ivan LIDAR Surveys, U.S. Geological Survey
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© 2009 Raymond P. Jefferis III
Lect 01 - 13
Reasons
• Allocation of resources
– mitigation efforts
– emergency response services
• Zoning law compliance
• Insurance
– risk assessment
– Claims data
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© 2009 Raymond P. Jefferis III
Lect 01 - 14
Conducting Watershed Analysis
M. Langland and T. Cronin ,
U.S. Department of the Interior,
Investigations Report 03-4123
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© 2009 Raymond P. Jefferis III
Lect 01 - 15
Reasons
• Flooding analysis
• Asset management – spatial distribution of
weather stations
• Well contamination analysis
(Note: wells can be geolocated)
• HAZMAT release analysis
• Radon survey and analysis
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© 2009 Raymond P. Jefferis III
Lect 01 - 16
Showing Real-Time Rainfall
National Weather Service, http://www.srh.noaa.gov/rfcshare/precip_analysis_new.php
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© 2009 Raymond P. Jefferis III
Lect 01 - 17
Reasons
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Flooding analysis
Anticipating emergency response needs
Crop growth/damage assessment
Economic impacts
Resource allocation
(Automatic weather reporting stations)
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© 2009 Raymond P. Jefferis III
Lect 01 - 18
Locating Rain Gauges
Arkansas-Red Basin River Forecast Center (ABRFC), http://www.srh.noaa.gov/abrfc/p1vol.html
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© 2009 Raymond P. Jefferis III
Lect 01 - 19
Reasons
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Rainfall mapping
Crop growth/damage assessment
Asset mapping
Placement analysis
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Lect 01 - 20
Presenting Precipitation Map
Arkansas-Red Basin River Forecast Center (ABRFC), http://www.srh.noaa.gov/abrfc/p1vol.html
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© 2009 Raymond P. Jefferis III
Lect 01 - 21
Reasons
• Crop yield and soil analysis
• Regional asset planning
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© 2009 Raymond P. Jefferis III
Lect 01 - 22
Viewing Radio Propagation
Radio wave propagation over rough topography - Hupfer et al. Senior Project
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© 2009 Raymond P. Jefferis III
Lect 01 - 23
Reasons
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Radio, television, cell phone coverage
Emergency communications assessment
Tower location planning
Transmitted power allocation
Interference prediction
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© 2009 Raymond P. Jefferis III
Lect 01 - 24
TV Transmitter Power Allocation
An FCC member in Philadelphia said the
problem (with 6ABC) could be with broadcast
power from the station, the governmentsubsidized converter boxes or antennas. I think
it's lack of broadcast power on the VHF
spectrum.
6ABC quadrupled the strength of its TV
signal over the weekend. The emergency signal
boost was granted under temporary authority by
the FCC and lasts for six months. A 6ABC
official said the station would seek FCC
permission to make it permanent.
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© 2009 Raymond P. Jefferis III
Lect 01 - 25
Add Your Own Examples …
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© 2009 Raymond P. Jefferis III
Lect 01 - 26
Course Objectives
• Study of Geographic Information Systems (GIS)
– Analytical methods
– Computations - apply methods learned to
real data files
– Graphical representations
– Applications (Simple examples only)
 Radio reception (wave propagation)
 Hydrology (watershed analysis)
 Magnetic survey (buried HazMat drums)
Note: Students are not expected to be topical area specialists
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© 2009 Raymond P. Jefferis III
Lect 01 - 27
Course Web Site URL
http://muse.widener.edu/~rpj0001/courses/Engr694A/engr694A.htm
Please check site (especially “Bulletins”) weekly.
Site will include many course materials:
Bulletins (Please read often)
Class Notes (Note: © 2009 R. P. Jefferis)
Homework assignments
Models and Mathematica® programs
Syllabus (current revision – may change)
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© 2009 Raymond P. Jefferis III
Lect 01 - 28
Course Conduct
Notes:
will be posted on Web site
Homework:
will be posted on Web site
Bulletins:
will be posted on Web site
Models:
will be posted on Web site
Data:
may be posted on Web site
Interaction: will include class sessions and reports,
which will usually include problem statements,
methods, programming, and plotted or tabular
results
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© 2009 Raymond P. Jefferis III
Lect 01 - 29
Special Features of Course
• In addition to the material posted on the course
Web site, and in the Textbook, students will each
have an extra CD of radar altimetry data to
process.
• Students are expected to use a mathematical
software package to process geographic data.
[Mathematica® or MATLAB® recommended]
Course examples will be in Mathematica®
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© 2009 Raymond P. Jefferis III
Lect 01 - 30
Contents of Extra Data CD
• Metadata
– A file describing the data.
