1. Session 5 Forestry and Change Detection Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 dcivco@canr.uconn.edu Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 dcivco@canr.uconn.edu CORSE 2000 June 26-29, 2000 University of Southern Mississippi Gulf Park Conference Center CORSE 2000 June 26-29, 2000 University of Southern Mississippi Gulf Park Conference Center

2. http://www.safnet.org/pubs/jof/index.html June 2000 Volume 98 Number 6 Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information SolutionsRemote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information Solutions Foresters' Roles in Remote SensingForesters' Roles in Remote Sensing From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land ManagersFrom Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Managers Remote 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 ManagementIntegrating Data and Information for Effective Forest Management Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information SolutionsRemote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information Solutions Foresters' Roles in Remote SensingForesters' Roles in Remote Sensing From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land ManagersFrom Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Managers Remote 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 ManagementIntegrating Data and Information for Effective Forest Management

3. http://www.safnet.org/pubs/jof/index.html Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information SolutionsRemote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information Solutions Kathleen Bergen, John Colwell, & Frank SapioKathleen Bergen, John Colwell, & Frank Sapio Remote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information SolutionsRemote Sensing and Forestry: Collaborative Implementation for a New Century of Forest Information Solutions Kathleen Bergen, John Colwell, & Frank SapioKathleen Bergen, John Colwell, & Frank Sapio “... new forest management paradigms and rapid technological advances together create an organizational and technological challenge, as well as a great opportunity for advancing forestry.” “... new forest management paradigms and rapid technological advances together create an organizational and technological challenge, as well as a great opportunity for advancing forestry.”

4. http://www.safnet.org/pubs/jof/index.html From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land ManagersFrom Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Managers Henry Lachowski, Paul Maud, and Norm RollerHenry Lachowski, Paul Maud, and Norm Roller From Pixels to Decisions: Digital Remote Sensing Technologies for Public Land ManagersFrom Pixels to Decisions: Digital Remote Sensing Technologies for Public Land Managers Henry Lachowski, Paul Maud, and Norm RollerHenry Lachowski, Paul Maud, and Norm Roller “… forest managers need information about the geospatial infrastructure, including the location, amount, and condition of the forest’s natural and cultural resources.” “… forest managers need information about the geospatial infrastructure, including the location, amount, and condition of the forest’s natural and cultural resources.”

5. DeforestationDeforestation Deforestation is the permanent destruction of forests and woodlands. The increasing population requires greater food production - deforestation occurs as the forests are converted for agricultural and urban uses. In the past three decades one fifth of all tropical forests were lost. Currently, 12 million hectares of forests are cleared annually. Most deforestation occurs in the moist forests and open woodlands of the tropics. http://ps.ucdavis.edu/classes/ire001/env/deforest.htm http://www.wri.org/forests/index.htmlhttp://ps.ucdavis.edu/classes/ire001/env/deforest.htm

6. Deforestation in the Tropics http://www.dpi.inpe.br/Amazonia/pg13.html Overview Map Degree of Deforestation Landsat Image

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

8. The Northeast Landscape In the beginning, there was forest...

9. After near total conversion to farmland, much forest has returned... After near total conversion to farmland, much forest has returned... The Northeast Landscape

10. Now, farm and forest are being converted to developed land, particularly subdivisions. The Northeast Landscape

11. Is urban sprawl, deforestation, and habitat fragmentation the future of the Northeast? The Northeast Landscape

12. The Power to Visualize ZZZZZ... wa*ter*shed n. 1. An area of land draining to a common outlet.

13. HMMM... The Power to Visualize

14. AWESOME ! The Power to Visualize

15. ? ? What the…?! ? ? ? Picasso The Power to Confuse

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

17. 3-D to drive home the point 3D Visualizations ADAR TM DEM

18. Thematic Mapper Band 6, Thermal, Resampled to 30 Meter Resolution Make the Obvious Even More Obvious By enhancing visualization...

19. 3-D Surface of Temperature Differences Cooler Warmer What are heat sinks? What will reduce thermal gains?

20. Roads are built. Forest Fragmentation

21. Developed areas follow. Forest Fragmentation

22. Patches of contiguous forest become smaller. Forest Fragmentation

23. Forest resources are fragmented. Forest Fragmentation

24. “Let me see this fragmentation”

25. Impervious overlay from planimetric data80 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 overlay Multiresolution Comparison

26. Steamboat Willie In 1928, Disney made history with the release of first talkie animation film Steamboat Willie featuring Mickey Mouse.

