GIS and Remote Sensing for Geospatial Analysis

A journey
to
Geographical Information System
and
Remote Sensing Technology
Dr. Nishant Sinha

Journey Expectations
▪ GIS
– Basics of GIS
– Components of GIS
– GIS Data Models (Raster andVector)
– GIS DataTypes and Metadata
– Various GIS Data formats and GIS Data
Products
– Process of GIS Data Generation/creation to
Analysis
– Data Conversions
– WebGIS –WMS,WFS

▪ Remote Sensing
– Basics of Remote Sensing
– Image Processing and Enhancements techniques
(Image algebra, Band Rationing, NDVI)
– Image classification (Supervised and Unsupervised)
and other recent trends in classification
– Image ClassificationAccuracyAssessment, Post
processing, Rectification and finalization for
preparing Map Layouts for printing
▪ Cartography
– Scale and types of scales
– Projections and coordinate systems
– Map and Map elements, UnderstandingToposheets

▪ 3D Data representation and visualization
– Real world objects (buildings, towers, etc.) and
rendering on Google Earth
– Digital Elevation Model (DEM)
– Walk through / fly through
▪ GPS ( (In a Nut Shell)
– How it works and its functionalities
– Capturing GPS Points and transferring to GIS
environment
– Few Applications

Spatial is Special
▪ “Everything is related to everything else,
but near things are more related than
distant things”
Tobler, W. 1970. A computer movie simulating urban growth
in the Detroit region. Economic Geography 46, 234–40
▪ Sometimes called the First Law of
Geography (because it is generally true!).

How do we describe geographical features?
▪ by recognizing two types of data:
– Spatial data which describes location (where)
– Attribute data which specifies characteristics at that location
(what, how much, and when)
How do we represent these digitally in a GIS?
▪ by grouping into layers based on similar characteristics (e.g
hydrography, elevation, water lines, sewer lines, grocery sales) and
using either:
– vector data model
– raster data model
▪ by selecting appropriate data properties for each layer with respect to:
– projection, scale, accuracy, and resolution
How do we incorporate into a computer application system?
▪ by using a relational Data Base Management System (RDBMS)
Representing Geographic Features

▪ Continuous
▪ Elevation
▪ Rainfall
▪ Ocean salinity
▪ Discrete
– Polygon areas:
▪ unbounded: landuse, market areas, soils, rock type
▪ bounded: city/county/state boundaries, ownership parcels, zoning
– Line networks
▪ roads, transmission lines, streams
– Points Location :
▪ fixed: wells, street lamps, addresses
Spatial Data Types

Categorical
name
– nominal
▪ no inherent ordering
▪ land use types, county names
– ordinal
▪ inherent order
▪ road class; stream class
▪ often coded to numbers eg SSN but can’t
do arithmetic
Numerical
Known difference between values
– interval
▪ No natural zero
▪ can’t say ‘twice as much’
▪ temperature (Celsius or Fahrenheit)
– ratio
▪ natural zero
▪ ratios make sense (e.g. twice as much)
▪ income, age, rainfall
▪ may be expressed as integer [whole number] or
floating point [decimal fraction]
Attribute data tables can contain locational information, such as addresses or a list of X,Y coordinates. ArcView refers to these as event tables.
However, these must be converted to true spatial data (shape file), for example by geocoding, before they can be displayed as a map.
Attribute data types

ContainTables or feature classes in which:
– rows: entities, records, observations, features:
▪ ‘all’ information about one occurrence of a feature
– columns: attributes, fields, data elements, variables, items (ArcInfo)
▪ one type of information for all features
The key field is an attribute whose values uniquely identify each row
Parcel Table
Parcel # Address Block $ Value
8 501 N Hi 1 105,450
9 590 N Hi 2 89,780
36 1001 W. Main 4 101,500
75 1175 W. 1st 12 98,000
entity
AttributeKey field
Data Base Management Systems (DBMS)

Geographic Information System
A system that doesn't hold maps or pictures but holds a database

GIS Defined …..
▪ A computer-based system for the manipulation
and analysis of geospatial information in which
there is an automated link between a data object
and their spatial location.
http://www.spatialanalysisonline.com/
(Free on-line textbook)

Roger F. Tomlinson, (born 17
November 1933) is an English
geographer and the primary
originator of modern
computerized geographic
information systems (GIS), and
has been acknowledged CM as
the "father of GIS"

What is GIS?
One word at a time…

G Information S
▪ Data is a fact or collection of facts
▪ Data that is processed, organized, structured
or presented in a given context to make them
useful, are called Information

G Information System
A set of components for:
Storing
Displaying
Analyzing
DATA
Information System
Data Storage
Query Information
One example of an Information System:
Microsoft Access database

What is the S in GIS?
▪ 1980s: Geographic Information Systems
– technology for the acquisition and management of spatial information
– software for professional users, e.g. cartographers
– Example: MapInfo
▪ 1990s: Geographic Information Science
– comprehending the underlying conceptual issues of representing data and
processes in space-time
– the science (or theory and concepts) behind the technology
– Example: design spatial data types and operations for querying
▪ 1990s: Geographic Information Studies
– understanding the social, legal and ethical issues associated with the application
of GISy and GISc
▪ 2000s: Geographic Information Services
– Web-sites and service centers for casual users, e.g. travelers
– Service (e.g., GPS, mapquest) for route planning

A means of:
Storing
Mapping
Analyzing
Spatial Data
Information System
Geographic Position

Leaving us with a simple way to start learning about GIS:
A tool for deriving information from any data with a geographic /
spatial component

What is GIS?
Basics of Storing, Mapping, and Analyzing Spatial Data…

What is GIS?
and answers the following…

Location - What is at………….?
The first of these questions seeks to find out
what exists at a particular location.
A location can be described in many ways,
using, for example place name, post code, or
geographic reference such as longitude/latitude
or x/y.

Condition - Where is it………….?
The second question is the converse
of the first and requires spatial data
to answer.
Instead of identifying what exists at
a given location, one may wish to
find location(s) where certain
conditions are satisfied (e.g., an
unforested section of at-least 2000
square meters in size, within 100
meters of road, and with soils
suitable for supporting buildings)

Trends - What has changed since…………..?
The third question might involve
both the first two and seeks to find
the differences (e.g. in land use or
elevation) over time.

Patterns - What spatial patterns exists….?
This question is more sophisticated
One might ask this question to
determine whether landslides are
mostly occurring near streams. It might
be just as important to know how many
anomalies there are that do not fit the
pattern and where they are located.

Modelling - What if………..?
"What if…" questions are posed to
determine what happens,
if a new road is added to a network or if
a toxic substance seeps into the local
ground water supply.
Answering this type of question requires
both geographic and other information
(as well as specific models). GIS permits
spatial operation.

Aspatial Questions
"What's the average number of people
working with GIS in each location?" is an
aspatial question
the answer to which does not require the
stored value of latitude and longitude; nor
does it describe where the places are in
relation with each other.

Spatial Questions
" How many people work with GIS in the
major centres of Delhi" OR "Which centres
lie within 10 Kms. of each other? ", OR "
What is the shortest route passing through
all these centres".
These are spatial questions that can only
be answered using latitude and longitude
data and other information such as the
radius of earth. Geographic Information
Systems can answer such questions.