• Data files
– A number of files containing the data
(various formats may be used)
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© 2009 Raymond P. Jefferis III
Lect 01 - 31
Resources Needed for Course
• Textbook (per Syllabus)
• Computer or access to computer
• Mathematical software package
[Mathematica® or MATLAB® recommended]
• Printer (preferably with color capability)
• Internet access
• Data disk (to be supplied)
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© 2009 Raymond P. Jefferis III
Lect 01 - 32
Other Software Available
ArcGIS/ArcINFO (ESRI, Inc.)
This seems to be the prominent package in
the industry. There are one or more copies
available in laboratories on campus. There
are many references to this software in the
textbook. The course will NOT address this
software specifically.
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© 2009 Raymond P. Jefferis III
Lect 01 - 33
ASTER GDEM Data
• http://asterweb.jpl.nasa.gov/gallerydetail.asp?name=gdem
• Account needed (sign up)
• Download data needed
• Very highly processed data
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© 2009 Raymond P. Jefferis III
Lect 01 - 34
Seamless USGS Data
• http://seamless.usgs.gov/products/1arc.php
(1 arc second pixels – about 30m)
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© 2009 Raymond P. Jefferis III
Lect 01 - 35
Additional Public Data (ftp site)
http://www2.jpl.nasa.gov/srtm/
http://eros.usgs.gov/products/eleva
tion/srtmdted.php
The SRTM/DTED folder contains elevation data
from the Shuttle Radar Topography Mission
(SRTM). The Version 2 folder is corrected to Level
2. [This is a BIG file, which becomes HUGE when
unzipped!]
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© 2009 Raymond P. Jefferis III
Lect 01 - 36
SRTM Naming Conventions
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© 2009 Raymond P. Jefferis III
Lect 01 - 37
Geographically Referenced Data
• Location
(each data point contains a location reference)
– Latitude (degrees, minutes, seconds)
– Longitude (degrees, minutes, seconds)
• Attribute(s)
(one or more attribute values are stored for each
data point)
– Values (altitudes, radiation levels, rainfall, etc.)
– Vectors (gradients, velocities, road segments, etc.)
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© 2009 Raymond P. Jefferis III
Lect 01 - 38
Latitude and Longitude
Longitude, λ
Latitude, ϕ
Basic Geodesy, Issue 5 (May 2005), National Geospatial-Intelligence Agency
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© 2009 Raymond P. Jefferis III
Lect 01 - 39
Longitude
Longitude, λ, is the equatorial angle from the
earth's center east or west of the Prime
Meridian, to a Great Circle intersecting a
given point on the earth's surface.
By convention Greenwich, England is taken
as 0 degrees. Angles to the East are taken as
positive and to the West as negative.
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© 2009 Raymond P. Jefferis III
Lect 01 - 40
Latitude
• Latitude, ϕ, is the angle measured at the
earth's center, at constant longitude,
between the Equator and a given point on
the earth's surface .
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© 2009 Raymond P. Jefferis III
Lect 01 - 41
Geographic Information Types
• Position Information
• Attribute Information
• Relational Information
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© 2009 Raymond P. Jefferis III
Lect 01 - 42
Position Information Sources
• Astronomical reckoning (surveying)
• Global Positioning Satellites (GPS)
Wide Area Augmentation System (WAAS) for higher
position accuracy (3 – 30 meters, depending on satellite
visibility and signal strength)
• InSAR (Interferometric Synthetic Aperture
Radar)
Uses signal phase to resolve altitude (vertical distance)
with greater precision
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© 2009 Raymond P. Jefferis III
Lect 01 - 43
Attribute Information [Sensing]
• Radar (altitude, rainfall, wind, etc.)
• Infrared (temperature, radiation, etc.)
• Weather stations (temperature, precipitation,
etc.)
• Magnetometry (magnetic fields and gradients)
• Gravitometry (local gravitational acceleration)
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© 2009 Raymond P. Jefferis III
Lect 01 - 44
GPS Configuration
Multiple (24 –
32) satellites are
used to
determine local
position with
accuracy.
GEODESY FOR THE LAYMAN, U S Naval Observatory, Report No. DMA TR 80-03
3/14/2016
© 2009 Raymond P. Jefferis III
Lect 01 - 45
GPS Methodology
• Many navigation satellites are in orbit, each
having a very precise atomic clock
• Satellite transmits exact time from an
accurately known orbital position.
• Receiver calculates distance from time lag.
