Remote sensing

ND GIS Users Workshop BismarcND GIS Users Workshop Bismarck, ND October 24-26, 2005k, ND October 24-26, 2005

Introduction to Introduction to Remote SensingRemote Sensing

Gregory VandebergGregory Vandeberg

Assistant Professor of Assistant Professor of GeographyGeography

Image: NASA 2005


OutlineOutline

Remote Sensing DefinedRemote Sensing Defined ResolutionResolution Electromagnetic Energy (EMR)Electromagnetic Energy (EMR) TypesTypes InterpretationInterpretation ApplicationsApplications


Remote Sensing DefinedRemote Sensing Defined

Remote Sensing is:Remote Sensing is:

““The art and science of obtaining The art and science of obtaining information about an object without being in information about an object without being in direct contact with the object” (Jensen direct contact with the object” (Jensen 2000).2000).

There is a medium of transmission involved.There is a medium of transmission involved.




EnvironmentalEnvironmental Remote Sensing: Remote Sensing:

… … the collection of information about Earth the collection of information about Earth surfaces and phenomena using sensors not in surfaces and phenomena using sensors not in physical contact with the surfaces and physical contact with the surfaces and phenomena of interest. phenomena of interest.

We will focus on data collected from an We will focus on data collected from an overhead perspective via transmission of overhead perspective via transmission of electromagnetic radiation.electromagnetic radiation.


Source: Jensen (2000)



Remote Sensing Includes:Remote Sensing Includes:

A) The mission plan and choice of sensors;A) The mission plan and choice of sensors;

B) The reception, recording, and processing B) The reception, recording, and processing of the signal data; andof the signal data; and

C) The analysis of the resultant data.C) The analysis of the resultant data.


Energy Source or Illumination (A)

Radiation and the Atmosphere (B)

Interaction with the Target (C)

Recording of Energy by the Sensor (D)

Transmission, Reception, and Processing (E)

Interpretation and Analysis (F)

Application (G)

Source: Canadian Centre for Remote Sensing

Remote Sensing Process Components


ResolutionResolution

AllAll remote sensing systems have remote sensing systems have four four typestypes of resolution: of resolution:

SpatialSpatial

SpectralSpectral

TemporalTemporal

RadiometricRadiometric


High vs. Low?

Spatial Resolution




SpectralResolution


Temporal Resolution

Time

July 1 July 12 July 23 August 3

11 days

16 days

July 2 July 18 August 3


Radiometric Resolution

6-bit range0 63

8-bit range0 255

010-bit range

1023


Electromagnetic RadiationElectromagnetic Radiation


Electromagnetic SpectrumElectromagnetic Spectrum


Signature SpectraSignature Spectra


Types of Remote SensingTypes of Remote Sensing

Aerial PhotographyAerial Photography

MultispectralMultispectral

Active and Passive Microwave and Active and Passive Microwave and LIDARLIDAR


Aerial PhotosAerial Photos

Balloon Balloon photography photography (1858)(1858)

Pigeon cameras Pigeon cameras (1903)(1903)

Kite photography Kite photography (1890)(1890)

Aircraft (WWI and Aircraft (WWI and WWII)WWII)

Space (1947)Space (1947)

Images: Jensen (2000)



MultispectralMultispectral

NOAA-AVHRR (1100 m)NOAA-AVHRR (1100 m) GOES (700 m)GOES (700 m) MODIS (250, 500, 1000 m)MODIS (250, 500, 1000 m) Landsat TM and ETM (30 – 60 m)Landsat TM and ETM (30 – 60 m) SPOT (10 – 20 m)SPOT (10 – 20 m) IKONOS (4, 1 m)IKONOS (4, 1 m) Quickbird (0.6 m)Quickbird (0.6 m)


AVHRR (Advanced Very High AVHRR (Advanced Very High Resolution Radiometer) NASAResolution Radiometer) NASA


GOES (Geostationary GOES (Geostationary Operational Environmental Operational Environmental

Satellites) IR 4Satellites) IR 4

http://www.goes.noaa.gov/GIFS/WCIR.JPG


MODIS (250 m)MODIS (250 m)


Landsat TM Landsat TM (False Color Composite)(False Color Composite)


SPOT (2.5 m)SPOT (2.5 m)


QUICKBIRD (0.6 m)QUICKBIRD (0.6 m)


IKONOS (4 m Multispectral) IKONOS (4 m Multispectral)


IKONOS (1 m Panchromatic)IKONOS (1 m Panchromatic)


RADAR RADAR (Radio Detection and Ranging)(Radio Detection and Ranging)

Image: NASA 2005


LIDAR LIDAR (Light Detection and Ranging)(Light Detection and Ranging)

Image: Bainbridge Island, WA courtesy Pudget Sound LIDAR Consortium, 2005

http://rocky2.ess.washington.edu/data/raster/lidar/ld2.jpg

http://rocky2.ess.washington.edu/data/raster/lidar/KIT0617_.jpg


Elements of Image Elements of Image InterpretationInterpretation

Shape:Shape: Many natural and human-made features Many natural and human-made features

have unique shapes.have unique shapes.

