Session 5Forestry and Change Detection
Session 5Forestry and Change Detection
Daniel L. CivcoLERIS / NRME
University of ConnecticutStorrs CT 06269
Daniel L. CivcoLERIS / NRME
University of ConnecticutStorrs CT 06269
CORSE 2000 June 26-29, 2000 University of Southern MississippiGulf Park Conference Center
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
http://www.safnet.org/pubs/jof/index.html
•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.”
http://www.safnet.org/pubs/jof/index.html
•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.
The Northeast LandscapeThe Northeast Landscape
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 Northeast LandscapeThe Northeast Landscape
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
The Power to The Power to VisualizeVisualize
AWESOME !AWESOME !AWESOME !AWESOME !
AWESOME !AWESOME !AWESOME !AWESOME !
AWESOME !AWESOME !AWESOME !AWESOME !
The Power to The Power to VisualizeVisualize
The Power to The Power to VisualizeVisualize
? ?? ?? ?? ?
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.
Forest FragmentationForest Fragmentation
Patches of contiguous forest become smaller.
Patches of contiguous forest become smaller.
Forest FragmentationForest Fragmentation
Forest resources are fragmented.
Forest resources are fragmented.
Forest FragmentationForest Fragmentation
““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
http://terra.nasa.gov/Gallery/http://terra.nasa.gov/Gallery/
Remote Sensing in Action:The MODIS Sensor Aboard TerraRemote Sensing in Action:The MODIS Sensor Aboard Terra
http://terra.nasa.gov/Gallery/http://terra.nasa.gov/Gallery/
Global Normalized Difference Global Normalized Difference Vegetation Index (NDVI)Vegetation Index (NDVI)
Global Normalized Difference Global Normalized Difference Vegetation Index (NDVI)Vegetation Index (NDVI)
http://terra.nasa.gov/Gallery/http://terra.nasa.gov/Gallery/
Global Normalized Difference Global Normalized Difference Vegetation Index (NDVI)Vegetation Index (NDVI)
Global Normalized Difference Global Normalized Difference Vegetation Index (NDVI)Vegetation Index (NDVI)
http://terra.nasa.gov/Gallery/http://terra.nasa.gov/Gallery/
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
Clearcutting Near Olympic National Park, WA
http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.htmlhttp://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html
Clearcutting Near Olympic National Park, WA
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
Clearcutting Near Olympic National Park, WA
Clearcutting Near Olympic National Park, WA
http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.htmlhttp://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html
Clearcutting Near Olympic National Park, WA
Clearcutting Near Olympic National Park, WA
http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.htmlhttp://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html
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
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
Basic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and Change
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
Basic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and Change
April 24, 1973resampled MSS
May 5, 1976resampled MSS
October 31, 1978resampled MSS
March 4, 1981resampled MSS
April 18, 1983resampled MSS
May 4, 1988 TM
April 25, 1993 TMMay 8, 1995 TM
April 26, 1985 TM
Results to Date, Salmon River Watershed
Basic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and ChangeBasic Land Cover Characterization and Change
April 24, 1973resampled MSS
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
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
Data, Salmon River Watershed
Landsat TM April 26,
1985
Landsat TM August 9,
1985
Landsat TM May 8, 1995
Landsat TM August 28,
1995
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
Results to Date, Salmon River Watershed
1985 Land Cover 1995 Land Cover
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
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
Results to Date, Salmon River Watershed
- Change to Urban Land- Other Change- No Change
Total urban land gain 1,333 ha
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
Forest Land Losses: 1985 to 1995 (Hectares)
68.7
345.5
892.4
853.0
63.9
Water
Wetland
Urban
Grassland
Barren
Results to Date, Salmon River Watershed
- Change from Forest Land- Other Change- No Change
Total forest land loss 2,223 ha
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
Grassland Losses: 1985 to 1995 (Hectares)
2.8
801.55.9
353.4
47.7
Water
Forest
Wetland
Urban
Barren
Results to Date, Salmon River Watershed
- Change from Grassland- Other Change- No Change
Total grassland loss 1,211 ha
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
Results to Date, Salmon River Watershed
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
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
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
Forest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban SprawlForest Fragmentation and Urban Sprawl
• 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
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
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
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
Data, springtime Landsat imagery
April 24, 1973resampled MSS
May 5, 1976resampled MSS
April 18, 1983resampled MSS
May 4, 1988 TM
May 8, 1995 TM
April 26, 1985 TM
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
Data, summertime Landsat imagery
August 9, 1985 TM
August 30, 1990 TM
August 28, 1995 TM
August 31, 1999 TM
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
Springtime 1973-1995 Animation
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
Summertime 1985-1999 Animation
Temporal Image SequencingTemporal Image Sequencing
Image Visualization ResearchImage Visualization ResearchImage Visualization ResearchImage Visualization Research
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)
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
North American Landscape
Characterization (NALC)
North American Landscape
Characterization (NALC)
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 ...
Session 5Forestry and Change Detection
Session 5Forestry and Change Detection
Daniel L. CivcoLERIS / NRME
University of ConnecticutStorrs CT 06269
Daniel L. CivcoLERIS / NRME
University of ConnecticutStorrs CT 06269
CORSE 2000 June 26-29, 2000 University of Southern MississippiGulf Park Conference Center
CORSE 2000 June 26-29, 2000 University of Southern MississippiGulf Park Conference Center