Rs lect 1day_1

Dr.-Ing. Görres Grenzdörffer Remote Sensing

Remote Sensing Basics 1

Universität Rostock, Professur für Geodäsie und Geoinformatik X 2006

Dr.-Ing. Görres Grenzdörffer

Basics and Applications ofRemote SensingBasics and Applications ofRemote Sensing

Universität Rostock, Professur für Geodäsie und Geoinformatik

Schedule Lectures

• Remote Sensing Basics (1st Day)• Introduction and history• The EM-spectrum• Reflectance properties of different objects• Spaceborne sensors

• Satellite Remote Sensing (1st Day)• Resolution• Examples• SRTM• Change Detection

• Airborne Remote Sensing (2nd Day)• (digital) photography• Lidar• Examples

• Digital image Processing (2nd Day)• Pixels and mixels• Spectral bands / low level image operations• MS-classification• Object oriented classification




Schedule Exercises

• How to obtain satellite data via internet ? (1st Day)• Google Earth / Virtual Earth• Earthview• GLCF• Digital Globe / Space Imaging

• Change detection Satellite Remote Sensing data (1st Day)• Histogram manipulations and visual image analysis

• Exercise with ArcView 3.x (2nd Day)• Unsupervised and supervised classification of a satellite scene


Content – Remote Sensing Basics

• Introduction, Definitions, History• What is Remote Sensing?, history

• Physical basics• Light, atmosphere and reflection properties of the earth surface

• Sensor systems• Earth observation satellite systems, availability, prices

• Remote sensing applications• Examples

• Basics of digital image processing / image analysis• filtering, radiometric and geometric image enhancements,

multispectral classification




Literature – selected textbooks

• Lillesand, T. M., Kiefer, R. W., Chipman, J. (2005): Remote Sensing and Image Interpretation.-5. ed.: 750 p.; John Wiley & Sons, NewYork

• Campell, J.B. (2007): Introduction to Remote Sensing.- 4Rev Ed., 546 p.; New York, London

• Jensen, J.R. (2004) Introductory Digital Image Processing, 3rd. Ed., 533 p. (Prentice Hall Series in Geographic Information Science) (Hardcover)


Remote Sensing tutorials in the internet

• NASA - The remote Sensing tutorial by Dr. N.M. Short• http://rst.gsfc.nasa.gov/

• The Journalists' Guide to Remote Sensing Resources on the Internet Version 2.2• http://www.american.edu/radiowave/

• The European Earth Observation Web Site for Secondary Schools • http://www.eduspace.eurisy.org/

• Radar Remote Sensing (Epsilon nought)• http://www.epsilon.nought.de/




Journals

PFG - Deutsche Gesellschaft für Geoinformation, Photogrammetrie und Fernerkundunghttp://www.dgpf.de

(PE&RS) PhotogrammetricEngineering & Remote Sensinghttp://www.asprs.org/publications/pers/

Photogrammetric Recordhttp://www.rspsoc.org/publications/the-photogrammetric-record/

Remote Sensing of Environmenthttp://www.rspsoc.org/publications/remote-sensing-of-environment/

ISPRS Journalhttp://www.itc.nl/isprsjournal/

Int. Journal of Remote Sensinghttp://www.tandf.co.uk/journals/tf/01431161.html


How can satellite images help you ?

• Observe: land cover, boundaries, threats, damage, topography…

• Measure: areas, distances, height/elevation…

• Detect: fires, resource use violations…

• Monitor: change in forest cover, crop and range condition, land

use…

• Classify: into vegetation and land use categories, habitats…




Why is Remote Sensing (RS) so attractive to developing countries ?

• Current Situation: Local and regional authorities commonly have no accurate and up to data spatial data (maps, cadastre etc.), which is also often controlled by the military

• Advances of RS• RS allows for cost efficient unbiased planning information on a

national / regional and local level with current data• RS is efficient in low density populated areas• A cadastre based on RS is much cheaper than terrestrial surveys• With modern data transfer technologies (DSL etc.) the data and

results may transferred faster than via common transportation routes• Problems of RS

• Data availability, skilled personnel, administrative structures …


What is Remote Sensing about ?

Data Capture Data Analysis

Sensorsystems

Data products Interpretation andAnalysis

Informationproducts

User

Referenceinformation

visual

digital

Images

CD, DVD,Tapes

Earth surface

Source of energy

Atmosphere

Physical Basics

14

3

12




Remote Sensing Domains

• Monitoring:• vegetation health, biomass, crop maturation, sediment load,

proportion of bare/impervious ground in urban areas, urban sprawl, land use changes through time, etc…..

