Intro. To GIS

Post Midterm Review March 25th , 2013

Vector vs. Raster

Characteristic

Vector Raster

Data Structure Usually complex Usually simple

Storage Requirements

Small for most datasets

Large for most data

Coordinate Conversion

Simple May be slow and require resampling

Analysis Preferred for network analysis

Easy for continuous data, combining layers

Positional Precision

Limited only by positional measurements (scale)

Floor set by cell size

Vector Data• Vector data

represented by coordinates– Points have X and

Y coordinate pairs– Lines (arcs)

connect two or more points

– Polygons are a series of connected lines

Raster Data

• Many cells make up a raster grid/image

• Size of cells can vary• Each cell has a value

• Think of a digital photograph…– Pixels = cells

Cont’d • Question 4 and Question 5: Page 132• Question 6 ??? Com’on

REVIEW: Scale?

Datums

• Reference surfaces used for mapping– Tied to a specific ellipsoid– Based on many precise measurements– Both horizontal (e.g. ellipsoid) and vertical

(Geoid) datums• Common US horizontal (2D) datums:– North American Datum (NAD) 1927 or 1983– World Geodetic System of 1984 – U.S. DOD

(used worldwide)

Earth Shape: Sphere and Ellipsoid

Earth Models and Datums

Horizontal Datums: Ellipsoids

• Bulge at the equator• Flattened at the poles

• A theoretical surface which fits the Earth best (globally/regionally)

– Semi-major axis– Semi-minor axis

f=𝑎−𝑏𝑎 Flattening

a

b

Ellipsoid vs. Geoid

• Ellipsoids are idealized (mathematical) models

• Geoids are more complex and representative (of the Earth surface)

• Different ellipsoids work better in certain parts of the world– In North America, usually WGS 1984 or

GRS 80

Types of Projections• Planar (Azimuthal)• Cylindrical (Mercator)• Conical

Review: Transverse Mercator (TM)

Central MeridianCentral Meridian changes with the specific region for which the projection is done

Isopleth/Contour Map

Question 14• Based on USGS standards, a 1:24,000

map scale has 24ft horizontal accuracy

Earth Observing (EO)/Infrared (IR) Remote Sensing Systems

• Space borne– CORONA– IKONOS / Geoeye (high spatial res.)– Quickbird / WorldView (high spatial res.)– Landsat/ SPOT (medium spatial res.)– MODIS/VIIRS/AVHRR (low spatial res.)

• Airborne (UAV)– AVIRIS– Predator– Global Hawk

Scale and Generalization

• Yellow generalization for smaller scale maps

03_19_Figure

Spatial Resolution

03_03_Figure

Need at least 4 satellites to find X, Y, Z, and error in receiver’s clock (Time)

DGPS Site

x+30, y+60

x+5, y-3

True coordinates = x+0, y+0

Correction = x-5, y+3

DGPS correction = x+(30-5) and y+(60+3)

True coordinates = x+25, y+63

x-5, y+3

DGPS ReceiverReceiver

Differential GPS (DGPS)

• Realtime (RTK)

Sources of Errors When Positioning with GPS

• Standard Positioning Service (SPS ): Civilian Users• Source Amount of Error

Satellite clocks: 0.5 to 1 meter Orbital errors (ephemeris): < 1 meter Ionosphere: 5.0 to 10.0 meters Troposphere: 0.5 to 1 meter Receiver noise: 0.3 to 1.5 meters Multipath: 0.6 to 1.0 meters Selective Availability (SA) Does not exist any more User error: Up to a kilometer or more

• Errors are cumulative and increased by DOP.• Note that the numbers are not current (absolute). However, you

can get a feel for which errors are more significant than the other (relative).

• The satellites broadcast two types of data, Almanac and Ephemeris. • Almanac data is coarse orbital parameters for all SVs. Each SV

broadcasts Almanac data for ALL SVs (Satellite Vehicles). This Almanac data is not very precise and is considered valid for up to several months.

• Ephemeris data by comparison is very precise orbital and clock correction for each SV and is necessary for precise positioning. EACH SV broadcasts ONLY its own Ephemeris data. This data is only considered valid for about 30 minutes. The Ephemeris data is broadcast by each SV every 30 seconds.

• When the GPS is initially turned on after being off for more than 30 minutes, it "looks" for SVs based on where it is based on the almanac and current time. With this information, appropriate SVs can be selected for initial search. When the GPS receiver initially locks onto a SV, the Garmin display then shows "hollow" signal strength bars. At this time, the Ephemeris data has yet to be completely collected. Once the ephemeris data is collected from EACH SV in turn, the associated signal strength bar will turn "solid" black and then the data from that SV is considered valid for navigation. If power is cycled on a GPS unit, and when turned back on, the Ephemeris data is less than 30 minutes old, lock-on will be very quick since the GPS does not have to collect new Ephemeris data.

Difference between Almanac and Ephemeris

N

S

W E

Poor Satellite Geometry

Spectral Resolution

REVIEW: Spectral Resolution

Refle

ctan

ce (%

)

• Electromagnetic Spectrum

Transformation types: Affine

The affine transformation function is:x’ = Ax + By + Cy’ = Dx + Ey + F

• where x and y are coordinates of the input layer and x’ and y’ are the transformed coordinates.

• The C and F parameters control shift in origin (translation)

• A, B, D, E control scale and rotation • their values are determined by comparing the

location of source and destination control points.