Storing Geographic Data
One GIS data layer combines both
Geographic Features and their Attributes
Geographic Features indicate “where”

Storing “Everyday” Geographical Objects
▪ Points
▪ The fundamental primitive is the point, a 0-dimensional
(0-D) object that has a position in space but no length.
– home, day-care, health clinics, schools, retail and tobacco outlets,
crimes & graffiti, bus stops, neighborhood anchor institutions,
community assets, resources and risks
▪ Lines
▪ A line is a 1-D geographic object having a length and is
composed of two or more 0-D point objects.
– roads, railway, pathways, walking or bus routes, rivers
▪ Areas (Polygons)
▪ A polygon is a geographic object bounded by at least three
1-D line objects or segments with the requirement that
they must start and end at the same location (i.e., node)
– census unit, ZIP code, school district, police precinct, health
service areas, counties, states, provinces, watersheds

Mapping Geographic Data – India States
India Airports (point layer)
India States (polygon layer)

Analyzing Geographic Data
• Query GIS data layers based on
attributes or geography, or both
 Which states’ population was more
than 75 million in 2011?

Analyzing Geographic Data
• Query GIS data layers based on
attributes or geography, or both
 Which are the neighboring states
of Madhya Pradesh

What is GIS?
In more details…

Scale of GIS data
Global to Local

What is Scale?
▪ Ratio of distance on a map, to equivalent distance on the earth's surface.
– Large scale: large detail, small area covered (1”=200’ or 1:2,400)
– Small scale -->small detail, large area (1:250,000)
– A given object (e.g. land parcel) appears larger on a large scale map
– Scale can never be constant everywhere on a map
because of map projection
– Scale representation
▪ Verbal: (good for interpretation.)
▪ Representative fraction (RF)
(good for measurement)
(smaller fraction=smaller scale:
1:2,000,000 smaller than 1:2,000)
▪ Scale bar (good if enlarged/reduced)
0ne inch each equals one statute mile
1: 63,360
Miles
0 1 2

Scale Examples
Common Scales
1:200 (1”=16.8ft)
1:2,000 (1”=56 yards; 1cm=20m)
1:20,000 (5cm=1km)
1:24,000 (1”=2,000ft)
1:25,000 (1cm=.5km)
1:50,000 (2cm=1km)
1:62,500 (1.6cm=1km; 1”=.986mi)
1:63,360 (1”=1mile; 1cm=.634km)
1:100,000 (1”=1.58mi; 1cm=1km)
1:500,000 (1”=7.9mi; 1cm=5km)
1:1,000,000(1”=15.8mi; 1cm=10km)
1:7,500,000(1”=118mi); 1cm=750km)
Large versus Small
large: above 1:12,500
medium: 1:13,000 - 1:126,720
small: 1:130,000 - 1:1,000,000
very small: below 1:1,000,000
( really, relative to what’s available for a given area; Maling 1989)
Map sheet examples:
1:24,000: 7.5 minute USGS Quads
(17 by 22 inches; 6 by 8 miles)
1:7,500,000 US wall map
(26 by 16 inches)
1:20,000,000: US 8.5” X 11”

Precision or Resolution
- it’s not the same as scale or accuracy!
Precision: the exactness of measurement or description
▪ the “size” of the “smallest” feature which can be displayed, recognized, or described
▪ Can apply to space, time (e.g. daily versus annual), or attribute (douglas fir v. conifer)
▪ For raster data, it is the size of the pixel (resolution)
– e.g. for NTGISC digital orthos is 1.6ft (half meter)
▪ raster data can be resampled by combining adjacent cells; this decreases resolution but saves storage
– eg 1.6 ft to 3.2 ft (1/4 storage); to 6.4 ft (1/16 storage)
▪ Resolution and scale
– generally, increasing to larger scale allows features to be observed better and requires higher resolution
– but, because of the human eye’s ability to recognize patterns, features in a lower resolution data set can sometimes be
observed better by decreasing the scale (6.4 ft resolution shown at 1:400 rather than 1:200)
▪ Resolution and positional accuracy
– you can see a feature (resolution), but it may not be in the right place (accuracy)
– Higher accuracy generally costs much more to obtain than higher resolution
– Accuracy cannot be greater (but may be much less) than resolution
▪ e.g. if pixel size is one meter, then best accuracy possible is one meter)
1.6ft
3.2ft
3.2ft

Accuracy: Rests on at least four legs, not one!
Positional Accuracy (sometimes called Quantitative accuracy)
– Spatial
▪ horizontal accuracy: distance from true location
▪ vertical accuracy: difference from true height
– Temporal
▪ Difference from actual time and/or date
Attribute Accuracy or Consistency: the validity concept in experimental design/stat. inf.
– a feature is what the GIS/map purports it to be
– a railroad is a railroad, and not a road
Completeness--the reliability concept from experimental design/stat. inf.
– Are all instances of a feature the GIS/map claims to include, in fact, there?
– Partially a function of the criteria for including features: when does a road become a track?
– Simply put, how much data is missing?
Logical Consistency: The presence of contradictory relationships in the database
– Non-Spatial
▪ Data for one country is for 2000, for another its for 2001
▪ Data uses different source or estimation technique for different years (again, lineage)
– Spatial
▪ Overshoots and gaps in road networks or parcel polygons

▪ Consists of discrete coordinates to store
the geographic position of
– Points
▪ Points: People or Cities (center)
– Lines
▪ Roads or Other Linkages
– Polygons
▪ CensusTract
▪ Vector Data Model
– Geographic features stored as X,Y
coordinate pairs
– Each vector layers has an attribute table
– Each feature corresponds to a row in the
table
Data Types: Vector Data

▪ Raster data represents a continuous surface
divided into a regular grid of cells
▪ Often used as background map layer
▪ Points: People or Cities (center)
– Lines
▪ Roads or Other Linkages
– Polygons
▪ CensusTract
▪ Raster Data Model
– Stores images as rows and columns of numbers,
forming a regular grid structure
– Great for computational analysis or modeling
– Bad for mapping precise locations
Data Types: Raster Data
Raster AttributeTables

Vector vs Raster
Vector
• Low data volume
• Faster display
• Can also store attributes
• Less pleasing to the eye
• Does not dictate how features
should look in the GIS
Raster
• High data volume
• Slower display
• Has no attribute information
• More pleasing to the eye
• Inherently stores how features
should look in the GIS

Coordinate Systems
▪ Describing the correct location and shape of
features requires a framework for defining
real-world locations
▪ A geographic coordinate system is used to
assign geographic locations to objects.
▪ GIS data layers must have a coordinate
system defined to integrate with other layers

Map Projections
Transforming 3-dimensional space (Earth) onto a 2-dimensional map (GIS)
Mercator Azimuthal Equidistant Albers Equal Area Conic
Lambert Conformal Conic Robinson

Map Projection is important
▪ Small-scale (large area) maps
– Interested in Comparing shapes, areas, distances, or directions of map features?
– Measurement errors can be quite substantial:
New
York
New
York
Los
AngelesLos
Angeles
Projection: Mercator
Distance: 3,124.67 miles
Projection: Albers Equal Area
Distance: 2,455.03 miles
Actual distance: 2,451 miles

Data collected may need to be reorganized and checked for
errors, before being used for spatial analysis, or mapping
project.
Error detection and correction may include:
- Compare data with input document
- Check topology of spatial objects
- Check attributes of spatial objects
- Check for missing spatial objects
Data Storage and Editing

Three major types of error:
(1) Entity error (positional error). Entity error can take three different forms:
missing entities, incorrectly placed entities, and disordered entities.
(2) Attribute error.Attribute error occurs in both vector and raster systems.
(3) Entity-attribute agreement error (logical consistency).
Of the three basic types of error found in GIS databases, the last two are the
most difficult to find.

Detecting and Editing Errors of Diff. Types
▪ Negative cases of the following statements will cause errors:
1. All entities that should have been entered are present.
2. No extra entities have been digitized.
3. The entities are in the right place and are of the correct shape and size.
4. All entities that are supposed to be connected to each other are connected .
5. All entities are within the outside boundary identified with registration marks.

