Marie-Louise Smith

Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests

Abstract Concern is resurfacing in the United States over the long-term effects of excess nitrogen (N) deposition and mobility in the environment. We present here a new synthesis of existing data sets for the northeastern United States, intended to answer a single question: Is N deposition altering the N status of forest ecosystems in this region? Surface water data suggest a significant increase in nitrate losses with N deposition. Soil data show an increase in nitrification with decreasing ratio of soil carbon to nitrogen (C:N) but weaker relationships between N deposition and soil C:N ratio or nitrification. Relationships between foliar chemistry and N deposition are no stronger than with gradients of climate and elevation. The differences in patterns for these three groups of indicators are explained by the degree of spatial and temporal integration represented by each sample type. The surface water data integrate more effectively over space than the foliar or soil data and therefore allow a more comprehensive view of N saturation. We conclude from these data that N deposition is altering N status in northeastern forests.

REGIONAL VARIATION IN FOLIAR CHEMISTRY AND N CYCLING AMONG FORESTS OF DIVERSE HISTORY AND COMPOSITION

Although understanding of nitrogen cycling and nitrification in forest ecosystems has improved greatly over the past several decades, our ability to characterize spatial patterns is still quite limited. A number of studies have shown linkages between canopy chemistry and N cycling, but few have considered the degree to which these trends can provide an indicator of forest N status across large, heterogeneous landscapes. In this study, we examined relationships among canopy chemistry, nitrogen cycling, and soil carbon:nitrogen ratios across 30 forested stands in the White Mountains of New Hampshire. Plots included a range of species (sugar maple, red maple, American beech, yellow birch, paper birch, red spruce, balsam fir, eastern hemlock) and were broadly grouped into two disturbance categories: those that were historically affected by intensive logging and/or fire and those that experienced minimal human disturbance. Across all plots, rates of net N mineralization and net nitrification were correlated wi...

DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN

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 rep- resents 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.

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.

Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data

Hyperspectral remote sensing methods are advancing rapidly and offer the promise of estimation of pigment, biochemical, and water content dynamics. The recent Earth Observer 1 (EO-1) Hyperion mission, and associated field campaigns, has allowed a range of biophysical and biochemistry attributes of eucalypt foliage to be analyzed in conjunction with remotely sensed spectra. This paper reports on a study at Tumbarumba (Bago-Maragle State Forest), Australia, which has a wide variety of eucalypt species, ranging in productivity and age. EO-1 Hyperion imagery was obtained in April 2001, and a field program was undertaken involving the establishment of plots, collection of standard forestry inventory data, and green leaf samples. Leaf nitrogen (N) content was measured from leaf samples using wet chemistry techniques and canopy N concentration estimated using leaf mass and proportional species leaf area index data. A number of models were developed from Hyperion reflectance, absorbance, and derivate transformations using partial least squares regression and multiple linear regression. The most significant calibration model predicted N with a correlation coefficient (r)=0.9 (82% variance explained) and a validation r/sup 2/=0.62 (P<0.01). The standard error of the estimate of foliar N was 0.16% equating to 13% of the mean observed %N at the site. These initial results indicate that predictions of canopy foliar N using Hyperion spectra is possible for native multispecies eucalypt forest. Similar studies worldwide, particular those associated with the flux tower network, will allow these findings to be placed in context with other biomes and functional types.

Sixty years of management and natural disturbance in a New England forested landscape

Abstract Changes in species composition of overstory trees (percent of basal area) and size class were monitored over 60 years on 441 cruise plots located on the Bartlett Experimental Forest, a 1052 ha experimental forest in the White Mountains of New Hampshire. The plots were analyzed by elevation class, landtype (deciduous and coniferous), and year (1931–32, 1939–40, and 1991–92) within managed and unmanaged stands. The primary changes in species composition over the 60-year period were due to natural succession, which resulted in marked increases (doubling) of the eastern hemlock ( Tsuga canadensis (L.) Carr.) component, and consistent decreases in paper birch ( Betula papyrifera Marsh.), yellow birch ( B. alleghaniensis Britton) (at medium or low elevations), and aspen ( Populus spp.). Timber management resulted in small decreases in the beech ( Fagus grandifolia Ehrh.) and red spruce ( Picea rubens Sarg.) component and slight increases in sugar maple ( Acer saccharum Marsh.). Natural disturbances (beech-bark disease and hurricane damage) had only minor effects on species occurrence. No consistent evidence of red spruce ( Picea rubens Sarg.) decline was detected. Eastern hemlock, a climatically sensitive species in northern New England with a limited elevational range, increased dramatically at moderate to low elevations, but showed little tendency to invade the highest elevation class; apparently, the warming trend reported elsewhere in New Hampshire is not occurring, or the species are not responding in terms of changes in elevational distribution. The results emphasize the resilience of New England forests and their resistance to exogenous disturbance.

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.

Comparison of green leaf eucalypt spectra using spectral decomposition

Vegetation function and dynamics are key parameters in terrestrial carbon-cycle models. The strong linkages between biochemical constituents in foliage with photosynthetic capacity and ecosystem productivity makes the development of methods to characterise patterns of foliage biochemistry a potentially powerful approach for estimating leaf function and carbon fluxes at a variety of scales. Eucalypt foliage spectra were obtained over a range of species and locations in southern New South Wales, covering a significant productivity and climatic gradient. We applied a spectral decomposition technique, based on multivariate factor analysis, which allows inter-correlations of underlying factors affecting a set of variables to be assessed. A small number of factors capture virtually all of the variation observed in the foliage spectra and each factor contains significant information relating to species and plot variation over the region. Factor analysis indicated that key chlorophyll, nitrogen, protein and water absorption features could be accurately identified across the spectra. In addition, significant correlations existed between factor loadings and environmental data of the region, including mean annual rainfall and a soil fertility index.

Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data

BackgroundA simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire.ResultsNet primary production (NPP) predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within ± 2.5% of the mean of plot estimates for annual NPP.ConclusionAlthough MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF.

Community and edaphic analysis of upland northern hardwood communities, central Vermont, USA

Abstract An ecological multifactor approach was used to identify and describe upland northern hardwood site types and species groups on the central Green Mountains, Vermont. Seven ecological types and ten species groups were identified by a method combining plot sampling of vegetative and soil-site variables and numerical analysis. Two-way indicator species analysis and canonical correspondence analysis (CCA) were used to evaluate the distinctness of the identified ecological types and species groups and to compare the discriminating abilities of different ecosystem components (landscape position, vegetation, and soil). Each ecological type was a characteristic combination of physiography, landscape position, soil, and vegetation. Descriptions of the relationships of species groups to gradients of soil moisture and fertility, inferred from landscape position and substrate are presented. CCA demonstrated that most species within defined groups had similar ecological responses. Few groups occurred in only one ecological type. In all cases evaluation of topographic and soil factors in conjunction with species groups facilitated identification of ecological types.