Session 5 Forestry and Change Detection Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 [email protected] Daniel L. Civco LERIS

Session 5Forestry and Change Detection


Daniel L. CivcoLERIS / NRME

University of ConnecticutStorrs CT 06269

[email protected]



[email protected]

CORSE 2000 June 26-29, 2000 University of Southern MississippiGulf Park Conference Center


http://www.safnet.org/pubs/jof/index.html

June 2000 Volume 98 Number 6 June 2000 Volume 98 Number 6 June 2000 Volume 98 Number 6 June 2000 Volume 98 Number 6 •Remote Sensing and Forestry: Collaborative Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Implementation for a New Century of Forest Information SolutionsInformation Solutions•Foresters' Roles in Remote SensingForesters' Roles in Remote Sensing•From Pixels to Decisions: Digital Remote From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Sensing Technologies for Public Land ManagersManagers

Remote Sensing Data Sources and TechniquesRemote Sensing Data Sources and Techniques •Aerial Photography in the Next DecadeAerial Photography in the Next Decade•Digital Imaging Basics for Natural Resource ManagersDigital Imaging Basics for Natural Resource Managers•Videography for ForestersVideography for Foresters•The Earth Observing System and Forest ManagementThe Earth Observing System and Forest Management•Intermediate Multispectral Satellite SensorsIntermediate Multispectral Satellite Sensors•Selecting and Interpreting High-Resolution ImagesSelecting and Interpreting High-Resolution Images•Forest Information from Synthetic Aperture RadarForest Information from Synthetic Aperture Radar•Lidar Remote Sensing for ForestryLidar Remote Sensing for Forestry•Using Hyperspectral Data to Assess Forest StructureUsing Hyperspectral Data to Assess Forest Structure•Map Data in Support of Forest ManagementMap Data in Support of Forest Management•Image and Spatial Analysis Software ToolsImage and Spatial Analysis Software Tools•Field Applications for Statistical Data and TechniquesField Applications for Statistical Data and Techniques•Integrating Data and Information for Effective Forest Integrating Data and Information for Effective Forest ManagementManagement

•Remote Sensing and Forestry: Collaborative Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Implementation for a New Century of Forest Information SolutionsInformation Solutions•Foresters' Roles in Remote SensingForesters' Roles in Remote Sensing•From Pixels to Decisions: Digital Remote From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Sensing Technologies for Public Land ManagersManagers

Remote Sensing Data Sources and TechniquesRemote Sensing Data Sources and Techniques •Aerial Photography in the Next DecadeAerial Photography in the Next Decade•Digital Imaging Basics for Natural Resource ManagersDigital Imaging Basics for Natural Resource Managers•Videography for ForestersVideography for Foresters•The Earth Observing System and Forest ManagementThe Earth Observing System and Forest Management•Intermediate Multispectral Satellite SensorsIntermediate Multispectral Satellite Sensors•Selecting and Interpreting High-Resolution ImagesSelecting and Interpreting High-Resolution Images•Forest Information from Synthetic Aperture RadarForest Information from Synthetic Aperture Radar•Lidar Remote Sensing for ForestryLidar Remote Sensing for Forestry•Using Hyperspectral Data to Assess Forest StructureUsing Hyperspectral Data to Assess Forest Structure•Map Data in Support of Forest ManagementMap Data in Support of Forest Management•Image and Spatial Analysis Software ToolsImage and Spatial Analysis Software Tools•Field Applications for Statistical Data and TechniquesField Applications for Statistical Data and Techniques•Integrating Data and Information for Effective Forest Integrating Data and Information for Effective Forest ManagementManagement


•Remote Sensing and Forestry: Remote Sensing and Forestry: Collaborative Implementation for a New Collaborative Implementation for a New Century of Forest Information SolutionsCentury of Forest Information Solutions

•Kathleen Bergen, John Colwell, & Frank SapioKathleen Bergen, John Colwell, & Frank Sapio

•Remote Sensing and Forestry: Remote Sensing and Forestry: Collaborative Implementation for a New Collaborative Implementation for a New Century of Forest Information SolutionsCentury of Forest Information Solutions

•Kathleen Bergen, John Colwell, & Frank SapioKathleen Bergen, John Colwell, & Frank Sapio

“ “ ... new forest management ... new forest management paradigms and rapid paradigms and rapid technological advances technological advances together create an together create an organizational and organizational and technological challenge, as technological challenge, as well as a great opportunity for well as a great opportunity for advancing forestry.”advancing forestry.”