Spatial Errors
▪ Dangling node, can be defined as a
single node connected to a single
line entity. Dangling nodes are also
called dangles.
▪ Dangles can result from three
possible mistakes:
(1)Failure to close a polygon
(2)Failure to connect the node to the
object it was supposed to be connected
to (called an undershoot)
(3)Going beyond the entity you were
supposed to connect to (called an
overshoot).

Source of Errors
▪ Dangles can also be a result of incorrect placement of the digitizing
puck, or improper fuzzy tolerance distance setting.
Distance between left dangle and
its above line segment is 0.25mm
Fuzzy tolerance = 0.1mm, if you
change it to o.3mm, dangle will
disappear.

Spatial Errors
▪ Sliver polygons
▪ This occurs when the software uses a
vector model that treats each
polygon as a separate entity. (or
spatial object)
▪ Solution: Use a GIS that does not
require digitizing the same line twice.
▪ Weird polygons
▪ Polygons with missing nodes.
▪ Missing Arcs/segments

Attribute Errors: Raster and Vector
Missing attributes
For raster:
A. Missing row
B. Incorrect or misplaced attributes
ForVector
Incorrect attribute values are very difficult to detect.

Checklist to Avoid Errors
As geospatial analyst, you should always approach a project with the
obvious sources of error discussed firmly on you mind. Therefore, when
given a task to perform, and the associated data, the following should act
as a good checklist:
– Is the data current?
– Were the data mapped at the correct scale? Do they have the same
accuracies?
– What is the resolution of the data? Will it support the kinds of analysis
we want to perform?
– Do we have all the data for the project areas, or is there some data
missing?
– If we need other data sets, are they available, or will we have trouble
getting them?

Obvious Errors
▪ The statement “to err is human” is very applicable to creating spatial data. Humans make a
lot of errors. Typing in the wrong value in a computer is a common mistake that humans
make. However, there are other sources of obvious error besides human error:
– Age: a map is a representation of real-world objects at a given point in time. The reliability of a
dataset typically goes down as it gets older. This is especially true of data that would frequently
change such as housing within a city. Many GIS projects take years to complete, and it is entirely
possible that much of the data collected in the beginning of a project may be out of date by the end
of the project.
– Map Scale: In general, larger scale maps show more detail than smaller scale maps. Also, larger
scale maps tend to have greater accuracy than smaller scale maps, especially maps within the “same
family” such as the differences between 1:250,000, 1:100,000 and 1:24,000 GIS will process any of
your data, whether the processing is appropriate or not. Therefore, you can combine data from
different scales rather easily, however, doing so may not be a good idea due to the different
accuracies of the products.
– Data Format: The way we represent data also presents an obvious source of error. For example, a
raster map of landuse represented by 10 meter grid cells will differ significantly from a raster map
of landuse represented by 100 meter grid cells. The following is a grid of landuse values around
Ithaca, NewYork. You can see the differences in representation between a map with 10 meter grid
cells, 30 meter grid cells, and 100 meter grid cells.

Problems with Age
The following maps show the different land cover types between 1968 and
1995. You can see how the data has changed over 30 years, and why using
older data might present a problem.

Components of Data Quality
▪ Positional Accuracy
▪ Attribute Accuracy
▪ Resolution
▪ Completeness

Spatial Accuracy
▪ positional accuracy relates to the coordinate values for the
geographic objects. But, even positional accuracy is divided into two
different categories:
– Absolute accuracy: refers to the actual X,Y coordinates of a geographic object.
If one knows the correct position of the geographic object, they can compare
the differences with the position represented in the geographic database.
Typically, absolute accuracy will measure the total different between an object,
or the difference in the X coordinate and the difference in theY coordinate.
– Relative accuracy: refers to the displacement of two or more points on a map (in
both the distance and angle), compared to the displacement of those same
points in the real world.

Errors Associated with Spatial Analysis
▪ Errors in Digitizing a Map
– Source errors
▪ Distortion
▪ Boundaries drawn on a map have a “thickness”
– 1 mm line
▪ 1.25 m wide on 1:250 map
▪ 100m wide on 1:100000
▪ Estimates show that 10% of a 1:24000 soil map may represent the boundary lines
alone
– Digital Representation
▪ Curves are approximated by many vertices
▪ Boundaries are not absolute, but should have a confidence interval

Errors Resulting from Natural Variations from Original
Measurements
▪ Measurement Error
– Accuracy vs. Precision
▪ Accuracy: extent to which an estimated value approaches the true value
▪ Precision: measure of dispersion of observations about a mean

Accuracy and Precision
▪ Accuracy is defined as displacement of a
plotted point from its true position in relation
to an established standard while Precision is
the degree of perfection; or repeatability of a
measurement.
▪ For mapping, accuracy is associated with
position of an object to its true position.
▪ Precision is then the ability to repeat a
measurement, or how likely you are to return
to the same location time and time again.
▪ The figures to the right illustrate the
differences between accuracy and precision.
▪ Therefore, if there are natural variations in
either the instruments used for
measurement, or the object you are
measuring, the accuracy or precision may be
effected.

Digitizing errors from duplicate lines include slivers and missing labels for
the sliver polygons. Slivers are exaggerated for the purpose of illustration.
Digitizing errors

Digitizing errors of overshoot (left) and undershoot (right)
Digitizing Errors- Overshoot & Undershoots

Digitizing errors of an unclosed polygon
Digitizing errors-Unclosed Polygon

Pseudo nodes, shown by the diamond symbol, are nodes that are not located at
line intersections
Digitizing errors- Pusedo Nodes

The from-node and to-node of an arc determine the arc’s direction.
Digitizing Arc

Digitizing error of multiple labels due to unclosed polygons
Digitizing Unclosed Polygon –Multi labels

The dangle length specified by the CLEAN command can remove an overshoot if the
overextension is smaller than the specified length. In this diagram, the overshoot a is removed
and the overshoot b remains.
Removing Dangles - Using Clean Command

Typical Digitizing Situations
this is ideal, but...
overshoot, and what
to do with it
undershoot,
and what to do

Remote Sensing
The science and art of acquiring of information about an object without being in physical contact with it

Remote Sensing
As you view the screen of your
computer monitor, you are actively
engaged in remote sensing.
HOW ?
THE ANSWER IS
A physical quantity (light) emanates
from the screen, which is a source of
radiation.The radiated light passes
over a distance, and thus is "remote" to
some extent.

Remote Sensing
1
2
3 5
6
1. energy source
2. atmospheric
interaction
3. ground object
4. data recording /
transmission
5. ground receiving
station
6. data processing
7. expert interpretation
/ data users
4
7

Balloon Remote Sensing, Paris, 1858

Wilbur Wright and his
first aerial photograph of
France.

LandSAT Satellite
Ox-Bow of the
Mississippi

Indian Remote
Sensing (IRS) Satellite
Bihar State Map
on AWiFS Data

▪ Area is covered by grid with (usually) equal-sized cells
▪ Cells often called pixels (picture elements); raster data often called image data
▪ Attributes are recorded by assigning each cell a single value based on the
majority feature (attribute) in the cell, such as land use type.
▪ Easy to do overlays/analyses, just by ‘combining’ corresponding cell values:
“yield= rainfall + fertilizer” (why raster is faster, at least for some things)
▪ Simple data structure:
– directly store each layer as a single table
(basically, each is analagous to a “spreadsheet”)
– computer data base management system not required
(although many raster GIS systems incorporate them)
Representing Data using Raster Model
corn
wheat
fruit
clover
fruit
oats