27. .. and haven’t we come a long way since? Mt Fuji volcano flyby created completely from ASTER data http://terra.nasa.gov/Gallery/

28. Remote Sensing in Action: The ASTER Sensor Aboard Terra http://terra.nasa.gov/Gallery/

29. Remote Sensing in Action: The MODIS Sensor Aboard Terra http://terra.nasa.gov/Gallery/

30. Global Normalized Difference Vegetation Index (NDVI) http://terra.nasa.gov/Gallery/

31. Global Normalized Difference Vegetation Index (NDVI) http://terra.nasa.gov/Gallery/

32. Mount St. Helens http://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelens April 1980 May1980 June 1980

33. Mount St. Helens http://edcwww.cr.usgs.gov/earthshots/slow/MtStHelens/MtStHelens Landsat MSS 1973 1983 1988 1992 1996

34. EarthShotsEarthShots http://edcwww.cr.usgs.gov/earthshots/slow/tableofcontents

35. Rondonia, Brazil: 1975-1992 http://edcwww.cr.usgs.gov/earthshots/slow/Rondonia/Rondonia 1975 1986 1992

36. Fires in Wyperfeld National Park, Victoria, Southeast Australia http://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeld 1975 1985 1999

37. Fires in Wyperfeld National Park 1979 to 1997 http://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeld

38. Fires in Wyperfeld National Park 1979 to 1997 http://edcwww.cr.usgs.gov/earthshots/slow/Wyperfeld/Wyperfeld

39. Clearcutting Near Olympic National Park, WA http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html

40. Clearcutting Near Olympic National Park, WA http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html 1991 1986 1987 1988

41. Clearcutting Near Olympic National Park, WA http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html

42. Clearcutting Near Olympic National Park, WA http://svs.gsfc.nasa.gov/imagewall/LandSat/olympic.html 1984to1995

43. Deforestation near Santa Cruz, Bolivia from 1984 to 1998 http://svs.gsfc.nasa.gov/imagewall/LandSat/santa_cruz.html 150 miles 200 miles

44. Urban Growth in the DC Area: 1973-1996 http://svs.gsfc.nasa.gov/imagewall/LandSat/dc_growth.html

45. Urban Growth Histories Baltimore- Washington Corridor http://www.ncgia.ucsb.edu/projects/gig/

46. Urban Growth History MarlboroughSubdivisionGrowth resac.uconn.edu

47. Urban Growth Projections San Francisco Bay Area Eastern Pennsylvania http://www.essc.psu.edu/~dajr/chester/animation/

48. Even WE cause fragmentation http://www.sp.uconn.edu/~wwwucimt/pano/images/towers.mov Click photo File / Open Movie / Uconn from Towers.mov Resize Windows Pan Right-Left

49. NAUTILUS Research Objectives Better land cover Sprawl metrics Forest fragmentation metrics Better impervious cover

50. 60 meter thermal 60 meter thermal 15 meter panchromatic 15 meter panchromatic 30 meter multispectral 30 meter multispectral Landsat ETM+ Data

51. IKONOS ADAR 5500 ADAR 5500 and IKONOS IKONOS coverage ADAR coverage Acquired through the NASA Scientific Data Purchase Program High Resolution Airborne & Satellite Data

52. ASTERMODIS Earth Observer 1 SPOT Hyperion ALI Other Satellite Data

53. Basic 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

54. Procedures, Salmon River Watershed Basic Land Cover Characterization and Change ISODATA clustering into 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) Calculate category areas

55. Data, Salmon River Watershed Basic Land Cover Characterization and Change April 24, 1973 resampled MSS May 5, 1976 resampled MSS October 31, 1978 resampled MSS March 4, 1981 resampled MSS April 18, 1983 resampled MSS May 4, 1988 TM April 25, 1993 TM May 8, 1995 TM April 26, 1985 TM

56. Results to Date, Salmon River Watershed Basic Land Cover Characterization and Change April 24, 1973 resampled MSS May 5, 1976 MSS October 31, 1978 MSS March 4, 1981 MSS April 18, 1983 MSS May 4, 1988 TM April 25, 1993 TM May 8, 1995 TM April 26, 1985 TM

57. Forest Fragmentation and Urban Sprawl Objectives To develop a practical method for assessing forest fragmentation and urban sprawl Use ArcView GIS and extensions exclusively

58. Procedures ISODATA clustering into 100 clusters (ArcView Image Analyst) Land Cover (7 classes) Spring & Summer TM Images Derive Spatial Statistics (ArcView Spatial Analyst) Label clusters into 7 classes Forest Fragmentation and Urban Sprawl

