- Related Research Areas
- Carbon Cycle & Ecosystems
Hyperspectral data have considerable potential for mapping plant functional types (PFTs), species and for providing improved estimates of canopy biophysics and biochemistry. We propose using high-spatial resolution Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and multitemporal AVIRIS and SPOT-5 to evaluate spatial, spectral and temporal requirements for a spaceborne hyperspectral mission. We will use a common set of analysis tools, primarily centered around Multiple Endmember Spectral Mixture Analysis and hyperspectral indices applied to AVIRIS data acquired over a diversity of North American ecosystems with a variety of PFTs. AVIRIS data will be spatially and spectrally degraded to synthesize several broadband sensors and more spectrally limited hyperspectral sensors at spatial scales ranging from 4 to 60 m. Ecosystems include temperate rainforest, semi-arid shrublands, interior mixed conifer forest, oak savanna, broadleaf deciduous and evergreen forest and forested wetlands. All study sites include high quality field data, existing high-resolution AVIRIS, and existing or planned lidar. Key questions addressed include: 1) What are the optimal sensor (spatial and spectral) requirements needed to map plant species and PFTs across sites representative of several major ecosystems within North America? ; What are the optimal sensor requirements for accurate estimates of canopy biophysical, chemical and physiological properties, including photosynthetic and non-photosynthetic cover, canopy moisture, above ground biomass and light use efficiency?; 3) What is the impact of temporal sampling on the ability to discriminate PFTs and estimate cover fractions?; and 4) What are the synergies between hyperspectral-physiology/biochemistry and lidar vertical structure including how height and height change improve PFT mapping and quantify the value of a hyperspectral measure? This proposal addresses Sub Element 2.1.1 of the Terrestrial Ecology RFP, but also addresses key components of 2.1.2 and 2.2 by investigating synergies between lidar and hyperspectral data and building off of findings from our current Carbon Cycle Science research.
Project PI: Dar Roberts/University of California, Santa Barbara
3611A Ellison Hall,Geography Department University of California, Santa Barbara Santa Barbara, CA 93106-4060
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