The Yellowstone Project Talk...
Transcript of The Yellowstone Project Talk...
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Remote sensing of riparian resources: Remote sensing of riparian resources: ethical considerations and the role of geographersethical considerations and the role of geographers
W. Andrew MarcusW. Andrew MarcusDepartment of GeographyDepartment of Geography
University of OregonUniversity of Oregon
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Collaborators and FundingCollaborators and Funding
•• Richard Aspinall, Montana State UniversityRichard Aspinall, Montana State University
•• Mark Fonstad, Texas State UniversityMark Fonstad, Texas State University
•• Carl Legleiter, UC Santa BarbaraCarl Legleiter, UC Santa Barbara•• Chuck Robertson, Idaho State UniversityChuck Robertson, Idaho State University
•• Photos: Carter Photos: Carter GowlGowl•• Field Assistants: Rob Field Assistants: Rob AhlAhl, Jim Rasmussen, Jim Rasmussen
Talk OrganizationTalk Organization
•• The Yellowstone projectThe Yellowstone project•• field area and methodsfield area and methods
•• mapping results: Inmapping results: In--stream habitats, stream habitats, depths, woody debris, vegetation, depths, woody debris, vegetation, wetlands & algaewetlands & algae
•• future researchfuture research•• My journey into ethical realmsMy journey into ethical realms
•• legal constraintslegal constraints•• military applicationsmilitary applications•• resource managementresource management
The Yellowstone ProjectThe Yellowstone ProjectOverarching goal: determine if HSRH imagery can be used to Overarching goal: determine if HSRH imagery can be used to map key environmental variables in complex stream settings.map key environmental variables in complex stream settings.
Soda Butte Creek, 9ssg68, Oct 10, 1999Soda Butte Creek, 9ssg68, Oct 10, 1999
From Wright et al., 2000From Wright et al., 2000
Remote mapping of streamsRemote mapping of streams
Some Personal HistorySome Personal History
MetalsMetals
ConcernsConcernsabout applications about applications
Talk OrganizationTalk Organization
•• The Yellowstone projectThe Yellowstone project•• field area and methodsfield area and methods
•• mapping results: Inmapping results: In--stream habitats, stream habitats, depths, woody debris, vegetation, depths, woody debris, vegetation, wetlands & algaewetlands & algae
•• My journey into ethical realmsMy journey into ethical realms•• legal constraintslegal constraints•• military applicationsmilitary applications•• resource managementresource management
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Advances due to fine spectral resolutionAdvances due to fine spectral resolution
cottonwood
sediment
doug fir
water
128128bandsbands
The ProbeThe Probe--1 hyperspectral sensor and 1 hyperspectral sensor and spectra from the Lamar River, WYspectra from the Lamar River, WY
Greater spectral resolution Greater spectral resolution detects subtle featuresdetects subtle features
pc 3,2,1pc 3,2,1
pc 12,11,10pc 12,11,10
pc 9,8,7pc 9,8,7
pc 6,5,4pc 6,5,4
true colortrue color
Advances due to high spatial resolutionAdvances due to high spatial resolution–– even from space!even from space!
TM (30 m)TM (30 m)
AVIRIS (17 m)AVIRIS (17 m)
Probe1 (8 m)Probe1 (8 m)
Probe1 (5 m)Probe1 (5 m)
IKONOS (0.85 m)IKONOS (0.85 m)
Animation byAnimation byRichard AspinallRichard Aspinall
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Field Area and Field Area and Collection of HSHR ImageryCollection of HSHR Imagery
Field Sites: NE YellowstoneField Sites: NE Yellowstone
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Lamar River
Soda Butte Creek
33rdrd
orderorder
44 thth
orderorder
55thth
orderorder
Cache Creek
Landscapes: Lower Soda Butte CreekLandscapes: Lower Soda Butte Creek
Landscapes: Lamar RiverLandscapes: Lamar River Methods: spectral data collectionMethods: spectral data collection
The Probe1 sensor:The Probe1 sensor:
•• 128 bands at 12 to 16 128 bands at 12 to 16 nm resolutionnm resolution
•• spectral range from 440 spectral range from 440 to 2,543 nmto 2,543 nm
•• 512 m image width at 1 512 m image width at 1 m resolutionm resolution
•• VERYVERY expensive expensive ($25,000/day rental)($25,000/day rental)
Probe1, misc9, Aug 4, 1999Probe1, misc9, Aug 4, 1999
Methods: Helicopter platformMethods: Helicopter platformTo collect 1To collect 1--m resolution data, the Probe1 m resolution data, the Probe1 sensor was mounted on a helicopter and sensor was mounted on a helicopter and flown 600 m above the ground surface.flown 600 m above the ground surface.
