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WE1.L09 - DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION
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WE1.L09 - DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION

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  • Introduce:Critical situation wrt global biodiversityRecognition of importance by decadal survey
  • Introduce:Critical situation wrt global biodiversityRecognition of importance by decadal survey – they specifically included biodiversity
  • The DESDynI mission summary from the Decadal Survey.They specifically mention habitat structure under the biodiversity and habitat component and identify both lidar and radar as the sensors to retrieve habitat structure for biodiversity science & conservation management
  • Introduce definitionsIntroduce how they relate to each otherThe Red lettering always denotes where a sensor other than radar or lidar is more likely to supply the needed information (e.g. multispectral optical or hyperspectral)
  • This slide talks about habitat
  • An example for habitatThese variables translate into DESDynI variablesExample for one bird speciesAgain – text in red may be better retrieved by passive optical (e.g. floristics, species…)
  • This slides about biodiversity (as opposed to habitat)
  • An example for biodiversity
  • Another example for biodiversityIllustrating relationship with key variable of vertical profile
  • Only room here for just two examples, one from each sensor, can note that there are many more.HOWEVER: there are no real examples of radar lidar fusion for biodiversity – yet.
  • Introduce how these definitions/concepts relate to vegetation 3-D structureMap on left was actually derived from multi-frequency polarimetric radar – landscape structureOn right is map of biomass derived from radar and canopy height profile derived from Lidar
  • Within the Teakettle Experimental Forest, 60% area identified as high biomass and low stress showed significantheight gain between 1999 and 2008. 40% area identified as high biomass and high stress showed significant height loss between 1999 and 2008.
  • May not need to use this slide.This would need to be updated if you use it, but there has been so much back and forth I’m not certain what the latest is!This is from the Fall meeting at Goddard with Paul Rosen.Feel free to modify.
  • Provide supporting evidence and illustrate examples of requirements for DESDynI for BD&HFocus on the 4 main variables
  • Provide supporting evidence and illustrate examples of requirements for DESDynI for BD&HFocus on the 4 main variablesYou may be able to improve some of my “Fusion” statements, feel free.
  • Transcript

    • 1. DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION
      1
      Kathleen Bergen, Ralph Dubayah, Scott Goetz
      Presented by Ralph Dubayah
      IGARSS 2010
      Special Session on DESDynI Fusion
    • 2. Outline
      • Introduction
      • 3. Identification by Decadal Survey
      • 4. Ecological science basis
      • 5. How does woody vegetation 3D structure influence:
      • 6. a) Habitat selection?
      • 7. b) Biodiversity patterns?
      • 8. Lidar & Radar for Biodiversity & Habitat
      • 9. Capabilities & two examples
      • 10. Lidar-Radar Fusion: Biodiversity Key Variables
      • 11. What precisions, temporal and spatial coverage of lidar and radar-derived measurements are needed for fusion?
      • 12. What are the most important variables for lidar-radar fusion?
      2
    • 13. Science Objectives
      CHARACTERIZE THE EFFECTS OF CHANGING CLIMATE AND LAND USE ON TERRESTRIAL CARBON CYCLE, ATMOSPHERIC CO2, AND SPECIES HABITATS
      Characterize global distribution of aboveground vegetation biomass
      Quantify changes in terrestrial biomass resulting from disturbance and recovery
      Characterize habitat structure for biodiversity assessments
    • 14. Science Objective 3: Habitat Structure
      Characterize habitat structure for biodiversity assessments
      Various forest structure products with specified accuracies (includes both gridded data and ungridded transect data)
      Desired Final Data Products
      Forest canopy structure including height, canopy profile, canopy cover, canopy roughness, biomass, vertical diversity
      Measurement Objectives
      Multi-beam lidar, polarimetric L-band SAR
      Instruments
    • 15. Introduction: Identification by Decadal Survey
      5
    • 16. The Decadal Survey
      • National Research Council. 2007. Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond
      • 17. Chapter 7: Land-Use Change, Ecosystem Dynamics, and Biodiversity
      6
    • 18. Roots of the Lidar-Radar Mission Biodiversity & Habitat Component
      • Mission Summary—Ecosystem Structure and Biomass
      • 19. Variables: Standing biomass; vegetation height and canopy structure; habitat structure
      • 20. Sensor(s): Lidar and InSAR/SAR
      • 21. Orbit/coverage: LEO/global
      • 22. Panel synergies: Climate, Health, Solid Earth
      • 23. New science: Global biomass distribution, canopy structure, ecosystem extent, disturbance, recovery
      • 24. Applications: Ecosystem carbon and interactions with climate, human activity, disturbance (including deforestation, invasive species, wildfires); carbon management; conservation and biodiversity
      7
    • 25. DESDynI
      Structure, Climate Change and Policy
      • California Spotted Owl (CASPO)
      • 26. Prefer “old-growth” (high biomass, tall trees, high canopy cover, etc)
      • 27. Climate change -> increased fires, insect damage, etc
      • 28. Healthy Forest Initiative
      • 29. Called for stand thinning
      • 30. Prevent Catastrophic fires
      • 31. Support local economy
      • 32. Carbon Policy
      • 33. Create carbon sinks through management
      • 34. Preserve existing biomass
      • 35. Encourage new growth
      • 36. Reforestation, afforestation
    • Structure, Climate Change and Policy
      Preserve
      Habitat
      Prevent
      Fires
      Quantitative assessment of policy options and impacts requires vertical and spatial forest structure
      Promote
      Sinks
      DESDynI
    • 37. Ecological Science Basis
      10
    • 38. Key Concepts
      Biodiversity:combination of richness and abundance
      Habitat: the environmental conditions required by a species for survival and reproduction
      Floristics:The vegetation composition (flora) comprising habitat
      Landscape Structure: patches and the spatial heterogeneity of an area composed of interacting habitat patches
      Vertical Structure: the bottom to top configuration or complexity of above-ground vegetation
      Vertical Structure
      Floristics
      Landscape Structure
      Habitat
      Habitat Heterogeneity
      Biodiversity
    • 39. Habitat and Vegetation Structure
      • Generalist vs. Specialist
      • 40. Many appear to have structural preferences
      • 41. Birds
      • 42. Most frequently studied WRT vegetation structure and habitat preference
      • 43. About one-third of the total number of studies in the literature.