“ “ ... new forest management ... new forest management paradigms and rapid paradigms and rapid technological advances technological advances together create an together create an organizational and organizational and technological challenge, as technological challenge, as well as a great opportunity for well as a great opportunity for advancing forestry.”advancing forestry.”


•From Pixels to Decisions: Digital Remote From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Sensing Technologies for Public Land ManagersManagers

•Henry Lachowski, Paul Maud, and Norm RollerHenry Lachowski, Paul Maud, and Norm Roller

•From Pixels to Decisions: Digital Remote From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Sensing Technologies for Public Land ManagersManagers

•Henry Lachowski, Paul Maud, and Norm RollerHenry Lachowski, Paul Maud, and Norm Roller

“… “… forest managers need forest managers need information about the information about the geospatial infrastructure, geospatial infrastructure, including the location, including the location, amount, and condition of the amount, and condition of the forest’s natural and cultural forest’s natural and cultural resources.”resources.”

“… “… forest managers need forest managers need information about the information about the geospatial infrastructure, geospatial infrastructure, including the location, including the location, amount, and condition of the amount, and condition of the forest’s natural and cultural forest’s natural and cultural resources.”resources.”

DeforestatioDeforestationn

DeforestatioDeforestationn

Deforestation is the Deforestation is the permanent destruction of permanent destruction of forests and woodlands. forests and woodlands. The increasing population The increasing population requires greater food requires greater food production - deforestation production - deforestation occurs as the forests are occurs as the forests are converted for agricultural converted for agricultural and urban uses. and urban uses. In the In the past three decades one past three decades one fifth of all tropical forests fifth of all tropical forests were lost.were lost. Currently, 12 Currently, 12 million hectares of forests million hectares of forests are cleared annually. Most are cleared annually. Most deforestation occurs in deforestation occurs in the moist forests and the moist forests and open woodlands of the open woodlands of the tropics.tropics.

Deforestation is the Deforestation is the permanent destruction of permanent destruction of forests and woodlands. forests and woodlands. The increasing population The increasing population requires greater food requires greater food production - deforestation production - deforestation occurs as the forests are occurs as the forests are converted for agricultural converted for agricultural and urban uses. and urban uses. In the In the past three decades one past three decades one fifth of all tropical forests fifth of all tropical forests were lost.were lost. Currently, 12 Currently, 12 million hectares of forests million hectares of forests are cleared annually. Most are cleared annually. Most deforestation occurs in deforestation occurs in the moist forests and the moist forests and open woodlands of the open woodlands of the tropics.tropics.

http://ps.ucdavis.edu/classes/ire001/env/deforest.htmhttp://ps.ucdavis.edu/classes/ire001/env/deforest.htm

http://www.wri.org/forests/index.html

http://ps.ucdavis.edu/classes/ire001/env/deforest.htmhttp://ps.ucdavis.edu/classes/ire001/env/deforest.htm

http://www.wri.org/forests/index.html

Deforestation in the TropicsDeforestation in the TropicsDeforestation in the TropicsDeforestation in the Tropics

http://www.dpi.inpe.br/Amazonia/pg13.htmlhttp://www.dpi.inpe.br/Amazonia/pg13.html

Overview Map Overview Map

Degree of Deforestation Degree of Deforestation

LandsatLandsat ImageImage

FragmentationFragmentationFragmentationFragmentation• Habitat Fragmentation, Modification or Loss Sources

of habitat fragmentation include:– Agriculture: Conversion of prairie and forest areas to

intensive agriculture eliminates nesting cover.

– Forestry: Harvesting and regeneration modify the Forestry: Harvesting and regeneration modify the forest landscape and alter the structural and plant forest landscape and alter the structural and plant species diversity.species diversity.

– Urbanization: Urban sprawl to accommodate a growing human population progressively consumes natural areas.

– Linear development: Roads, pipelines and hydro rights-of-way open up previously difficult-to-access territory to human use.