Raster Array Representations
▪ Raster data comprises rows and columns, by one or more characteristics or arrays
– elevation, rainfall, & temperature; or multiple spectral channels (bands) for remote sensed data
– how organise into a one dimensional data stream for computer storage & processing?
▪ Band Sequential (BSQ)
– each characteristic in a separate file
– elevation file, temperature file, etc.
– good for compression
– good if focus on one characteristic
– bad if focus on one area
▪ Band Interleaved by Pixel (BIP)
– all measurements for a pixel grouped together
– good if focus on multiple characteristics of geographical area
– bad if want to remove or add a layer
▪ Band Interleaved by Line (BIL)
– rows follow each other for each characteristic
File 1: Veg A,B,B,B
File 2: Soil I,II,III,IV
File 3: El. 120,140,150,160
A,I,120, B,II,140 B,III,150 B,IV,160
A,B,I,II,120,140 B,B,III,IV,150,160
Note that we start in lower left.
Upper left is alternative.
A B
B B
III IV
I II 150 160
120 140
Elevation
Soil
Veg

The generic raster data model is actually implemented in several different computer file
formats:
▪ GRID is ESRI’s proprietary format for storing and processing raster data
▪ Standard industry formats for image data such as JPEG, TIFF and MrSid formats can be
used to display raster data, but not for analysis (must convert to GRID)
▪ Georeferencing information required to display images with mapped vector data
– Requires an accompanying “world” file which provides locational information
File Formats for Raster Spatial Data
Image Image File World File
TIFF image.tif image.tfw
Bitmap image.bmp image.bpw
BIL image.bil image.blw
JPEG image.jpg image.jpw

Remote Sensing: Imagery Types
TES
1 m
Quickbird
61cm
IRS 1D
23.5 m
IRS 1D
5.8 m
High Resolution Imagery Low Resolution Imagery
Panchromatic Imagery Multi-spectral Imagery

Resolution
▪ The Ability to Discriminate
▪ Types of Resolution
– Spatial: Discrimination by Distance
– Spectral: Discrimination by Wave length
– Radiometric: Discrimination by energy levels
– Temporal: Discrimination byTime
5.8m
5.8m
Radiometric
Resolution
8-bit (0-255)
Spectral
Resolution
0.4-0.7 μm
Day 1
Day 48
Day 96

What is an image?
▪ Data that are organized in a grid of columns and rows
▪ Usually represents a geographical area
X-axis

An image refers to any pictorial
representation, regardless of what
wavelengths or remote sensing device has
been used to detect and record the
electromagnetic energy.
A photograph refers specifically to images
that have been detected as well as
recorded on photographic film.
Based on these definitions, we can say that
all photographs are images, but not all
images are photographs.
Difference between Image and Photographs

Pixels
▪ Resulting images are made of a grid of
pixels
• Each pixel stores a digital number (DN)
measured by the sensor
• Represents individual areas scanned by
the sensor
• The smaller the pixel, the easier it is to
see detail

Continuous data
Two types:
• Panchromatic ( 1 Band/layer)
• Multispectral ( 2 or more Bands)

Viewing continuous images
▪ Each band or layer is viewable as a separate image
Thematic Mapper Band 1
Band 4
Band 5

Blue
Green
Red
NIR
SWIR
Part of spectrum
Monitor
color guns
Viewing images
▪ Three bands are viewable simultaneously
Band
4
Band
3
Band
2
Band
4
Band
5
Band
3
Band
1
Band
2
Band
3

Geomteric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪ Geometric corrections intended to compensate for these distortions
▪ Required so that geometric representation of the imagery is as close as
possible to the real world
▪ Geometric registration of the imagery to a known ground coordinate
system must be performed

▪ Radiometric Corrections
– changing the image data BVs to correct for errors or distortions
▪ atmospheric effects (scattering and absorption)
▪ sensor errors
▪ Geometric Corrections
– changing the geometric/spatial properties of the image data
– Also called image rectification or rubber sheeting
Image Preprocessing

Geometric Registration
▪ Image-to-map registration
– Involves identifying the image coordinates (i.e. row, column) of
several clearly discernible points, called ground control points (or
GCPs), in the distorted image (A - A1 to A4), and matching them to
their true positions in ground coordinates (e.g. latitude, longitude).
– True ground coordinates are measured from a map (B - B1 to B4),
either in paper or digital format
▪ Image-to-image registration
– Performed by registering one (or more) images to another image,
instead of geographic coordinates
▪ Several types of transformations applied on image co-
ordinates to transform into real world coordinates:
– Plane transformations - keep lines straight, being on the first order
– Curvilinear (polynomial) - higher order transformations that do not
necessarily keep lines straight and parallel
– Triangulation.
– Piecewise transformations - Break the map into regions, apply
different transformations in each region

Geometric Corrections
▪ All remote sensing imagery inherently subject to geometric distortions
caused by various factors
▪ Geometric corrections intended to compensate for these distortions
▪ Required so that geometric representation of the imagery is as close as
possible to the real world
▪ Geometric registration of the imagery to a known ground coordinate
system must be performed

▪ Distortions factors
– the perspective of the sensor
optics
– the motion of the scanning
system
– the motion of the platform
– the platform altitude
– attitude, and velocity
– the terrain relief and
– the curvature and rotation of
the Earth
▪ Distortions type
– Systematic (predictable in
nature)
▪ Accounted through
accurate modeling of sensor
and platform motion and
▪ Geometric relationship of
the platform with the Earth
– Unsystematic (random)
errors cannot be modeled
and corrected
Earth Rotation AltitudeVariation PitchVariation
SpacecraftVelocity RollVariation YawVariation
Non
Systematic
Distortions
Systematic
Distortions
Image distortions
Scanner distortions
Actual
Velocity
Nominal
Velocity
Mirror
Angle
time
Mirror velocity variations Scan Skew

Geometric Correction
▪ Four Basic Steps of Rectification
1. Collect ground control points (GCPs)
2. “Tie” points on the image to GCPs.
3. Transform all image pixel coordinates using mathematical
functions that allow “tied” points to stay correctly mapped to GCPs.
4. Resample the pixel values (BVs) from the input image to put values
in the newly georeferenced image

Geometric Correction
▪ ThreeTypes of Resampling
– Nearest Neighbor - assign the
new BV from the closest input
pixel.This method does not
change any values
– Bilinear Interpolation - distance-
weighted average of the BVs
from the 4 closest input pixels
– Cubic Convolution - fits a
polynomial equation to
interpolate a “surface” based on
the nearest 16 input pixels; new
BV taken from surface
1
2
3
4
1
2
3
4

Image Enhancements
▪ Procedures of making a raw image more interpretable for a particular
application
▪ Improve the visual impact of the raw remotely sensed data on the human
eye
▪ Classification
– Contrast (global) enhancement: Transforms raw data using statistics computed over
whole data set
▪ Examples - Linear contrast, histogram equalized and piece-wise contrast stretch
– Spatial (local) enhancement - Local conditions considered only that vary over image
▪ Examples - Image smoothing and sharpening

Image Enhancements
▪ Procedures of making a raw image more
interpretable for a particular application
▪ Improve the visual impact of the raw
remotely sensed data on the human eye
▪ Contrast (global) enhancement:
Transforms raw data using statistics
computed over whole data set (Examples -
Linear contrast, histogram equalized and
piece-wise contrast stretch)
▪ Spatial (local) enhancement - Local
conditions considered only that vary over
image (Examples - Image smoothing and
sharpening)

Image Enhancement: Example
▪ Contrast Enhancement - “stretching” all or part of input BVs from
the image data to the full 0-255 screen output range

Image Fusion
LISS III PAN
Brovey Multiplicative PCA Wavelet

Image Classification
▪ To label the pixels in the image with meaningful information of the
real world.
▪ Classification of complex structures from high resolution imagery
causes obstacles due to their spectral and spatial heterogeneity
▪ Two types
– Unsupervised classification
– Supervised classification
117