..
J. Boardman, Probe1, misc6, 08/04/99J. Boardman, Probe1, misc6, 08/04/99
The helicopter platform created unique problems:The helicopter platform created unique problems:
•• more vibration, pitch, yaw and roll than fixed wing aircraft more vibration, pitch, yaw and roll than fixed wing aircraft
•• gyroscope overcompensation led to serious image swirlgyroscope overcompensation led to serious image swirl
•• rotor blades reflected GPS signal, causing loss of coordinate darotor blades reflected GPS signal, causing loss of coordinate da tata
•• greater difficulty in coregistering field maps to imagery and GIgreater difficulty in coregistering field maps to imagery and GIS dataS data
Aug 4, 1999, misc5Aug 4, 1999, misc5
99080308b, Lamar River99080308b, Lamar RiverAug3. 1999Aug3. 1999
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Riparian features examinedRiparian features examinedRiparian variables examined:Riparian variables examined:
•• inin--stream habitats (riffles, pools, glides)stream habitats (riffles, pools, glides)•• stream depthsstream depths•• large woody debrislarge woody debris•• vegetation cover typesvegetation cover types•• amphibian habitatamphibian habitat
All these variables:All these variables:•• are key to habitat managementare key to habitat management•• are sensitive to human impacts are sensitive to human impacts
•• are difficult to map at watershed scales with are difficult to map at watershed scales with groundground--based methodsbased methods
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Mapping inMapping in--stream habitatsstream habitats
InIn--stream habitatsstream habitats
Eddy drop zone Eddy drop zone (fine sediment(fine sedimentaccumulation)accumulation)
GlideGlide
High gradientHigh gradientriffle (whiteriffle (whitewater)water)
Low gradient riffleLow gradient riffle(no white water)(no white water)
Scour poolScour pool
Round Prairie, 9srp104, October, 1999Round Prairie, 9srp104, October, 1999
Methods: field mapping forMethods: field mapping forimage training and validationimage training and validation
Morphologic units, stream depth, Morphologic units, stream depth, substrate size, and vegetation communsubstrate size, and vegetation commun--ities and species were mapped directly ities and species were mapped directly to 1 m images to insure coregistrationto 1 m images to insure coregistration
J. Rasmussen, Aug, 1999 (misc12)J. Rasmussen, Aug, 1999 (misc12) C. Legleiter, Aug, 1999 (misc13)C. Legleiter, Aug, 1999 (misc13)
Lamar River
9sul03,9sul03,Nov16, 1999Nov16, 1999
Field map of morphologic units Field map of morphologic units with 3 pixel buffer zone removedwith 3 pixel buffer zone removed
9mul89mul8--26, Aug 26, 199926, Aug 26, 1999
Image 99080309Image 99080309
PC images: The power of hyperspectral PC images: The power of hyperspectral Take raw imageTake raw image
Mask to calculate Mask to calculate covarcovar--ianceiance statistics for waterstatistics for water
Create pc data that highlight Create pc data that highlight withinwithin--stream variationsstream variations
Mask of mapped unitsMask of mapped units
PC bands 8,4,1 PC bands 8,4,1
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InIn--stream habitat mapping accuraciesstream habitat mapping accuraciesLamar River, WY: 5Lamar River, WY: 5thth Order StreamOrder Stream
