Remote Sensing of Nitrogen and Carbon Isotope Compositions in Terrestrial Ecosystems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009-11-05

AUTHORS

Lixin Wang , Gregory S. Okin , Stephen A. Macko

ABSTRACT

Stable isotopes have been frequently used to indicate processes occurring in soils, plants, and the atmosphere at scales from individual organisms to an entire ecosystem. Remote sensing, on the other hand, is a powerful tool used to identify ecosystem patterns and processes at larger scales. A union of these two approaches would hold promise for spatially continuous estimates of isotope compositions, thus providing unprecedented information into the patterns and processes within the Earth’s ecosystems. To date, however, the combination of isotope and remote sensing techniques is still in the exploratory stage. Here, two examples, one utilizing high spectral resolution remote sensing and the other employing high spatial resolution remote sensing, are suggested as possible approaches for the integration of stable isotope and remote sensing techniques. First, the feasibility of using high-resolution spectral data to estimate the vegetation δ15N from leaf to canopy levels is tested. Experimental results have shown that there is a strong correlation between foliar δ15N and spectral reflectance (R) in certain visible and near-infrared wavelengths. Stepwise regression indicates that the first-difference of the log 1/R explains 76–92% of the variation in foliar δ15N, providing the most reliable correlations in bands near 600 and 700 nm. Second, if the vegetation-soil δ13C relationship can be quantified on ground, soil δ13C distribution can be estimated by high-resolution satellite. Although limitations exist, because of the non-destructive and continuous nature, remote sensing is a promising tool to expand measurements of terrestrial δ15N and δ13C spatial patterns and dynamics. More... »

PAGES

51-70

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-90-481-3354-3_3

DOI

http://dx.doi.org/10.1007/978-90-481-3354-3_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011605403


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