Position is “triangulated” from intersecting
time-spheres of many inputs (typically ≥ 4)
Ref: http://en.wikipedia.org/wiki/Global_Positioning_System
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© 2009 Raymond P. Jefferis III
Lect 01 - 46
GPS
• Receiver clock is synchronized from the
signals of multiple satellites
• Signals are CDMA, using 1024-bit codes
• Receiver crosscorrelates to obtain signal
from each satellite individually
• 4 satellites needed for receiver location,
more satellites give greater accuracy
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© 2009 Raymond P. Jefferis III
Lect 01 - 47
Data Models
• Raster (grid of points, attributes separate)
• Vector (points and directions, attributes
stored separately)
• Object (points and their attributes stored
together)
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© 2009 Raymond P. Jefferis III
Lect 01 - 48
Raster Data
• The area is divided into a grid of regular
rows and columns
• The cells, or pixels, need are typically
rectangular
• Each cell in this grid contains location coordinates and attribute values
• The ordering of the data determines the
spatial location of each cell
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© 2009 Raymond P. Jefferis III
Lect 01 - 49
Raster Satellite Data
LandSat Section of PA - U.S. Department of the Interior, U.S. Geological Survey
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© 2009 Raymond P. Jefferis III
Lect 01 - 50
Characteristics of Raster Data
• Discrete raster lines
• Raster lines not on exact North/South or
East/West axes
• Pixel size corresponds to land area
• Attributes exhibited by color, line texture,
or simulated 3D using shadowing
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© 2009 Raymond P. Jefferis III
Lect 01 - 51
Vector Data
• Comprised of lines or arcs, defined by beginning
and end points
• Vectors meet at nodes, having geo-location.
• Geographic features (objects) are approximated
by a series of lines or arcs.
• Each object is referenced by a unique identifier.
• Geo-location and attribute information stored
separately
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© 2009 Raymond P. Jefferis III
Lect 01 - 52
Vector Habitat Model
Vector Overlay of Coral Habitat, NOAA Coastal Services Center
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© 2009 Raymond P. Jefferis III
Lect 01 - 53
Object Oriented Model
• Integrates vector and raster data on GIS objects
in single database
• Objects in logical groups ("classes”)
• Class has predefined properties built directly into
that object
• Operations on objects are stored with the class
• There can be multiple instances of objects of a
class.
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© 2009 Raymond P. Jefferis III
Lect 01 - 54
Paleo-ecological Object Example
Portion of "Site data" model, Gärtner and Bergmann, GeoComputation 1999.
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© 2009 Raymond P. Jefferis III
Lect 01 - 55
Layers of Data
National Weather Service, Wakefield, VA 09:17 PM EDT Wed Jun 27 2007
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Lect 01 - 56
Layers to Note on Map
• Geographic features (water, land, elevation)
• Roads
• Political boundaries
– States
– Counties
• Storm boundaries (color for intensity)
• Warning areas (polygons)
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© 2009 Raymond P. Jefferis III
Lect 01 - 57
Data Processing
Geometric Transformation
Removal of outliers (bad data points)
Filtering and smoothing (preprocessing)
Computation based on attributes
Feature extraction
Presentation [Display]
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© 2009 Raymond P. Jefferis III
Lect 01 - 58
Display
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Viewpoints
Shading to indicate features
Coloring to indicate attributes
Cross-sections [slices]
Contours of constant attribute value
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© 2009 Raymond P. Jefferis III
Lect 01 - 59
Digital Elevation Model (DEM)
• 7.5 - minute grid (Approx. 30 x 30 meters)
• Universal Transverse Mercator projection
• Elevations typically in feet (see Metadata)
• Data ordered South to North in profiles
ordered West to East
Note: Computer displays are oriented North
to South, West to East. Data may need to be
inverted before display.
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© 2009 Raymond P. Jefferis III
Lect 01 - 60
Reading DEM (Raster) Files
• Place the DEMdata files on your high speed disk
drive
• In the folder for your assigned sector:
– Open and read the Metadata file
– Example: For the Malvern quadrant this is the file:
1670812.dem.sdts
– This file describes the data as shown in the following
slides.
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© 2009 Raymond P. Jefferis III
Lect 01 - 61
1670812.dem.sdts
ITEM_TYPE: SDTS DEM - 7.5X7.5 GRID
CELL_NAME: Malvern
STATE: PA
MRC_CODE: 40075-A5
BEST_AVAILABLE: T
SALEABLE: T
DATA_LEVEL: 2
X_RESOLUTION: 30
Y_RESOLUTION: 30
XY_UNITS: Meter
Z_RESOLUTION: 1.000
Z_UNITS: Foot
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© 2009 Raymond P. Jefferis III
Lect 01 - 62
1670812.dem.sdts (continued)
ORIG_LOAD_DATE: 14-SEP-01
CREATE_DATE: 14-SEP-01
PUBLICATION_DATE: 17-SEP-01
SOURCE_DATE: 1983
INSP_REV_DATE: 1995
INSP_REV_NAME: Inspection
HORIZONTAL_DATUM: North American Datum
of 1927
PROJECTION: Transverse Mercator
ZONE: 18
VERTICAL_DATUM: NGVD 1929
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© 2009 Raymond P. Jefferis III
Lect 01 - 63
1670812.dem.sdts (continued)
DIGITAL_FORMAT_SPEC: DEM SDTS
(Part 5: Raster Profile and Extensions
(RPE))
STANDARDS_NAME: Standards for Digital Elevation
Models 1998, Header with Pre-1995 Content
PROCESS_STEP: DLG/Hypsography LINETRACE,
LT4X Complex Linear
MIN_ELEVATION: 105
MAX_ELEVATION: 720
SUSPECT_VOID: 0
PERCENT_VOID: 0
LARGE_CONTOUR_INT:
SMALL_CONTOUR_INT: 10
CONTOUR_UNITS: Foot
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© 2009 Raymond P. Jefferis III
Lect 01 - 64
1670812.dem.sdts (continued)
RMSE: 2
SAMPLE_SIZE: 30
NORTH_LATITUDE:
SOUTH_LATITUDE:
WEST_LONGITUDE:
EAST_LONGITUDE:
3/14/2016
40.125000
40.000000
-75.625000
-75.500000
© 2009 Raymond P. Jefferis III
Lect 01 - 65
Reading DEM Files (cont’d)
• Write a program to read in and display the
files.
