DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION <br />1<br />Kathleen Bergen, Ralph...
Outline<br /><ul><li>Introduction
Identification by Decadal Survey
Ecological science basis
How does woody vegetation 3D structure influence:
a) Habitat selection?
b) Biodiversity patterns?
Lidar & Radar for Biodiversity & Habitat
Capabilities & two examples
Lidar-Radar Fusion:  Biodiversity Key Variables
What precisions, temporal and spatial coverage of lidar and radar-derived measurements are needed for fusion?
What are the most important variables for lidar-radar fusion?</li></ul>2<br />
Science Objectives<br />CHARACTERIZE THE EFFECTS OF CHANGING CLIMATE AND LAND USE ON TERRESTRIAL CARBON CYCLE, ATMOSPHERIC...
Science Objective 3: Habitat Structure<br />Characterize habitat structure for biodiversity assessments<br />Various fores...
Introduction:  Identification by Decadal Survey<br />5<br />
The Decadal Survey <br /><ul><li>National Research Council. 2007. Earth Science and Applications from Space: National Impe...
Chapter 7:  Land-Use Change, Ecosystem Dynamics, and Biodiversity</li></ul>6<br />
Roots of the Lidar-Radar Mission Biodiversity & Habitat Component<br /><ul><li>Mission Summary—Ecosystem Structure and Bio...
Variables: Standing biomass; vegetation height and canopy structure;    habitat structure
Sensor(s): Lidar and InSAR/SAR
Orbit/coverage: LEO/global
Panel synergies: Climate, Health, Solid Earth
New science: Global biomass distribution, canopy structure, ecosystem extent, disturbance, recovery
Applications: Ecosystem carbon and interactions with climate, human activity, disturbance (including deforestation, invasi...
DESDynI<br />Structure, Climate Change and Policy<br /><ul><li>California Spotted Owl (CASPO)
Prefer “old-growth” (high biomass, tall trees, high canopy cover, etc)
Climate change -> increased fires, insect damage, etc
Healthy Forest Initiative
Called for stand thinning
Prevent Catastrophic fires
Support local economy
Carbon Policy
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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 &amp; 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&amp;HFocus on the 4 main variables
  • Provide supporting evidence and illustrate examples of requirements for DESDynI for BD&amp;HFocus on the 4 main variablesYou may be able to improve some of my “Fusion” statements, feel free.
  • WE1.L09 - DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION

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