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Outline
– Remote Sensing: Evolution & Applications• Geospatial Technologies
• Brief History of Remote Sensing
• Remote Sensing measures variation– Spatial
– Spectral
– Temporal
– Radiometric (will not cover this)
• IndianaView
• GABBs
• MultiSpec
• Issues to Monitor
• Future
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Geospatial Technologies(my view)
• GPS (global positioning system)– Car GPS systems, yield monitors, smart phones
• RS (remote sensing)– Satellite or Aircraft imagery
– Your camera
• GIS (geographic information system)– Combines layers of information
– Can include GPS and RS information
– Provides analytic tools
– ArcGIS, ArcGIS Online, QGIS, GoogleEarth?
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Unconventional Definitions of
Remote Sensing
Remote Sensing is the most expensive way to make a picture.
- Andrew Bashfield, Intergraph Corporation
Source: Canada Centre for Remote Sensing www.ccrs.nrcan.gc.ca/ccrs/eduref/misc/rs_defne.html
The art of dividing up the world into little multi-coloured
squares and then playing computer games with them to
release unbelievable potential that's always just out of
reach.
- Jon Huntington, CSIRO Exploration, Geoscience,
Australia
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What Is Remote Sensing
Remote sensing may be broadly defined as the
collection of information about an object without being in
physical contact with the object. Aircraft and satellites
are the common platforms from which remote sensing
observations are made. The term remote sensing is
restricted to methods that employ electromagnetic
energy as the means of detecting and measuring target
characteristics.
- Sabins, Floyd F. Jr.
"Remote Sensing Principles and Interpretation",
W.H. Freeman and Company, San Francisco. 1978, p1
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Aerial Reconnaissance
– Hot Air Balloons:1840’s
– Avian Remote Sensing
– Aircraft
• U2 Plane
• NASA Aircraft
• Private Sector Aircraft
European Pigeon Fleet: Late
19th Century
Bavarian Castle Photo by Pigeon
Source: NASA Goddard RS Tutorial
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Airborne Remote Sensing
NASA Wallops Island
Hot Air Balloon
Airborne remote sensing has
been the method of choice for
high resolution over flights,
utilizing experimental sensors &
pre-launch testing of precursor
instrumentation. A UAS System at Research FarmsFrom: www.youtube.com/watch?v=Dm9-QfIBR4M
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Satellite Remote Sensing
• Classified satellites (now declassified)– Corona, Lanyard, Argon (1960-1972): 6-8 ft.
• US Civilian satellites/sensors– TIROS Program (1958 - 1967)
– Landsat Series (1972 – present)
– AVHRR (1978 – present)
– NASA’s EOS ASTER, MODIS (1999 – present)
• Other nation’s satellites– Brazil, Canada, France, India, Japan, Russia, S. Korea, …
• Private Sector– Ikonos, Quickbird, GeoEye, WorldView, …
Illustration of Landsat 8 satellite orbit:www.youtube.com/watch?v=ONaJSkCpDhg
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How Remote Sensing Works
Source: Copyright © 1998 USC Remote Sensing Lab and the Board of Trustees of the University of South Carolina
www.cla.sc.edu/geog/rslab/
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Reflectance from the Earth’s Surface
Smooth Surface Rough Surface
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RS Measures Spatial Variations
30 meter or 100 foot pixels 0.3 meter or 1 foot pixels
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Spatial Resolution
A graphic representation showing the differences in
spatial resolution among some well known sensors.
Source: Copyright © 1998 USC Remote Sensing Lab and the Board of Trustees of the University of South Carolina
www.cla.sc.edu/geog/rslab/
DigitalGlobe
QuickBird 0.6 m
Space Imaging IKONOS - 1m
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RS Measures Spectral Variations
Color Color Infrared Red
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RS Measures Spectral Variations
False Color (7,5,4) Thermal
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RS Measures Spectral Variations
Color L8 channel 6 water band
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June 11, 2008 Flood: Knox & Daviess Counties;
Landsat 5 false color (5, 4, 3)
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The Electromagnetic Spectrum
Infrared does not always mean thermal or heat!!!