59. 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 Sprawl

60. Results to Date, Salmon River Watershed 1985 Land Cover1995 Land Cover Forest Fragmentation and Urban Sprawl

61. 1985 Land Cover 1995 Land Cover Detail Areas of Change 1995 Land Cover

62. 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 Sprawl

63. 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 Sprawl

64. Results to Date, Salmon River Watershed - Change from Grassland - Other Change - No Change Total grassland loss 1,211 ha Forest Fragmentation and Urban Sprawl

65. Results to Date, Salmon River Watershed Forest Fragmentation and Urban Sprawl

66. Results to Date, Salmon River Watershed GRASSLAND 1985, 10.1 % of the total area 1995, 9.6 % of the total area FOREST 1985, 79.2 % of the total area 1995, 77.1 % of the total area URBAN 1985, 3.9 % of the total area 1995, 6.7 % of the total area 165 ha 892 ha 853 ha 801 ha 353 ha 62 ha Forest Fragmentation and Urban Sprawl

67. To create a visual demonstration of actual change occurring in the landscape through the use of animations Objectives Temporal Image Sequencing Image Visualization Research

68. 1995 TM Original 1985 TM ER Mapper Algorithm Linear Transforms Non-linear transform 1995 TM Histogram- matched 1985 TM Procedures, Histogram Matching Temporal Image Sequencing Image Visualization Research

69. 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 Sequencing Image Visualization Research

70. Data, springtime Landsat imagery April 24, 1973 resampled MSS May 5, 1976 resampled MSS April 18, 1983 resampled MSS May 4, 1988 TM May 8, 1995 TM April 26, 1985 TM Temporal Image Sequencing Image Visualization Research

71. Data, summertime Landsat imagery August 9, 1985 TM August 30, 1990 TM August 28, 1995 TM August 31, 1999 TM Temporal Image Sequencing Image Visualization Research

72. Springtime 1973-1995 Animation Temporal Image Sequencing Image Visualization Research

73. Summertime 1985-1999 Animation Temporal Image Sequencing Image Visualization Research

74. Intertnet Sources of Forest- related Information http://www.wri.org/gfw/http://www.wri.org/gfw/ / http://www.fanweb.org/index.shtml/ http://www.forestwatch.sr.unh.edu/

75. Where Can You Find Additional Educational Resources on Remote Sensing? … how about the Internet?

76. ASPRS Remote Sensing Core Curriculum http://research.umbc.edu/~tbenja1/index.html

77. NASA On-Line Remote Sensing Tutorial http://rst.gsfc.nasa.gov/

78. Canada Center for Remote Sensing Fundamentals http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.html

79. Where Can You Find Digital Remote Sensing Image Data For Forest Characterization ? … how about the Internet?

80. North American Landscape Characterization (NALC) http://edcdaac.usgs.gov/pathfinder/pathpage.html#nalc 19731980 1990 Landsat MSS Triplicates

81. North American Landscape Characterization (NALC) http://edcdaac.usgs.gov/pathfinder/pathpage.html#nalc Landsat MSS and DEM 1990DEM

82. TerraServer http://www.terraserver.com/

83. Global Land Information System Landsat TM August 20, 1998 http://edcwww.cr.usgs.gov/webglis

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

85. http://images.jsc.nasa.gov/iams/html/ Other Sites for Data http://svs.gsfc.nasa. gov/imagewall.html http://terra.nasa.gov/

86. … or Visit NASA’s RESAC at UConn http://resac.uconn.edu

87. … where you’ll find... http://resac.uconn.edu.. and...

88. Research & Education Watersheds A range of land covers and issues Presumpscot SuAsCo Salmon Stonybrook

89. Salmon 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

90. 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

91. SuAsCo Watershed, MA 377 Sq. Miles Sudbury Assabet Concord 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

92. Presumpscot 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

93. Alternative/Emerging Classification Approaches Knowledge Based Expert Systems, Procedures Decision Tree Source Data and Derivatives Final Classifications

94. Alternative/Emerging Classification Approaches Neural Networks, Procedures Principal Components Input Layers Back-propagation May 8, 1995 TM Neural Network Training Neural Network Classification Output Layer

95. Neural Network Based Land Cover Change Proof of Concept Study Backpropagation neural network Forest to non- forest land cover change

96. Land Cover Mapping and Change Detection Using ArcView …. In tomorrow’s breakout session we’ll look at... …. In tomorrow’s breakout session we’ll look at...

97. Session 5 Forestry and Change Detection Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 dcivco@canr.uconn.edu Daniel L. Civco LERIS / NRME University of Connecticut Storrs CT 06269 dcivco@canr.uconn.edu CORSE 2000 June 26-29, 2000 University of Southern Mississippi Gulf Park Conference Center CORSE 2000 June 26-29, 2000 University of Southern Mississippi Gulf Park Conference Center