04/19/23 © 2009 Raymond P. Jefferis III Lect 01 - 1

Geographic Information Processing

Introduction to Geographic Information Processing

GPS Satellite - NASA

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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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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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Examples

• Navigation systems

• Position tracking systems

• Mapping systems

• Precision farming

• Flood plain management

• Land use management

• Radio wave propagation

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Emergency Vehicle Tracking

Resource Tracking -

1/21/2007

Red line shows track to fire location and returning.

DR-6755 (ARC)

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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Locating Pumping Stations

Photo: Trans-Alaska Pipeline System (TAPS)

Reasons

• Correlation of maintenance with– weather– earthquakes

• Asset mapping

• Planning

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Mapping Mahogany Trees

Map: Carlos D. Rodriguezon: U.S. Forestry Service

Reasons

• Asset mapping and management

• Correlation of growth with location

• Fertilizer application

• Law enforcement

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PA, NJ, DE Tornado Locations

Tornado Touchdown Points, 2000 - 2004, The National Atlas of the United States of America ｨ

Reasons

• Allocation of resources– monitoring instruments– emergency response services

• Insurance– risk assessment– claims

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Defining Damage Assessment

Photos: Hurricane Ivan LIDAR Surveys, U.S. Geological Survey

Reasons

• Allocation of resources– mitigation efforts– emergency response services

• Zoning law compliance

• Insurance– risk assessment– Claims data

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Conducting Watershed Analysis

M. Langland and T. Cronin , U.S. Department of the Interior,Investigations Report 03-4123

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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Showing Real-Time Rainfall

National Weather Service, http://www.srh.noaa.gov/rfcshare/precip_analysis_new.php

Reasons

• Flooding analysis

• Anticipating emergency response needs

• Crop growth/damage assessment

• Economic impacts

• Resource allocation(Automatic weather reporting stations)

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Locating Rain Gauges

Arkansas-Red Basin River Forecast Center (ABRFC), http://www.srh.noaa.gov/abrfc/p1vol.html

Reasons

• Rainfall mapping

• Crop growth/damage assessment

• Asset mapping

• Placement analysis

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Presenting Precipitation Map

Arkansas-Red Basin River Forecast Center (ABRFC), http://www.srh.noaa.gov/abrfc/p1vol.html

Reasons

• Crop yield and soil analysis

• Regional asset planning

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Viewing Radio Propagation

Radio wave propagation over rough topography - Hupfer et al. Senior Project

Reasons

• Radio, television, cell phone coverage

• Emergency communications assessment

• Tower location planning

• Transmitted power allocation

• Interference prediction

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TV Transmitter Power Allocation

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An FCC member in Philadelphia said the problem (with 6ABC) could be with broadcast power from the station, the government-subsidized 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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Add Your Own Examples …

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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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http://muse.widener.edu/~rpj0001/courses/Engr694A/engr694A.htm

Course Web Site URL

Please check site (especially “Bulletins”) weekly.

Site will include many course materials:Bulletins (Please read often)Class Notes (Note: © 2009 R. P. Jefferis)Homework assignmentsModels and Mathematica® programsSyllabus (current revision – may change)

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Course ConductNotes: will be posted on Web siteHomework: will be posted on Web siteBulletins: will be posted on Web siteModels: will be posted on Web siteData: may be posted on Web siteInteraction: will include class sessions and

reports, which will usually include problem statements, methods, programming, and plotted or tabular results

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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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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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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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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.

ASTER GDEM Data

• http://asterweb.jpl.nasa.gov/gallery-detail.asp?name=gdem

• Account needed (sign up)

• Download data needed

• Very highly processed data

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Seamless USGS Data

• http://seamless.usgs.gov/products/1arc.php(1 arc second pixels – about 30m)

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Additional Public Data (ftp site)

http://www2.jpl.nasa.gov/srtm/

http://eros.usgs.gov/products/elevation/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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SRTM Naming Conventions

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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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Latitude and Longitude

Longitude, λLatitude, ϕ

Basic Geodesy, Issue 5 (May 2005), National Geospatial-Intelligence Agency

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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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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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Geographic Information Types

• Position Information

• Attribute Information

• Relational Information

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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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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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GPS Configuration

GEODESY FOR THE LAYMAN, U S Naval Observatory, Report No. DMA TR 80-03

Multiple (24 – 32) satellites are used to determine local position with accuracy.

