Describing Tundra Vegetation Cover Using Spectral Data

Karl Fred Huemmrich, NASA GSFC/UMBC, karl.f.huemmrich@nasa.gov (Presenter)
Sergio Armando Vargas, University of Texas at El Paso, savargas@utep.edu
Petya Krasteva Entcheva Campbell, JCET/UMBC, petya.campbell@nasa.gov
Craig E. Tweedie, University of Texas at El Paso, ctweedie@utep.edu
John A Gamon, University of Nebraska, jgamon@gmail.com

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem function. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Partial Least Squared Regression is applied to ground measured spectral reflectance to develop algorithms to estimate vegetation functional type coverage, along with other biophysical characteristics, such as chlorophyll concentration. These algorithms are then used with AVIRIS NG imagery to map tundra variation across regions of the North Slope of Alaska.

Associated Project(s): 

• Gamon-01
• Huemmrich-01

Poster Location ID: 35

Session Assigned: Vegetation Dynamics and Distribution