Landsat 8 spectral range
18Source: en.wikipedia.org/wiki/Solar_irradiance
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Landsat 8 Spectral Bands
(OLI & TIRS)• Bands 1, 2, 3 & 4 in wavelength range our eyes are sensitive to
• Band 5 in near infrared
• Band 6 transition region, water vapor band
• Bands 7 & 8 in middle infrared
• Bands 9 & 10 thermal infrared
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7 8 9 10
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Reflectance Curves for Some Features
Landsat 812 3 4 5
67 8
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5
2
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Plant Canopy Reflectance
visible
near
infrared
middle infrared
Chlorophyll
(N)
Leaf &
Canopy
Structure
Water
Content
Bars represent Landsat TM bands
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Interaction between Plant &
Electromagnetic Radiation (Sun)
Transmitted
Reflected
Absorbed
From: Remote Sensing in Precision Agriculture: An Educational Primer (www.amesremote.com/contents.htm)
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Three
Generations
of Sensors
6-bit data
MSS1968
8-bit data
TM
1975
10-bit data
Hyperspectral
1986
Spectral Resolution‘Sampling Interval’
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Example of Side by Side Multispectral Image
Channel number and/or description
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RS Measures Temporal Variations
Landsat 5 – May 4, 1985 Landsat 8 – September 25, 2014
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MODIS NDVI Composites
Combination of
Terra and Aqua
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Temporal Changes (weekly)
11 May 8 July 14 July4 June 15 Sept3 Aug
The Crop Calendar
A
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Sun Angle/Row Direction Affects
12:25 PM10:50 AM
Soybeans - 91 cm rows
Temporal Changes (hourly)
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Primary Purpose of Remote
Sensing Analysis
Convert multispectral image
with thousands or more
different measurements
Management decisions are then made based on the information categories,
usually along with other “layers” of information … such as in a GIS
To image with a dozen or
fewer different information
categories
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Remote Sensing Software
• ERDAS Imagine
• Exelis ENVI
• Trimble eCognition
• Esri ArcGIS
• Quantum GIS (QGIS)
• IDRISI
• Drone-based packages (DroneDeploy)
• …
• Freeware/Open Source – MultiSpec, GRASS, … 30
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IndianaView Initiatives
Provide access to geospatial data (Landsat & ortho-data)
Fund remote sensing / geospatial mini-grants in Indiana
Results high lighted in fact sheets (www.indianaview.org/fact_sheets.html)
Partnership with GENI (Geographers’ Educators Network of Indiana)
Created Geospatial Interactives for High School and Middle School
www.iupui.edu/~geni/
2014-2016: funded student scholarships
7 scholarships funded; 19 applications submitted.
6 scholarships will be funded in 2016.
Participate in AmericaView with 40 other state-views
Participate in AmericaView’s Earth Observation Day (October)
AmericaView has Education Resources Sharing Portal:
www.americaview.org/resources
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www.indianaview.org/glovis/IN_County_Landsat_Data.html
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IndianaView-GloVis
Graphical Interface for
viewing & downloading
remote sensing image
data
More than 300 Landsat
TM scenes of Indiana are
available
Link to high resolution
aerial images of Indiana
stored on IU Spatial Data
Some products like
NASS Crop Data Layers
& MODIS LAI for Indiana
IndianaView GloVis link: www.indianaview.org/glovis/index.html
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Indiana Spatial Data Portal
gis.iu.edu/
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Project Goals
• Enabling geospatial modeling and analysis online
• Anyone can create an online app and share
• Anyone can share geospatial data
• Building blocks can be used by other projects
Building for self service (DIY) – Leverage successful
software – Develop building blocks
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A GABBs Driving Examples
• Multi-scale and multi-disciplinary data and modeling for addressing hydrologic and ag economic issuesMultiSpec Online
mygeohub.org/tools/multispec/
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Gray-scale Image
Divide one band by another
NDVI (normalized difference vegetation index):
Image Enhancement
Spectral Ratioing
NIR - Red
NIR + Red
Color IR Image
NDVI
Pseudo-color Image
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Many types of classifiers to group pixels into categories
Pixel Classifier ECHO
Spectral-Spatial Classifier
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Some will label field as corn or soybeans. Others as corn or soybeans with grass waterways.
