Satellite images for assessing environmental problems in aquatic ecosystems Eduardo Eiji Maeda
Satellite images for assessing environmental problems in aquatic
ecosystems Eduardo Eiji Maeda
• What is remote sensing• Remote sensing platforms• Physical principles of remote sensing• Basic concepts in remote sensing
Part 1• Mapping Global Water Surface• Monitoring water quality• Oil spill response
Part 2
What is remote sensing
“The acquisition of information about an object or phenomenon without making physical contact with the object” –Wikipedia
“Remote sensors collect data by detecting the energy that is reflected from the object” - NOAA
In remote sensing, there are 3 essential elements:
1 - a platform to hold the instrument2 - a sensor to observe the target 3 - a target object to be observed
Remote sensing platforms and acquisition levels
1880, Arthur Batut, France
Bavarian Pigeon Corps, 1903
Remote sensing platforms and acquisition levels
Sensors
Passive sensors• use external energy sources(for example: the sun)
Active sensors• relies on its own sources of radiation
AdvantagesSimpler to analyzeLower energy requirement
Advantages Can penetrate clouds, light rain and snowCan be operated during day and night
Objects
Remote sensing techniques are implemented in function of what needs to be observed.
RS of vegetation RS of water
RS of the atmosphere
Physical principles-electromagnetic radiation
Jožef Stefan (1835–1893)
Max Planck (1858 - 1947)
Ludwig Boltzmann (1844-1906)
Wilhelm Wien (1864 -1928)
Physical principles-electromagnetic radiation
Stefan–Boltzmann law
The energy radiated by a black body across all wavelengths per unit time, is directly proportional to the fourth power of the black body's thermodynamic temperature T:
E = σT4
All objects with temperatures greater than absolute
zero emit radiation
Where:
k is the Boltzmann constant
h is Planck's constant
c is the speed of light
Physical principles-electromagnetic radiation
https://www.youtube.com/watch?v=IXxZRZxafEQ
Physical principles-electromagnetic radiation
Planck Radiation Law
where
R is the spectral radiance of the surface of the black body
T is its absolute temperature
ν is the frequency of the emitted radiation
λ is its wavelength
kB is the Boltzmann constant,
h is the Planck constant
c is the speed of light.
Planck's law describes the electromagnetic radiation emitted by a black body in thermal equilibrium at a definite temperature.
Physical principles-electromagnetic radiation
Wien's displacement law:
where
λ = Peak Wavelength [m]
b = 0.0029 [mK] (Wien's constant)
T = Temperature [K]
Wilhelm Wien Example:
Sun surface temperature ~ 5778 K=sun peak radiation at 500 nm
Physical principles-electromagnetic radiation
Physical principles-electromagnetic radiation
Interactions with the Atmosphere
EMR is attenuated by its passage through the atmosphere
Attenuation = scattering + absorption
Scattering is the redirection of radiation by reflection and refraction
Physical principles-electromagnetic radiation
Rayleigh Scattering
Caused by molecules with diameter < wavelength
Primarily due to oxygen and nitrogen molecules
Blue is scattered 4 x more than red radiation
Responsible for blue sky
Physical principles-spectral signature of objectsVegetation
Physical principles-spectral signature of objectsSoil
False color RGB: Red (near-infrared), Green (red), Blue(green)
Physical principles-spectral signature of objects
Water
Three types of possible reflectance from a water body:
a) Specular reflectanceb) Bottom reflectance
c) Volume reflectance
Contains information relating to water quality.
Physical principles-spectral signature of objects
Water
• High absorption at near infrared and beyond. • Turbid water has a higher reflectance in the visible region • Same for waters containing high chlorophyll concentrations.
Physical principles-spectral signature of objects
Water
Physical principles-spectral signature of objects
Water
An algal bloom of cyanobacteria (coast of Scotland)Source: NASA
Reflectance patterns are used to detect algae colonies as well as contaminations such as oil spills or industrial waste water
Basic characteristics of remote sensing data
• Spatial resolution
• Spectral resolution
• Temporal resolution
• Radiometric resolution
Basic characteristics of remote sensing data
• Spatial resolution
Ground dimensions of each pixel orPixel size of satellite images covering the earth
The ability to "resolve," or separate, small details
Ghulam et al (2015)
Basic characteristics of remote sensing data
• Spatial resolution
Rule of thumb:To detect a feature, the spatial resolution of the sensor should be less than one-half the size of the feature (measured in its smallest dimension)
Basic characteristics of remote sensing data
• Number and dimension (size) of wavelength
Intervals to which the sensor in sensitive
• Each wavelength interval is refered to as ”Bands”
• Spectral resolution
Basic characteristics of remote sensing data
• Spectral resolution
Basic characteristics of remote sensing data
• Spectral resolution
Basic characteristics of remote sensing data
• The amount of time needed to revisit and acquire data for the exact same location
• Temporal resolution
•NOAA AVHRR: < 1 day•MODIS: 1–2 days•Ikonos: 16 days (1.5–3 days off-nadir)•Landsat ETM+: 16 days•SENTINEL-2 constellation: 5 days
Basic characteristics of remote sensing data
• Radiometric resolution
Describes the ability of the sensor to discriminate differences in energy input
8 bit = 28 = 256 levels
1 bit = 21 = 2 levels
•coastal habitat mapping •thermal characteristics of coastal waters •sea-level rise •shoreline erosion •change detection •coastal hazards •Pollution monitoring
Part 2 - Assessing environmental problems in aquatic ecosystems
Mapping Global Water Surface
3 million satellite scenes collected over the past 30 years
10 000 computers running in parallel for 10 million hours
Over 1823 Terabytes of data (= 546 million MP3 songs)
Mapping Global Water Surface
https://global-surface-water.appspot.com/
Sweden and Finland rank in 10th and 12th position worldwide as the countries with the largest area of permanent water bodies.