Supervised vs. Unsupervised Approaches
– Unsupervised: statistical "clustering" algorithms used to
select spectral classes inherent to the data, more
computer-automated
Posterior Decision
– Supervised: image analyst "supervises" the selection of
spectral classes that represent patterns or land cover
features that the analyst can recognize
Prior Decision

Edit/evaluat
e signatures
SelectTraining
fields
Classify
image
Evaluate
classification
Identify
classes
Run clustering
algorithm
Evaluate
classification
Edit/evaluate
signatures
Supervised Unsupervisedvs

Band Combinations
▪ Features can become more obvious
Vegetation
Urban
2,3,1 (RGB) 4,3,2 (RGB)4,5,3 (RGB)

Keys to Image Interpretation
▪ Shape
▪ Size
▪ Shadows
▪ Tone
▪ Color
▪ Texture
▪ Pattern

Interpretation Principles
• Shape
• Size
• Shadow
• Tone/Color
• Texture
• Pattern
• Relationship to Surrounding Objects

▪ The photo on the right is a black
and white photo of the City of
Ithaca and the Cornell
University campus taken in
1991. More specifically, it was
taken on April 4, 1991 (look in
the upper left hand corner).
▪ So lets take a quick tour of the
photograph
Image Interpretation

▪ Size: the size of an object is one of the
most useful clues to its identity. Also,
understanding the size of one object
may help us understand the sizes of
other objects.
▪ For example, most of us have a feeling
for the size of a baseball field, and
football field. When we observe these
objects on a photograph, it will help us
to understand the sizes of other
objects on the photograph.
▪ For example, on another part of the
photograph we have a trailer park.
This could easily be confused with a
parking lot, but when we understand
the size of the objects we will realize
that the objects in the trailer park are
much too large to be cars.

▪ Shape: Shapes can often give away
an object’s identity. For example, a
cloverleaf is a very distinctive
feature of a highway, while a
stream’s meandering gives away its
identity.
▪ And again, the baseball diamond
we just looked at also has a
distinctive shape.

▪ Shadow: shadows often give us an
indication of the size and shape of
an object. When we look at aerial
photographs we often see a
vantage point we are not used to:
an overhead view.
▪ Shadows can let us “cheat” alittle
to see the side of an object. The
photos on the right show the
CornellTheory Center, which casts
a rather large shadow, indicating
the building size, and a water tower
on one of the farms on campus. If
you look closely, you can see the
“legs” of the watertower.

▪ Shadow: while shadows are
helpful, they can also be a
hindrance. As we try to look down
into the gorge on the Cornell
campus, we can see very little due
to the shadows cast.

▪ Tone:You can see the tonal
contrast between Cayuga Lake and
the land area. Also, there is good
tone representation for wet or dry
soils.

▪ Texture: In this photo we see the Cornell
Plantations and Botanical Garden, as well
as the experimental agricultural plots.
Especially in the Plantations, you will see
the different textural characteristics
between the mowed lawns and the grassy
areas. Notice too, the small pond in the
Plantations (an example of tone)
▪ Additionally, around another natural area
on campus you can see the textural
difference of trees vs. more of a grassland
area.
▪ And again, as you look at the agricultural
plots you will notice a different texture
from the forested areas.
▪ Finally, in the golf course shown below
there are obvious patterns between
managed lawns vs. the unmanaged lawns,
in addition to the tonal differences
between the lawns and sand traps.

▪ Pattern:There are so many
examples related to pattern. These
would include the rectilinear
pattern of the older, urban
neighborhoods in Ithaca, the
straight lines of trees in an orchard,
the rectilinear shape of the
experimental agricultural plots, and
the configuration of a parking lot.
▪ Also, the pattern of the golf course
with greens, tees, traps, and
fairways is very easy to spot.

▪ Pattern: the drainage
pattern for a particular
property on this photo is
easy to see. Also, because
the drainage is relatively
straight, we can assume
that a moderate to steep
slope exists, as water did
not have much opportunity
to meander.

▪ Relationship: observing relationships on
photographs is one of the most fun
observations. For example, a school and a
plaza are interpreted differently due to
relationships:
– While both have many large structures on
them, schools typically have playing fields
– Also, plazas usually have larger parking
areas
▪ Here we see the East Hill Shopping Plaza
(no athletic fields, but a campus of
buildings), and the Ithaca High School
campus (with athletic fields)

▪ Relationship: here is another example of
relationship that shows a middle school
and an elementary school. Notice that it
have buildings like the high school, and a
parking lot, but no real athletic fields to
speak of. What it does have, however, is
what appears to be a playground, and is
surrounded by a residential community.
▪ The structures on the top are an
apartment complex. They could be
tractor trailers, but “size” gives them
away.They are too large to be tractor
trailers when you consider the size of the
schools below.
▪ Notice that just north of the apartment
complex is a large pool. How do we
know it’s a pool, well, the tone gives us a
clue…
Apartments
School
School

▪ interpretation is putting together our
observations bit by bit to form a coherent
understanding of the image. For instance,
identifying the water treatment plant
forces us to use shape, pattern, tone, and
relationship to make the connection:
– We see the water holding areas in
black (tone)
– We see the large tanks (shape)
– And when you’ve seen one treatment
plant, you’ve seen them all (pattern)!!
▪ Notice that across the water is a park.
Why do we know it’s a park? Well, again,
we see multiple ball fields, not enough
buildings to be a school, and a very large
pool.

Cartography
The foremost cartographers of the land have prepared this for you; it’s a map of the area that you’ll be traversing

Spatial is Special
“Everything is related to everything else,
but near things are more related than
distant things”
Tobler, W. 1970. A computer movie simulating urban growth in the Detroit region.
Economic Geography 46, 234–40.
Sometimes called the First Law of
Geography (because it is generally true!).

Cartography – amalgam of art, science
and technology
▪ The art, science and
technology of making maps
▪ Help to produce and analyze
maps to enable
communication of ideas
▪ Basic principles of map making
– Has a clear motive or goal
– Is directed toward an audience
– Uses appropriate design elements
to clearly convey its message

Cartography dynamics – An example
▪ Phone call Cartography

Cartography – a communication bridge
The geographic
environment Compile Recognize
Select
Classify
Simplify
Symbolize
Read
Analyze
Interpret
Imagine
Map user
MapMap
CartographerReality
Reality?

Cartographic elements
▪ Maps show how different
features are related
▪ All maps, however, share the
same common elements
– Title
– Legend
– Scale
– Directional Indicator
– Inset Maps
– Projection System
Beauty is NOT the MAIN objective

The Elements of a Map: Title
▪ Most important element of the
map for acquiring information
efficiently is the title
▪ Identifies the map area and the
type of map
▪ Cartographers may list the title
simply or artistically
▪ Typically appear at the top of
the map, but not always
Map of Indiana Showing Its History, Points of Interest,1967, GRMC, Ball State University Libraries

The Elements of a Map: Legend
▪ Another important feature on a
map is the legend or map key
▪ Contains information needed to
read a map
▪ Most maps use symbols or
colors to represent different
geographic features, the
meaning of which is determined
by legend
This legend simply identifies the roads ofTexas, but the cartographer chose to be creative in the
design of the legend to add character to the map. (Texas Guide Map, 194-, GRMC, Ball State
University Libraries).

The Elements of a Map: Scale
▪ Almost all maps have scales
▪ Scales compare a distance
measured on the map to the
actual distance on the surface of
the earth.
▪ Scales appear on maps in
several forms, but most
cartographers draw a line scale
as a point of reference
This scale from a historical map of Kentucky features an image of GeorgeWashington surveying the
land. (Kentucky:The Dark and Bloody Ground, An Historical and Geographical Map of the State of
Kentucky, 1933, GRMC, Ball State University Libraries).