• Mapping:• updating existing maps, land cover, some land uses

Satellite data image processing scene understanding


Applications of Remote Sensing

Hydrology

Meteorology

Climatology

Astronomy

Architecture

SurveyingCartography

Geography

Geology

Geomorphology

Soil Science

ForestryAgriculture

Animal Science

Planning

Archaeology




Spatial and temporal resolution of selected applications

There are spatial and temporal resolution considerations that must be made for certain remote sensing applications.


Highlights of Satellite Earth Observation Remote Sensing

• Before 1972 - primarily aerial and satellite photographs

• 1972 - First Landsat satellite launched• 1986 - SPOT satellite launched• 1988 - Indian Remote Sensing Satellite

launched• 1995 - Radarsat launched• 1999 - IKONOS satellite launched and NASA

launched Terra satellite• 2006 – TerraSAR-X and Radarsat 2

launched• Today many new satellite instruments are

being developed and launched




Analogue satellite images during the Cold War – Corona Satellites KH 1 – KH 4

1975/12/23 1967/06/11 GSD: 0.6 – 1.2 m GSD: 6 – 10 m

• 880,000 photographic images between 1959 and 1972

• Declassified imagessince 1996

• Not georeferenced• Often with clouds• Complex geometry• Price 30 USD /

scanned Photo

http://edc.usgs.gov/products/satellite/declass1.html


Electro magnetic spectrum

Photographic Images

Multi spectral scanner

Thermal scanner

X-RaysUltra-violett

NearInfrared

middle and far Infrared

Micro-waves

Radio-waves

0.01µm 0.1µm 1µm 10µm 100µm 1 mm 10mm 100mm 1 m

0.5µm 0.6µm 0.7µmVisible light

0.4µmUV IR

Human Eye

blau grün gelb rot

RADAR

1.0µm




Active and passive sensor systems

E E S/E

ActiveSystems

Passive Systems

Reflectedsolar radiation

Thermal –radiation

Reflectedartificial radiation

Photography,Multi spectralscanner

Thermal scanner RADAR, LIDAR


Reflection-, scattering and absorption processes in the atmosphere and at the earth surface

Solar radiation

Scattering in theAtmosphereby aerosols

Absorption byH20, C02 and O 3

Diffuse sky radiation

Satellite sensor

Airborne-sensor

Emission from clouds, H20, C02, trace gases

Scattering in theAtmosphereby aerosols

Absorption and Emission (Heat)

diffusereflection

mirrorreflection Emission

Reflection at clouds

Heat

Atmosphere

Earth surface

Outer space




Solar energy and absorption bands of the earth atmosphere

Atmospheric windows are very important,because they define the available spectraldomains of passive optical remote sensingsensors

Solar radiation outside the atmosphere

Radiation of a black body at 5900° K

Solar radiation at sea level

Kronberg, 1984


Haze Removal

• Software• ATCOR - Erdas Imagine

• AdvanDayss• Automated process• Better SNR and higher

image quality• Better visual interpretation

• Limitations and Problems• Not based on a physical

model, no radiance units• Results depend upon image

statistics

Landsat ETM, 11.04.2000, Vietnam




Original RGB-image

Atmospheric correction - a prerequisite for time series analysis

• Software• Atcor 2 (based on modtran2)

• Advances• haze removal and better SNR• compatible reflectance values to

field measurements• Multitemporal images may be

compared and analyzed correctly

• Limitations and Problems• thresholds in cloudy scenes

(>50 %)• introduction of artifacts in hazy

images• Complex and time consuming

procedure

29.5.2001

Atmospherically corrected image


Atmospheric correction with ATCOR

Reference spectra and measured spectra

Spectral target definition

Definition of atmospheric conditionsand band wise calibration parameters




Radiometric Correction for Terrain

© CNES 2000

Necessary - but complicated - for MS-classification of mountainous areas


0,4 0,5 0,6 0,7 0,8 0,9

Wavelength λ [µm]

Spe

ctra

l ref

lect

ance

[%]

20

40

60

80Lime stone

Fresh snowForestVital grassland

Desert sandDry grassland Road pavement

ter

Reflection curve = spectralReflection ρ in relation to thewavelengthCharacteristic signatures fordifferent surfaces

Spectral signatures of different surfaces




Spectral signature of a green leaf

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.60

10

20

30

40

50

60

Wavelength [µm]

Spectral

Healthy leaf

Unhealthy leaf

ChlorophyllAbsorption Water absorption

Cell structure/Leaf pigments

Reflection [%]

Water contentBoundary reflection

Reflection properties of the leaf • Leaf organs partially transparent• in the visible light chlorophyll absorption (blue, red)• in the infra red spectrum boundary reflection (cell boundaries, hollows)


Wait! – It’s not that easy!

