Remote sensing

ND GIS Users Workshop Bismarck, ND October 24-26, 2005ND GIS Users Workshop Bismarck, ND October 24-26, 2005
Introduction toIntroduction to
Remote SensingRemote Sensing
Gregory VandebergGregory Vandeberg
Assistant Professor of GeographyAssistant Professor of Geography
Image: NASA 2005

ND GIS Users Workshop Bismarck, ND October 24-26, 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 informationThe art and science of obtaining information
about an object without being in direct contact withabout an object without being in direct contact with
the object” (Jensen 2000).the object” (Jensen 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 surfacesthe collection of information about Earth surfaces
and phenomena using sensors not in physical contactand phenomena using sensors not in physical contact
with the surfaces and phenomena of interest.with the surfaces and phenomena of interest.
 We will focus on data collected from an overheadWe will focus on data collected from an overhead
perspective via transmission of electromagneticperspective via transmission of electromagnetic
radiation.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 of theB) The reception, recording, and processing of the
signal data; andsignal 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 haveremote sensing systems have four typesfour types ofof
resolution:resolution:
 SpatialSpatial
 SpectralSpectral
 TemporalTemporal
 RadiometricRadiometric

High vs. Low?
Spatial Resolution

Spectral
Resolution

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 range
0 63
8-bit range
0 255
0
10-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 LIDARActive and Passive Microwave and LIDAR

Aerial PhotosAerial Photos
 Balloon photographyBalloon photography
(1858)(1858)
 Pigeon camerasPigeon cameras
(1903)(1903)
 Kite photographyKite photography
(1890)(1890)
 Aircraft (WWI andAircraft (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 HighAVHRR (Advanced Very High
Resolution Radiometer) NASAResolution Radiometer) NASA

GOES (Geostationary OperationalGOES (Geostationary Operational
Environmental Satellites) IR 4Environmental Satellites) IR 4

MODIS (250 m)MODIS (250 m)

Landsat TMLandsat 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)

RADARRADAR
(Radio Detection and Ranging)(Radio Detection and Ranging)
Image: NASA 2005

LIDARLIDAR
(Light Detection and Ranging)(Light Detection and Ranging)
Image: Bainbridge Island,
WA courtesy Pudget Sound
LIDAR Consortium, 2005

Elements of Image InterpretationElements of Image Interpretation
 Shape:Shape:
 Many natural and human-made features haveMany natural and human-made features have
unique shapes.unique shapes.
 Often used are adjectives like linear,Often used are adjectives like linear,
curvilinear, circular, elliptical, radial, square,curvilinear, circular, elliptical, radial, square,
rectangular, triangular, hexagonal, star,rectangular, triangular, hexagonal, star,
elongated, and amorphous.elongated, and amorphous.

Jensen (2000)
ShapeShape

 Shadow:Shadow:
 Shadow reduction is of concern in remote sensingShadow reduction is of concern in remote sensing
because shadows tend to obscure objects thatbecause shadows tend to obscure objects that
might otherwise be detected.might otherwise be detected.
 However, the shadow cast by an object may beHowever, the shadow cast by an object may be
the only real clue to its identity.the only real clue to its identity.
 Shadows can also provide information on theShadows can also provide information on the
height of an object either qualitatively orheight of an object either qualitatively or
quantitatively.quantitatively.

Jensen (2000)
ShadowShadow

 Tone and Color:Tone and Color:
 AA bandband of EMR recorded by a remote sensingof EMR recorded by a remote sensing
instrument can be displayed on an image ininstrument 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 beThese shades are called “tones”, and can be
qualitatively referred to as dark, light, orqualitatively 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 reflectedTone is related to the amount of light reflected
from the scene in a specific wavelength intervalfrom the scene in a specific wavelength interval
(band).(band).

Jensen (2000)
Tone and ColorTone and Color

 Texture:Texture:
 Texture refers to the arrangement of tone or colorTexture refers to the arrangement of tone or color
in an image.in an image.
 Useful because Earth features that exhibit similarUseful because Earth features that exhibit similar
tones often exhibit different textures.tones often exhibit different textures.
 Adjectives include smooth (uniform,Adjectives include smooth (uniform,
homogeneous), intermediate, and rough (coarse,homogeneous), intermediate, and rough (coarse,
heterogeneous).heterogeneous).

Jensen (2000)
TextureTexture

 Pattern:Pattern:
 Pattern is the spatial arrangement of objects onPattern is the spatial arrangement of objects on
the landscape.the landscape.
 General descriptions include random andGeneral descriptions include random and
systematic; natural and human-made.systematic; natural and human-made.
 More specific descriptions include circular, oval,More specific descriptions include circular, oval,
curvilinear, linear, radiating, rectangular, etc.curvilinear, linear, radiating, rectangular, etc.

Jensen (2000)
PatternPattern

 Height and Depth:Height and Depth:
 As discussed, shadows can often offer clues to theAs discussed, shadows can often offer clues to the
height of objects.height of objects.
 In turn, relative heights can be used to interpretIn turn, relative heights can be used to interpret
objects.objects.
 In a similar fashion, relative depths can often beIn a similar fashion, relative depths can often be
interpreted.interpreted.
 Descriptions include tall, intermediate, and short;Descriptions include tall, intermediate, and short;
deep, intermediate, and shallow.deep, intermediate, and shallow.

