we1.l09 - desdyni biodiversity and habitat key variables and implications for lidar-radar fusion
TRANSCRIPT
DESDynI
DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS
FOR LIDAR-RADAR FUSION
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Kathleen Bergen, Ralph Dubayah, Scott Goetz
Presented by Ralph Dubayah
IGARSS 2010Special Session on DESDynI Fusion
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Outline
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 & HabitatCapabilities & 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?
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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
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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)
Forest canopy structure including height, canopy profile, canopy cover, canopy roughness, biomass, vertical diversity
Multi-beam lidar, polarimetric L-band SAR
Desired Final Data Products
Measurement Objectives
Instruments
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Introduction: Identification by Decadal Survey
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The Decadal Survey
National Research Council. 2007. Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond
Chapter 7: Land-Use Change, Ecosystem Dynamics, and Biodiversity
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Roots of the Lidar-Radar Mission Biodiversity & Habitat Component
Mission Summary—Ecosystem Structure and Biomass 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, invasive species, wildfires); carbon management; conservation and biodiversity
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Structure, Climate Change and Policy
California Spotted Owl (CASPO) Prefer “old-growth” (high biomass, tall
trees, high canopy cover, etc) Climate change -> increased fires, insect
damage, etc
Carbon Policy Create carbon sinks through management Preserve existing biomass Encourage new growth
Reforestation, afforestation
Healthy Forest Initiative Called for stand thinning Prevent Catastrophic fires Support local economy
DESDynIDESDynI
Structure, Climate Change and Policy
Quantitative assessment of policy options and impacts requires vertical and spatial
forest structure
PreserveHabitat
PromoteSinks
PreventFires
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Ecological Science Basis
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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
Habitat Heterogeneity
Vertical Structure
Floristics
Habitat
Landscape Structure
Biodiversity
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Habitat and Vegetation Structure
Generalist vs. Specialist Many appear to have structural
preferences
Birds Most frequently studied WRT
vegetation structure and habitat preferenceAbout one-third of the total number of
studies in the literature.
Other Taxa Mammals, primates, reptiles,
amphibians and arthropods / insects.
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Habitat Example
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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)
Pine Warbler Habitat: Closed canopy forest
Uneven or broken canopies Trees older than 30 years Overstory taller than 30 ft Well-developed underlayer Large patch sizes (non-fragmented) Upland pine species
Lidar-Radar Variables: Canopy cover Biomass (age-height-density) Height Canopy vertical profile Patch size and shape
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Biodiversity and Vegetation Structure
Vegetation diversity may influence animal biodiversity A hypothesis: greater structural
complexity creates more “niches” and thus greater species diversity. Foliage Height Diversity (FHD)
MacArthur and MacArthur (1961)
Landscape heterogeneity
Biodiversity patterns of animals WRT structure Biodiversity patterns of birds are
most widely studied WRT vegetation structure
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 e.g. under forest canopy
herbaceous plants
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Biodiversity Example 1
Relationships with Biomass/VolumeExample: 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.)