Ground “Truth” or Ground “Reference”
Depends very much on one’s perspective
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Some Issues about Remote
Sensing Data to Monitor
- Are data values saturated?
- Are data registered well between channels?
- Are data ‘blurred’ because of resampling algorithm?
- Have data been compressed to point that artifacts exist?
- Is the ‘stitching’ of several images very noticeable?
- Are image over-scanned or under-scanned or just right?
These issues deal with tradeoffs.
- Resampling algorithm used may make overall image
look better, but small detail may be blurred
- Compressed data is easier to transfer but quality
may be affected.
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Examples of Issues
Data not saturated Data is saturated
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Examples (continued)
No Compression SID 100SID 20
Original Data Resampled (smoothed)
Key:
Work with
vendor for
best tradeoffs
for user.
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Examples (continued)
Key: Work with vendor for best tradeoffs for user.
Notice “stitching problem” Very much improved.
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Over-Scan/Under-Scan- Ideally, the scanner (aerial or satellite) moves the exact
width of the image scan line during the time of a single
scan.
IdealWidth of scan lines
Distance scanner
moves in one scan.
Under-scan
- If the scanner moves more than than the width of the image
scan line, then the scene is “Under-Scanned”. Part of the scene
is not included in any of the scan lines.
Over-scan
- If the scanner moves less than the width of the image scan
line, then the scene is “Over-Scanned”. Part of the scene is
included in more than one scan line.
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Crop Residue influences on Soil Patterns
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Soil Patterns as influenced by
Timber and Grassland Vegetation
From IKONOS Satellite
Space Imaging, Inc.
May 24, 2000
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Example of weather ‘remote sensing’
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- Infrared image of indoor marijuana growing facility. The red shades of the image indicate hotspots emitted by the high energy sodium lights
- Case went to court using the thermal radiation picture. Charge of invasion of privacy was ruled out because image only measures energy emitted out from house
- Privacy will continue to be an issue
Source: www.x20.org/library/thermal/IR_in_the_courts.htm
Thermal Radiation
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Contrast between Daytime and
Nighttime Thermal Images
Nighttime
• Water appears cooler (darker) than its surroundings during the day and
warmer (lighter) than the surroundings at night.
• Kinetic water temperature has changed little between day and night but
the land areas have cooled considerably.
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Future of Remote Sensing(just a few of many)
• UAS (Unmanned Aerial Systems)
• Agriculture (Site Specific Farming)
• Image sensors in personal devices
• Sensors on Vehicles
• Autonomous sensors (“throw” in forest, ocean)
• Web enabled tools (examples)
– GABBs (mygeohub.org/groups/gabbs)• MultiSpec Online (mygeohub.org/tools/multispec)
– U2U project (agclimate4u.org)
• …
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Geospatial Technology Careers
• GIS Specialists
• Programmers
• Web developers
• Engineers/technicians
– UAS’s,
– vehicles image sensors
– Sensors in general
– Fire monitoring
– Change detection
– Image cameras as part of manufacturing
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Useful Web Site Links
• Freeware Application to view remote Sensing Data
• MultiSpec: Available for Macintosh & Windows platforms
• engineering.purdue.edu/~biehl/MultiSpec/
• Source for Image Data
• IndianaView & AmericaView:
• www.indianaview.org/ &
• www.americaview.org/k-12-earth-observation-day
• County Landsat Images:
• www.indianaview.org/glovis/IN_County_Landsat_Data.html
• Aerial high spatial resolution images:
• gis.iu.edu/downloadData/index.php
• Geography Educators’ Network of Indiana (GENI):
• www.iupui.edu/~geni/
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So is remote sensing magic?