Mapping Global Water Surface
by the 1960s the water started being diverted to irrigate cotton fields.
Aral Sea
by 2015 less than 10% of the original area remained
Mapping Global Water Surface
A new desert was formed: the Aralkum desert
Mapping Global Water Surface
Belo Monte Dam: Brazilian Amazon
Mapping Global Water Surface
Belo Monte Dam: Brazilian Amazon
Mapping Global Water Surface
Water quality monitoring
Water quality monitoring
Objectives: To use Landsat Enhanced Thematic Mapper (ETM+)
imagery to quantify chlorophyll-a (chl-a) concentrations
Chlorophyll-a concentration – North Island lakes of New Zealand (Allan et al 2011)
Water quality monitoring
16 surface samples of chl-a Most samples collected within 2 days of image
capture
2 dates: 24 January 2002 and 23 October 2002
Band µm Resolution
1 0.45-0.515 30 m
2 0.525-0.605 30 m
3 0.63-0.69 30 m
4 0.775-0.90 30 m
5 1.55-1.75 30 m
6 10.4-12.5 60 m
7 2.08-2.35 30 m
8 0.52-0.9 15 m
•Temporal Resolution: 16 days
•Image Size: 183 km X 170 km
Chlorophyll-a concentration – North Island lakes of New Zealand (Allan et al 2011)
Water quality monitoring
Chlorophyll-a concentration – North Island lakes of New Zealand (Allan et al 2011)
Water quality monitoring
Chlorophyll-a concentration – North Island lakes of New Zealand (Allan et al 2011)
Water quality monitoring
Water quality monitoring
Objectives: Estimate concentration and spatial distribution of
Cyanobacteria using Landsat TM sensor
Phycocyanin detection for mapping cyanobaterial blooms in Lake Erie (Vincent et al 2004)
Water quality monitoring
Phycocyanin detection for mapping cyanobaterial blooms in Lake Erie (Vincent et al 2004)
PC= Phycocyanin content = indicative of the presence of cyanobacteria.
Multivariate regression models:
Water quality monitoring
Phycocyanin detection for mapping cyanobaterial blooms in Lake Erie (Vincent et al 2004)
Water quality monitoring
July 1st September 27
Phycocyanin detection for mapping cyanobaterial blooms in Lake Erie (Vincent et al 2004)
Deepwater Horizon oil spill
https://www.youtube.com/watch?v=ECamImhCNuY
Deepwater Horizon oil spill
Objectives:Discuss the role of RS in the DWH response
Deepwater Horizon oil spill
based on Leifer et al (2012)
Key question for resource allocation: • How much oil has been released? • Where is the oil? • What type of oil was spilled? • When (and how) was the oil released? • What types of ecosystems are threatened?
Amount of oil in the spill can be estimated based on the slick’s spatial pattern and temporal dynamics
Deepwater Horizon oil spill
The role of RS in oil spills
Previous studies have found limited spill response applicability (Fingas and Brown,1997)Mostly simple visual observation (only confirming what was already known)
Some of the limitations are:-Coarse Spatial resolution
based on Leifer et al (2012)
Deepwater Horizon oil spill
The role of RS in oil spills
Some of the limitations are:-Coarse Temporal resolution
based on Leifer et al (2012)
Deepwater Horizon oil spill
The role of RS in oil spills
Some of the limitations are:-Cloud coverage (passive sensors)
based on Leifer et al (2012)
Deepwater Horizon oil spill
The role of RS in oil spills
New technologies and tools have emerged, but:Acceptance requires a proven reliability track record with well-understood physical mechanisms
DWH provided unique opportunity to test new sensor technologies:• Persistent and high magnitude spill (4.9 million barrels / 780,000 cubic meters)• Vast extent (~100 000 km2)
based on Leifer et al (2012)
Deepwater Horizon oil spillThe role of RS in oil spills
based on Leifer et al (2012)
Primary response for thick oil identification was done by Airborne human observations
Deepwater Horizon oil spill
The role of RS in oil spills
based on Leifer et al (2012)
Human observations were supplemented by remote sensing data
Underlining spectroscopy:
4 mm thickness
Deepwater Horizon oil spill
The role of RS in oil spills
based on Leifer et al (2012)
Human observations were supplemented by remote sensing data
Underlining spectroscopy:
Deepwater Horizon oil spillThe role of RS in oil spills
based on Leifer et al (2012)
Airborne spatial and temporal coverage was extended and supplemented by satellite passive visible data
Deepwater Horizon oil spillThe role of RS in oil spills
based on Leifer et al (2012)
Multispectral imaging: maps of oil thickness classes
A. Oil distribution and thickness map from multispectral image
Deepwater Horizon oil spill
based on Leifer et al (2012)
Hyperspectral data: quantified thickness maps for the response
The role of RS in oil spills
A. False color AVIRIS image, including clouds in scene. B. RGB map of band absorption strength, which correlates with oil thickness. C. True color AVIRIS oil scene. D. Tetracorder oil-to-water emulsion ratio map.
Deepwater Horizon oil spill
based on Leifer et al (2012)
True-color MODIS imagery was important due to the spill's vast extent and the accessibility of Rapid Response Products.
The role of RS in oil spills
https://www.youtube.com/watch?v=2AAa0gd7ClM
Deepwater Horizon oil spill