– Climate change: When growing conditions are altered, habitat availability is affected, especially for species at the edge of their range

• Habitat Fragmentation, Modification or Loss Sources of habitat fragmentation include:– Agriculture: Conversion of prairie and forest areas to

intensive agriculture eliminates nesting cover.

– Forestry: Harvesting and regeneration modify the Forestry: Harvesting and regeneration modify the forest landscape and alter the structural and plant forest landscape and alter the structural and plant species diversity.species diversity.

– Urbanization: Urban sprawl to accommodate a growing human population progressively consumes natural areas.

– Linear development: Roads, pipelines and hydro rights-of-way open up previously difficult-to-access territory to human use.

– Climate change: When growing conditions are altered, habitat availability is affected, especially for species at the edge of their range

http://www.cws-scf.ec.gc.ca/canbird/pif/habitat.htmhttp://www.cws-scf.ec.gc.ca/canbird/pif/habitat.htm

The Northeast LandscapeThe Northeast LandscapeIn the beginning, there was forest...In the beginning, there was forest...

After near total conversion to farmland,

much forest has returned...

After near total conversion to farmland,

much forest has returned...

The Northeast LandscapeThe Northeast Landscape

Now, farm and forest are being converted to developed land, particularly subdivisions.

Now, farm and forest are being converted to developed land, particularly subdivisions.


Is urban sprawl, deforestation, and habitat fragmentation the future of the Northeast?Is urban sprawl, deforestation, and habitat fragmentation the future of the Northeast?


The Power to VisualizeThe Power to VisualizeThe Power to VisualizeThe Power to Visualize

ZZZZZ...ZZZZZ...

wa*ter*shed wa*ter*shed nn. . 1. An area of 1. An area of land draining to land draining to a common a common outlet.outlet.

wa*ter*shed wa*ter*shed nn. 1. An . 1. An area of land draining area of land draining to a common outlet.to a common outlet.

wa*ter*shed wa*ter*shed nn. 1. An . 1. An area of land draining area of land draining to a common outlet.to a common outlet.

HMMM...HMMM...

The Power to The Power to VisualizeVisualize


AWESOME !AWESOME !AWESOME !AWESOME !





? ?? ?? ?? ?

What the…?!What the…?!What the…?!What the…?!

? ? ?? ? ?? ? ?? ? ?

Picasso

The Power to The Power to ConfuseConfuse

The Power to The Power to ConfuseConfuse

What is a Watershed?What is a Watershed?What is a Watershed?What is a Watershed?

A Watershed is an area of land that drains to a single outlet.

A Watershed is an area of land that drains to a single outlet.

3-D to drive home the point3-D to drive home the point

3D Visualizations

ADARTMDEM

Thematic Mapper Band 6, Thermal,Thematic Mapper Band 6, Thermal, Resampled to 30 Meter ResolutionResampled to 30 Meter Resolution

Make the Obvious Even More ObviousMake the Obvious Even More Obvious

By enhancing By enhancing visualization ...visualization ...By enhancing By enhancing visualization ...visualization ...

3-D Surface of Temperature Differences3-D Surface of Temperature Differences

Cooler

Warmer

What are heat sinks?What will reduce thermal gains?

Roads are built. Roads are built.

Forest FragmentationForest Fragmentation

Developed

areas follow. Developed

areas follow.


Patches of contiguous forest become smaller.

Patches of contiguous forest become smaller.


Forest resources are fragmented.

Forest resources are fragmented.


““Let me see this fragmentation”Let me see this fragmentation”““Let me see this fragmentation”Let me see this fragmentation”

Impervious overlay from planimetric data

80 meter MSS multispectral80 meter MSS w/ impervious overlay30 meter TM 7 band multispectral30 meter TM w/ impervious overlay10 meter SPOT panchromatic10 meter SPOT w/ impervious overlay1 meter DOQ panchromatic1 meter DOQ w/ impervious overlay1 meter ADAR 4 band multispectral1 meterADAR w/ impervious overlayMultiresolution ComparisonMultiresolution Comparison

Steamboat WillieSteamboat WillieSteamboat WillieSteamboat Willie

In 1928, Disney made history with the release of first talkie

animation film Steamboat Willie featuring Mickey Mouse.

.. and haven’t we come a long way .. and haven’t we come a long way since?since?

