Sensors and Data Interpretation II

Michael Horswell

Def

inin

g re

mo

te s

ensi

ng

1. When was the last time you did any remote sensing?“acquiring information about something without direct contact”

2. What are the requirements for remote sensing to possible?

- ability to detect something that defines the characteristics of what is being observed,

- electromagnetic radiation (EMR) that objects either emit or reflect

The problematic view from space…

• Unlimited perspectives on ourselves, the world and the cosmos around us

• Benefits cannot be over estimated

• Deployed against the environmental threat, to prevent new ecological and economic falling dominoes and enhance global security

• Rather than being embedded participants in the reality depicted, earth system scientists become disengaged observers of that reality

• When quantifiabilitymonopolizes the mantle of legitimacy, qualitative values are given short shrift

• Decontextualization

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=WfLDt-SKHChj5M&tbnid=5ISl5_v44IFbGM:&ved=0CAUQjRw&url=http://petapixel.com/tag/animaleyes/&ei=ZfqaU_zCI_Hn7AaFjoHYDA&bvm=bv.68911936,d.ZWU&psig=AFQjCNHck3Sm0O_qpi5plGy-Zma8j71NKA&ust=1402751676680442

A r

efin

ed (

form

al)

def

init

ion

“Remote sensing is the practice of deriving information about the Earth’s land and water surfaces using images acquired from an overhead perspective, by employing electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the Earth’s surface”

(Campbell 2006 p. 6)

History of remote sensing

1825:

Photography

1850:

From balloons

1873:

Electromagnetism

1909:

Airplanes

1914:

WWI

reconnaissance

The Great Depression

1939-1945:

WWII: Non-visible radiation

1956:

Infra-red & plant health

1972:

Landsat 1

1980s:

Hyperspectral RS

1990s:

Hi-Res spaceborne images

Present:

Global RS systems & LiDAR

Interpreting images

Why interpret images?

• Classification: assigning discernible areas or features to classes

• Enumeration: counting or listing discrete items visible on an image

• Measurement: photogrammetry – applying knowledge of image geometry to the derivation of accurate measurement

• Delineation: defining observable regions

Methods of interpretation

Field observations

• If knowledge of interpreter is inadequate, a field visit may be required

• Field visits are routine as a method of checking accuracy

Direct recognition

• Qualitative, subjective interpretation based on experience, skill and judgement

Interpretation by inference

• Using an observable characteristics to infer the presence on a non-observable characteristic e.g. using landform and vegetation to infer soil characteristics

Methods of interpretation

Probabilistic interpretation

• Integrating other information into the interpretation process, e.g. knowledge of crop rotation and crop calendar can help to identify the crop growing in a region – if winter wheat is harvested in June, the crop on your August image is unlikely to be wheat

• A decision rule is created which expresses the knowledge as a probability, e.g. there is only a 2% probability that the crop is wheat

Methods of interpretation

Deterministic interpretation

• Based on quantitative relationships inherent in the imagery itself, e.g. using stereo imagery pairs to develop elevation data

Image interpretation will more than likely involve

a mixture of interpretative strategies

Elements of interpretation

Tone: refers to shades of grey (B&W) or colours in the image. Tone of the same feature type may vary seasonally.

Texture: the result of changes in tone, or the arrangement of tone on a landscape (described as e.g. fine, medium, or coarse)

Shadow: gives clues to the profile, and shape of landscape features but can obscure detail in other features, as well as alter the spectral signature of other features

Pattern: the arrangement of objects into recurrent forms can facilitate their recognition

Shape: is an important clue to identifying an object and helps, in particular, to distinguish between constructed and natural features

Size: relative size - the size of an unknown object in relation to the size of a known object; absolute size - the actual size of a landscape features

Association: some types of features can be identified despite no compliance with any other interpretative element, but through relationships with identifiable features

Co

nve

rgen

ce o

f ev

iden

ce • Image interpretation is a deductive process

• Features that can be detected and identified lead the interpreter to the location and identification of other features

• Deductive interpretation requires either the conscious or unconscious consideration of all of the elements of image interpretation

• The completeness and accuracy of an interpretation is related to the interpreter’s understanding of the "how and the why" of the elements, and the techniques and methods of interpretation

• This is convergence of evidence – combination of knowledge, expertise, image elements, interpretative strategies to come up with a defensible interpretation

Peo

ple

vs.

co

mp

ute

rs• Jelly bean that made the news…

• People are extremely good at image interpretation – pattern seeking and recognition

• Believed to have significant evolutionary value at little cost

• Computers are more sceptical…

When is enough proof enough?

