Integrating remote sensing and ecosystem process models for landscape- to regional-scale analysis of the carbon cycle

Citation data:

BioScience, ISSN: 0006-3568, Vol: 54, Issue: 6, Page: 573-584

Publication Year:
2004
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Repository URL:
https://scholarworks.umt.edu/ntsg_pubs/143
DOI:
10.1641/0006-3568(2004)054[0573:irsaep]2.0.co;2
Author(s):
David P. Turner; Scott V. Ollinger; John S. Kimball
Publisher(s):
Oxford University Press (OUP); ScholarWorks at University of Montana
Tags:
Agricultural and Biological Sciences
review description
A growing body of research has demonstrated the complementary nature of remote sensing and ecosystem modeling in studies of terrestrial carbon cycling. Whereas remote sensing instruments are designed to capture spatially continuous information on the reflectance properties of landscape and vegetation, models focus on the underlying biogeochemical processes that regulate carbon transformation, often over longer temporal scales. Remote sensing capabilities, developed over the past several decades, now provide regular, high-resolution (10-meter to 1-kilometer) mapping and monitoring of land surface characteristics relevant to modeling, including vegetation type, biomass, stand age class, phenology, leaf area index, and tree height. Integration of these data sets with ecosystem process models and distributed climate data provides a means for regional assessment of carbon fluxes and analysis of the underlying processes affecting them. Applications include monitoring of carbon pools and flux in response to the United Nations Framework Convention on Climate Change.