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Remote Sensing 101

(Is it magic?)

February 2, 2016

Larry Biehl

biehl@purdue.edu

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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?