The Elements of a Map: Directional Indicator
▪ A directional indicator on a map
helps determine the orientation
of the map.
▪ Some cartographers place an
arrow that points to the North
Pole on the map.This is a “north
arrow.”
▪ Other maps indicate direction
by using a “compass rose,” with
arrows pointing to all four
cardinal directions
Ancient cartographers drew elaborate, artistic directional indicators—most commonly a compass
rose. (Floridae Americae Provinciae: Recens & Exactissima Descriptio, 1564, engraved reproduction,
GRMC, Ball State University Libraries).

The Elements of a Map: Inset Map
▪ Some maps feature inset maps -
smaller maps on the same sheet
of paper.
▪ Provide additional information
not shown on the larger map
▪ Drawn at a larger, more
readable scale
▪ Usually feature areas of interest
related to the larger map
The larger map above is a modern map of the NileValley.The inset map shows the ancient Egyptian
Empire in the same area.(National GeographicThe NileValley: Land of the Pharaohs, 1963, GRMC,
Ball State University Libraries).

▪ Earth is round and maps are flat, getting information from a curved surface to a flat one involves a
mathematical formula called a map projection , or simply a projection
▪ This process of flattening the earth will cause distortions in one or more of the following spatial
properties and No projection can preserve all these properties:
– Distance
– Area
– Shape
– Direction
▪ Type of projections based on different types of distortion
– EQUIVALENCY Correct representation of area
– CONFORMALITY Correct representation of shapes
– EQUIDISTANCY Correct representation of distance
– AZIMUTHALCY Correct representation of direction
▪ Type of projections according to the projection surface
– CONIC
– CYLINDRICAL
– PLANAR
In simple terms where the ‘paper touches the Earth’ there are no distortions. But the further the
‘paper’ is away from the surface of the Earth the greater distortions. The mathematics in
different projections attempt to overcome this problem – but none remove all distortions.
The Elements of a Map: Map Projections

Earth Models and Datums
Geoid
Ellipsoid
Sphere
Sea Level
Height
Figure 2.4 Elevations defined with reference to a sphere, ellipsoid, geoid, or local sea level will all
be different. Even location as latitude and longitude will vary somewhat. When linking field data
such as GPS with a GIS, the user must know what base to use.
Terrain

The Datum
▪ An ellipsoid gives the base elevation for mapping, called a
datum.
▪ Examples are NAD27 and NAD83 Everest
▪ The geoid is a figure that adjusts the best ellipsoid and the
variation of gravity locally.
▪ It is the most accurate, and is used more in geodesy than
GIS and cartography.

Geographic Coordinates
Parallels
Equator
PrimeMeridian
PrimeMeridian
Meridians

Geographic Coordinates as Data

Equivalency
▪ Also known as Equal area Projection,
that preserves the area of displayed
features
▪ Shape, distance and angles are distorted
▪ The meridians and parallels may not
intersect at right angles but they are
marked in such a way that the area
represented in each quadrangle is same
with the adjacent one
▪ Shapes of the features are generally
distorted for a larger area
▪ in smaller areas it is difficult to visualize
unless it is measured
Example: Albers Equal-Area Conic projection

Conformality
▪ Also known as Conformal Projection,
which preserves the local shape
▪ To preserve the individual angles
describing the spatial relationship this
projection must show the
perpendicular graticules intersecting
at right angles
▪ Issue - Shapes of the larger areas
cannot be preserved
▪ Used for smaller regions i.e. for large-
scale maps
▪ Useful for navigational charts and
weather maps
Example: Mercator projections

Equidistancy
▪ Also known as Equidistant
Projection, which preserves the
distance between two points
▪ Scale is not maintained correctly
throughout an entire map
▪ There are one or two lines on a map
along which the scale is maintained
correctly irrespective of the fact that
whether they are great or small circles
and straight or curved
Example:

Azimuthalcy
▪ Also known as True direction
Projection
▪ Preserves or maintains some of the
great circle arcs, giving the directions
or azimuths of all points on the map
correctly with respect to the center
Example: Azimuthal Equidistant projections

Conic Projections
▪ Based on the concept of the ‘piece of paper’
being rolled into a cone shape and touching
the Earth on a circular line
– Tip of the cone is positioned over a Pole
– Line of latitude where the cone touches
the Earth is called a Standard Parallel
▪ Used for regional/national maps of mid-
latitude areas – such as Australia and the
United States of America.
▪ Characteristics
– Fan shaped when used to map large areas
– Have distortions increasing away from
the central circular line (the ’touch point
of the paper‘)
– Have very small distortions along the
central circular line (the ’touch point of
the paper‘)
– Shapes are shown correctly, but size is
distorted
– Usually have lines of longitude fanning
out from each other and have lines of
latitude as equally spaced open
concentric circlesThis is a typical example of a world map based on the Conic Projection technique. This map is centred on central Australia and the Standard Parallel is 25°
South. Note how the shapes of land masses near the Standard Parallel are fairly close to the true shape when viewed from space – see the images at the
beginning of this section. This includes Australia, South America and the ’tip‘ of Africa. Also note how land masses furthest away from the Standard
Parallel are very distorted when compared to the views from space. Particularly note how massively large northern Canada and the Arctic icecaps look.

Cylindrical Projections
▪ Based on the concept of the ‘piece of
paper’ being rolled into a cylinder and
touching the Earth on a circular line
– Cylinder is usually positioned over
the Equator, but this is not essential
▪ Usually used for world maps or
regional/national maps of Equatorial
areas – such as Papua New Guinea
▪ Characteristics
– Rectangular or oval shaped – but this
projection technique is very variable in
its shape
– Have lines of longitude and latitude at
right-angles to each other
the central circular line (the ‘touch
point of the paper’)
– Have very small distortions along the
central circular line (the ‘touch point of
the paper’)
– Show shapes correctly, but size is
distorted..
his is an example of a cylindrical map projection and it is one of the most famous projections ever developed. It was created by a Flemish cartographer
and geographer – Geradus Mercator in 1569. It is famous because it was used for centuries for marine navigation. The sole reason for this is that any line
drawn on the map was a true direction. However, shapes and distances were distorted.
Notice the huge distortions in the Arctic and Antarctic regions, but the reasonable representation of landmasses out to about 50° north and south.

Planar Projections
▪ Based on the ‘flat piece of paper’
touching the Earth at a point. The point
is usually a Pole, but this is not essential
– Cylinder is usually positioned over
the Equator, but this is not essential
▪ Also known as Azimuthal or Zenithal
Projection
▪ Characteristics
the central point
– Have very small distortions near the
centre point (the ’touch point of the
paper‘)
– Compass direction is only correct from
the centre point to another feature –
not between other features
– Not usually used near the Equator,
because other projections better
represent the features in this area
When the centre of the map is the North or South Pole maps produced using Azimuthal Projections techniques have lines of longitude fanning out
from the centre and lines of latitude as concentric circles. These projections are often called polar projections.

What are my cartographic objectives?
Why?Map
objectives
Convey information
Illustrate analysis results
Highlight spatial relationships
Easier comprehension of complex events
How? Design
objectives Fulfill map objectives
Assign meaningful symbology
Ensure truthful depiction of reality
Fulfill communication objectives

What are my communication objectives?
PopulationSoils
Focused information
Importance can vary
Symbols can dominate
Variety of information
Equal importance
Subtle symbology
General map
Thematic map
Qualitative Quantitative

▪ More than 3000
▪ No perfect projection
▪ All have distortions
Cylindrical
Conical
Azimuthal
Direction
Distance
Shape
Area
What projection should I chose?

Displace?
Aggregate?Omit?
Simplify?
Collapse?
Typify?
Exaggerate?Classify?Refine?
C
E
B
C2
C3 E1
B1
E5C5
B4
How much detail should I include?

Color
Shape
Texture/Pattern
Gray tone value
Size
Qualitative
Quantitative
What symbols should I use?

