[PDF] Fundamentals of Remote Sensing PDF

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[PDF] Fundamentals of Remote Sensing

Remote Sensing data is one of the primary data sources in GIS analysis The objective of this material is to provide fundamentals of Remote Sensing technology

What is remote sensing in GIS PDF?
Remote sensing is the common name for all methods used to collect data at a distance from the object under study by some kind of recording device.
What is GIS and remote sensing?
Remote sensing is a surveying and data collection technique, used to survey and collect data regarding an object while GIS is a computer system that consists of software used to analyze the collected data and hardware that the software would operate in.
What is remote sensing PDF download?
Remote sensing is the process of acquiring information, detecting, analyzing, monitoring the physical characteristics of an area by recording the radiation energy that is reflected and emitted without having any physical contact with the object under study. This is done by capturing the reflected radiation / energy.
"Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information."

Preface

Ihopeenjoyit

KoKoLwin

DivisionofSpatialInformationScience

UniversityofTsukuba

2008
2

Part I: Fundamentals of Remote Sensing

1. Remote Sensing Overview

1.1 Definition

1.2 Remote Sensing Concept

1.3 Components in Remote Sensing

1.4 Types of Remote Sensing

1.5 Multistage Remote Sensing Data Collection

1.6 Stereo Pair Remote Sensing Data Collection

1.7 Types and Uses of Satellites

2. Remote Sensing Data Acquisition

2.1 Electromagnetic Waves Used in Remote Sensing

2.2 Properties of Electromagnetic Waves

2.3 Spectral Reflectance and Earth Surface Interaction

2.4 Multi-spectral Remote Sensing Data (Image)

2.5 Spectral Properties and Principal Applications

2.6 Spectral Reflectance to DN (Digital Number)

2.7 Structure of Remote Sensing Data

2.8 Resolutions in Remote Sensing3

3. Remote Sensing Data Processing and Analysis

3.1 Remote Sensing Data Pre-processing

3.2 Visual Interpretation (Band combination)

3.3 Apply Algorithms

3.4 Multi-spectral Classification

3.5 Scene Selection Criteria for Multi-spectral Classification

Part II: Remote Sensing Data Applications in GIS

4. Remote Sensing Data Applications in GIS

4.1 Land Cover Classification form Landsat ETM+

4.2Land Cover Classification form ALOS

4.3 Urban Greenness

4.4 Monitoring of Deforestation Process

4.5 Surface Steepness Measurement from LIDAR

4.6 ViewshedAnalysis and Resort Site Selection

4.7 Watershed and Environmental Impact Assessment

Appendix A Remote Sensing Learning Resources

Appendix B Remote Sensing Data Resources

Appendix C Remote Sensing Software Resources4

Part I: Fundamentals of Remote Sensing

1.Remote Sensing Overview

1.1Definition

WhatisRemoteSensing?Ifyouarereadingthis

sentence,nowyouaredoingRemoteSensing.In fact,anyinformationacquiredfromtheobject withouttouchingisRemoteSensing.Following isascientificdefinitionofRemoteSensing.

Thescienceofacquiringinformationaboutthe

earthusinginstrumentswhichareremotetothe

Instrumentsmayusevisiblelight,infraredor

radartoobtaindata.Remotesensingoffersthe dataforGIS.(Source:digimap) 6

© 2008 Ko Ko Lwin

1.2 Remote Sensing and GIS Work Flow

Solar radiation from

the sun

Data transmission

Platform (satellite)

Ground receiving station &

data warehouse

DATA ACQUISITION

DATA PRE-PROCESSINGDATA PROCESSING & ANALYSIS

Atmospheric correction

Radiometric correction

Geometric correction

etc. Earth Sun

Algorithm development

Multi-spectral classification

Spatial data modeling along

with other GIS dataset

Example in optical Remote Sensing

23© 2008 Ko Ko Lwin

1.3 Components in Remote Sensing

Platform

The vehicle which carries a sensor. i.e. satellite, aircraft, balloon, etc...

Sensors

Device that receives electromagnetic radiation and converts it into a signal that can be recorded and displayed as either numerical data or an image.