.. and haven’t we come a long way .. and haven’t we come a long way since?since?

Mt Fuji volcano flyby created completely from ASTER data

http://terra.nasa.gov/Gallery/http://terra.nasa.gov/Gallery/

Remote Sensing in Action:The ASTER Sensor Aboard Terra

Remote Sensing in Action:The ASTER Sensor Aboard Terra


Remote Sensing in Action:The MODIS Sensor Aboard TerraRemote Sensing in Action:The MODIS Sensor Aboard Terra


Global Normalized Difference Global Normalized Difference Vegetation Index (NDVI)Vegetation Index (NDVI)






Mount St. HelensMount St. HelensMount St. HelensMount St. Helens

http://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelenshttp://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelens

April 1980April 1980 May1980May1980 June 1980June 1980

Mount St. HelensMount St. HelensMount St. HelensMount St. Helens

http://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelenshttp://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelens

Landsat MSSLandsat MSS

19719733

19819833

19819888

19919922

19919966

EarthShotsEarthShotsEarthShotsEarthShots

http://edcwww.cr.usgs.gov/earthshots/slow/tableofcontentshttp://edcwww.cr.usgs.gov/earthshots/slow/tableofcontents

Rondonia, Brazil: 1975-1992Rondonia, Brazil: 1975-1992

http://edcwww.cr.usgs.gov/earthshots/slow/Rondonia/Rondoniahttp://edcwww.cr.usgs.gov/earthshots/slow/Rondonia/Rondonia

19719755

19819866

19919922

Fires in Wyperfeld National Park, Victoria, Southeast

Australia

Fires in Wyperfeld National Park, Victoria, Southeast

Australia

http://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeldhttp://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeld

19719755

19819855

19919999

Fires in Wyperfeld National Park 1979 to 1997

Fires in Wyperfeld National Park 1979 to 1997

http://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeldhttp://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeld

Clearcutting Near Olympic National Park, WA


http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.htmlhttp://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html




1991991 1

1981986 6

1981987 7

1981988 8







1984 1984 toto

1995 1995

Deforestation near Santa Cruz, Bolivia from 1984 to 1998

Deforestation near Santa Cruz, Bolivia from 1984 to 1998

http://svs.gsfc.nasa.gov/imagewall/LandSat/santa_cruz.html

150 miles150 miles

200 miles200 miles

Urban Growth in the DC Urban Growth in the DC Area: 1973-1996Area: 1973-1996

Urban Growth in the DC Urban Growth in the DC Area: 1973-1996Area: 1973-1996

http://svs.gsfc.nasa.gov/imagewall/LandSat/dc_growth.htmlhttp://svs.gsfc.nasa.gov/imagewall/LandSat/dc_growth.html

Urban Urban Growth Growth

HistoriesHistories

Urban Urban Growth Growth

HistoriesHistories

Baltimore-Baltimore-WashingtoWashington Corridorn Corridor

http://www.ncgia.ucsb.edu/projects/gig/http://www.ncgia.ucsb.edu/projects/gig/

Urban Urban GrowtGrowt

h h HistorHistor

yy

Urban Urban GrowtGrowt

h h HistorHistor

yyMarlborougMarlboroug

hhSubdivisionSubdivision

GrowthGrowth

resac.uconn.eduresac.uconn.edu

Urban Growth Urban Growth ProjectionsProjections

Urban Growth Urban Growth ProjectionsProjections

San Francisco Bay AreaSan Francisco Bay Area Eastern PennsylvaniaEastern Pennsylvania

http://www.essc.psu.edu/~dajr/chester/animation/http://www.essc.psu.edu/~dajr/chester/animation/

Even Even WEWE cause cause fragmentationfragmentation

Even Even WEWE cause cause fragmentationfragmentation

http://www.sp.uconn.edu/~wwwucimt/pano/images/towers.movhttp://www.sp.uconn.edu/~wwwucimt/pano/images/towers.mov

Click photoClick photoFile / Open Movie / File / Open Movie / Uconn from Towers.movResize WindowsPan Right-Left