Ground Truth (percent)
Classification
eddy drop zones
riffles
pools
glides
eddy drop zones 84.8 4.1 2.1 1.3 riffles 3.5 75.9 2.6 3.9 pools 11.6 11.1 90.5 7.9
glides .2 8.9 4.8 86.8 Total 100 100 100 100
Overall accuracy = 9432/11,033 = Overall accuracy = 9432/11,033 = 85.5%85.5%Kappa = Kappa = 0.740.74
Data for supervised classification on pc bands 1Data for supervised classification on pc bands 1--25, 1 m 25, 1 m imagery with buffered morphologic unitsimagery with buffered morphologic units
Results of 4 Unit Stream ClassificationResults of 4 Unit Stream Classification
RifflesRiffles
GlidesGlides
PoolsPoolsEddy drop zoneEddy drop zone
1 meter classification1 meter classificationField mapField map
Classification Results inClassification Results in33rdrd, 4, 4thth and 5and 5thth Order StreamsOrder Streams
0.740.74
0.280.28
0.520.52
0.350.35
KappaKappapoolspoolsglidesglidesrifflesriffleseddy eddy drop drop zoneszones
6767676775755252343433Cooke CityCooke City
8585878791917676858555Lamar RiverLamar River
71717878737362629944FootbridgeFootbridge
7171666684845555444444Round PrairieRound Prairie
Overall Overall AccurAccur--acyacy (%)(%)
Producer’s Accuracy Producer’s Accuracy (%)(%)Stream Stream
orderorderLocationLocation
Discussion: Reasons for varying results in Discussion: Reasons for varying results in 33rdrd, 4, 4thth and 5and 5thth order streamsorder streams
Image 9scc04, Oct 4, 1999Image 9scc04, Oct 4, 1999
Cooke City Reach, 3Cooke City Reach, 3 rdrd OrderOrder
Image 9srp104, Oct 4, 1999Image 9srp104, Oct 4, 1999
Round Prairie Reach, Round Prairie Reach, 44thth OrderOrder
Footbridge Reach, 4Footbridge Reach, 4thth OrderOrder
Image 9sfb79, Sept, 1999Image 9sfb79, Sept, 1999
Lamar River,Lamar River,55 thth OrderOrder
Image 9sul05, Nov., 1999Image 9sul05, Nov., 1999
Soda Butte Creek, 9msb10Soda Butte Creek, 9msb10--4, Oct, 4, 19994, Oct, 4, 1999
Are HSHR maps better than field maps? Are HSHR maps better than field maps?
low gradient rifflelow gradient riffleglideglidepoolpoolrunrun
high gradient rifflehigh gradient riffle
eddy drop zoneeddy drop zone
Classification mapClassification map
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Stream DepthsStream Depths
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Stream depthStream depthWater depth is a key control on aquatic habitat and is a Water depth is a key control on aquatic habitat and is a variable in most hydrologic and geomorphic models.variable in most hydrologic and geomorphic models.
Continuous depth variations cause point measurements Continuous depth variations cause point measurements to miss much of the natural variationto miss much of the natural variation ..
Soda Butte Creek, 9ssg53, Oct 10, 1999Soda Butte Creek, 9ssg53, Oct 10, 1999
Jim Rasmussen, misc14, Aug, 1999Jim Rasmussen, misc14, Aug, 1999
Reflectance is a function of:Reflectance is a function of:•• depthdepth•• andand turbulenceturbulence•• andand substrate substrate
Soda Butte Crk, July, 1999, Untitled 1Soda Butte Crk, July, 1999, Untitled 1-- 22
Stratifying by morphologic Stratifying by morphologic unit should therefore help unit should therefore help to normalize the relation of to normalize the relation of depth to reflectance. depth to reflectance.