While the general patterns may or may not remain the same, the spectra of features change both over time and over distance

However, we can recognize spectral patterns for vegetated vs. non-vegetated areas, for certain classes of vegetation, and geologic and mineral properties

- depending on the sensor type!




Spectral Bands

The satellite sensor acquires the reflection spectra with several images (bands) at once, each recording a specific part of the EM-spectrum. When viewed, each individual band looks like a black and white photograph


Basic characteristics of satellites operating in the optical spectrum

• Satellite orbit characteristics

• Spatial resolution

• Coverage area / swath width

• Spectral resolution

• Radiometric properties

• Pointing

• Revisit period

• Data availability




Satellite Orbit

• “Satellites move in an elliptical path with the planet at one of the foci of the ellipse”

• Most earth observation satellites fly in circular orbits • Characteristics

• Altitude• Period• Inclination• Equatorial crossing time

• Most important types• Polar (or near-polar) orbit - Sun-synchronous?• Geostationary orbit


Sun synchronous and geostationary satellites

Polar Orbit:

• Earth observation remote sensing• 500-1.000 km altitude• Sun synchronous• Orbit period 95-105 min.• High ground resolution• Low revisit rate

Geostationary Orbit:

• Weather-, Telecommunication• 35.790 km above the earth• Orbit period 23 h 56 min. 4 sec• Low ground resolution• High revisit rate




Sun synchronous satellite orbit of Landsat 7

Sun-synchronous orbit:Satellite always crossed theequator at precisely the samelocal time.


Swath Width and Field of View

185 km

Field of View

175kmscene

LandsatLandsat

Satellite ground tra

ck705km

Spatial Resolution

Pixel size=(30x30m)




Definition of highest – high – medium resolution EO satellites

Highest resolution

High resolution Medium resolution

Ground res. < 5 m 5 – 100 m > 100 m – 1.000 m Swath width < 20 km 20 – 200 km 200 – 2000 km Repetition rate* < 100 days 5 – 25 days 1 – 5 days Spectral bands 4 4 – 100 4 – 100 Scanner Pushbroom Pushbroom,

Whiskbroom Whiskbroom

Examples IKONOS, Quickbird, Orbview

3, EROS …

ASTER, SPOT, LAndsat, IRS

MODIS, AVHRR

* without pointing or oblique viewing


Interdependence between spatial and temporal resolution of space imaging

10 m1 m 100 m 1000 m

1 M

o.15

min

1 d

2 W

o.

5000 m

Meteosat

NOAA-AVHRR

Landsat-TM

SPOTIRS-1C

IKONOS

IKONOSIRS - WiFSSPOT

Spatial resolution

Tem

pora

l res

olut

ion

10 m1 m 100 m 1000 m

1 M

o.15

min

1 d

2 W

e.

5000 m

Meteosat

NOAA-AVHRR

Landsat-TM

SPOTIRS-1C

IKONOS

IKONOS(oblique) IRS - WiFSSPOT

(oblique)




Spatial coverage of selected satellites – Example North Vietnam

3.000 km

NOAA 17, AVHRR

Landsat 7170 km

Quickbird (16 km)

Hanoi


Ground resolution

Quickbird MS (2.4 m) Landsat ETM (30 m)




Continuous global capture of terrestrial environmental parameters by remote sensing

• Conditions• varying properties of

reflection of earth surface

• Clouds• Vegetation parameters, e.g.

• Vegetation and Biomass development

• Absorbed fraction of photosyntheticalactive radiation (FAPAR)

Vegetation indexEurope Juli 2001

ISIS/DLRNOAA-AVHRR9.5.2002 - 12:57 UTC ISIS/DLR


MODIS Vegetation Continuous Fields – Percent tree cover

Description• Proportional estimates of cover are

developed from global training data derived using high-resolution imagery and regression analysis

• life form (proportion of woody vegetation, herbaceous vegetation, or bare ground)

• Leaf type (proportion of woody vegetation that is needleleaf or broadleaf)

• leaf longevity (proportion of woody vegetation that is evergreen or deciduous)

• Percent tree cover, generated from monthly composites of 500 meter resolution surface reflectance 8 day composites MODIS data. Clouds and cloud shadows are removed.

http://glcf.umiacs.umd.edu/data/modis/vcf/description.shtm

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