GST 101 Introduction to Geospatial Technology � �Unit 8 - Introduction to Remote � Sensing and Imagery �Module 8.1 – Basic Remote Sensing � Concepts ����� �

Ann Johnson

Associate Director

ann@baremt.com

​

Based upon work supported by the National Science Foundation under Grants DUE 1304591, DUE 164409, DUE 1700496, DUE 1937177, Due 1938717 DUE 1937237, 2030206 and 2015927. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Topics Covered in Unit 8 Modules 8.1, 8.2 and 8.3

• Module 8.1
• What is Remote Sensing and Electromagnetic Spectrum
• Platforms: ground based, aerial (manned/unmanned), and satellites
• Electromagnetic Spectrum and sensor wavelength data format
• Passive and Active Remote Sensing
• Resolutions: Spatial, Spectral, Radiometric and Temporal
• Module 8.2
• Finding satellite data – sources of data
• Accessing Remote Sensing Data
• Module 8.3
• Pixels: Brightness and Digital Numbers
• Use of Signature graphs to identify objects
• Composite images
• Image classification

​

This is a very brief introduction to Remote Sensing. A full Remote Sensing course is recommended and available from GeoTech Center

Please Note – Remote Sensing Modules

• Remote Sensing technology and methods are changing rapidly
• The content for these modules is “up to date” as of early 2021
• Please use current Tutorials on sites providing data access
• Remote Sensing imagery data use requires understanding underlying concepts of how the data was collected and how different spectral values can be used
• This module will introduce these topics, but a full course is recommended
• Some imagery data will be (deprecated) removed or updated in mid-2021
• Links to these data will have to be updated after that date

​

Remote Sensing Imagery - A Source for More Information

​

In the past imagery was mainly used as a backdrop or basemap for GIS projects, but it provides much more information than just a picture – it can help identify what is observed in the image

​

​

​

​

​

In Unit 1, Model 1.1 a definition of remote sensing was provided from the USGS

​

​

​

​

Acquiring information about a natural feature or phenomenon, such as Earth’s surface, without being in contact with it.

ASTER Spectral Image of fire burn scar and smoke

Terra.nasa.gov

Sensors are used to detect and acquire information about features without being directly in contact with them

The human eye is a remote sensor

and the brain processes the

data and produces a

visualization!

• Wavelengths are measured from crest to crest
• Frequency is the number of wavelengths per unit of time
• As wavelength decrease, energy and frequency increases

This is a graphic of the of Electromagnetic Spectrum

​

The data our eyes use comes from a small portion of the electromagnetic spectrum

​

​

Other sensors can detect the electromagnetic radiation that our eyes cannot see which can be used for remote sensing analysis

​

​

​

​

Using the Sun’s Energy – the Electromagnetic Spectrum

Watch this Tour of the Electromagnetic Spectrum video From NASA https://science.nasa.gov/ems/01_intro

Source of Electromagnetic Energy

• All objects above absolute 0 Kelvin emit electromagnetic energy (radiation)

​

​

​

• The amount and type of the emitted energy depends on the temperature of the object and its physical and chemical properties
• Hotter objects emit more energy with shorter wavelengths
• The ability to use sensors to collect emitted energy from objects is the basis of the use of remote sensing to provide information about the Earth

​

Collecting Electromagnetic Radiation Data� Two Types of Remote Sensing Sensors

• Passive – Sun as the “energy source”
• Landsat
• MODIS
• Aster
• Active – Energy source is “provided” by the sensor
• LiDAR – Light Detection and Ranging using pulsed laser beam (of varying wavelengths) creating a point cloud
• SAR – Synthetic Aperture Radar – pulses of radio wavelengths

​

What about our eyes – Active or Passive?

Aerial Remote Sensing Imagery

P. Alejandro Díaz - March 30, 2002 - Catalina Island's Airport-in-the-Sky (KAVX)

USGS streamgage with rainbow in the background. (Credit: Robert Swanson)

https://landsat.gsfc.nasa.gov/sites/landsat/

• Platforms
• balloons, kites, manned aircraft or unmanned aircraft (UAV or UAS) and satellites
• Historically, photographic film was used
• Black and White, Panchromatic or color
• Platforms now carry digital sensing devices -sensors
• Many cities, counties, states and industries may contract to have imagery flown – one time or on a set schedule
• The spatial resolution can be very high (cm’s to m’s) to very low (km’s)

A

Top of Atmosphere

*Adapted from: http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9309

​

Reflected

Absorbed

Passive Remote Sensing Components*

A = energy source - Sun

B and C = targets – features on Earth

D = sensor(s) - on satellite platform (TOA)

E = collection – ground station or recorders

F = processing method

G = a distribution method (Internet)

B and C

D

E

F

G

EROS:

Earth Resources Observation and Science Center in Sioux Falls, SD

Proposed Timeline For Sensor Data to be Processed and Corrected to a Collection 2 level (surface reflectance/surface temperature) for Landsat 7, 8 and Future Landsat 9

For More Information see: htps://www.usgs.gov/media/images/landsat-collection-2-generation-timeline

Active Remote Sensing - Lidar� See NOAA Lidar Tutorial for more information: https://coast.noaa.gov/digitalcoast/training/intro-lidar.html

Collected by aerial (planes, drones) or land vehicles creating a point cloud of data

View This Video Explaining and Demonstrating Use of Lidar

• https://www.youtube.com/watch?v=chSywRqgIGY&list=PLzmugeDoplFM5pPI80wwi3qmtZH99Ism2&index=1&t=3s

Lidar and Archeology – Revealing the past by removing the forest and vegetation of the present

Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize, Chase, et al, http://archive.archaeology.org/1007/etc/caracol.html

​

Remote Sensing Imagery “Resolutions”