Depth results, Lamar RiverDepth results, Lamar River
Multiple regression of pc scores (x) Multiple regression of pc scores (x) vsvs depth (ydepth (y)
But… sample size issues are significantBut… sample size issues are significant
In-stream habitat Number of sites
Number of pc's included in regression
Adjusted R2
Eddy drop zone 17 4 93.8%High gradient riffle 35 6 67.0%Low gradient riffle 11 6 91.7%Pool 5 1 72.3%Rough water runs 10 3 82.5%Runs 9 4 93.1%Glides 17 11 98.6%
Methods: The HABMethods: The HAB--1 model1 model
Basis for model:Basis for model:•• Manning equationManning equation•• Jarret’sJarret’s roughnessroughness•• Q, S and W are measuredQ, S and W are measured
Key assumptions:Key assumptions:•• roughness estimate is reasonableroughness estimate is reasonable•• triangle approximation: triangle approximation: DDmaxmax = 2*= 2*DDavgavg•• avgavg depth depth αα avgavg reflectance signal (implies depth reflectance signal (implies depth driving all variations in reflectance)driving all variations in reflectance)
.
TEST SECTION TEST SECTION DN’sDN’s HABHAB--1 Depths1 Depths
Darkest DN Max DepthDarkest DN Max Depth
Average DN Average DN AvgAvg DepthDepth
Brightest DN Shallow DepthBrightest DN Shallow Depth
(relates to…)
Repeat with multiple XRepeat with multiple X--sections to produce sections to produce A wellA well--defined Depthdefined Depth--toto--DN relationshipDN relationship
Applying the HABApplying the HAB--1 Model:1 Model:•• estimate estimate DDavgavg and and DDmaxmax at multiple cross sectionsat multiple cross sections•• determine determine avgavg, max, min reflectances at cross sections, max, min reflectances at cross sections•• regress reflectance (x) regress reflectance (x) vsvs depth (y)depth (y)•• apply regression to remainder of remote sensing image apply regression to remainder of remote sensing image
Methods: The HABMethods: The HAB--1 model1 model
Smooth water transects Smooth water transects (green)(green)
Rough water transectsRough water transects (yellow)(yellow)
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Results: TM5 band 3 Results: TM5 band 3 vsvs DepthDepth
HABHAB--1 depth 1 depth vsvs measured depthmeasured depth
Band 3 Band 3 vsvs HABHAB--1 estimated depth1 estimated depth
Discussion: Are the depths accurate? Discussion: Are the depths accurate? rr22 = 0.34, seems low, but = 0.34, seems low, but
•• general trend captured (slope general trend captured (slope ≅≅ 1, y int. 1, y int. ≅≅ 0)0)•• cross sections seem real:cross sections seem real:
IKONOS Image of Lamar& Soda Butte System, YNP August 7, 2001
HABHAB--1 Cross1 Cross--SectionsSections
XS 1
XS 2
XS 3
XS 4
XS 5
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Reasons for misclassificationReasons for misclassification
•• natural variations within a feature typenatural variations within a feature type
•• “false” turbulence“false” turbulence
•• viewing angle relative to sun angle viewing angle relative to sun angle
•• variations in background (mixed pixels)variations in background (mixed pixels)
•• obstructions (e.g., trees, woody debris)obstructions (e.g., trees, woody debris)
•• inconsistent field mappinginconsistent field mapping
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Soda Butte Crk, Silver Gate, Oct, 1999 (9ssg57)Soda Butte Crk, Silver Gate, Oct, 1999 (9ssg57)
Variations within a feature typeVariations within a feature type
Soda Butte Crk, Round Prairie, Oct, 1999 (9srp96)Soda Butte Crk, Round Prairie, Oct, 1999 (9srp96)
Pools with varying substrate Pools with varying substrate and surface turbulenceand surface turbulence
Reflectance variations from turbulenceReflectance variations from turbulence
Downstream hydraulic riffle
Downstream hydraulic riffle
Up valley wind creating
Up valley wind creating
waves on a glidewaves on a glide
““False” turbulenceFalse” turbulence