C:\Program Files\QuickTime\QuickTimePlayer.exe

NAUTILUS Research Objectives

NAUTILUS Research Objectives

Better land Better land covercover

Better land Better land covercover

Sprawl Sprawl metricsmetricsSprawl Sprawl

metricsmetrics

Forest Forest fragmentation fragmentation

metricsmetrics

Forest Forest fragmentation fragmentation

metricsmetrics

Better Better impervious impervious

covercover

Better Better impervious impervious

covercover

60 meterthermal

60 meterthermal

15 meterpanchromatic

15 meterpanchromatic

30 metermultispectral

30 metermultispectral

Landsat ETM+ Data

Landsat ETM+ Data

IKONOS

ADAR 5500

ADAR 5500 and IKONOSIKONOS coverage

ADAR coverage

Acquired through the NASA Scientific Data Purchase Program

High Resolution Airborne & Satellite DataHigh Resolution Airborne & Satellite Data

ASTER MODISEarth Observer 1Earth Observer 1 SPOT

Hyperion ALI

Other Satellite DataOther Satellite Data

Basic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and Change

Objectives

• To identify and quantify general land cover change over a 25 year period

• Perform classifications using traditional classification techniques

Procedures, Salmon River Watershed


ISODATA clusteringinto 200 clusters(for each date)

Land Cover (7 classes)

Label clusters into 7 classes

MSS & TM

Identify best signatures from 1973MSS &

1985TM,Perform Maximum

Likelihood Classifier

Adjust classifications to remove unlikely changes due to classification error

(i.e. urban to forest)

HECTARESUrb/Bare Ag/Grass Forest

1973 2484 3516 312981976 2479 3917 305941978 2612 3060 321731981 3674 3024 306671983 2407 3851 308901985 3927 4151 291161988 5292 3673 281521993 5024 2483 295731995 5110 4072 28059

Calculate category areas

Data, Salmon River Watershed


April 24, 1973resampled MSS

May 5, 1976resampled MSS

October 31, 1978resampled MSS

March 4, 1981resampled MSS


May 4, 1988 TM

April 25, 1993 TMMay 8, 1995 TM

April 26, 1985 TM

Results to Date, Salmon River Watershed



May 5, 1976 MSS

October 31, 1978 MSS

March 4, 1981 MSS

April 18, 1983 MSS

May 4, 1988 TM

April 25, 1993 TMMay 8, 1995 TM

April 26, 1985 TM

Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl

Objectives

• To develop a practical method for assessing forest fragmentation and urban sprawl

• Use ArcView GIS and extensions exclusively

Procedures

ISODATA clusteringinto 100 clusters

(ArcView Image Analyst)

Land Cover (7 classes)

Spring & Summer TM Images

Derive Spatial Statistics

(ArcView Spatial Analyst)

Label clusters into 7 classes

Total Class Area(ha)

Number ofPatches (n)

Mean Patch Size(ha)

Class 1985 1995 1985 1995 1985 1995URBAN 1,495 2,578 812 1,856 2.42 1.68FOREST 30,565 29,751 2,524 2,785 11.97 10.52GRASS 3,909 3,713 3,823 3,463 0.99 1.05


Data, Salmon River Watershed

Landsat TM April 26,

1985

Landsat TM August 9,

1985

Landsat TM May 8, 1995

Landsat TM August 28,

1995



1985 Land Cover 1995 Land Cover


1985 Land Cover 1985 Land Cover 1985 Land Cover 1985 Land Cover 1985 Land Cover 1985 Land Cover 1985 Land Cover 1985 Land Cover 1995 Land Cover 1995 Land Cover 1995 Land Cover 1995 Land Cover

Detail Areas of ChangeDetail Areas of Change

1995 Land Cover 1995 Land Cover 1995 Land Cover 1995 Land Cover

Urban Land Gains: 1985 to 1995 (Hectares)

7.7

892.4

36.4

353.4

42.9

Water

Forest

Wetland

Grassland

Barren


- Change to Urban Land- Other Change- No Change

Total urban land gain 1,333 ha


Forest Land Losses: 1985 to 1995 (Hectares)

68.7

345.5

892.4

853.0

63.9

Water

Wetland

Urban

Grassland

Barren


- Change from Forest Land- Other Change- No Change

Total forest land loss 2,223 ha


Grassland Losses: 1985 to 1995 (Hectares)

