Scaling and Evaluation of Ecosystem Carbon Uptake Through Multi-Scale Remote Sensing of Canopy Nitrogen

Scott V. Ollinger, Mary .E. Martin, Julian .P. Jenkins, Lucie C. Plourde, Andrew D. Richardson, University of New Hampshire, Durham, NH

Marie-Louise Smith, David Y. Hollinger, USDA Forest Service, Northeastern Research Station, Durham, NH

Spatial patterns of carbon assimilation in terrestrial ecosystems are influenced by two important classes of vegetation variables: those related to canopy structure (e.g. foliar biomass and leaf area index) and those related to photosynthetic capacity (e.g. leaf nitrogen and pigment concentrations). Although EOS-era remote sensing instruments have greatly improved assessment of vegetation productivity, focus has been concentrated on detection of LAI and related structural attributes that are used in models designed to be driven by these variables. Information on plant traits related to photosynthetic capacity has lagged behind and is typically only available through use of biome-specific look-up tables that lack important sources of spatial variability.

 Here, we report on an investigation that examines the degree to which carbon assimilation in forests can be related to both local and regional variation in canopy nitrogen. Field measurements collected at a diverse array of forested research sites within the AmeriFlux network have been combined with hyperspectral remote sensing data from the airborne AVIRIS and spaceborne Hyperion instruments. Resulting coverages of canopy nitrogen concentrations have been used to relate tower-based estimates of carbon assimilation capacity to canopy N for the local landscapes surrounding each tower.

Results indicate a significant positive relationship between canopy N and GPPmax. Because existing methods of canopy N detection are labor intensive and are restricted to small landscapes, a parallel investigation involves developing generalizeable canopy N detection methods that would enable more widespread application of these results. Results of this effort indicate that a single PLS regression equation can accurately predict canopy N concentrations at independent sites covering a wide range of site types. Further, we show that a substantial fraction of the variation in canopy N can be related to simple spectral features available from broad-band sensors.

Across all research sites and spectral features we examined, simple measures reflecting the height of the NIR plateau appeared to bear the strongest and most consistent relationship with plot-level foliar N concentrations.

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BartlettHarvard

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Austin Carey

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Willow Creek

Niwot Ridge

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Figure 1. Location of study sites used in the Canopy N--Carbon flux scaling experiment. Estimates of canopy nitrogen content are shown for several eastern U.S. sites within the AmeriFlux network. Coverages were derived using PLS regression of field-measured canopy N (% by foliar mass) against spectral reflectance data from NASA’s AVIRIS sensor. Data have been acquired for all sites indicated and are being used to derive (a) a continental-scale relationship between canopy N and vegetation carbon assimilation capacity (GPPmax, or canopy level Amax) and (b) generalizeable methods for N detection using broad-band sensors.

Funding From:

NIGECNIGEC

ABSTRACT

IS GLOBAL DETECTION OF CANOPY NITROGEN FEASIBLE?Questions: Can existing methods using high spectral resolution remote sensing be generalized?

Are there methods by which some degree of canopy N detection can be achieved using broad-band multi-spectral sensors?

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Figure 5. Relationships between predicted and observed foliar %N across sites in the U.S., Costa Rica and Australia using (a) site-specific calibration, (b) a general calibration spanning all sites and (c) a general calibration with each site iteratively dropped and predicted from the relationship derived from remaining sites. Site abbreviations are: DF-Duke Forest, NC; ACMF-Austin Carey Memorial Forest, FL; HF-Harvard Forest, MA; HOW-Howland Forest, ME; BEF-Bartlett Experimental Forest, NH, BAGO-Bago Maragle Forest, NSW Australia; LS-La Selva Costa Rica; AP-Adirondack Park, NY.

Figure 6. Measured foliar %N from plots shown in Figure 5 in relation to reflectance values for broad spectral bands representing the Advanced Land Imager. Raw reflectance values from AVIRIS and/or Hyperion were aggregated to reproduce the 9 ALI bands in the visible, near infrared (NIR) and short wave infrared (SWIR) regions of the spectrum. Foliar N data are stand-level multi-species averages from ~20 x 20 plots in N. America, Costa Rica and Australia.

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Figure 7. Plot-level foliar %N in relation to NIR reflectance (a, b) and NDVI (c, d) using spectral bands contained on the Advanced Land Imager simulated with AVIRIS and/or Hyperion. Reflectance values from AVIRIS and/or Hyperion were aggregated to reproduce 9 ALI bands in the visible, near infrared (NIR) and short wave infrared (SWIR) regions of the spectrum. Across all research sites and spectral bands we examined, the height of the NIR plateau appeared to bear the strongest and most consistent relationship with plot-level foliar N (e). Site abbreviations are the same as in Figure 5.

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Global Foliar N Calibration with Hyperspectral Data Foliar N and Broad-Band Reflectance

Canopy N and GPP Canopy N and GPP maxmax

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TEMPERATE FORESTS SHOW STRONG CARBON-NITROGEN LINKAGES THAT ARE REFLECTED VIA PATTERNS OF CANOPY N

Question: To what degree can this pattern be extended across a range of ecosystems?

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Figure 2. Canopy nitrogen (% by foliar mass) in relation to (a) aboveground net primary production (ANPP) and (b) leaf area index (LAI) at a series of plots covering a range of forest types at the Bartlett Experimental Forest, NH and the Harvard Forest, MA. Canopy N is a strong and positive correlate with ANPP across both sites. Canopy N and LAI are more weakly related and result in trends between LAI and ANPP that are either weak (Bartlett) or absent (Harvard Forest). The implication of these results is that methods for remote sensing of canopy N should allow characterization of a substantial degree of variation in growth, beyond that which can be explained by LAI alone. ANPP and LAI data for Harvard Forest are courtesy of Tom Gower, Dave Turner, Warren Cohen and the Bigfoot project.

Plot-level forest productivity

FIGURE 3. Predicted and observed daily gross primary productivity at the Harvard Forest Ameriflux site. Temporal trends include an upward trend over the period from 1992-2004, interrupted by a sharp dip in 1998. (a) Running PnET-Day using daily climate and a mean foliar nitrogen value produced a roughly 10 % underestimate and did not track the observed temporal trends. (b) For a second set of model runs, we compiled vegetation inputs that included annual values of measured foliar N and canopy mass from a nearby study (John Aber, chronic N study control plots). Interestingly, these data showed an upward trend in foliar N over time (c) and produced model predictions that more closely followed the observed patterns. (d) Interestingly, independent estimates of canopy Amax derived through inversion of a simple statistical model yielded a significant relationship with annual foliar N values.

Temporal Trends in Flux Tower Carbon Exchange

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Figure 4. Maximum carbon assimilation capacity (Canopy Amax) at 6 AmeriFlux sites in relation to landscape-scale foliar N concentrations, as determined by hyperspectral remote sensing estimates. Each foliar N value represents the average of predictions generated with AVIRIS (Figure 1) for a 250 m radius area surrounding each flux tower. This suggests that the well-known leaf-level trend between foliar N and Amax scales to whole forest canopies and that methods for broad-scale N detection would greatly improve regional estimates of carbon assimilation.

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