Title: Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen
Author: Smith, Marie-Louise; Ollinger, Scott V.; Martin, Mary E.; Aber, John D.; Hallett, Richard A.; Goodale, Christine L.;
Source: Ecological Applications. 12(5): 1286-1302.
Publication Series: Scientific Journal (JRNL)
Description: The concentration of nitrogen in foliage has been related to rates of net photosynthesis across a wide range of plant species and functional groups and thus represents a simple and biologically meaningful link between terrestrial cycles of carbon and nitrogen. Although foliar N is used by ecosystem models to predict rates of leaf-level photosynthesis, it has rarely been examined as a direct scalar to stand-level carbon gain. Establishment of such relationships would greatly simplify the nature of forest C and N linkages, enhancing our ability to derive estimates of forest productivity at landscape to regional scales. Here, we report on a highly predictive relationship between whole-canopy nitrogen concentration and aboveground forest productivity in diverse forested stands of varying age and species composition across the 360 000-ha White Mountain National Forest, New Hampshire, USA. We also demonstrate that hyperspectral remote sensing can be used to estimate foliar N concentration, and hence forest production across a large number of contiguous images. Together these data suggest that canopy-level N concentration is an important correlate of productivity in these forested systems, and that imaging spectrometry of canopy N can provide direct estimates of forest productivity across large landscapes.
Keywords: foliar chemistry, forest C and N linkages, forest canopy-level N concentration, forest productivity, nitrogen, remote sensing, hyperspectral, AVIRIS, White Mountain National Forest, New Hampshire
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Smith, Marie-Louise; Ollinger, Scott V.; Martin, Mary E.; Aber, John D.; Hallett, Richard A.; Goodale, Christine L. 2002. Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecological Applications. 12(5): 1286-1302.
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