Nasa.gov. (i.e. multispectral scanners)

Introduction to Remote Sensing

INT 527 Lecture 11 19 October 2015nasa.gov

What is “Remote Sensing”

• “The measurement or acquisition of information of some property or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study.”• American Society of

Photogrammetry, Manual of Remote Sensing

• “The detection, recognition, or evaluation of reflected or emitted electromagnetic energy.”• Paine, An Introduction to Aerial

Photography for Natural Resource Management

Interactions with the Atmosphere• Solar radiation is either absorbed or reflected (scattered) by atmospheric particles, or transmitted through the atmosphere.

• Scattering occurs based on the size of atmospheric particles relative to the incoming solar radiation.

• Absorption and scattering reduce the amount of energy incident on the target, and thus recorded by the sensor.

Interactions with the Atmosphere

Transmission through the atmosphere

• Atmospheric particles and gases absorb EMR at specific wavelengths called ’absorption bands’• Remote sensing systems must operate in ‘atmospheric windows’ where transmission is high.

Transmission through the atmosphere

Transmission through the atmosphere

Transmission through the atmosphere

(i.e. multispectral scanners)

Transmission through the atmosphere

Interactions with surface features

• I : Incident radiation• T : Transmittance• A : Absorbance • R : Reflectance

• So what is not absorbed by the object or transmitted through the object is reflected by the object

• Reflected energy is what most remote sensor records (some also have detectors that record emitted thermal energy)

Sensors: Two Primary Modes

Passive: Instrument records solar energy reflected from surface features

Active: Instrument emits energy and records the reflection of that energy from surface features

optical sensors

(multispectral scanners)Radar, Lidar

Multi-spectral scanners

Synthetic Aperture Radar

• SAR transmits pulses of microwaves and measures the strength and the time delay of the energy that is scattered back to the antenna.

Sensors: Two Primary Modes• Pros / cons between Passive Optical vs. Active Radar?

•Different applications?

So much data!

• So many acronyms…• TLAs• FLAs

•Overwhelmed by it all?

• Let’s organize things by some basics:• Sensor characteristics• Science & Applications

•Use multispectral sensors (particularly from the Landsat program) as demonstrative and hopefully useful examples

Sensor Characteristics

•Spatial resolution (pixel size) and spatial extent (swath width)

• Temporal resolution (revisit period) and extent (duration of operational image acquisition)

•Spectral resolution (band position/width) and extent (portion of EM spectrum sampled)

•Radiometric resolution (sensitivity to different levels of reflected energy)

Spatial resolution

The spatial resolution (pixel size) of the image is a product of the system’s instantaneous field of view (IFOV) and orbital altitude

• The sensor’s optical system determines the field of view• The platform’s orbital altitude

and IFOV determine how much ground area the sensor ‘sees’• All energy propagating

toward the instrument within the IFOV contributes to detector response at any given instant

Spatial Resolution

Earth resources satellites categorized according to their spatial resolution (pixel size)

• Moderate resolution sensors• Landsat Thematic Mapper (30 m)• Landsat Multispectral Scanner (80 m)• SPOT High Resolution Visible / Infrared (20m, 10 m)

• Coarse resolution sensors• Advance Very High Resolution Radiometer (1.1 km)• Moderate Resolution Imaging Spectrometer (250 m, 500 m, 1

km)• SPOT Vegetation (1 km)

• High resolution sensors• Ikonos (4 m)• Quickbird (2.5m)

Spatial Resolution

Spatial Resolution

• Resolution is application dependent – spatial resolution may be coarse or fine depending on the size of the objects to be resolved

• To resolve individual objects, generally need several pixels to fall completely within the object’s boundaries• A pixel that falls on a boundary between two types of features records a mixture of the reflectance signatures of the two types – this is a mixed pixel

Spatial Resolution

• Resolution is application dependent – spatial resolution may be coarse or fine depending on the size of the objects to be resolved

• Trade-offs of fine spatial resolution data• Smaller area coverage (spatial extent)• Low temporal resolution / irregular acquisitions and

shorter time-series records• Lower spectral resolution & fewer bands• Lower radiometric resolution & signal-to-noise• Data volume and processing• High co$t

Spatial Extent (swath width)

Remote Sensing Data

Temporal characteristics

Platform / Sensor(s)

Resolution (revisit time)

Length of record

AVHRR (1km) daily 35 years (1979-)

MODIS (250-500-1000m) daily 15 years (1999-)

Landsat MSS (80m) 16 days

41 years (1972-2013)

Landsat TM/ETM+/OLI (30m)

8-16 days 33 years (1982-)

SPOT HRV (20m) 26 days 29 years (1986-)

Ikonos (4m) Variable (tasking) 15 years (1999-)

Quickbird (2.5m) Variable (tasking) 13 years (2001-)

Temporal resolution

Importance of temporal resolution:• Identification / mapping of land use or land cover, where the use of multiple images may improve results (e.g. tree species identification, crop classification)•Detection / mapping of transient land cover conditions, where the proper timing of image acquisition will improve results (e.g. disturbance, crop condition)•Monitoring land use / cover change, where an image time series is used to document or infer a process of interest (e.g. urban expansion, vegetation regrowth, vegetation stress or chronic disturbance)

Temporal resolution

Improved temporal resolution (daily or weekly coverage) allows you to produce clear-sky composite imagery and do time-series trend analysis

Data availability, acquisition•Data source• USGS: aerial imagery, satellite remote sensing data• Digital Globe: high-resolution commercial imagery

• Subject area• NASA Distributed Active Archive Centers (DAAC): Land

Processes (EROS), Biogeochemical Dynamics (ORNL)• National Snow and Ice Data Center (NSIDC)

• Region of interest• Maine Office of GIS

• Project-specific• NASA Campaigns (e.g. LBA, CARVE, ABoVE Science

Cloud)

Landsat data

•Worldwide Reference System (WRS) indexed by Path (column) / Row • Scene size is ~185 x 185 km• 8 scenes cover Maine

• Pixel size is 30 x 30 m (~1/4 acre)• 16-day revisit time• Landsats 5&7 and 7&8 off-set

orbits mean nominal 8-day repeat coverage

• Imagery archived and catalogued by USGS EROS Data Center (Sioux Falls)