Often used are adjectives like linear, Often used are adjectives like linear, curvilinear, circular, elliptical, radial, curvilinear, circular, elliptical, radial, square, rectangular, triangular, square, rectangular, triangular, hexagonal, star, elongated, and hexagonal, star, elongated, and amorphous.amorphous.


Jensen (2000)

ShapeShape



Shadow:Shadow: Shadow reduction is of concern in remote Shadow reduction is of concern in remote

sensing because shadows tend to obscure sensing because shadows tend to obscure objects that might otherwise be detected.objects that might otherwise be detected.

However, the shadow cast by an object may However, the shadow cast by an object may be the only real clue to its identity.be the only real clue to its identity.

Shadows can also provide information on Shadows can also provide information on the height of an object either qualitatively the height of an object either qualitatively or quantitatively.or quantitatively.


Jensen (2000)

ShadowShadow



Tone and Color:Tone and Color: A A bandband of EMR recorded by a remote sensing of EMR recorded by a remote sensing

instrument can be displayed on an image in instrument can be displayed on an image in shades of gray ranging from black to white.shades of gray ranging from black to white.

These shades are called “tones”, and can be These shades are called “tones”, and can be qualitatively referred to as dark, light, or qualitatively referred to as dark, light, or intermediate (humans can see 40-50 tones).intermediate (humans can see 40-50 tones).

Tone is related to the amount of light Tone is related to the amount of light reflected from the scene in a specific reflected from the scene in a specific wavelength interval (band).wavelength interval (band).


Jensen (2000)

Tone and ColorTone and Color



Texture:Texture: Texture refers to the arrangement of tone or Texture refers to the arrangement of tone or

color in an image.color in an image.

Useful because Earth features that exhibit Useful because Earth features that exhibit similar tones often exhibit different similar tones often exhibit different textures.textures.

Adjectives include smooth (uniform, Adjectives include smooth (uniform, homogeneous), intermediate, and rough homogeneous), intermediate, and rough (coarse, heterogeneous).(coarse, heterogeneous).


Jensen (2000)

TextureTexture



Pattern:Pattern: Pattern is the spatial arrangement of objects Pattern is the spatial arrangement of objects

on the landscape.on the landscape.

General descriptions include random and General descriptions include random and systematic; natural and human-made.systematic; natural and human-made.

More specific descriptions include circular, More specific descriptions include circular, oval, curvilinear, linear, radiating, oval, curvilinear, linear, radiating, rectangular, etc.rectangular, etc.


Jensen (2000)

PatternPattern



Height and Depth:Height and Depth: As discussed, shadows can often offer clues As discussed, shadows can often offer clues

to the height of objects.to the height of objects.

In turn, relative heights can be used to In turn, relative heights can be used to interpret objects.interpret objects.

In a similar fashion, relative depths can In a similar fashion, relative depths can often be interpreted.often be interpreted.

Descriptions include tall, intermediate, and Descriptions include tall, intermediate, and short; deep, intermediate, and shallow.short; deep, intermediate, and shallow.


Height and DepthHeight and Depth



Association:Association: This is This is veryvery important when trying to important when trying to

interpret an object or activity.interpret an object or activity.

AssociationAssociation refers to the fact that refers to the fact that certain features and activities are certain features and activities are almost always related to the presence almost always related to the presence of certain other features and of certain other features and activities.activities.


Jensen (2000)

AssociationAssociation




Imaging Tools and DataImaging Tools and Data

Google EarthGoogle Earth

ERDAS ImagineERDAS Imagine

Digital Northern Digital Northern Great PlainsGreat Plains


Case Study 1: Case Study 1: Identification and Identification and

Characterization of Mining Characterization of Mining Waste Using Landsat TM Waste Using Landsat TM

Imagery, Cherokee Imagery, Cherokee County, KSCounty, KS

Gregory S. VandebergGregory S. Vandeberg


ProblemProblem

Mining, milling and Mining, milling and smelting have disturbed smelting have disturbed more than 240,000 kmmore than 240,000 km22 earth’s surface (Moore earth’s surface (Moore and Luoma 1990)and Luoma 1990)

100,000 – 500,000 100,000 – 500,000 abandoned mine lands abandoned mine lands in U.S. (Hauff 2000)in U.S. (Hauff 2000)

Mapping and Mapping and characterization of characterization of these areas problematicthese areas problematic

Source: http://www.cma.junta-andalucia.es/guadiamar/accidente_aznalcollar/aznalcollar_1.html


HypothesesHypotheses

Metal mining Metal mining wastes/tailings in wastes/tailings in Cherokee County, KS Cherokee County, KS can be identified and can be identified and mapped using Landsat mapped using Landsat TM imageryTM imagery

Landsat TM data can Landsat TM data can also be used to also be used to characterize the characterize the mineralogy of these mineralogy of these wasteswastes


Previous StudiesPrevious Studies

Use of aerial photographs to identify Use of aerial photographs to identify abandoned coal mine lands in KS (Kenny and abandoned coal mine lands in KS (Kenny and McCauley, 1982), and WV (Peplies et al. 1982)McCauley, 1982), and WV (Peplies et al. 1982)