What colors should I use?
▪ Connotations
▪ Conventions
▪ Preferences
▪ For screen or paper
R G B Y
100 70 40 10%
20 30 50 85%
Hue
Value
Saturation
Dimensions

12 colors Maximum 7- 8 shades
What are the eye limitations?

What about the color impaired?
▪ Avoid pure green / red
▪ Vary shapes, textures
▪ Use brightness contrast (not more colors)
Normal eye
Red defective
Green defective
Blue defective

How do I represent names on my map?
▪ Legibility issues
– Text color vs background color
– Uppercase vs lowercase
– No fancy fonts
River
RIVER
River
Form
Tigris
Orlando
Color
Baghdad
Basra
Style
Qualitative
San Diego
Redlands
Size
Redlands
San Diego
Value
Tigris
Orlando
Color
River
RIVER
River
Form
Quantitative

• Readability issues
Makramville
Jackville1 2
How do I label names?
How do I place names at POINTS?
• Ambiguity issues
How do I place names at LINES?
• Faster reading
issues
How do I label contours?

0 3 ft
Are these dog houses? Must be visually
easy to read
Correct? Easy to use?
?
What about my scale?

Objective Map form
Quantity of information
Easy / Complex
Quality
Authenticity
Symbol size, color
Reality
Audience
Conditions of use
Technical limits
Scale
Generalization
What factors control my design?

Global Positioning System
“Geographers never get lost.They just do accidental field work.”

What is Global Positioning System ?
24+ satellites revolving around earth
provide 24-hour, real time, all-
weather, global coverage
Satellites are equipped with atomic
clocks
Precise time signals are broadcast on
L-band radio frequencies
Four satellite signals enable
receivers to triangulate position

Satellites broadcast
•Precise time
•Orbit data
•Satellite health
Receiver measures time delay from satellites,
and by triangulation calculates
• Location
• Elevation
•Velocity
How GPS System Works?

GPS applications
Car Navigation
▪ On-board navigation
▪ Fleet management
▪ Roadside assistance
▪ Stolen vehicle recovery
Consumer/Recreational
▪ Portable GPS receivers for
fishermen, hunters, hikers,
cyclists, etc.
▪ Recreational facilities -- golf
courses, ski resorts
▪ Integration of GPS into
cellular phones
▪ E-911 requirement

GPS applications
Surveying/Mapping/GIS
▪ Rural electrification
▪ Telecom tower
placement
▪ Pipelines
▪ Oil, gas, and mineral
exploration
▪ Flood Plain Mapping
▪ Sub-centimeter accuracy
▪ 100%-300% savings in time,
cost, & labor
Tracking/Machine Control
▪ Package/cargo delivery
▪ Fleet and asset
management
▪ Theft recovery
▪ Public safety and services
▪ Farming, mining, and
construction equipment
▪ DGPS required for many
applications

3-D GIS
The application of GIS is limited only by the imagination of those who use it

• 3D Data representation and
visualization
– Real world objects (buildings, towers,
etc.) and rendering on Google Earth
– Digital Elevation Model (DEM)
– Walk through / fly through

3D GIS
▪ The earth is not flat. In the real world,
surfaces with vertical dimensions exist
• True 3D
– Store data in structures that actually reference
locations in 3D space (x,y,z)
– Here z is not an attribute but an element of the
location of the point
– If z is missing, object does not exist!
▪ 3D enable interactive perspective
viewing and navigation, including pan,
zoom, rotate, tilt, fly-through
simulations, and export utilities for
display on the Web
▪ 3D GIS assist
– Construction of surface models such asTINs and
raster from any data
– Extrude buildings and vector features from a
surface
– Aerial photography can be draped onto a 3D
model to project more realistic look
– Supply analytical functions to calculate slope,
aspect and hill shading
The Eagle map of the United States engraved for rudiments of National Knowledge. 1833.

3D Prevue: “Coastal Terrain Model”
… a surface that integrates topography and bathymetry
+ =
Integrated Topo-Bathy Model
BathymetryTopography

Why do we need 3D GIS?
▪ Simulation of complex systems provide
understanding on how the system
operates different perspectives aided by
high quality visualization and interaction
▪ Observation of system features that would
be too small or too large to be seen on a
normal scale system
▪ Access to situations that would otherwise
be dangerous or too remote or
inaccessible
▪ Enable high degree of interaction
▪ Provide a sense of immersion of the
environment
– Where the user can appreciate the scale of change
and visualise the impact of building design on the
external environment and the inhabitants

Why do we need 3D GIS?
▪ To offer consensual reading, with which
people will have an immediate perception
of relief, orientation, slope and visibility
▪ Allow to export to popular multimedia
formats such as video (.avi or .mpeg) or
VRML (.vrl or .vrml) that provide the
following benefits
– No prior knowledge of 3D GIS required
– Only require intuitive and easy to use
interface to operate the 3D model
– Inherent flexibility/adaptability – these
multimedia are 3D cross-platform display
and non-browser specific which enable
expensive data to be used more widely
– Fast and slow time simulation – Ability to
control timescale by incorporating a
sequence of captured events into the key
frames of the motion videos

Surface defined...
▪ Surfaces involve a third 'z' dimension (height/elevation/magnitude,
quantity) in addition to x,y planimetric location
▪ Any type of continuous data can be represented as a surface,
whether it be ground elevation, barometric pressure, rainfall, crop
yield, noise levels, population density, sales intensity, land value,
income, crime rates, etc.
▪ 3 basic methods for representing a surface
– DEM (digital elevation model)
– TIN (Triangulated Irregular Network)
– Contour lines

Digital Elevation Model (DEM)
▪ Set of regularly spaced sampled ground
points in the x and y dimensions (although
spacing not necessarily the same in each)
accompanied by an elevation measure (z
dimension)
▪ Terminology introduced by USGS
▪ General term digital terrain model (DTM)
may be used to refer to any of the above
surface representations when in digital form
▪ Two concepts used for determining elevation
at points within the grid cells:
– Lattice: each mesh point represents a value on
the surface only at the center of the grid cell.The
z-value is approximated by interpolation
between adjacent sample points; it does not
imply an area of constant value
– Surface grid considers each sample as a
square/rectangular cell with a constant surface
value

Triangulated Irregualr Network (TIN)
▪ A set of adjacent, non-overlapping triangles
computed from irregularly spaced points,
with x, y horizontal coordinates and z vertical
elevations
▪ Terminology introduced by USGS
▪ General term digital terrain model (DTM)
may be used to refer to any of the above
surface representations when in digital form
▪ Two concepts used for determining elevation
at points within the grid cells:
– Lattice: each mesh point represents a value on
the surface only at the center of the grid cell.The
z-value is approximated by interpolation
between adjacent sample points; it does not
imply an area of constant value
– Surface grid considers each sample as a
square/rectangular cell with a constant surface
value

Triangulated Irregular Network (TIN)
Irregular set of points Irregular set of points connected
through triangulation
Triangulation to create surface
• TIN
– A set of adjacent, non-overlapping triangles computed from
irregularly spaced points, with x, y horizontal coordinates and z
vertical elevations.
• Why triangulation?
– Can capture significant slope features (ridges, etc)
– Efficient since require few triangles in flat areas
– Easy for certain analyses: slope, aspect, volume
– Avoids “weak” geometry and “thin” triangulation
– Creates a unique network (barring a minor exception)

Contour (isolines) Lines
▪ Contour lines, or isolines, of constant
elevation at a specified interval,
▪ Advantages
– Familiar to many people
– Easy to obtain mental picture of surface
• Close lines = steep slope
• UphillV = stream
• DownhillV or bulge = ridge
• Circle = hill top or basin
▪ Disadvantages
– Poor for computer representation: no formal
digital model
– Must convert to raster orTIN for analysis
– Contour generation from point data requires
sophisticated interpolation routines, often
with specialized software such as Surfer from
Golden Software, Inc., or ArcView Spatial
Analyst extension