A definitive determination of landcover based on remotely sensed data is achieved when all relevant elements of interpretative process have been considered, and the assessment of no single element stands in strong contradictionto the overall assessment

More than meets the eye:Multispectral imagery

The electromagnetic spectrum

Continuous range of electromagnetic radiation from radio waves (low frequency, long wavelengths) to gamma rays (high frequency, short wavelengths)

Reflection and absorption

• Objects reflect and absorb different parts of the EMS

• The combination of these generates a set of observable characteristics that allow us to identify the object

Beyond the visible…

• Although we cannot see outside of the visible spectrum, we can detect these wavelengths using sensors

• Pictures depict flowers refection in the UV part of the spectrum –bee’s eye view

Sp

ectr

al

sig

natu

res • The particular reflectance and absorption characteristics of

EM radiation from the surface of an object is called its spectral signature

• All surfaces on earth have a specific spectral signature

• A surface type or material can be identified if the sensor has adequate spectral resolution do distinguish it from other materials

• The spectral signature of an object is a plot of the fraction of radiation reflected as a function of the incident wavelength

Mu

ltis

pectr

al

imag

ery

• Measures of radiation in more than one part of the spectrum – not only the visible part

• Each part in which measurement occurs is called a spectral band - a discrete part of the EMR spectrum

• Spectral bands of satellite-based remote sensors have been selected to enable the discrimination of major surface materials

• The specific parts of the EMR spectrum that a sensor can detect is called its spectral resolution

Lan

dsa

t sp

ectr

al

ban

ds

Landsat 8: OLI & TIRSBand Wavelength EMS Area / Target

1 0.43-0.45 Coastal aerosol2 0.45-0.51 Blue3 0.53-0.59 Green4 0.64-0.67 Red5 0.85-0.88 Near infrared (NIR)6 1.57-1.65 Short-wave infrared (SWIR) 17 2.11-2.29 Short-wave infrared (SWIR) 28 0.5-0.68 Panchromatic9 1.36-1.38 Cirrus10 10.6-11.19 TIRS 111 11.5-12.51 TIRS 2

Landsat 4,5: TM & Landat 7: ETM+Band Wavelength EMS Area / Target

- -1 0.45-0.52 Blue2 0.52-0.60 Green3 0.63-0.69 Red4 0.77-0.90 Near infrared (NIR)5 1.55-1.75 Short-wave infrared (SWIR) 17 2.09-2.35 Short-wave infrared (SWIR) 28 0.52-0.90 Panchromatic

- -

6 10.4-12.5 Thermal Infrared

Sp

ectr

al r

esp

on

se c

urv

es • Represent the reflectance of target objects across the EMS

• Define the objects due to the characteristic reflectance of objects

• Equivalent to ‘tone’ in traditional image interpretation

Vegetation reflectance

• Chlorophyll absorbs red and blue parts of the visible spectrum

• Palisade mesophyll grouped as it is, means green light is reflected

• Chlorophyll transmits NIR, but the arrangement of cells in the spongy mesophyll layer causes high reflection in NIR

Ch

ara

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rist

ic r

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GIS & Remote Sensing Applications 27

Feature Reflectance Response

Water Bodies

Generally reflect high in the visible spectrum, however, clearer water has less reflectance than turbid water. In the Near IR and Mid-IR regions water increasingly absorbs the light making it darker. This is dependent upon water depth and wavelength. Increasing amounts of dissolved inorganic materials in water bodies tend to shift the peak of visible reflectance toward the red region from the green region (clearer water) of the spectrum.

Soil

Northern latitudes have black soils and tropical regions have red soils. Soil reflectance decreases as organic matter increases. As soil moisture increases, reflectance of soil decreases at all wavelengths. Texture of soil will cause increased reflectance with decreased particle size, i.e., the bigger particles (rocks, sand, and soils) basically cast a larger shadow.

Vegetation

The spectral reflectance is based on the chlorophyll and water absorption in the leaf. Needles have a darker response than leaves. There will be various shades of vegetation based on type, leaf structure, moisture content and health of the plant. Healthy vegetation has high IR reflectance – decreases with plant stress.

Man-Made Materials

Concrete and asphalt both display spectral curves that generally increase from the visible through the Near IR and Mid-IR regions. However, as concrete ages, it becomes darker and as asphalt ages it becomes lighter.

Snow and Ice

Old snow may develop a compacted crust and the moisture content increases which make it less reflective in the Near IR and Mid-IR region. It is possible to compare old and new snow by its Mid-IR reflectance.