Platform NameSensor Name

Landsat TMThematic Mapper

(Passive: Optical sensor)

Landsat ETMEnhanced Thematic Mapper

(Passive: Optical sensor)

ALOSPRISM (Passive: Optical sensor)

AVNIR-2 (Passive:Optical sensor)

PALSAR (Active: Microwave sensor)

One platform can carry more than

one sensor. For example: 8

Platform

Sensor

1.4 Types of Remote Sensing

Passive Remote Sensing and Active Remote Sensing

Active Remote Sensing

Remotesensingmethodsthatprovide

theirownsourceofelectromagnetic radiationtoilluminatetheterrain.

Radarisoneexample.

PassiveRemoteSensing

Remotesensingofenergynaturally

reflectedorradiatedfromtheterrain. 9

Own energy source

© 2008 Ko Ko Lwin

1.5 Multistage Remote Sensing Data Collection

Satellite based remote sensing

Advantages: Less geometric errors (platform is stable) Disadvantages: Need to wait a time for certain event

Fixed spatial resolution

Aerial surveying

Advantages: Acquire any times any events

Variable spatial resolution by

changing flight altitude and camera focal length

Disadvantages: High geometric errors;

require sophisticated geometric correction model

Costly for specific area, specific purpose

Ground based remote sensing GBRS or

Low Altitude Remote Sensing

Scientific experiment purposes

(e.g. study about canopy, soil contamination, etc.) 10

Earth surface

© 2008 Ko Ko Lwin

Stereo Pair Images

Different days from different orbits

Onboard two or more inclined sensors

e.g. SPOT5, ALOS PRISM (Panchromatic Remote- sensing Instrument for Stereo Mapping)

Swath width : 35Km

1.6 Stereo Pair Remote Sensing Data Collection

11 Nadir

West pointedEast pointed

Nadir

Forward

Backward

Nadir (2.5m)

Forward

Backward

ALOS PRISM

SPOT-5ALOS PRISM Sensor

DEM Digital Elevation Model can be

generated from stereo pair images by using

Photogrammetric technology.

1.7 Types and Uses of Satellites

Types of satellites can be classified by their orbit characteristics. Type 1: Low Earth Orbits/Satellites: Normally used in spy satellite (Military purposes) Type 2:Sun-synchronous Orbits/Satellites: a polar orbit where the satellite always crosses the Equator at the same local solar time. Most of the earth resources satellites are sun-synchronous orbit. 12

Examples

Landsat TM/ETM

SPOT ALOS

IKONOS

QuickBird

Earth rotation

Satellite orbit

Type 3: Geostationary Orbits/Satellites: Satellites at very high altitudes, which view the same portion of the Earth's surface at all times. Especially used in metrological applications. 13

Fixed position on specific location

Same speed as earth rotation speed

Wide area coverage

Especially designed for weather monitoring

Earth rotation

Satellite orbit

2. Remote Sensing Data Acquisition

14 GammaX-raysUltravioletVisibleIR (Infrared)MicrowaveRadio-wave

10-14 10-12 10-8 10-6 10-4 10-21 (wavelength in meter)

SUN

Electromagnetic spectrum

400 500 600 700 (wavelength in nanometer)

Optical remote sensing (Passive Remote Sensing)

Microwave remote sensing (Active Remote Sensing)

SUN

2.2PropertiesofElectromagneticWaves

Incident energy

Reflected energy

Transmitted energy

or Refracted energy

Emitted energy

(Emitted by substance; e.g. Surface Temperature)

Use in Passive Remote Sensing

SUN

2.3 Spectral Reflectance and Earth Surface Interaction

Surface categoryLow reflectanceHigh reflectance

WaterN-IR (Near -Infrared)Blue (Visible)

VegetationM-IR (Mid-Infrared)N-IR (Near-Infrared)

SoilBlue (Visible)M-IR (Mid-Infrared)

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5

Reflectance %

BGR (VISIBLE)wavelength

in mN-IR (Near-InfraredM-IR (Mid-Infrared)

Reflectance form water

Reflectance form vegetation

Reflectance form soil

Spectral reflectance and earth surface interaction 70
60
50
40
30
20 10 0

2.4 Multi-spectral Remote Sensing Data (Image)

Composed with more than one spectral band and each band represents specific wavelength Example in Landsat TM (Total 7 bands, Band 6 Thermal band omitted in here)

TM Band 1: High reflectance in waterTM Band 4: High reflectance in vegetationTM Band 7: High reflectance in bare land (soil)17