      • 44. Other Taxa
      • 45. Mammals, primates, reptiles, amphibians and arthropods / insects.
      12
    • 46. Habitat Example
      13
      • Pine Warbler Habitat:
      • 47. Closed canopy forest
      • 48. Uneven or broken canopies
      • 49. Trees older than 30 years
      • 50. Overstory taller than 30 ft
      • 51. Well-developed underlayer
      • 52. Large patch sizes (non-fragmented)
      • 53. Upland pine species
      • 54. Lidar-Radar Variables:
      • 55. Canopy cover
      • 56. Biomass (age-height-density)
      • 57. Height
      • 58. Canopy vertical profile
      • 59. Patch size and shape
      Example (right): Habitat for pine warbler in the Great Lakes Region is tall, dense (high biomass) pine, but not short sparse pine; also require large patch sizes (Bergen et al., 2007)
    • 60. Biodiversity and Vegetation Structure
      • Vegetation diversity may influence animal biodiversity
      • 61. A hypothesis: greater structural complexity creates more “niches” and thus greater species diversity.
      • 62. Foliage Height Diversity (FHD) MacArthur and MacArthur (1961)
      • 63. Landscape heterogeneity
      • 64. Biodiversity patterns of animals WRT structure
      • 65. Biodiversity patterns of birds are most widely studied WRT vegetation structure
      • 66. But also small mammals, primates, arthropods and amphibians
      Songbird species richness over a landscape in southern Wisconsin, USA. ( Lesak et al., submitted, 2009).
      • Vegetation structure can also influence diversity of other plants
      • 67. e.g. under forest canopy herbaceous plants
      14
    • 68. Biodiversity Example 1
      • Relationships with Biomass/Volume
      • 69. Example: Total breeding bird density (pairs per 25 ha) as a function of total vegetation volume (TVV) for Arizona study sites ranging from desert-grasslands to woodlands to forests. Regression equation: y = 290x – 1.0. (Miller et al.)
      15
    • 70. Biodiversity Example 2
      • Relationships with Height Vertical Profile:
      • 71. Example: Foliage height diversity (FHD) vs. bird species diversity (BSD) (Wilson, 1974)
      16
    • 72. Lidar & Radar for Biodiversity & Habitat
      17
    • 73. Radar and Lidar Capabilities
      18
      Vegetation Type
      Upland conifer
      Lowland conifer
      Northern hardwoods
      Aspen/lowland deciduous
      Grassland
      Agriculture
      Wetlands
      Open water
      Urban/barren
      Lidar and Radar can Map and Measure Vertical Structure & Biomass
      Radar and Lidar Can Map and Measure Landscape Structure
      Vegetation 3D Structure & Biomass: Radar and Lidar
      Together
      High: 30 kg/m2
      Biomass
      Low: 0 kg/m2
      Low: 0 kg/m2
    • 74. Lidar Heights and Avian Biodiversity
      19
      • Relationships of avian biodiversity with height & vertical canopy distribution [Goetz et al., 2007]
      • 75. Forest bird richness increased linearly with height (and vertical complexity)
      • 76. Shrub bird species richness decreased with increasing height
    • Ivory-billed Woodpecker
      Lidar used to predict potential habitat to guide search
      Large trees, open midstory, crown dieback
      20
      Historic Range
    • 77. Range
      Atomic
      BIOCLIM
      Logistic
      Species
      Occurrence: point samples from field
      Modeling: GARP (or GLM, GAM, MaxEnt, etc)
      SAR:
      volumetric structure
      -biomass
      Landsat:
      horizontal structure
      -majority
      -variety
      Landsat:
      land-cover composition
      Modeled Habitat
      Radar Biomass for Avian Habitat
      • Simultaneous characterization of “multi-dimensional” structure – both horizontal (landscape structure) and volumetric (biomass)
      • 78. Landscape structure from optical sensors (e.g. Landsat)
      • 79. Volumetric structure (i.e. biomass, height) from SAR, InSAR, and/or Lidar
      Bergen, Gilboy & Brown, 2007
    • 80. Radar Biomass for Avian Habitat
      Pine Warbler
      Known Primary habitat:
      Mature conifers
      Secondary habitat:
      Larger sapling conifers
      • Best model included vegetation type, biomass, and patch size (> 20% improvement in accuracy over vegetation type alone)
      • 81. The above model created more realistic habitat models and maps:
      • 82. Only conifer areas selected
      • 83. Higher biomass conifer areas selected
      • 84. Majority layer
      • 85. allowed habitat selection if surrounded by a majority of suitable habitat;
      • 86. de-selected highly fragmented areas
      Bergen, Gilboy & Brown, 2007
    • 87. Height of bars (biomass)
      750 Mg/ha
      0
      Low stress, biomass > 200 Mg/ha
      More stress, biomass >200 Mg/ha
      Biomass < 200Mg/ha
      Swatantran et al., submitted
      Lidar/Hyperspectral Fusion
    • 88. Lidar-Radar Fusion: Biodiversity Key Variables
      24
    • 89. Space Mission: Current DESDynI Ecosystems Level 1 Requirements
      • Biomass:
      • 90. The DESDynI Mission shall produce global estimates of aboveground woody biomass within the greater of 20 Mg/ha or 20% (errors not to exceed 50 Mg/ha), at a spatial resolution of 250 m globally at end of mission.
