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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)


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


https://www.youtube.com/watch?v=IXxZRZxafEQ


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.


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


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


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.


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.


Water


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



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)



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)


• Number and dimension (size) of wavelength

Intervals to which the sensor in sensitive

• Each wavelength interval is refered to as ”Bands”



• 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


• 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)


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.


by the 1960s the water started being diverted to irrigate cotton fields.

Aral Sea

by 2015 less than 10% of the original area remained


A new desert was formed: the Aralkum desert


Belo Monte Dam: Brazilian Amazon


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)


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



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)



PC= Phycocyanin content = indicative of the presence of cyanobacteria.

Multivariate regression models:


July 1st September 27


Deepwater Horizon oil spill

https://www.youtube.com/watch?v=ECamImhCNuY


Objectives:Discuss the role of RS in the DWH response


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


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




Some of the limitations are:-Coarse Temporal resolution




Some of the limitations are:-Cloud coverage (passive sensors)




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)


Deepwater Horizon oil spillThe role of RS in oil spills


Primary response for thick oil identification was done by Airborne human observations




Human observations were supplemented by remote sensing data

Underlining spectroscopy:

4 mm thickness




Human observations were supplemented by remote sensing data

Underlining spectroscopy:



Airborne spatial and temporal coverage was extended and supplemented by satellite passive visible data



Multispectral imaging: maps of oil thickness classes

A. Oil distribution and thickness map from multispectral image



Hyperspectral data: quantified thickness maps for the response


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.



True-color MODIS imagery was important due to the spill's vast extent and the accessibility of Rapid Response Products.


https://www.youtube.com/watch?v=2AAa0gd7ClM


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