Lucie C. Plourde

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earths climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle–climate models.

Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor

Field studies among diverse biomes demonstrate that mass-based nitrogen concentration at leaf and canopy scales is strongly related to carbon uptake and cycling. Combined field and airborne imaging spectrometry studies demonstrate the capacity for accurate empirical estimation of forest canopy N concentration and other biochemical constituents at scales from forest stands to small landscapes. In this paper, we report on the utility of the first space-based imaging spectrometer, Hyperion, for estimation of temperate forest canopy N concentration as compared to that achieved with the airborne high-altitude imaging spectrometer, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Overall accuracy of Hyperion estimates of forest canopy N concentration, as compared with field measurements, were within 0.25% dry mass, and AVIRIS-based estimates were within 0.19% dry mass, each well within the accuracy required to distinguish among forested ecosystems in nitrogen status.

Sampling method and sample placement: How do they affect the accuracy of remotely sensed maps?

for accuracy against the reference data. A widely accepted proThe accuracy of remotely sensed forest stand maps is tra- cedure for comparing these data is the generation of an error ditionally assessed by comparing a sample of the map data matrix (Card, 1982; Congalton et al., 1983; Story and Congalton, with actual ground conditions. Samples most often comprise 1986; Congalton, 1991). clusters of pixels within homogeneous areas, thereby avoiding An error matrix is an especially effective accuracy assessproblems associated with accurately mapping “edges” (e.g., ment tool because it provides a starting point for a series of statransition areas between two forest types). Consequently, they tistical techniques to further examine accuracy (Congalton and may well overestimate accuracy, but the degree of overestima- Green, 1999). One such analytical technique is the Kappa analtion is unknown. This paper examines two important factors ysis, a discrete multivariate technique for comparing error maregarding accuracy assessment that are not well studied: the trices (Congalton et al., 1983; Hudson and Ramm, 1987; Coneffect on estimates of accuracy of (1) the sampling method galton, 1991; Ma and Redmond, 1995; Stehman, 1996; Stehand (2) the exact placement of the samples. Overall accuracy, man, 1999; Congalton and Green, 1999). Kappa analysis, which normalized accuracy, and the KHAT statistic are computed from assumes a multinomial distribution, generates a KHAT statistic error matrices generated from simple random sampling, stra- that measures the difference between actual and chance (or rantified random sampling, and systematic sampling using totally dom) agreement between the map and reference data. It can also random sample placement and samples chosen from homog- be used to test for significant differences between two error eneous areas only. The results indicate that Kappa appears matrices. to be as appropriate to use with systematic sampling and The only sampling method that satisfies Kappa’s assumpstratified random sampling as it is with simple random sam- tion of a multinomial model is simple random sampling. The pling, but suggests that sample placement may have more of effect of other sampling schemes on the outcome of the Kappa an effect on estimates of accuracy than sampling method analysis has not been well studied. In addition, samples are ofalone. ten chosen only if they occur within the interior of homogeneous pixel groupings in order to avoid problems with sam

Estimating species abundance in a northern temperate forest using spectral mixture analysis

Effective, reliable methods for characterizing the spatial distribution of tree species through remote sensing would represent an important step toward better understanding changes in biodiversity, habitat quality, climate, and nutrient cycling. Towards this end, we explore the feasibility of using spectral mixture analysis to discriminate the distribution and abundance of two important forest species at the Bartlett Experimental Forest, New Hampshire. Using hyperspectral image data and simulated broadband sensor data, we used spectral unmixing to quantify the abundance of sugar maple and American beech, as opposed to the more conventional approach of detecting presence or absence of discrete species classes. Stronger linear relationships were demonstrated between predicted and measured abundance for hyperspectral than broadband sensor data: R 2 � 0.49 (RMSE � 0.09) versus R 2 � 0.16 (RMSE � 0.19) for sugar maple; R 2 � 0.36 (RMSE � 0.18) versus R 2 � 0.24 (RMSE � 0.33) for beech. These results suggest that spectrally unmixing hyperspectral data to estimate species abundances holds promise for a variety of ecological studies.

Reply to Fisher: Nitrogen–albedo relationship in forests remains robust and thought-provoking

Fishers (1) primary concerns have overlooked important methodological aspects of our study (2), whereas other concerns are consistent with our own presentation of the findings. We did not exclude photosynthetically active radiation (PAR) wavelengths, as Fisher states. Instead, we related canopy nitrogen to mean reflectance across the entire imaging spectrometer detection range of 400–2,500 nm and to independent estimates of shortwave albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS), which include both PAR and near-infrared (NIR) wavelengths. Snowfall was not a factor because our analysis only included imagery from the midgrowing season to match our field sampling. Given the size of MODIS pixels, there is undoubtedly some influence of canopy gaps or nonvegetative surfaces, but their effect was minimized by focusing on large tracts of closed canopy forest.