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Carbon Cycle & Ecosystems

The overall goal of the proposed research is to develop consistent remote sensing methods to assess above ground vegetation biomass and forest carbon at multiple scales, with improved accuracy and uncertainty estimates, by using affordable lidar measurements and MODIS vegetation products. More detailed research objectives are to: (1) provide a consistent method for estimating vegetation structure, aboveground biomass, and carbon at local scales using small footprint scanning lidar data collected at worldwide locations, including the U.S., Canada, Norway, and Taiwan; (2) develop a robust method for calibrating MODIS vegetation products and scaling-up carbon and canopy estimates from local, i.e., lidar-derived, to regional and sub-continental scales, i.e., MODIS-derived; and (3) quantify major sources of variability in the regional estimates of biomass and carbon. Educational objectives include: (1) develop a World Wide Web (Web) portal for online lidar processing, multispectral image integration, including MODIS, and accessibility to a lidar data archive, software, and instructional materials; (2) organize the Remote Sensing Event for the high-school Science Olympiad with a Carbon Cycle and Ecosystems theme;(3) integrate research findings into graduate education, national workshops, Science Olympiad coach clinics, and a lidar textbook currently under publication agreement with Taylor&Francis.; Ties between local and regional scale estimates will be derived by using: (1) regression analysis relating ground measurements to remote sensing observations at multiple scales; and (2) non parametric methods based on classification and regression tree analysis (CART). Ties are constructed by scaling up in a multi-phase sampling technique with the following observations levels: (1) ground plot measurements of individual trees and US Forest Service Forest Inventory and Analysis data (FIA); (2) airborne scanning lidar with the capability to automatically measure individual trees and characterize canopy biophysical parameters over local scale areas; and (3) MODIS products with regional coverage. Results and conclusions drawn from this study have the ability to improve future NASA operational systems related to monitoring and managing terrestrial ecosystems, estimating carbon budgets and uncertainties, and calibrating sensors and standard products, and to educate college and high-school students, along with remote sensing and ecosystem science practitioners via effective means, such as inquiry-rich courses, workshops, and the Web.

Project PI: Sorin Popescu/Texas Agricultural Experiment Station

1500 Research Plaza, Office 221D College Station, TX 77845, USA

Phone: (979) 862-2614

Fax: (979) 862-2607

Email: s-popescu@tamu.edu

http://essm.tamu.edu/people-info/faculty/popescu-sorin.aspx

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Started: Aug 09, 2010

Last Activity: Dec 09, 2010

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