Viewing angle relative to sunViewing angle relative to sun
Looking down at same time Looking down at same time and site (arrows show same and site (arrows show same transect)transect)
Looking towards sunLooking towards sun
Soda Butte Crk,Silver Gate, Oct 1999 (9ssg54)Soda Butte Crk,Silver Gate, Oct 1999 (9ssg54)
Soda Butte Crk,Silver Gate, Oct 1999 (9ssg55)Soda Butte Crk,Silver Gate, Oct 1999 (9ssg55)
Mixed pixels Mixed pixels
Soda Butte Crk, Oct 1999 (9srp94)Soda Butte Crk, Oct 1999 (9srp94)
One pixelOne pixel
Obstructed view of stream
pool
Logsover pool 1069 nm reflectance
(highlights woody debris)
493
nm r
efle
ctan
ce
Reflectance of Mapped Pool Sites
Pure pool spectra
Spectra for logs overhangingpool
Discussion: The power of visualization Discussion: The power of visualization
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Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Woody Debris Woody Debris Large woody debrisLarge woody debris
Wood Wood alters stream morphology, stores sediment and organic alters stream morphology, stores sediment and organic debris, provides fish cover, and debris, provides fish cover, and provides habitat and nutrients for provides habitat and nutrients for invertebrates. Mapping woody debris is a pain.invertebrates. Mapping woody debris is a pain.
Soda Butte near Amphitheatre Crk, P001314, June, 2000Soda Butte near Amphitheatre Crk, P001314, June, 2000
Field Map of Woody DebrisField Map of Woody DebrisLamar River, Aug., 1999Lamar River, Aug., 1999
Matched Filter Mapping of Woody DebrisMatched Filter Mapping of Woody Debris
Woody debrisWoody debris(orange)(orange)
False color mageFalse color mageby J. Rasmussenby J. Rasmussen
Proportion of wood in each pixel,Proportion of wood in each pixel,red = high, green red = high, green -- medium, blue = lowmedium, blue = low
Matched filter imageMatched filter imageby J. Boardmanby J. Boardman
Overall classification accuracy = 82%Overall classification accuracy = 82%
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Amphibian HabitatAmphibian HabitatAlgae and wetlandsAlgae and wetlands
•• key for many species, including amphibianskey for many species, including amphibians
•• frequently small (< several mfrequently small (< several m22), widely dispersed, ), widely dispersed, and missed by standard photos, maps, and groundand missed by standard photos, maps, and ground--based surveys.based surveys.
•• Census techniques areCensus techniques areneeded that guide us toneeded that guide us topotential habitat in apotential habitat in afast, efficient manner. fast, efficient manner.
Algae site 17, Cache Creek, WY, Aug, 2000Algae site 17, Cache Creek, WY, Aug, 2000Photos by Chuck PetersonPhotos by Chuck Peterson
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Initial Wetland ResultsInitial Wetland Results
Matched filter image by W.A. Marcus and Matched filter image by W.A. Marcus and Chuck PetersonChuck PetersonProbe 1 5 m imagery from Aug 25, 1999Probe 1 5 m imagery from Aug 25, 1999
Intentional Intentional overpredictionoverprediction (errors of (errors of commission) to avoid missing any commission) to avoid missing any amphibian habitat. In a preliminary amphibian habitat. In a preliminary survey, no wetland sites were missed out survey, no wetland sites were missed out of 15 visited. Three new amphibian sites of 15 visited. Three new amphibian sites were found.were found.
#5#5
#6#6
Imagery led us to find wetlands in Imagery led us to find wetlands in unexpected sitesunexpected sites
Cache Creek, WY, August 2000Cache Creek, WY, August 2000Photo by Chuck PetersonPhoto by Chuck Peterson
Spectral Angle Mapping of AlgaeSpectral Angle Mapping of AlgaeS.A.M. requires that the user define theS.A.M. requires that the user define thethreshold at which the feature is “found.”threshold at which the feature is “found.”