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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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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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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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 co-ordinates and attribute values

• The ordering of the data determines the spatial location of each cell

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Raster Satellite Data

LandSat Section of PA - U.S. Department of the Interior, U.S. Geological Survey

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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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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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Vector Habitat Model

Vector Overlay of Coral Habitat, NOAA Coastal Services Center

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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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Paleo-ecological Object Example

Portion of "Site data" model, Gärtner and Bergmann, GeoComputation 1999.

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Layers of Data

National Weather Service, Wakefield, VA 09:17 PM EDT Wed Jun 27 2007

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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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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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Display

• Viewpoints

• Shading to indicate features

• Coloring to indicate attributes

• Cross-sections [slices]

• Contours of constant attribute value

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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 EastNote: Computer displays are oriented North to South, West to East. Data may need to be inverted before display.

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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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1670812.dem.sdtsITEM_TYPE: SDTS DEM - 7.5X7.5 GRIDCELL_NAME: MalvernSTATE: PAMRC_CODE: 40075-A5BEST_AVAILABLE: TSALEABLE: TDATA_LEVEL: 2X_RESOLUTION: 30Y_RESOLUTION: 30XY_UNITS: MeterZ_RESOLUTION: 1.000Z_UNITS: Foot

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1670812.dem.sdts (continued)

ORIG_LOAD_DATE: 14-SEP-01CREATE_DATE: 14-SEP-01PUBLICATION_DATE: 17-SEP-01SOURCE_DATE: 1983INSP_REV_DATE: 1995INSP_REV_NAME: InspectionHORIZONTAL_DATUM: North American Datum of 1927PROJECTION: Transverse MercatorZONE: 18VERTICAL_DATUM: NGVD 1929

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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 ContentPROCESS_STEP: DLG/Hypsography LINETRACE,

LT4X Complex LinearMIN_ELEVATION: 105MAX_ELEVATION: 720SUSPECT_VOID: 0PERCENT_VOID: 0LARGE_CONTOUR_INT: SMALL_CONTOUR_INT: 10CONTOUR_UNITS: Foot

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1670812.dem.sdts (continued)

RMSE: 2SAMPLE_SIZE: 30NORTH_LATITUDE: 40.125000SOUTH_LATITUDE: 40.000000WEST_LONGITUDE: -75.625000EAST_LONGITUDE: -75.500000

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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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Mathematica® ExampleySR = 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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Intermediate Results

{{446655, 457365}, {4427685, 4441605}}

– Bottom left and Top right corners [meters]{0, 720}

– Min. and Max. altitude [feet]

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Compute Parameters of Data

dims = Dimensions[yDat];ncols = dims[[2]]nrows = dims[[3]]minval = yER[[1]]maxval = yER[[2]]aratio = nrows/ncolsrelief = maxval - minval

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Intermediate Results

465 number of data columns358 number of data rows0 min. altitude720 max. altitude358/465 aspect ratio of quadrangle720 differential altitude

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To Look at Some Data

y = yDat[[1, 465]]Gives the following result (358 cells):{0, 0, 0, 418, 408, 402, 394, 387, 375, 372, 386, 394, 393, 386, 378, 375, 372, 373, 373, 372, 371, 369, 361, 354, 348, 350, 350, 350, 350, 353, 354, 357, 358, 361, 364, 366, 368, 369, 379, 397, 407, 397, 388, 376, 367, 359, 352, 345, 340, 334, 329, 323, 316, 310, 306, 304, 304, 304, 305, 306, 308, 310, 312, 315, 320, 326, 330, 334, 337, 338, 338, 337, 334, 327, 322, 316, 304, 298, 292, 288, 286, 285, 285, 286, 290, 294, 302, 313, 321, 334, 350, 381, 400, 442, 486, 497, 483, 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, 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}

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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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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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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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Discussion