      • 91. Disturbance:
      • 92. The DESDynI Mission shall map global areas of disturbance at 1 ha resolution annually and measure subsequent regrowth to an accuracy of 4 Mg/ha/yr* at 1 ha resolution.
      • 93. Canopy Profiles:
      • 94. Provide transects of vegetation vertical canopy profiles over all biomes at 25 m spatial resolution, 30 m along-transect posting, with a maximum of500m across-transect posting at end of mission and 1 m vertical resolution up to conditions of 99% canopy cover.
      * for areas disturbed at least 4 years prior to last observation and where the resulting biomass is less than 80 Mg/ha
      25
    • 95. Height
      Ground
      Overstory cover
      Midstory cover
      Understory cover
      Energy height quantiles
      DESDynIWaveform Metrics
    • 96. Variables Important for Biodiversity and Habitat
      27
    • 97. Variables Important for Biodiversity and Habitat
      28
    • 98. Variables Important for Biodiversity and Habitat
      29
    • 99. Conclusions: Key Variables and Fusion
      Canopy height: a key habitat characteristic, forest height has been correlated with biodiversity, including plant and avian species richness.
      Fusion: Only fusion of lidar and radar together will provide canopy height wall-to-wall maps that are highly desired by conservation managers.
      Canopy height profile: provides observations on presence of different strata (e.g. overstory, understory) for vegetation vertical structure & diversity metrics. Correlated with biodiversity and with habitat suitability . Highly sought after by biodiversity scientists and conservation managers.
      Fusion: IFSAR?
      Biomass: is an indicator of the type of structure, age or maturity of a forest, and forest productive ability; the amount of total biomass in a patchhas been correlated with habitat use by species. Ranks high as desired by wildlife managers.
      Fusion: Only fusion of lidar and radar together will provide spatially continuous biomass maps that are highly desired by conservation managers and biodiversity scientists.
      Canopy cover:is also related to tree age and density and has been correlated with habitat suitability for species of birds, mammals, amphibians, and reptiles.
      Fusion: Lidar is the primary variable of the two, high spatial resolution radar would be needed for useful fusion.
      30
    • 100. Conclusions: Key Capabilities and Fusion
      Global coverage of forested ecosystems: landscape and forest structures are rapidly changing worldwide, and implications include extinctions and invasive species; data from all forested ecosystems will be required to assess the global extent of change.
      Fusion: will benefit from dense coverage of lidar transects, fusion with radar will provide wall-to-wall global coverage for all fusion variables.
      Contiguous along-track lidar footprints: Continuous profiles of vegetation along-track for calculating structure correlation lengths and other metrics, identification of edges, maximizing observation of ground to maximize precision of height estimates, identification of rare ecosystem features
      Fusion: this is a lidar variable, but the benefits of contiguous along-track lidar footprints will carry over into increased precisions of radar-lidar fusion for heights, biomass, texture, edge mapping, landscape pattern, surface roughness and potentially other fusion variables.
      Targeted response for events: Periodic or stochastic disturbance events such as hurricanes, fire, wind blow downs and insects have impacts on vegetation 3D structure and consequently on biodiversity and habitat of plants and animals
      Fusion: Wherever radar and lidar overlap in disturbance areas the lidar will be useful to increase the confidence and precision in radar observations and provide additional unique information on within-canopy structure where applicable.
      31
    • 101. Acknowledgements
      • The authors would like to thank Dr. Diane Wickland and the other science and technology members of the NASA Decadal Survey Radar-Lidar Mission Ecosystems Science Study Team
      • 102. And all of the many scientists working on lidar-radar vegetation 3D structure who are advancing its applications including for biodiversity and habitat.
      32