2.8

801.55.9

353.4

47.7

Water

Forest

Wetland

Urban

Barren


- Change from Grassland- Other Change- No Change

Total grassland loss 1,211 ha



Total Class Area(ha)

Number ofPatches (n)

Mean Patch Size(ha)

Class 1985 1995 1985 1995 1985 1995URBAN 1,495 2,578 812 1,856 2.42 1.68FOREST 30,565 29,751 2,524 2,785 11.97 10.52GRASS 3,909 3,713 3,823 3,463 0.99 1.05


Results to Date, Salmon River WatershedGRASSLAND1985, 10.1 % of the total area1995, 9.6 % of the total area

FOREST1985, 79.2 % of the total area1995, 77.1 % of the total area

URBAN1985, 3.9 % of the total area1995, 6.7 % of the total area

165 ha

892 ha

853 ha

801 ha

353 ha62 ha


• To create a visual demonstration of actual change occurring in the landscape through the use of animations

Objectives

Temporal Image SequencingTemporal Image Sequencing

Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research

1995 TM

Original 1985 TM

ER Mapper Algorithm

Linear Transforms

Non-linear transform

1995 TM

Histogram-matched 1985 TM

Procedures, Histogram Matching



1991 Interpolated TM

Formula Editor

[((1990 * 4) + (1995 * 1))/5] = 1991

.GIF Movie Creator

.AVI Format Movie

Procedures, Interpolation and Movie Creation

1990 TM

1995 TM



Data, springtime Landsat imagery


May 5, 1976resampled MSS


May 4, 1988 TM

May 8, 1995 TM

April 26, 1985 TM



Data, summertime Landsat imagery

August 9, 1985 TM

August 30, 1990 TM

August 28, 1995 TM

August 31, 1999 TM



Springtime 1973-1995 Animation



Summertime 1985-1999 Animation



Intertnet Sources of Forest-Intertnet Sources of Forest-related Informationrelated Information

Intertnet Sources of Forest-Intertnet Sources of Forest-related Informationrelated Information

http://www.wri.org/gfw/http://www.wri.org/gfw/http://www.wri.org/gfw/http://www.wri.org/gfw/

http://www.fanweb.org/index.shtml//http://www.fanweb.org/index.shtml//

http://www.forestwatch.sr.unh.edu/http://www.forestwatch.sr.unh.edu/

Where Can You Find Additional Educational Resources on Remote

Sensing?

Where Can You Find Additional Educational Resources on Remote

Sensing?

… … how about the Internet?how about the Internet?… … how about the Internet?how about the Internet?

ASPRS Remote Sensing ASPRS Remote Sensing Core CurriculumCore Curriculum

ASPRS Remote Sensing ASPRS Remote Sensing Core CurriculumCore Curriculum

http://research.umbc.edu/~tbenja1/index.htmlhttp://research.umbc.edu/~tbenja1/index.html

NASA On-Line NASA On-Line Remote Remote Sensing Sensing TutorialTutorial

NASA On-Line NASA On-Line Remote Remote Sensing Sensing TutorialTutorial

http://rst.gsfc.nasa.gov/http://rst.gsfc.nasa.gov/

Canada Center for Remote Canada Center for Remote Sensing FundamentalsSensing Fundamentals

Canada Center for Remote Canada Center for Remote Sensing FundamentalsSensing Fundamentals

http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.htmlhttp://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.html

Where Can You Find Digital Remote Sensing Image

DataFor Forest Characterization

?

Where Can You Find Digital Remote Sensing Image

DataFor Forest Characterization

?

… … how about the Internet?how about the Internet?… … how about the Internet?how about the Internet?

