Biodiversity Studies in the NASA Remote Sensing Programs Greg Asner, Scott Goetz, Kathleen Bergen, Nicholas Coops, Weihong Fan, Mick Follows, Joanne Nightingale, Matt Oliver, Volker Radeloff, Tom Smith, Richard Waring and colleagues NASA CC&E Plenary 2008

The Importance of Biological Diversity Biosphere-atmosphere interactions Climate system Secondary production/Fisheries Carbon storage and loss Water quantity and quality Cultural, recreational, aesthetic value Biodiversity underpins nearly all of the services provided by ecosystems to humans

Traditional Biodiversity Mapping Capabilities ► ► Global-scale models without remote sensing   Resolution: low spatially, low taxonomically and functionally ► ► Field-based assessments   Extent: local   Resolution: taxonomically high, spatially medium-to-high

Current Limitations ► We lack regional and global knowledge of biodiversity  Extent: millions of sq. km  Resolution: high spatially and taxonomically ► So what?  We don’t know what’s out there  We can't truly understand biogeochemical cycling, including water cycling  We can’t determine if diversity is changing with climate  We can’t track insipient effects of land and ocean use, and invasive species

Land cover, Structure, Chemistry and Physiology Functional Types Organismal Association/Alliance Observed Species Richness and Abundance Correlated Species Richness and Abundance Remote Sensing and Biodiversity Research Satellite and Airborne Measurements, Models, and Field Observations Remote sensing provides access to biodiversity information at scales that can’t be reached using ground-based observations alone.

Low biological productivity zones have increased in the last 5 years Total Area of Oligotrophic Biomes Mapping Ocean Biomes from Color and Temperature Data (Aqua) Matt Oliver (U. Delaware) and colleagues

Bear density in 2000 MODIS Landcover In 1990, the Soviet Union broke down, as did it’s control over eastern Europe. Since then, land use intensity has decreased, and parts of Eastern Europe are re-wilding. MODIS Land Cover and Bear Density MODIS Land Cover and Bear Density Volker Radeloff and colleagues

Spectral Discrimination of Plant Species (Hyperion) Spectral Discrimination of Plant Species (Hyperion) Phil Townsend and colleagues Townsend and Foster % 71.0% 76.9% Forest Hard Pine White Pine Hemlock Red Oaks White Oak Mixed Conifer / Oaks Successional Red Oak Chestnut Oak Black Oak Scarlet Oak Conifers Broadleaf Deciduous Hardwoods 88.3% 100% 84.6% 37.5% 62.5% 47.6% 53.3% Overall 96.5% 79.8%59.3%

Kilauea Iki Kilauea Volcano Canopy Water Kilauea Iki Kilauea Volcano Leaf Nitrogen Leaf Nitrogen Canopy Water 2500  m 2.5 % 0  m 0 % Canopy Nitrogen Concentration Canopy Water Content Asner and Vitousek 2005 Plant Functional Types from AVIRIS Plant Functional Types from AVIRIS Greg Asner and colleagues Montane Rainforest in Hawaii Volcanoes National Park

Kilauea Caldera Canopy Chemistry  Invasive Species Hedychium in forest understory (high canopy water) Myrica invasion front (high leaf nitrogen) Myrica infestations (high leaf nitrogen and high canopy water) Asner and Vitousek 2005

AVIRIS Data Bird and Plant Species Interactions and Invasion: Bird and Plant Species Interactions and Invasion: Combining Plant Species Identification from AVIRIS with Field Bioacoustics for Birds Asner et al Metrosideros polymorpha (Ohia) Morella faya (Fire Tree) - INVADER Other native species… Plant Species Cover (%) shrub savanna woodland forest shrub savanna woodland forest Native Gradient Invaded Gradient Forest Savanna Shrubland Forest Savanna Shrubland Native Ecosystems Invaded Ecosystems AVIRIS

Boelman et al Bird and Plant Species Interactions and Invasion: Bird and Plant Species Interactions and Invasion: AVIRIS and Bioacoustics AVIRIS Area under acoustic frequency curve  avian abundance Field-based bioacoustics station Bioacoustic Spectra Processed bioacoustic Spectra Bird diversity and the ratio of native to invasive birds is highly correlated with vegetation composition.