• Spectral – specific wavelength of spectrum collected by a sensor
• Spatial – size of area on the ground of one pixel and size of image footprint
• Temporal – how often data (imagery) is acquired for a location
• Radiometric – the sensitivity of sensor to collect very slight differences in emitted or reflected energy

​

• See an iGETT Remote Sensing YouTube video on Resolutions at

https://www.youtube.com/watch?v=AdXBewR-u2A

Spectral Resolution �Part of Electromagnetic Spectrum Energy Captured By One or More Sensors

• Ability of a sensor to capture specific band or wavelength intervals
• Number and width of bands on a platform such as the satellite platforms of Landsat, Terra, Aqua, and Sentinal-2
• Multispectral platforms generally collect wider and few bands (such as Landsat) in the visible and infrared spectrum

​

​

​

• Hyperspectral platforms generally collect 100s of narrower bands

​

​

Webinar by Austin Coates, Sales Engineer Manager for L3Harris Geospatial

Spectral Signature form USGS Spectral Library of Chamise (shrub)

Atmospheric Windows

• Atmospheric gases absorb electromagnetic energy in very specific regions of the spectrum, they influence where (in the spectrum) we can "look" for remote sensing purposes

​

• Those areas of the spectrum that are not severely influenced by atmospheric absorption are useful to remote sensors and are called atmospheric windows

Webinar by Austin Coates, Sales Engineer Manager for L3Harris Geospatial

�Atmospheric Windows � Note: for this graphic gray shading indicates “good” atmospheric windows.

Note where Landsat and Sentinel-2 sensors focus on collecting data

Spatial Resolution � Size of Pixel and Extent of Area of Footprint ��

• High spatial resolution:
• Meter to sub meter pixel size
• Small objects can be identified
• Small area for each image footprint

​

• Moderate spatial resolution
• Generally, 30 meter pixels (Landsat)
• Object identification generally greater than 30 meters
• Moderate area image footprint

​

• Low spatial resolution
• 1 KM or larger pixels (MODIS)
• Objects smaller than 1 KM not observable
• Very large footprint

Jensen, 2000

Spatial Resolution

The fineness of detail visible in an image

1 meter

10 meters

30 meters

Graphic:

John McCombs NOAA

Waquiot Bay, MA�NAIP Imagery – False Color

Spatial Resolution Increases the Amount of Information

8 Data Samples in 1x1 m pixels

32 Data Samples in 1/2x1/2 m pixels

Same spatial extent, but more data samples

​

Help identify unique object composition rather than homogenize mixed pixel compositions

​

Other techniques for determining the pixel composition will be discussed in Module 8.3

Temporal Resolution

• How often data is collected of the same location
• Only once
• Daily – or multiple times a day
• “Frequently” – every so many days
• Landsat missions
• Once every 16 days – but . . . . To be collected and archived:
• Must be clear (or have a percent cloud coverage)
• Must be “important” (U.S. and outside U.S.)
• With Landsat 7 and 8 the repeat time for the same location would be 8 days
• Sentinel-2
• With two satellites in polar orbits the repeat times are 5 days

Two Orbital Pathways For Satellites and Temporal Resolution

• Geostationary or geosynchronous equatorial orbit – Its circular orbital path keeps it over the same Earth location as the earth rotates on its axis
• General repeat 1-2 day
• Polar (near polar) or Sun-synchronous – it orbits from pole to pole (generally north to south on the sun facing orbit and from south to north on the side away from the sun)
• Landsat uses this orbit and collects data of the same location every 16 days

https://seos-project.eu/remotesensing/remotesensing-c02-ws01-t.html

���������For Example: Landsat 8 Polar Orbit and GOES Stationary Orbit

Graphic:USGS

Graphic:NASA

http://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=11481

Geostationary Operational Environmental Satellite (GOES) – Weather Service Satellite at 3600 km above equator

One day repeat

So many satellites! Resources:

• Satellite Viewer

http://science.nasa.gov/iSat/?group=visual&satellite=14484

• EarthNow! Landsat Image Viewer
• Real time view as data is collected showing current path of satellite
• http://earthnow.usgs.gov/earthnow_app.html?sessionId=fdbe7bc05944802eda2c68d1e603ed8462919

​

Radiometric Resolution – Sensor Sensitivity

• Radiometric Resolution is the range of values of energy differences that it can detect and is specified by its “bit depth” – its sensitivity
• Sensors with low radiometric resolution detect only large differences in energy
• Landsat 7 pixels can have 256 possible values (2⁸) and is 8-bit
• Sensors with high radiometric resolution can detect smaller differences in energy
• Landsat 8 pixels can have 65536 possible values and is 16-bit or (2¹⁶)
• Sentinel-2 pixels can have 4096 possible values and is 12-bit or (2¹²)
• Pixel values (Digital Numbers) are used by software to display imagery layer brightness derived from its energy level using shades of black to white

​

​

​

Image for one band using Digital Numbers to scale brightness from black to white

�����Higher Radiometric Resolution�

Greater range of values (16 bit versus 8 bit) of radiometric resolution provides even better observable details without higher spatial resolution

Graphic: Canadian Center for Remote Sensing Tutorial Figure 1

​

16-bit or (2¹⁶) with a possible range of values of 0 to 65536

8-bit (2⁸) with a range of values from 0 to 255

Pan Sharpening

• Combining higher spatial resolution data with higher spectral resolution data is called pansharpening

Quickbird data (50 cm panchromatic, 2 m multispectral)

See GeoTech Center website (https://geotechcenter.org) �for additional Model Courses and other curriculum resources. ������Note: some content is a derivative of other authors���

Ann Johnson

Associate Director

ann@baremt.com

​

3-17-2021 V8