Visualizing non-visible wavelengths

Band 2

Band 3

Band4

Band5

Band6

Band7

Ban

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n: 432 • The "natural colour" band combination

• Because the visible bands are used in this combination, • ground features appear in colours similar to their appearance to the human

visual system, • healthy vegetation is green, • recently cleared fields are very light, unhealthy vegetation is brown and yellow,• roads are grey, and shorelines are white

• This band combination provides the most water penetration and superior sediment and bathymetric information, it is also used for urban studies

• Cleared and sparsely vegetated areas are not as easily detected here as in the 562 or 543 combination

• Clouds and snow appear white and are difficult to distinguish• Vegetation types are not as easily distinguished as the 562

combination• The 432 combination does not distinguish shallow water from soil as

well as the 764 combination does

Ban

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n: 432

Ban

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n: 543

• The standard "false colour" composite• Vegetation appears in shades of red, urban areas are cyan

blue, and soils vary from dark to light browns• Coniferous trees will appear darker red than hardwoods

• Deep red hues indicate broad leaf and/or healthier vegetation while lighter reds signify grasslands or sparsely vegetated areas

• Ice, snow and clouds are white or light cyan• Urban areas are shown in light blue• This band combination gives results similar to traditional

colour infrared aerial photography• This band combination is useful for vegetation studies,

monitoring drainage and soil patterns and various stages of crop growth.

Ban

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n: 543

Ban

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n: 562

• Healthy vegetation appears in shades of reds, browns, oranges and yellows

• Soils may be in greens and browns, urban features are white, cyan and grey, bright blue areas represent recently clear-cut areas and reddish areas show new vegetation growth, probably sparse grasslands

• Clear, deep water will be very dark in this combination, if the water is shallow or contains sediments it would appear as shades of lighter blue

• The addition of the Mid-IR band increases sensitivity of detecting various stages of plant growth or stress; however care must be taken in interpretation if acquisition closely follows precipitation

• Use of OLI5 and OLI6 shows high reflectance in healthy vegetated areas

• Compare flooded areas and red vegetated areas with the corresponding colours in the 432 combination to assure correct interpretation

Ban

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n: 562

Ban

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ns:

564

• This combination offers added definition of land-water boundaries and highlights subtle details not readily apparent in the visible bands alone

• Inland lakes and streams can be located with greater precision when more infrared bands are used

• Vegetation type and condition show as variations of hues (browns, greens and oranges), as well as in tone

• Demonstrates moisture differences and is useful for analysis of soil and vegetation conditions.

• Generally, the wetter the soil, the darker it appears, because of the infrared absorption capabilities of water.

Ban

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n: 564

Ban

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n: 753 • Penetrates atmospheric particles and smoke.

• Healthy vegetation will be a bright green and can saturate in seasons of heavy growth, grasslands will appear green, pink areas represent barren soil

• Dry vegetation will be orange and water will be blue

• Sands, soils and minerals are highlighted in a multitude of colours

• It is useful for geological, agricultural and wetland studies

• Fires would appear red. This combination is used in the fire management applications for post-fire analysis.

• Urban areas appear in varying shades of magenta

• Grasslands appear as light green. The light-green spots inside cities indicate grassy land cover - parks, cemeteries, golf courses

• Olive-green to bright-green hues normally indicate forested areas with coniferous forest being darker green than deciduous.

Ban

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n: 753

Ban

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n: 764 • Penetrates atmospheric particles, smoke and haze

• Vegetation appears in shades of dark and light green during the growing season, urban features are white, grey, cyan or purple; sands, soils and minerals appear in a variety of colours

• Provides well defined coast lines and highlighted sources of water within the image.

• Snow and ice appear as dark blue

• Hot surfaces such as forest fires and volcano calderas saturate the Mid-IR bands and appear in shades of red or yellow

• Flooded areas should look very dark blue or black, compared with the 432 combination in which shallow flooded regions are difficult to distinguish.

Ban

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n: 764

Ban

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n: 765

• This combination involves no visible bands

• It provides the best atmospheric penetration

• Coast lines and shores are well defined

• It may be used to find textural and moisture characteristics of soils

• Vegetation appears blue

• This band combination can be useful for geological studies

Ban

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n: 765

Ban

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n: 654

• This combination provides the user with a great amount of information and colour contrast

• Healthy vegetation is bright green and soils are mauve

• Use of Band 6 ensure maximal agricultural information

• This combination is useful for vegetation studies, and is widely used in the areas of timber management and pest infestation

Ban

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n: 654

Rem

emb

er…

CONVERGENCE OF EVIDENCE

Spectral response alone may not be enough to determine the characteristics of an observed object