• Use the file: MalvernExper.nb - to be
found in the Models directory of the Course
Web Site.
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© 2009 Raymond P. Jefferis III
Lect 01 - 66
®
Mathematica Example
ySR = Import[
"~/Desktop/DEMdata/PA/Malvern/1670812.dem.sdts.tgz",
{"SDTS", "SpatialRange"}]
yER = Import[
"~/Desktop/DEMdata/PA/Malvern/1670812.dem.sdts.tgz",
{"SDTS", "ElevationRange"}]
yDat = Import[
"~/Desktop/DEMdata/PA/Malvern/1670812.dem.sdts.tgz",
{"SDTS", "Data"}];
yGrf = Import[
"~/Desktop/DEMdata/PA/Malvern/1670812.dem.sdts.tgz",
"SDTS"];
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© 2009 Raymond P. Jefferis III
Lect 01 - 67
Intermediate Results
{{446655, 457365}, {4427685, 4441605}}
– Bottom left and Top right corners [meters]
{0, 720}
– Min. and Max. altitude [feet]
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© 2009 Raymond P. Jefferis III
Lect 01 - 68
Compute Parameters of Data
dims = Dimensions[yDat];
ncols = dims[[2]]
nrows = dims[[3]]
minval = yER[[1]]
maxval = yER[[2]]
aratio = nrows/ncols
relief = maxval - minval
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© 2009 Raymond P. Jefferis III
Lect 01 - 69
Intermediate Results
465
358
0
720
358/465
720
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number of data columns
number of data rows
min. altitude
max. altitude
aspect ratio of quadrangle
differential altitude
© 2009 Raymond P. Jefferis III
Lect 01 - 70
To Look at Some Data
y = yDat[[1, 465]]
Gives the following result (358 cells):
{0, 0, 0, 418,
372, 373, 373,
357, 358, 361,
352, 345, 340,
310, 312, 315,
304, 298, 292,
381, 400, 442,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
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408, 402, 394,
372, 371, 369,
364, 366, 368,
334, 329, 323,
320, 326, 330,
288, 286, 285,
486, 497, 483,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
387, 375, 372, 386, 394, 393, 386, 378, 375,
361, 354, 348, 350, 350, 350, 350, 353, 354,
369, 379, 397, 407, 397, 388, 376, 367, 359,
316, 310, 306, 304, 304, 304, 305, 306, 308,
334, 337, 338, 338, 337, 334, 327, 322, 316,
285, 286, 290, 294, 302, 313, 321, 334, 350,
461, 449, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0}
© 2009 Raymond P. Jefferis III
Lect 01 - 71
Meaning of Result
• The result is the last column of the DEM
data, taken from North to South (inverted).
• Each number is the altitude of ONE CELL,
approximately 30 x 30 meters in extent.
• The zeros are present because the satellite
path is not exactly aligned with the
quadrangle borders.
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© 2009 Raymond P. Jefferis III
Lect 01 - 72
Form Table and Plot Data
elev = Table[yDat[[1, r, c]], {r, 1,
ncols, 1}, {c, 1, nrows, 1}];
ReliefPlot[elev, ColorFunction ->
"GreenBrownTerrain"]
See result on next slide -->
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© 2009 Raymond P. Jefferis III
Lect 01 - 73
Result - Malvern Quadrangle
Note that the data input
has reversed the South and
North directions to
conform with computer
plotting requirements and
give a typically oriented
plot with North at the top.
Column 465, at the right,
contains many nulls from
non-vertical raster. These
show as black pixels.
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© 2009 Raymond P. Jefferis III
Lect 01 - 74
Discussion
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© 2009 Raymond P. Jefferis III
Lect 01 - 75