Introduction to Remote Sensing

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Introduction to Remote Sensing

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Introduction

• Remote sensing is the acquisition of data, "remotely"

• Earth Observation Remote Sensing (EO/RS)

• For EO, "remotely" means using instruments (sensors) carried by platforms

• Usually we will think in terms of satellites, but this doesn't have to be the case – aircraft, helicopters, ...

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Definition

• Remote sensing is a technology for sampling electromagnetic radiation to acquire and

interpret non-immediate geospatial data of

earth object without being physical contact to that object.

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Why use satellite RS ?

• Source of spatial and temporal information

– land surface, oceans, atmosphere, ice

• monitor and develop understanding of environment

• information can be accurate, timely, consistent and large (spatial) scale

• some historical data (60s/70s+)

• move to quantitative applications

– data for climate (temperature, atmospheric gases, land surface, aerosols….)

• some 'commercial' applications

– Weather, agricultural monitoring, resource management

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Advantages of RS

• Provides regional view(Large scale monitoring)

• Provides repetitive looks at the same area(Temporal monitoring)

• Remote sensors see over a broad spectrum than human eye

• Provides georeferenced digital data

• Some remote sensors operate in all seasons, at night and in bad weather

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Remote Sensing Challenges

• Remote sensing has various issues

– Remote sensing can be expensive technology – It involves technically difficult processes

– NOT direct

• measure surrogate variables

• e.g. reflectance (%), brightness temperature (Wm^-2

 ^oK), backscatter (dB)

• RELATE to other, more direct properties.

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History of Remote Sensing

• Balloon photography (1858)

• Pigeon cameras (1903)

• Kite photography (1890)

• Aircraft (WWI and WWII)

• Space (1947)

Images: Jensen (2000)

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A Remote Sensing System Cycle

• Energy source

• platform

• sensor

• data recording / transmission

• ground receiving station

• data processing

• expert interpretation / data users

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Remote Sensing Sensors

• There are two basic types of sensors:

• Passive, and

• Active sensors.

• Passive sensors

–Record radiation reflected from the earth's surface.

–The source of this radiation must come from outside the sensor; in most cases, this is solar energy.

–Capture data during daylight hours.

–Examples: The Thematic Mapper (TM) sensor system on the Landsat satellite is a passive sensor.

• Active sensors

–Active sensors require the energy source to come from within the sensor.

–For example, a laser-beam remote sensing system is an active sensor that sends out a beam of light with a known wavelength and frequency. This beam of light hits the earth and is reflected back to the sensor, which records the time it took for the beam of light to return.

–Topographic LIDAR is an example.

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Remote Sensing & GIS Applications Directorate

Remote sensing cycle

• Remote Sensing Includes:

– A) The mission plan and choice of sensors

– B) The reception, recording, and processing of the signal data

– C) The analysis of the resultant data.

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Concept of RS

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Electromagnetic Radiation

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Electromagnetic Spectrum

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Signature Spectra

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Remote Sensing Platforms

• The vehicle or carrier for a remote sensor to collect and record energy reflected or emitted from a target or surface is called a platform.

• Ground Based Sensors: Ground vehicles and/or towers upto 50 m.

• Aerial Platforms: Airplanes, helicopters, high altitude aircrafts balloons, upto 50 Km

• Satellite Platforms: Rockets, Satellites, shuttles from about 100 to 36000 Km.

– Space Shuttle: 250 – 300 Km – Space Station: 300-400 Km

– Low Level Satellite: 700 – 1500 Km – High level satellites: About 36000 Km

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Remote Sensing: examples

•Platform depends on application

• What information do we want?

• How much detail?

• What type of detail?

upscale

http://www-imk.fzk.de:8080/imk2/mipas-b/mipas- b.htm

upscale upscale

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Fundamental term

• All remote sensing systems have four types of resolution:

– Spatial – Spectral – Temporal – Radiometric

Resolution

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Spatial Resolution

• The earth surface area covered by a pixel of an image is known as spatial resolution

• Large area covered by a pixel means low spatial resolution and vice versa

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High vs. Low?

Spatial Resolution

Source: Jensen (2000)

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Spectral Resolution

• Is the ability to resolve spectral features and bands into their separate components

• More number of bands in a specified bandwidth means higher spectral resolution and vice versa

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Spectral Resolution

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Temporal Resolution

• Frequency at which images are recorded/ captured in a specific place on the earth.

• The more frequently it is captured, the better or finer the temporal resolution is said to be

• For example, a sensor that captures an image of an agriculture land twice a day has better temporal resolution than a sensor that only captures that same image once a week.