Spatial Interpolation
▪ Critical component for raster surface creation
▪ Used to create surface which contain equally spaced cells from
irregularly spaced point data
▪ Five basic methods of interpolation
– Inverse Distance Weighting
– Spline
– Trend
– Natural Neighbor
– Kriging

Inverse Distance Weighting
▪ Inverse DistanceWeighting (IDW): assumes that each input point has a local influence that diminishes
with distance, which is achieved by weighting the points closer to the processing cell greater than those
farther away.
▪ Output values for each cell may be based on
– Nearest neighbor option which weights a specified number of points (the “k" nearest neighbors). If k=n then all points
are included.
– or Radius option which uses all points within a specified radius.
▪ A Power Parameter controls the significance of the surrounding points upon the interpolated value: the
higher the value the less influence from distant points.
– In essence, this is the rate of attenuation with distance; a higher value means a steeper slope for attenuation
▪ A barrier input line theme can be specified to represent a cliff, ridge, or some other interruption in a
landscape; points beyond the barrier are excluded from the weighting. A choice of No Barriers will use all
points specified in the No. of Neighbors or within the identified radius.
▪ Use IDW, for example, to interpolate a surface of consumer purchasing. More distant locations have less
influence, because people are more likely to shop closer to home; a barrier could be railroad lines assuming
nobody "crosses the tracks" to shop.
▪ An IDW surface passes through the measured points (referred to as an “exact interpolation”), and
interpolated surface is never above or below the highest/lowest measured points

Spline
▪ Spline is a general purpose interpolation method that fits a minimum-curvature surface exactly
through the input points.
▪ Conceptually, it is like bending a sheet of rubber to pass through the points, while minimizing the
total curvature of the surface.
▪ The Regularized option yields a smoother surface
▪ the weight parameter defines the “weight of the third derivatives of the surface in the curvature minimization
expression.”The higher its value the smoother the surface
-The Tension option tunes the stiffness of the surface according to the character of the modeled
phenomenon.
▪ the weight parameter defines the “weight of the tension”.The higher its value the coarser the surface
▪ The number of points parameter identifies the number of points per region used for local
approximation. Higher values produce smoother surfaces.
▪ The spline method is best for gently varying surfaces such as elevation, water table heights, or
pollution concentrations. It is not appropriate if there are large changes in the surface within a
short horizontal distance, because it can overshoot estimated values.
▪ Like IDW it is an “exact interpolation” in that the surface passes through the measured points,
but unlike IDW the surface can extend above or below measured points

Interpolation with cubic "natural" splines between three points
Note how smooth the curves of the terrain are; this is because
Spline is fitting a simply polynomial equation through the points

Trend
▪ Trend: Uses a polynomial regression to fit a least-squares surface to the input points.The surface is chosen so that
the sum of the squared differences (vertical distances on the z axis) between the original points and their estimate
on the surface is minimized for the entire surface.
▪ The resulting surface seldom passes through the original points since it performs a best fit for the entire surface,
thus it is an “inexact interpolation.” The lower the RMS error, the more closely the interpolated surface represents
the input points.
▪ Order of the polynomial used to fit the surface is specified by user.The most common order of polynomials is 1
through 3.
– A first-order linear trend surface interpolation simply performs a least-squares fit of a plane to the set of input points.
– When an order higher than 1 is used, the interpolator may generate a Grid whose minimum and maximum might exceed the
minimum and maximum of the input points.
– As the order of the polynomial is increased, the surface being fitted becomes progressively more complex. A higher order
polynomial will not always generate the most accurate surface, it is dependent upon the data.
▪ A logistic option is available for generating a trend surface for prediction of presence/absence of certain
phenomena (in the form of probability) for a given set of locations (x,y) in space.The z value is a categorized
random variable with only two possible outcomes, for instance, the existence of an endangered species or the lack
of existence of that species. These two values of z can be coded as 1 and 0, respectively. It creates a continuous
probability Grid with cell values between 1 and 0. A maximum likelihood estimation is used to calculate the logistic
probability surface model. Trend surface interpolation creates smooth surfaces. (see Interp MakeTrend Discussion in
AV Help)

Natural Neighbor
▪ Natural neighbor: similar to IDW in that it applies weights to a set of neighbor
points, so it is a local rather than global interpolation
– However, point selection is based on delauney triangles, and weighting is based on area of
thiessen polygons
▪ “robust” and “conservative, artifice free”
– interpolated heights will be within range of sample heights (none above or below) and it will
not produce peaks, ridges, etc. that are not in the sample data
▪ Exact in that surface passes through all input points
▪ Requires no user input such as search radius, sample count, etc..
▪ Can handle large numbers of points that may crash other methods
▪ Works with regularly and irregularly distributed points
▪ Also known as Sibson interpolation

Natural neighbor interpolation.The colored circles. which represent the interpolating weights, wi, are generated using the ratio of the shaded area to that of the cell area
of the surrounding points.The shaded area is due to the insertion of the point to be interpolated into the Voronoi tessellation

Kriging
▪ Kriging is an advanced procedure that assumes the distance or direction
between sample points shows spatial correlation that describes the surface.
▪ Kriging assumes that the same pattern of variation can be observed at all
locations on the surface.
▪ This pattern is estimated by constructing Variograms based on sets of n
points or all points within a specified radius.
▪ The best estimation method for generating the output surface is then based
on an interactive examination of theVariogram.
▪ This approach is most useful when you already know about spatially
correlated distance or direction bias within the data. It is most commonly
used in geology and soil science.

Example of one-dimensional data interpolation by kriging, with confidence intervals. Squares indicate the location of the data.The kriging interpolation is in red.The
confidence intervals are in green.

Data sampling
▪ Method of sampling is critical for subsequent interpolation...
Regular Random Transect
Stratified random Cluster Contour

Systematic sampling pattern
Easy
Samples spaced uniformly at fixed
X,Y intervals
Parallel lines
Advantages
Easy to understand
Disadvantages
All receive same attention
Difficult to stay on lines
May be biases
Systematic sampling

Random Sampling
Select point based on random
number process
Plot on map
Visit sample
Advantages
Less biased (unlikely to match pattern
in landscape)
Disadvantages
Does nothing to distribute samples
in areas of high
Difficult to explain, location of
points may be a problem
Random Sampling

Cluster Sampling
Cluster centers are established
(random or systematic)
Samples arranged around each
center
Plot on map
Visit sample
(e.g. US Forest Service, Forest
Inventory Analysis (FIA)
Clusters located at random then
systematic pattern of samples at
that location)
Advantages
Reduced travel time
Cluster Sampling

Adaptive sampling
Higher density sampling where
feature of interest is more variable.
Requires some method of
estimating feature variation
Often repeat visits (e.g. two stage
sampling)
Advantages
Efficient in large homogeneous
areas with higher spatial variation.
Disadvantages
If no method of identifying where
features are most variable then
several you need to make several
sampling visits.
Adaptive Sampling

3D GIS in action: DMRC
GPS Control: 5 cm accuracy Digital Elevation Model: 50 cm accuracy
triangulation

3D GIS in action: DMRC
3D Topology
Texturing 3D Visualization
3D Rendering

GIS and Remote Sensing for Geospatial Analysis

GIS and Remote Sensing for Geospatial Analysis

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à GIS and Remote Sensing for Geospatial Analysis

Similaire à GIS and Remote Sensing for Geospatial Analysis (20)

Plus de Nishant Sinha

Plus de Nishant Sinha (6)

Dernier

Dernier (20)

GIS and Remote Sensing for Geospatial Analysis