North American Landscape

Characterization (NALC)



http://edcdaac.usgs.gov/pathfinder/pathpage.html#nalc

19731973 19801980 19901990

Landsat MSS Triplicates Landsat MSS Triplicates Landsat MSS Triplicates Landsat MSS Triplicates





http://edcdaac.usgs.gov/pathfinder/pathpage.html#nalc

Landsat MSS and DEMLandsat MSS and DEMLandsat MSS and DEMLandsat MSS and DEM19901990 DEMDEM

TerraServer

TerraServer

http://www.terraserver.com/

Global Land Information Global Land Information SystemSystem

Global Land Information Global Land Information SystemSystem

Landsat TM August 20, 1998Landsat TM August 20, 1998

http://edcwww.cr.usgs.gov/webglis

http://edcwww.cr.usgs.gov/webglis

March 7, 2000 ETM+March 7, 2000 ETM+http://landsat7.usgs.gov/order.htmlhttp://edcimswww.cr.usgs.gov/pub/imswelcome/

http://landsat7.usgs.gov/order.htmlhttp://edcimswww.cr.usgs.gov/pub/imswelcome/

http://images.jsc.nasa.gov/iams/html/http://images.jsc.nasa.gov/iams/html/

Other Sites for DataOther Sites for Data

http://svs.gsfc.nasa.gov/imagewall.html

http://svs.gsfc.nasa.gov/imagewall.html

http://terra.nasa.gov/http://terra.nasa.gov/

… … or Visit NASA’s RESAC at or Visit NASA’s RESAC at UConnUConn

… … or Visit NASA’s RESAC at or Visit NASA’s RESAC at UConnUConn

http://resac.uconn.eduhttp://resac.uconn.edu

… … where you’ll find ...where you’ll find ...… … where you’ll find ...where you’ll find ...

http://resac.uconn.eduhttp://resac.uconn.edu

.. and ..... and ..... and ..... and ...

Research & Education Research & Education WatershedsWatershedsResearch & Education Research & Education WatershedsWatersheds

A range of land covers and issuesA range of land covers and issues

PresumpscotPresumpscot

SuAsCoSuAsCo

SalmonSalmon

StonybrookStonybrook

Salmon River Watershed, CTSalmon River Watershed, CT

140 Sq. Miles

• Focused watershed for NAUTILUS research

• Rapid Urbanization

• 5 out of 7 watershed towns are listed as the fastest growing towns in the State

• CES program and research already existing in watershed

• Key component of the lower Connecticut River Watershed

• State highway connects with major Hartford market

Stony Brook Millstone Watershed, NJ

Stony Brook Millstone Watershed, NJ

265 Sq. Miles

• Has a strong Watershed Association in existence

• Between New York City and Philadelphia

• Increased development pressures

• Loss of agriculture land to urban sprawl

SuAsCo Watershed, MA

SuAsCo Watershed, MA

377 Sq. Miles

SuSudburydburyAsAssabet sabet

CoConcord ncord Watershed Watershed

• Has a strong Watershed Coalition in existence

• Between the Boston metropolitan region and Worcester

• Rapid development

• New residential development replacing forests

• All river segments are Class B waters

PresumpscotWatershed, MEPresumpscot

Watershed, ME

200 Sq. Miles

• Existing NEMO Program

• Focus is on the lower portion of Presumpscot River

• Most urban and rapidly developing region in the State

• Significant water quality problems in the lower portion

• Adjacent Harraseeket River coastal watershed, which contains Freeport, is included in NAUTILUS Project

Alternative/Emerging Classification ApproachesAlternative/Emerging Classification ApproachesAlternative/Emerging Classification ApproachesAlternative/Emerging Classification Approaches

Knowledge Based Expert Systems, Procedures

Decision Tree

Source Dataand Derivatives

Final Classifications

Alternative/Emerging Classification ApproachesAlternative/Emerging Classification ApproachesAlternative/Emerging Classification ApproachesAlternative/Emerging Classification Approaches

Neural Networks, Procedures

Principal Components

Input Layers

Back-propagation

May 8, 1995 TM

Neural Network Training

Neural Network Classification

Output Layer

Neural Network Based Land Cover ChangeNeural Network Based Land Cover ChangeNeural Network Based Land Cover ChangeNeural Network Based Land Cover Change

Proof of Concept Study

Backpropagationneural network

Forest to non-forest land cover change

Land Cover Mapping and Change Detection Using

ArcView

Land Cover Mapping and Change Detection Using

ArcView

……. In tomorrow’s breakout. In tomorrow’s breakoutsession we’ll look at ...session we’ll look at ...……. In tomorrow’s breakout. In tomorrow’s breakoutsession we’ll look at ...session we’ll look at ...





[email protected]



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Documents

Session 5 Forestry and Change Detection Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 [email protected] Daniel L. Civco LERIS