Height and DepthHeight and Depth

 Association:Association:
 This isThis is veryvery important when trying toimportant when trying to
interpret an object or activity.interpret an object or activity.
AssociationAssociation refers to the fact that certainrefers to the fact that certain
features and activities are almost alwaysfeatures and activities are almost always
related to the presence of certain otherrelated to the presence of certain other
features and activities.features and activities.

Jensen (2000)
AssociationAssociation

Imaging Tools and DataImaging Tools and Data
 Google EarthGoogle Earth
 ERDAS ImagineERDAS Imagine
 Digital Northern GreatDigital Northern Great
PlainsPlains

Case Study 1:Case Study 1:
Identification andIdentification and
Characterization of MiningCharacterization of Mining
Waste Using Landsat TMWaste Using Landsat TM
Imagery, Cherokee County,Imagery, Cherokee County,
KSKS
Gregory S. VandebergGregory S. Vandeberg

ProblemProblem
 Mining, milling andMining, milling and
smelting have disturbedsmelting have disturbed
more than 240,000 kmmore than 240,000 km22
earth’s surface (Mooreearth’s surface (Moore
and Luoma 1990)and Luoma 1990)
 100,000 – 500,000100,000 – 500,000
abandoned mine lands inabandoned mine lands in
U.S. (Hauff 2000)U.S. (Hauff 2000)
 Mapping andMapping and
characterization of thesecharacterization of these
areas problematicareas problematic
Source: http://www.cma.junta-
andalucia.es/guadiamar/accidente_aznalcollar/
aznalcollar_1.html

HypothesesHypotheses
 Metal miningMetal mining
wastes/tailings inwastes/tailings in
Cherokee County, KSCherokee County, KS
can be identified andcan be identified and
mapped using Landsatmapped using Landsat
TM imageryTM imagery
 Landsat TM data can alsoLandsat TM data can also
be used to characterizebe used to characterize
the mineralogy of thesethe mineralogy of these
wasteswastes

Previous StudiesPrevious Studies
 Use of aerial photographs to identify abandonedUse of aerial photographs to identify abandoned
coal mine lands in KS (Kenny and McCauley,coal mine lands in KS (Kenny and McCauley,
1982), and WV (Peplies et al. 1982)1982), and WV (Peplies et al. 1982)
 Use of Landsat TM imagery and other remoteUse of Landsat TM imagery and other remote
sensing techniques (e.g. AVIRIS) to recognizesensing 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 monitorUse of Landsat TM imagery to monitor
vegetation and mining in Sudbury, Canadavegetation and mining in Sudbury, Canada
(Singhroy 2000)(Singhroy 2000)

Tri-State Mining DistrictTri-State Mining District
 Lead and zinc ores minedLead 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 andsurface water and
groundwatergroundwater
 Over 3 billion metric tonsOver 3 billion metric tons
of mine tailings producedof mine tailings produced
in district (often referredin district (often referred
to as chat)to as chat)
 More than 17 historicalMore 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 ofSupervised and Unsupervised Classification of
mining waste and tailings using Landsat 5mining 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 84Geometrically rectified to UTM Zone 15 WGS 84
using 11 ground control points and first orderusing 11 ground control points and first order
polynomial equation (ERDAS Imagine) afterpolynomial equation (ERDAS Imagine) after
subsetting image to county boundariessubsetting image to county boundaries
 Radiometric and atmospheric correction usingRadiometric and atmospheric correction using
Chavez (1996) COST model (Skirvin 2000)Chavez (1996) COST model (Skirvin 2000)
 Use of band ratios to identify broadUse 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 ofFalse Color TM Image of
Cherokee County, KS (4-3-2)Cherokee County, KS (4-3-2)

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

Unsupervised ClassificationUnsupervised Classification
False Color (7-4-2) Unsupervised

Unsupervised ClassificationUnsupervised Classification
AssessmentAssessment
MineMine
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
accuracyaccuracy
83.3% (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 ClassificationSupervised Classification
AssessmentAssessment
tailingstailings
OtherOther Row TotalsRow Totals
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 sameConducted using orthophotos from same
year with recognition of waste inyear with recognition of waste in
piles/barren areaspiles/barren areas
 Mining and milling wastes wereMining and milling wastes were
incorporated into roads, foundations, etc.incorporated into roads, foundations, etc.
so accuracy rates are likely higher thanso accuracy rates are likely higher than
presentedpresented

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

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

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

ConclusionsConclusions
 Mining wastes/tailings are recognizableMining wastes/tailings are recognizable
using Landsat TM imagery, but includeusing Landsat TM imagery, but include
many other classes (nonwaste).many other classes (nonwaste).
 Only iron oxide minerals readilyOnly iron oxide minerals readily
identifiable from Landsat TM imagery foridentifiable from Landsat TM imagery for
areaarea

Remote sensing

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Editor's Notes