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Temporal Resolution

Time

July 1 July 12 July 23 August 3

11 days

16 days

July 2 July 18 August 3

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Radiometric Resolution

• Sensitivity of the sensor to the magnitude of the received electromagnetic energy determines the radiometric resolution

• Finer the radiometric resolution of a sensor, if it is more sensitive in detecting small differences in reflected or emitted energy

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Radiometric Resolution

1023

6-bit range

0 63

8-bit range

0 255

0

10-bit range 2-bit range

0 4

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GOES (Geostationary Operational

Environmental Satellites) IR 4

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Landsat TM

(False Color Composite)

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MODIS (250 m)

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SPOT (2.5 m)

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QUICKBIRD (0.6 m)

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IKONOS (4 m Multispectral)

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Applications of Remote Sensing

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Applications of Remote Sensing

• Urbanization and Transportation

– Urban Planning – Change Detection

– Road Networks identification

• Agriculture

– Crop health analysis – Precision agriculture – Yield estimation

– Forest Application – Species detection

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Cont.d

• Natural Resource Management

– Habitat Analysis

– Environmental Assessment – Pest/disease outbreaks

– Impervious surfaces – Lake monitoring

– Hydrology

– Land-use Land-cover monitoring – Mineral Province

– Geomorphology – Geology

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• National Security

– Targeting

– Disaster mapping and monitoring – Damage assessment

– Weapon monitoring – Navigation

– Telecommunication planning – Coastal mapping

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Meteorological Application

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Medical Applications

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Exploration of Terrestrial Bodies

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Astronomy and Cosmology

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Types of Satellites

• Geostationary Satellites

– In high altitude orbits (~36000Km) – Orbital period of satellite matches

rotational speed of Earth

– Continuously observe same area on Earth

– Very High Temporal Resolution – Usually monitor meteorological

condition and sever storm development, including

hurricanes, tornadoes and floods

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Polar Orbiting Satellites

• In Low altitude Orbit(~700-800Km)

• Orbit around North and South Pole

• Earth rotates under satellite as it orbits, so each time satellite makes a pass over Earth It observes a new area

• Polar orbiting satellites same area on Earth once per day(or less)

• Low temporal resolution

• Global Coverage

• Used for variety of application including air quality, land cover, water quality and vegetation studies.

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Features of Remote Sensing Images

• Spatial resolution

– 10s cm - 100s km

– determined by altitude of satellite (across track), altitude and speed (along track), viewing angle

• Temporal Resolution

– minutes to days

– NOAA (AVHRR), 12 hrs, 1km (1978+) – MODIS Terra/Aqua, 1-2days, 250m++

– Landsat TM, 16 days, 30 m (1972+) – SPOT, 26(...) days, 10-20 m (1986+)

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Some Commonly Used Satellite Datasets

(For more detail visit: http://www.satimagingcorp.com)

• Satellite sensors (0.31-2m) resolution

– Geoeye(0.41m)

– Worldview 1,2,3 and 4(0.31-0.46m) – Quickbird(0.61m)

– IKONOS(0.82m) – SPOT(1.5 m)

Kutztown University,

Geoeye satellite, 2008 Palm Island, Dubai, Worldview 2

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• Satellite Sensors (15-30 m)

– Lansat 8 – Aster

Landsat 8 Image

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Structure of Vector Data Source

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Structure of Raster

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Data Models

• Geo-relational Data Model.

– Stores Geometries and Attributes in Split systems – Use Unique ID to link between Specific geometry

with specific attribute – Data Coverage Structure

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Data Coverage Structure

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Data Models

• Object Data Model

– Treats every geospatial element (data) as objects.

– Every new feature is represented by new object – Geo-Database

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Geo-Database

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Readings

Campbell, J. B. (1996) Introduction to Remote Sensing (2nd Ed), London:Taylor and Francis.

R. Harris, 1987. "Satellite Remote Sensing, An Introduction", Routledge & Kegan Paul.

Jensen, J. R. (2000) Remote Sensing of the Environment: An Earth Resource Perspective, 2000, Prentice Hall, New Jersey. (Excellent on RS but no image processing).

Jensen, J. R. (2005, 3rd ed.) Introductory Digital Image Processing, Prentice Hall, New Jersey. (Companion to above) BUT mostly available online at

http://www.cla.sc.edu/geog/rslab/751/index.html

Lillesand, T. M., Kiefer, R. W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing and Image Interpretation, John Wiley, New York.

Mather, P. M. (1999) Computer Processing of Remotely sensed Images‑ , 2nd Edition.

John Wiley and Sons, Chichester.

W.G. Rees, 1996. "Physical Principles of Remote Sensing", Cambridge Univ. Press