Use of Landsat TM imagery and other remote Use of Landsat TM imagery and other remote sensing techniques (e.g. AVIRIS) to recognize sensing techniques (e.g. AVIRIS) to recognize mining wastes in Cripple Creek Mining District, mining wastes in Cripple Creek Mining District, CO (Peters et al. 1996, Peters and Hauff 2000)CO (Peters et al. 1996, Peters and Hauff 2000)

Use of Landsat TM imagery to monitor Use of Landsat TM imagery to monitor vegetation and mining in Sudbury, Canada vegetation and mining in Sudbury, Canada (Singhroy 2000)(Singhroy 2000)


Tri-State Mining DistrictTri-State Mining District Lead and zinc ores mined Lead and zinc ores mined

from 1848-1968from 1848-1968 Legacy of mine tailings, Legacy of mine tailings,

metal-contaminated soils, metal-contaminated soils, surface water and surface water and groundwatergroundwater

Over 3 billion metric tons Over 3 billion metric tons of mine tailings produced of mine tailings produced in district (often referred in district (often referred to as chat)to as chat)

More than 17 historical More than 17 historical smelter sitessmelter sites

3 Superfund Sites3 Superfund Sites


(Spruill 1987)



(Ragan 1996)


(Photo: Gartung, 1931)



(KS Geological Survey)


(KS Geological Survey)


(Photo: Charles Martin, Kansas State)


(Photo: Kansas Geological Survey)






MethodsMethods Supervised and Unsupervised Classification of Supervised and Unsupervised Classification of

mining waste and tailings using Landsat 5 mining waste and tailings using Landsat 5 Thematic Mapper image (Path 26 and Row 34, Thematic Mapper image (Path 26 and Row 34, acquired June 27, 1992)acquired June 27, 1992) Geometrically rectified to UTM Zone 15 WGS 84 using Geometrically rectified to UTM Zone 15 WGS 84 using

11 ground control points and first order polynomial 11 ground control points and first order polynomial equation (ERDAS Imagine) after subsetting image to equation (ERDAS Imagine) after subsetting image to county boundariescounty boundaries

Radiometric and atmospheric correction using Chavez Radiometric and atmospheric correction using Chavez (1996) COST model (Skirvin 2000)(1996) COST model (Skirvin 2000)

Use of band ratios to identify broad Use of band ratios to identify broad mineralogical types (Peters and Hauff 2000)mineralogical types (Peters and Hauff 2000)


Spectral “Signatures”Spectral “Signatures”


False Color TM Image of False Color TM Image of Cherokee County, KS (4-3-2)Cherokee County, KS (4-3-2)


False Color TM Image (7-4-False Color TM Image (7-4-2)2)


Unsupervised ClassificationUnsupervised Classification

False Color (7-4-2) Unsupervised


Unsupervised Classification Unsupervised Classification AssessmentAssessment

Mine Mine waste/tailingswaste/tailings

OtherOther Row TotalsRow Totals

Mine waste/ Mine waste/ tailingstailings

1010 4040 5050

OtherOther 22 4848 5050

Column totalsColumn totals 1212 8888 100100

58% overall58% overall

accuracyaccuracy83.3% (I)83.3% (I)

20% (II)20% (II)54% (I)54% (I)

96% (II)96% (II)KAPPA (kKAPPA (khathat) = ) = 16%16%


Supervised ClassificationSupervised Classification

False Color (7-4-2) Supervised


Supervised Classification Supervised Classification AssessmentAssessment

Mine waste/Mine waste/

tailingstailingsOtherOther Row TotalsRow Totals

Mine waste/ Mine waste/ tailingstailings

88 4242 5050

OtherOther 11 4949 5050

Column totalsColumn totals 99 9191 100100

57% overall57% overall 89% (I)89% (I)

16% (II)16% (II)54% (I)54% (I)

98% (II)98% (II)KAPPA (kKAPPA (khathat) = ) = 14%14%


Accuracy AssessmentAccuracy Assessment

Conducted using orthophotos from Conducted using orthophotos from same year with recognition of waste same year with recognition of waste in piles/barren areasin piles/barren areas

Mining and milling wastes were Mining and milling wastes were incorporated into roads, foundations, incorporated into roads, foundations, etc. so accuracy rates are likely etc. so accuracy rates are likely higher than presentedhigher than presented


Mineralogy (3/4-3/1-5/7) Mineralogy (3/4-3/1-5/7) Iron oxidesIron oxides


Bands 3/1-5/4-5/7Bands 3/1-5/4-5/7Iron oxides vs. Iron oxides vs. Ferrous/ClayFerrous/Clay


Bands 5/7-3/1-4/3Bands 5/7-3/1-4/3Hydrothermal depositsHydrothermal deposits


ConclusionsConclusions

Mining wastes/tailings are Mining wastes/tailings are recognizable using Landsat TM recognizable using Landsat TM imagery, but include many other imagery, but include many other classes (nonwaste). classes (nonwaste).

Only iron oxide minerals readily Only iron oxide minerals readily identifiable from Landsat TM imagery identifiable from Landsat TM imagery for areafor area

Technology

Remote sensing