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Biodiversity Example 2
Relationships with Height Vertical Profile: Example: Foliage height
diversity (FHD) vs. bird species diversity (BSD) (Wilson, 1974)
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Lidar & Radar for Biodiversity & Habitat
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Radar and Lidar Capabilities
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Upland coniferLowland coniferNorthern hardwoodsAspen/lowland deciduousGrasslandAgricultureWetlandsOpen waterUrban/barren
Vegetation Type
Lidar and Radar can Map and Measure Vertical Structure & Biomass
Vegetation 3D Structure &
Biomass: Radar and Lidar Together
Radar and Lidar Can Map and Measure Landscape Structure
High: 30 kg/m2
Biomass
Low: 0 kg/m2
Low: 0 kg/m2
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Lidar Heights and Avian Biodiversity
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Relationships of avian biodiversity with height & vertical canopy distribution [Goetz et al., 2007] Forest bird richness increased linearly with height (and vertical complexity) Shrub bird species richness decreased with increasing height
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Ivory-billed Woodpecker
Lidar used to predict potential habitat to guide search Large trees, open midstory,
crown dieback
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Historic Range
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Radar Biomass for Avian Habitat
Simultaneous characterization of “multi-dimensional” structure – both horizontal (landscape structure) and volumetric (biomass)
Landscape structure from optical sensors (e.g. Landsat) Volumetric structure (i.e. biomass, height) from SAR, InSAR, and/or Lidar
Landsat:land-cover composition
RangeAtomicBIOCLIMLogistic
SAR:volumetric structure-biomass
SpeciesOccurrence: point samples from field
Modeling: GARP (or GLM, GAM, MaxEnt, etc)
Modeled Habitat
Landsat:horizontal structure-majority-variety
Bergen, Gilboy & Brown, 2007
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Radar Biomass for Avian Habitat
Best model included vegetation type, biomass, and patch size (> 20% improvement in accuracy over vegetation type alone)
The above model created more realistic habitat models and maps: Only conifer areas selected Higher biomass conifer areas
selected Majority layer
• allowed habitat selection if surrounded by a majority of suitable habitat;
• de-selected highly fragmented areas
Pine Warbler
Bergen, Gilboy & Brown, 2007
Known Primary habitat:Mature conifers
Secondary habitat:Larger sapling conifers
DESDynI Swatantran et al., submitted
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750 Mg/ha
Height of bars (biomass)
Low stress, biomass > 200 Mg/haMore stress, biomass >200 Mg/ha
Biomass < 200Mg/ha
Lidar/Hyperspectral Fusion
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Lidar-Radar Fusion: Biodiversity Key Variables
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Space Mission: Current DESDynI Ecosystems Level 1 Requirements
Biomass: 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.
Disturbance: 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.
Canopy Profiles: Provide transects of vegetation vertical canopy profiles over all biomes
at 25 m spatial resolution, 30 m along-transect posting, with a maximum of 500 m 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
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DESDynI Waveform Metrics
Height
Ground
Energy height quantiles
Overstory cover
Midstory cover
Understory cover
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Variables Important for Biodiversity and Habitat
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Variablea Radar Lidarb,c Precisionsd/Comments Fusion
Variables From Single Radar Pixels or Single Lidar Pulses
Canopy cover (%) (along the vertical profile and at desired heights)
no yes 10–20% M, 5% Dd Lidar
Max canopy heighte (m) no yes 2 m M, 1 m D Lidar, fusion
HOME (m) yes yes 2 m M, 1 m D Radar, lidar, fusion
Canopy height profile no yes 1 m quantile heights, within canopy
relative accuracy of ±5%e
Lidar
Dry biomass (t/ha) f yes yes ±20% or 10 tC/ha Lidar, radar, fusion
Basal area (approximates diameter × density)
yes yes Lidar, radar, fusion
Stem densityg (stems/ha) no no ±20% n/a
Diameter (cm) no no ±20% n/a
Physiognomy yesh no (e.g. hardwood vs conifer)
Radar
Species no no n/a
Snags (snags/ha) no ? n/a
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Variables Important for Biodiversity and Habitat
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Variablea Radar Lidarb,c Precisionsd/Comments Fusion
Landscape-Scale Variables
Canopy cover ? yes 10–20% M, 5% D Lidar, radar (?)
Canopy texture (standard deviation of heights) (m)
? yes ±20% M, ±10% D Lidar, fusion
Height size class distribution
no yes Lidar
Diameter size class distribution
no no n/a
Edge identification/mapping
yes yes within limits of pixel/pulse size
Lidar, radar, fusion
Landscape pattern (patch size and other landscape patterns)
yes yes within limits of pixel/pulse size, patch metrics or spatial statistics
Lidar, radar, fusion
Surface (topographic) roughness (m)
no yes ±20% M, ±10% D lidar
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Variables Important for Biodiversity and Habitat
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Variablea Radar Lidarb Precisions/Comments Fusion
Other Mission Capabilities
Fine spatial resolution data
yes yes 25 m (lidar, ~30 m radar
Lidar, radar, fusion
Map local landscapes yes no Wall-to-wall coverage
Radar, fusion
Contiguous along-track lidar plots
n/a yes 30 m spacing along-transect
Lidar, fusion
Global coverage yes yes Every 91 days Lidar, radar, fusion
Ability to target disturbance events
yes no Radar
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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 patch has 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.
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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
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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
And all of the many scientists working on lidar-radar vegetation 3D structure who are advancing its applications including for biodiversity and habitat.
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