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5

70
60
50
40
30
20 10 0

Reflectance %

B1B2B3B4B7B5

VISIBLE

Wavelength in m

N-IRM-IR

Reflectance form water

Reflectance form vegetation

Reflectance form soil

2.4 Multi-spectral Remote Sensing Data (Image) (Continued)

Example in Landsat TM/ETM (Band 6 omitted)

Band 1 : Blue (0.450 ~ 0.515 m) Band 2 : Green (0.525 ~ 0.605 m) Band 3 : Red (0.630 ~ 0.690 m)

Band 4 : Near-Infrared(0.750 ~ 0.900 m) Band 5 : Mid-Infrared (1.550 ~ 1.750 m) Band 7 : Mid-Infrared (2.090 ~ 2.350 m)

2.5 Spectral Properties and Principal Applications

Example in Landsat TM/ETM

BandWavelength (m)Principalapplications

B-10.45 -0.52 (Blue)Thisbandisusefulformappingcoastalwaterareas, anddetectingculturalfeatures. B-20.52 -0.60 (Green)Thisbandcorrespondstothegreenreflectanceofhealthy B-30.63 -0.69 (Red)Thisbandisusefulfordiscriminatingbetweenmanyplantquotesdbs_dbs14.pdfusesText_20

[PDF] [PDF] Fundamentals of Remote Sensing

What is remote sensing in GIS PDF?

What is GIS and remote sensing?

What is remote sensing PDF download?

Preface

Ihopeenjoyit

KoKoLwin

DivisionofSpatialInformationScience

UniversityofTsukuba

Contents

Part I: Fundamentals of Remote Sensing

1. Remote Sensing Overview

1.1 Definition

1.2 Remote Sensing Concept

1.3 Components in Remote Sensing

1.4 Types of Remote Sensing

1.5 Multistage Remote Sensing Data Collection

1.6 Stereo Pair Remote Sensing Data Collection

1.7 Types and Uses of Satellites

2. Remote Sensing Data Acquisition

2.1 Electromagnetic Waves Used in Remote Sensing

2.2 Properties of Electromagnetic Waves

2.3 Spectral Reflectance and Earth Surface Interaction

2.4 Multi-spectral Remote Sensing Data (Image)

2.5 Spectral Properties and Principal Applications

2.6 Spectral Reflectance to DN (Digital Number)

2.7 Structure of Remote Sensing Data

2.8 Resolutions in Remote Sensing3

3. Remote Sensing Data Processing and Analysis

3.1 Remote Sensing Data Pre-processing

3.2 Visual Interpretation (Band combination)

3.3 Apply Algorithms

3.4 Multi-spectral Classification

3.5 Scene Selection Criteria for Multi-spectral Classification

Part II: Remote Sensing Data Applications in GIS

4. Remote Sensing Data Applications in GIS

4.1 Land Cover Classification form Landsat ETM+

4.2Land Cover Classification form ALOS

4.3 Urban Greenness

4.4 Monitoring of Deforestation Process

4.5 Surface Steepness Measurement from LIDAR

4.6 ViewshedAnalysis and Resort Site Selection

4.7 Watershed and Environmental Impact Assessment

Appendix A Remote Sensing Learning Resources

Appendix B Remote Sensing Data Resources

Appendix C Remote Sensing Software Resources4

Part I: Fundamentals of Remote Sensing

1.Remote Sensing Overview

1.1Definition

WhatisRemoteSensing?Ifyouarereadingthis

Thescienceofacquiringinformationaboutthe

Instrumentsmayusevisiblelight,infraredor

© 2008 Ko Ko Lwin

1.2 Remote Sensing and GIS Work Flow

Solar radiation from

Data transmission

Platform (satellite)

Ground receiving station &

DATA ACQUISITION

DATA PRE-PROCESSINGDATA PROCESSING & ANALYSIS

Atmospheric correction

Radiometric correction

Geometric correction

Algorithm development

Multi-spectral classification

Spatial data modeling along

Example in optical Remote Sensing

23© 2008 Ko Ko Lwin

1.3 Components in Remote Sensing

Platform

Sensors

Platform NameSensor Name

Landsat TMThematic Mapper

Landsat ETMEnhanced Thematic Mapper

ALOSPRISM (Passive: Optical sensor)

AVNIR-2 (Passive:Optical sensor)

PALSAR (Active: Microwave sensor)

One platform can carry more than

Platform

Sensor