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

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

17. Chapter 7: Land-Use Change, Ecosystem Dynamics, and Biodiversity6

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 biodiversity7

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

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

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

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 shapeExample (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)

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

67. e.g. under forest canopy herbaceous plants14

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

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

75. Forest bird richness increased linearly with height (and vertical complexity)

78. Landscape structure from optical sensors (e.g. Landsat)

79. Volumetric structure (i.e. biomass, height) from SAR, InSAR, and/or LidarBergen, Gilboy & Brown, 2007

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 areasBergen, 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

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

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