Continental-scale Bird Diversity from Terra-MODIS Data Nicholas Coops, Richard Waring and colleagues Seasonality of fPAR from MODIS, Data from North American Breeding Bird Survey Coops et al. 2008

Dynamic Habitat Index Seasonality Productivity Minimum Cover Relationship between DHI and Total Bird Species Richness: r2 = 0.88, p < 0.001, n=420 species

LVIS Lidar Canopy Height Patuxent National Wildlife Refuge, MD Forest Structure and Bird Habitat from Airborne LiDAR (LVIS) Forest Structure and Bird Habitat from Airborne LiDAR (LVIS) Scott Goetz and colleagues From top left, clockwise: Tufted Titmouse, Brown Thrasher, Kentucky Warbler, and Carolina Wren. Photographs by Scott Somershoe, USGS. Goetz et al. 2006

Forest Structure and Bird Habitat from Airborne LiDAR Forest Structure and Bird Habitat from Airborne LiDAR Scott Goetz and colleagues Breeding bird survey (BBS) grid Goetz et al ► ► 5681 Individuals ► ► 90 species ► ► 6 guilds   Forest   Scrub/2nd Growth   Suburban   Pond/Wetland   Open-forest   Semi-open Forest

► ► 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 MODEL SAR: volumetric structure -biomass Species Occurrence: point samples from field Modeling: GARP (or GLM, GAM, MaxEnt, etc) Modeled HabitatLandsat: horizontal structure -majority -variety Bergen, Gilboy & Brown, 2007 Bird Habitat and Diversity from Multi-sensor Fusion Bird Habitat and Diversity from Multi-sensor Fusion Kathleen Bergen and colleagues

► ► Best model included vegetation type, biomass, and patch size (> 20% improvement in accuracy over vegetation type alone) Pine Warbler Bergen, Gilboy & Brown, 2007 Known Primary habitat: Mature conifers Secondary habitat: Younger conifers Bird Habitat and Diversity from Multi-sensor Fusion Bird Habitat and Diversity from Multi-sensor Fusion Kathleen Bergen and colleagues

Diversity Mapping for Conservation Prioritization Diversity Mapping for Conservation Prioritization Tom Smith and colleagues

(c ) Least suitable Most suitable Phenotypic Genetic Currently protected areas Areas of particularly high genetic and phenotypic turnover Wedge-billed woodcreeper Diversity Mapping for Conservation Prioritization Diversity Mapping for Conservation Prioritization Tom Smith and colleagues

Genetics and Physiology Physical and Chemical Environment Competition Interaction Predation Selection Ecosystem Structure and Function 78 initialized phytoplankton types Random assignment of physiological traits Simple allometric trade-offs MIT ocean circulation model N, P, Fe and Si cycles 2 grazers 99% of biomass in ~16 types Self-Organizing Ecosystem Model Biodiversity of Ocean Phytoplankton from Remote Sensing and Modeling Mick Follows (MIT) and colleagues

Prochlorococcus analogs Synechococcus & small eukaryotes Diatoms. Other large eukaryotes Emergent biogeography – organized into functional classes Biodiversity of Ocean Phytoplankton from Remote Sensing and Modeling Follows et al Science

If you could build a biodiversity sensor, what would it be? Chemistry and physiology 3-D Structure Geographic Scale and Resolution Temporal Resolution Low High Small/Fine Large/Coarse Integrated Airborne Imaging Spectroscopy and wLiDAR or SAR Spaceborne SAR/wLiDA R and HiFIS Formation Flying and Integrated Data Processing

A Few Take-home Messages There are an increasing number of approaches to address fundamental biodiversity questions with NASA imagery. Satellite and airborne imagery help us detect unique biological patterns that help us understand underlying processes. Satellite and airborne imagery have the potential to revolutionize biogeography and make it a leading biological discipline in the 21 st century (restoring some of the luster it held in the 19 th century for Darwin, Wallace, Hooker, Bates, and others). We need to develop and institute tools that allow researchers to bridge the gaps in scale (and related knowledge gaps) between biome and organisms and the molecular components of organisms. Observations and associated models are our principal tools. Future missions to support biodiversity research should measure vegetation structure, plant and plankton chemistry, and physiology in a fully integrated observation approach.