The user should therefore have The user should therefore have knowledge about the landscapeknowledge about the landscape
for feature ID to work.for feature ID to work.
SAM rule images by W.A. MarcusSAM rule images by W.A. MarcusProbe1 1 m data, Aug 03, 1999Probe1 1 m data, Aug 03, 1999
The algae training siteThe algae training site
Initial Algae ResultsInitial Algae Results
75% correct 75% correct classificaclassifica--tiontion out of 20 sites out of 20 sites visited. Found 4 new visited. Found 4 new amphibian sites in algae amphibian sites in algae areas.areas.
Site 18Site 18Probe1 1 m imagery, Aug 3, 1999Probe1 1 m imagery, Aug 3, 1999Matched filter image by W.A. Marcus and C. PetersonMatched filter image by W.A. Marcus and C. Peterson
Photos by C. PetersonPhotos by C. Peterson
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Vegetation MappingVegetation Mapping
Riparian Vegetation Riparian Vegetation
Riparian vegetation:Riparian vegetation:•• provides habitat for terrestrial and aquatic organismsprovides habitat for terrestrial and aquatic organisms•• stabilizes streams banksstabilizes streams banks•• is sensitive to disturbanceis sensitive to disturbance
Classical surveyClassical surveytechniques can betechniques can betime consuming andtime consuming andlocally destructive.locally destructive.
Hitching Post Willow Grid, P705Hitching Post Willow Grid, P705Soda Butte Creek, Aug, 1999Soda Butte Creek, Aug, 1999
Conifer 100%Conifer 100%
Sedge Sedge 96.6%96.6%
DecidDecid.. 100%100%
ThistleThistle 93.1%93.1%
AverageAverage 97.4%97.4%
% correct% correct
30 sites visited for 30 sites visited for each categoryeach category
Work by Kerry Halligan, YERCWork by Kerry Halligan, YERC
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Summary: high definition measurementSummary: high definition measurement
Overall Accuracies:Overall Accuracies:InIn--stream habitats:stream habitats: 76% 76% –– 91% 91% Depths:Depths: 67% 67% –– 99%99%Woody debris:Woody debris: 82%82%Algae:Algae: 75%75%Wetlands:Wetlands: 100%100%Vegetation:Vegetation: 93% 93% -- 100%100%
low gradient rifflelow gradient riffleglideglidepoolpoolrunrun
high gradient rifflehigh gradient riffle
eddy drop zoneeddy drop zone
Classification mapClassification map
Ethical ConcernsEthical ConcernsAdvances in remote mapping of riparian settings Advances in remote mapping of riparian settings –– with with examples from Yellowstone: examples from Yellowstone:
•• ability of high resolution imagery to “reveal all”ability of high resolution imagery to “reveal all”•• broad spatial coverage of spacebroad spatial coverage of space--based platformsbased platforms
•• ease of accessease of accessPerils of exposing natural resources and potential ethical Perils of exposing natural resources and potential ethical dilemmas:dilemmas:
•• unfriendly useunfriendly use•• exploitationexploitation
•• destruction of wildernessdestruction of wildernessWork to promote ethical uses of remote sensing data:Work to promote ethical uses of remote sensing data:
•• existing guidelinesexisting guidelines•• future roles for geographersfuture roles for geographers
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Mapping depth with 0.85Mapping depth with 0.85--m satellite m satellite imagery, Soda Butte Creek, WYimagery, Soda Butte Creek, WY
IKONOS analysis by Mark FonstadIKONOS analysis by Mark Fonstad
IKONOS (0.85 m)
High definition imagery from space, over the internetHigh definition imagery from space, over the internet
Satellite imagery Satellite imagery potentially available at potentially available at 0.75 to 4 m resolution for 0.75 to 4 m resolution for entire ecosystem entire ecosystem ––over the internetover the internet
IKONOS 3.8 m imagery
New technology and techniques enable:New technology and techniques enable:
•• greater mapping accuracy at finer spatial resolutions greater mapping accuracy at finer spatial resolutions than ever beforethan ever before
•• application and use of the high definition products at application and use of the high definition products at watershed, landscape and regional scaleswatershed, landscape and regional scales
•• global access to the high definition, regional imagery global access to the high definition, regional imagery and mapsand maps
Probe1 1 meter data, 8/03/99Probe1 1 meter data, 8/03/99MNF Image by W. A. MarcusMNF Image by W. A. Marcus
Applications and perils of exposing natural resources:Applications and perils of exposing natural resources:
•• military applications and potential unfriendly usemilitary applications and potential unfriendly use•• resource mapping and potential exploitationresource mapping and potential exploitation
•• potential destruction of the wildernesspotential destruction of the wildernessThe key question: to reveal, or not to reveal?The key question: to reveal, or not to reveal?