•Scales, skews, rotates, and translates •6 unknowns( A,B,C,D,E,F) so a minimum of three “displacement links” required•Little benefit from more than 18-30 links•The most common choice

Vector Data and Topology• Topology

– The arrangement for how point, line, and polygon features share geometry

– Or knowledge about relative spatial positioning

• Two types of vector models exist in a GIS– Geo-relational Vector Model

• Arc Coverage (has topology) >>> format: binay• Shape files (no topology) >>>> format: *.shp, *.shx,

*dbf, etc.– Object-based Vector Model

• Includes classes and geodatabases >>> format: *.mdb

Organizing Attribute Data

• Flat Files • Hierarchical • Relational (databases)• Object-oriented (database)

• Relational (What is commonly used in GIS)– Various tables (databases) are “linked”

through unique identifiers

Organizing Attribute Data

Non-spatial Data• Or Attributes (for a vector dataset)

Record

Field (Attribute)--- It could be either numeric or text)

The Shape Field/Object ID tells about the type of vector feature (point/polygon/line)… It is where the coordinates are also stored (you do not see them here)

add our one second error to the third receiver…

XX

…circle from 3rd satellite cannot intersect where other two do

purple dots areintersections of

2 satellites

define area of solutions …receivers calculate best solution(add or subtract time from each satellite)

With Only Three Satellites Visible to the receiver

position determined from multiple pseudo-range measurements4 satellites…three (X, Y, Z) dimensions and time

when clock offsets are determined, the receiver position is known

Fourth Satellite

Q 33 and 34

• Page 30• 1 degree longitude: 111km * cos

(phi)=81.2 km• Page 31

State Plane

02_11_Figure

UTM Coordinate System

02_11_Figure

Datum and UTM

• NAD83: (North American Datum) produced in 1983

• Q38– Page 38

• Q39– Blocking of signal due to the

trees/branches, etc.

Q 42

Radiometric Resolution

03_21_Figure

Radiometric Resolution • Radiometric res= where m is the number of

bits• Tells us about the dynamic range of pixel

numbers in an image

03_21_Figure

Jpeg Vs. Tiss• Jpeg is a lossy format• Jpeg does not inherently carry geo-coordinates

Spectral Resolution

Q 48• Land cover: nominal • Temperature: interval• Building numbers: ordinal/nominal• Population: ratio

• Check your email for further discription

Query: Select by Attributes

• Or Structured Query Language (SQL)

• Enter criteria for one or more fields– Numeric values =,<,>,<>– Nominal values = ‘text’

• Change criteria or narrow results based on additional criteria

REVIEW: Joins and Relates

• Many datasets are available in tabular format– Excel (.xls, .xlsx), comma-spaced values

(.csv), text

• Tables can be imported to ArcMap and linked points, lines, or polygons using a common ID

REVIEW: Joining Tables

• Tables downloaded as text or CSV may need to be opened and saved as Excel files first

• First row of table should contain short headers with no special characters (or spaces, ideally)

• Table must have an ID that matches geography

REVIEW: One-to-one

• A one-to-one relationship means that each record in one table has only one matching record in another table

REVIEW: Many-to-one

• Many-to-one means multiple records in the table match to one record in another table

REVIEW: Joining Tables

• Right-click the spatial data which will have the table joined to it, click Joins and Relates, then Join…

• Choose the table and the common ID fields

REVIEW: Spatial Join

• Join can be performed using spatial location to summarize/select data from another layer

• “Join data from another layer based on spatial location” option in the Join dialog box

• Choose another layer to join, then– Summarize numeric attributes OR– Give the attributes of the closest feature

REVIEW: Relating Tables

• Relates are used when tables have a one-to-many or many-to-many relationship

• Attributes are not appended to the table, but selecting a record in one table will select all related records in another table

• Right-click layer, choose Joins and Relates, then Relate…

Q 53-54• Page 73• Use the formula to find out the specification

(dpi) for the scanner? • dpi: Dot per inch

• Q54: ???

Topology• Concepts – Adjacency– Enclosure– Connectivity

• Terms to be defined– Node– Arc– Polygon

Non-spatial Data• Or Attributes (for a vector dataset)

Record

Field (Attribute)--- It could be either numeric or text)

The Shape Field/Object ID tells about the type of vector feature (point/polygon/line)… It is where the coordinates are also stored (you do not see them here)

REVIEW: Trade-off between Spatial and Spectral Resolution

E (or signal)

• In order to maintain a reasonable level of energy (or signal) reaching the camera (or imaging system), the relation between the pixel size (or pixel area) and spectral bandpass (channel width) must be considered:

Pixel areaSpectral bandpassEnergy

Example: Transformation

• Let’s do a simple example– We would like to calculate new coordinates for point A(x=1, y=1), i.e.,

we want to convert coordinate system (x,y) to (x’,y’).– We assume a 1st order (affine) transformation works fine– All the six coefficients (for affine transformation) are given (a0=1,

a1=1.1, a2=0.4 and b0=0.2,b1=1.8,b2=0.8)

– x’ and y’ are the new coordinates for (x,y) in the new coordinate system– Continue on next Slide >>>>

1

.5 , 8

Resampling• Let’s continue on… After the transformation, the question is:

– What is the pixel value for .5 , 8 ???? (That’s what we call resampling)

• The new coordinate system is, in fact, a new raster dataset (right), which is slightly rotated, scaled, skewed, or distorted depending on the order of polynomial.

• We need to estimate pixel values from the original raster data (left/yellow dot), i.e., resampling, for the new dataset (right/green)

coordinate 6865

70 80

Pixel valuex

x’

78 73 78

7469

y

1

1

2

32

31

2

3

12 3

y’

e.g., Average of 80 and 68 would be the pixel’s new value

Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)

Orthophoto Vs. Aerial photos (or Remotely sensed Imagery)

Homework & Lab• Read chapter 4 (Data quality) and

answer the question:– HW: all questions except Q1 and Q2

• Lab this week: Review on ArcGIS