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

DESDynI

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

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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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