Exposing natural resources: potential ethical dilemmasExposing natural resources: potential ethical dilemmas
Algae site 17, Cache Creek, WY, Aug, 2000Algae site 17, Cache Creek, WY, Aug, 2000
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Military trafficability analysisMilitary trafficability analysis
Unfriendly use of high resolution image productsUnfriendly use of high resolution image products
Pools (in blue), too deep for crossing
Rapids (in white): Passable for vehicles, but too swift for foot traffic
Fine grained sediment (in yellow), might bog down vehicles
Glides (in green) are shallow, hard bottomed areas good for both foot and vehicle traffic
Best Crossing
Unfriendly use of image productsUnfriendly use of image products
1111--20202121--30303131--4040
00--10 cm10 cm5151--60606161--70707171--100100
4141--50 cm50 cm
>100 cm>100 cm
FlowFlow
nasty neighbornasty neighbor
downstream neighbordownstream neighbor
boun
dary
boun
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Stream inventory and managementStream inventory and management
FISH!!!!!!FISH!!!!!!
Private and commercial use of image productsPrivate and commercial use of image products
Resource inventoryResource inventory
#5#5
#6#6
Imagery led us to find wetlands in Imagery led us to find wetlands in unexpected sitesunexpected sites
Matched filter image by W.A. MarcusMatched filter image by W.A. MarcusProbe 1 5 m imagery from Aug 25, 1999Probe 1 5 m imagery from Aug 25, 1999
In a preliminary survey, 1In a preliminary survey, 1--m imagery m imagery identified 35 new algae or wetland sites. identified 35 new algae or wetland sites. Seven new amphibian sites were found in Seven new amphibian sites were found in backcountry wilderness.backcountry wilderness.
Resource management and Resource management and commercial exploitationcommercial exploitation
Matched filter image by W.A. Marcus, C PetersonMatched filter image by W.A. Marcus, C PetersonProbe 1 5 m imagery from Aug 25, 1999Probe 1 5 m imagery from Aug 25, 1999
Chuck’s Toads and Frogs Chuck’s Toads and Frogs Wilderness Adventures!Wilderness Adventures!
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The destruction of wildernessThe destruction of wilderness
Aldo Leopold: Aldo Leopold:
“..speaking geographically, the end of “..speaking geographically, the end of the Unknown is at hand. Is it to be the Unknown is at hand. Is it to be expected that it shall be lost from expected that it shall be lost from human experience without something human experience without something likewise being lost from human likewise being lost from human character?”character?”
Where can we turn for Where can we turn for specificspecific guidelines on ethical guidelines on ethical use of remote sensing for natural resource mapping?use of remote sensing for natural resource mapping?
Ethics: “The science of moral duty…, the science of the ideal Ethics: “The science of moral duty…, the science of the ideal ends of human action.”ends of human action.”
