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Leaf to Landscape Scale Remote Sensing of Arctic Vegetation Structure and Function

Shawn Paul Serbin,  Brookhaven National Laboratory,  sserbin@bnl.gov (Presenter)
Daryl Yang,  Brookhaven National Laboratory,  dediyang@bnl.gov
Ran Meng,  Brookhaven National Laboratory,  mengran07@gmail.com
Andrew McMahon,  Brookhaven National Laboratory,  amcmahon@bnl.gov
Kim Ely,  Brookhaven National Laboratory,  kely@bnl.gov
Wouter Hantson,  University of Maine,  wouter.hantson@maine.edu
Daniel Hayes,  University of Maine,  daniel.j.hayes@maine.edu
Alistair Rogers,  Brookhaven National Laboratory,  arogers@bnl.gov
Stan D. Wullschleger,  Oak Ridge National Laboratory,  wullschlegsd@ornl.gov

The inadequate representation of plant trait variation across space and time in terrestrial biosphere models is a key driver of uncertainty in forecasts of terrestrial ecosystems. This is particularly relevant for the Arctic with sparse observational data availability. Uncertainty in the modeling of carbon uptake and associated processes and fluxes in the Arctic has been tied to the lack of key data on plant properties that regulate these processes. An approach is needed to bridge the scales between detailed ongoing in-situ observations in remote locations and the larger, landscape context needed to inform models on parameter variation in relation to biotic and abiotic drivers such as climate, soils, topography, and disturbance history. Remote sensing, particularly spectroscopy, high resolution imaging, and thermal infrared thermography, represent powerful observational datasets capable of scaling plant properties and capturing spatial and temporal dynamics in plant structure and functioning. We build on the success of scaling traits in temperate ecosystems and extend our approaches to the high Arctic to develop algorithms for mapping biochemical, morphological and physiological traits. We focuse on connecting a range of plant species and remote sensing data within our two core Alaska study areas. We link direct measurements of leaf function with measurements of leaf and canopy optical properties, TIR, and imagery from near-surface, unmanned aerial system (UAS) platforms, and NASA AVIRIS. Tram, UAS and AVIRIS platforms show a strong capacity to scale leaf-level traits to the larger landscape and capture patterns through time and across vegetation structure, derived using optical Structure from Motion (SfM). Despite strong variation in leaf and canopy structure we are finding a good potential for spectral models (i.e. R2 between 0.50 and 0.89) to capture trait variation and highlight the possibility to map traits in the high Arctic.

Poster: ASTM5_Poster_Serbin_8.pdf 

Associated Project(s): 

Poster Location ID: 3-10

Session: Modeling

Session Date: Wednesday (5/22) 4:30 PM

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