MSB-ECA: Leveraging NEON data to investigate remote sensing of biodiversity variables and scaling implications

Information

  • NSF Award
  • 1703062
Owner
  • Award Id
    1703062
  • Award Effective Date
    8/1/2017 - 6 years ago
  • Award Expiration Date
    7/31/2019 - 4 years ago
  • Award Amount
    $ 288,851.00
  • Award Instrument
    Standard Grant

MSB-ECA: Leveraging NEON data to investigate remote sensing of biodiversity variables and scaling implications

Periods in Earth's history can be divided into geological epochs that correspond to major changes in the evolution of life, such as the mass extinction of dinosaurs in the Cretaceous Period. A mass extinction event is currently underway that is many magnitudes greater than previous events recorded in the fossil record. Humanity is widely considered to be in a new period coined the Anthropocene Epoch on account of both extinction rates and the footprints our societies are recording on Earth?s surface. It is not surprising then that species numbers and biodiversity patterns are becoming increasingly relevant to human survival and a rising priority across the world. Biodiversity provides humanity with food, energy, and materials. The biodiversity of plant species can be measured on the ground using calculations such as the total number of species in an area, or the number of species weighted by the portion of area covered by each species. Global biodiversity trends can be collected from satellites but finer scale processes cannot be studied without an expanded network of ground and airborne observations of the same species over short-term intervals (every 1 to 5 years). Remotely sensed and ground based measurements from the National Ecological Observatory Network are well suited to complete this knowledge gap. This award will combine measurements collected simultaneously from the ground and the Network's Aerial Observation Platform, which has cutting edge optical imaging and three dimensional laser scanning systems onboard, to test the direct mapping of biodiversity variables. A method for measuring biodiversity will be demonstrated at eight Network sites located along a latitudinal gradient in eastern US temperate, broadleaf and mixed forest. Project activities will be conducted over two years at a Primarily Undergraduate Institution. Two workshops will be hosted that are designed to involve faculty and students across a network of Primarily Undergraduate Institutions through collaboration with Ecological Research as Education Network participants. Curriculum will be developed that focus on interdisciplinary training of students in field data analysis training, large dataset handling, macroecology theory and remote sensing. Value added remote sensing map products, educational material and the biodiversity processing framework generated from this project will be made available to National Ecological Observatory Network data users for testing other sites located in additional eco-climatic domains across the Continental US.<br/><br/>Documenting and understanding patterns of biodiversity is a central issue in macroecology. To date, continental to global scale plant diversity mapping has been based on occurrence data, biased by uneven sampling and narrowly focused only on species richness. Vegetation diversity or phytodiversity occurs along a range of temporal, biological, and spatial scales, from genes to species, ecosystems and global biodiversity. Patches or corridors of biodiversity across a local landscape can reflect areas where fine scale patterns of species diversity are mechanistically related to broad scale dynamics as represented by environmental variables. The first component of this project explores an emerging concept in imaging spectroscopy where spatial patterns of reflectance correlate with species diversity (the spectral variation hypothesis). The second component of this project addresses cross-scale interactions and will directly benefit macrosystems biology by enhancing theory on scaling challenges related to spatial heterogeneity and interactions between fine scale species diversity and broader environmental variables across macroscale gradients such as latitude and time since glaciation. The project investigates the following three questions: 1) what are the spatial and spectral detection limits for species diversity mapping with Network Aerial Observation Platform datasets and by extension, how limited is simple species diversity upscaling, 2) what coarser scale natural and human variables are driving species diversity patterns at the site level and where are local diversity hotspots predicted to occur, and 3) how do patterns of and controls on species diversity change along macroscale gradients and how is spatial heterogeneity expected to influence macrosystem mapping and modeling? The value added optical and structural diversity map products that are generated under this award will be useful to larger remote sensing and ecology communities and help provide a basis for new and synoptic exploration of relationships between functional and taxonomic diversity in the US. The optical diversity products can also be used to run simulations that evaluate the role of future satellite-based spectroscopy missions in mapping Essential Biodiversity Variables. Overall processing flows developed through this project will create new methods for identifying potential landscape and genetic diversity hotspots and results can be used to test global biodiversity theories related to latitudinal and elevational distributions.

  • Program Officer
    Elizabeth R. Blood
  • Min Amd Letter Date
    7/28/2017 - 6 years ago
  • Max Amd Letter Date
    7/28/2017 - 6 years ago
  • ARRA Amount

Institutions

  • Name
    Appalachian State University
  • City
    Boone
  • State
    NC
  • Country
    United States
  • Address
    P.O. Box 32174
  • Postal Code
    286082174
  • Phone Number
    8282627459

Investigators

  • First Name
    Jessica
  • Last Name
    Mitchell
  • Email Address
    mitchelljj@appstate.edu
  • Start Date
    7/28/2017 12:00:00 AM

Program Element

  • Text
    MACROSYSTEM BIOLOGY
  • Code
    7959

Program Reference

  • Text
    MACROSYSTEM BIOLOGY
  • Code
    7959