Underlying premise: moral actions promote the well being of Underlying premise: moral actions promote the well being of one or more living creatures, without harming othersone or more living creatures, without harming others
For remote sensing, Estep, 1973, p. 363: Goal is “providing a For remote sensing, Estep, 1973, p. 363: Goal is “providing a decent condition of living for all the peoples of the earth.”decent condition of living for all the peoples of the earth.”
Ethical remote sensing of natural resourcesEthical remote sensing of natural resources
Most professional codes focus on:Most professional codes focus on:•• quality of workquality of work
•• unfair competitionunfair competition•• intellectual propertyintellectual property
•• strengthening profession strengthening profession •• accessibility/privacyaccessibility/privacy
•• enforcement mechanisms and consequencesenforcement mechanisms and consequencesURISA, April, 2003 also has a social welfare emphasis:URISA, April, 2003 also has a social welfare emphasis:
•• work should be objective, clear, honestwork should be objective, clear, honest•• promote broad involvement and use of findingspromote broad involvement and use of findings
•• speak out about issuesspeak out about issues
Professional CodesProfessional CodesLegal Guidelines to Use of Remote SensingLegal Guidelines to Use of Remote Sensing
Sources to guide us:Sources to guide us:•• legislation legislation (not useful)(not useful)•• regulationregulation (nope)(nope)•• case lawcase law (some help)(some help)•• international treatiesinternational treaties (some help)(some help)
U.S. Case Law and PrivacyU.S. Case Law and PrivacyThe 4The 4thth Amendment prohibits unreasonable search by the Amendment prohibits unreasonable search by the
government (but not by other parties for commercial or government (but not by other parties for commercial or financial purposes)financial purposes)
Precedent is confusing:Precedent is confusing:•• Katz v. U.S., 1967 (the public phone booth case)Katz v. U.S., 1967 (the public phone booth case)
•• Dow v. U.S., 1981 (the air photo case) Dow v. U.S., 1981 (the air photo case) •• Oliver v. U.S., 1984 Oliver v. U.S., 1984 -- “an individual may not legitimately “an individual may not legitimately
demand privacy for activities conducted out of doors and demand privacy for activities conducted out of doors and in fields”in fields”
•• FLIR and recent casesFLIR and recent casesBut all this only applies to private propertyBut all this only applies to private property
International AgreementsInternational Agreements
Key guides:Key guides:
•• UN Declaration of Human Rights ( “Freedom to UN Declaration of Human Rights ( “Freedom to seek receive, and impart information and ideas seek receive, and impart information and ideas through any media and regardless of frontiers”) through any media and regardless of frontiers”)
•• UN Open Skies Policy (satellites are free to roam)UN Open Skies Policy (satellites are free to roam)
Consequence is open information for:Consequence is open information for:
•• entities with resources and expertiseentities with resources and expertise
•• groups/individuals with open internet accessgroups/individuals with open internet access
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Role of Geographers in Remote Sensing EthicsRole of Geographers in Remote Sensing Ethics
We can promote ethical use of remote sensing for mapping We can promote ethical use of remote sensing for mapping natural resources by:natural resources by:
•• Bringing a humanistic perspective to the engineering Bringing a humanistic perspective to the engineering dominated world of remote sensing (remote sensing is dominated world of remote sensing (remote sensing is more than techniques)more than techniques)
•• Providing creative avenues to engage in ethical thinking Providing creative avenues to engage in ethical thinking
•• Publishing on ethical uses of remote sensingPublishing on ethical uses of remote sensing
•• Promoting new laws, regulations and policies that apply Promoting new laws, regulations and policies that apply to remote sensing to remote sensing
•• Engaging in certification efforts and development of Engaging in certification efforts and development of ethical standards in professional societiesethical standards in professional societies
More immediate contributionsMore immediate contributions
Adopting ethics guiding human geography investigations to Adopting ethics guiding human geography investigations to remote sensing of natural resources:remote sensing of natural resources:
•• concepts of census data useconcepts of census data use
•• ethics guiding interviewsethics guiding interviews
•• human subject clearancehuman subject clearance
•• teachingteaching