Linking opticalsignals to functional change in arctic ecosystems

"Linking optical signals to functional changes in Arctic ecosystems"

PI: John A. Gamon

Affiliation: Department of Biology & Microbiology, California State University, Los Angeles

Address:

Department of Biology & Microbiology

California State University, Los Angeles

5151 State University Drive

Los Angeles, CA 90032

Phone: 323-343-2066

Fax: 323-343-6451

Email: jgamon@calstatela.edu

Budget summary:

Year 1 (2000): $186,882

Year 2 (2001): $182,480

II. Abstract

Ongoing global warming is expected to have strong impacts in the northern latitudes, and this warming is likely to affect the distribution of vegetation and the carbon balance of the region. Remotely sensed data is useful for the detection and description of these ecosystem responses. However, most recent studies employing remote sensing have used broadband sensor data with course spatial resolution. These conventional methods cannot fully distinguish subtle changes in cover, species composition or physiological activity, leaving the functional interpretation of remotely sensed signals unresolved. In contrast, hyperspectral (narrow-band) data can discriminate between many different types of tundra and boreal landscape components (e.g. mosses, lichens, grasses, shrubs, broadleaf and needle leaf trees, and bare soil), as well as provide information on the functional traits associated with these components (e.g. pigment content, moisture content, photosynthetic activity). We propose to collect hyperspectral reflectance data both on the ground and from aircraft, in conjunction with ongoing carbon flux measurements and ecosystem manipulations. Our primary objective will be to characterize spectral "signatures" associated with functionally distinct cover types. These measurements will provide baseline data needed for the detection of significant landscape change. A readily achievable outcome of this first objective will be an accessible web-based spectral library that will facilitate studies examining functional change in northern latitude ecosystems. Our secondary objective will be to clearly link these signatures to ecosystem function through experimental manipulations and association with spatial and temporal patterns. The outcome of this second objective will be improved regional carbon balance models incorporating the additional information provided by hyperspectral signatures. By specifically considering cover type, temperature, moisture status, and physiological state, we hope to improve our understanding of processes controlling system carbon fluxes in northern latitudes, and will improve our detection of significant ecosystem responses to climate change.

III. Project Description

Background

According to Global Circulation Model (GCM) predictions, arctic and boreal regions are likely to warm by several degrees in the next century (Schlesinger and Mitchell 1987, Kattenberg et al. 1995, Sellers et al.1996). Warming trends have already been observed in northern regions (Chapman and Walsh 1993). This heating is likely to cause changes in regional carbon balance, with the potential for further feedbacks on climate. Two predictions emerge:

1) Warmer temperatures could lengthen the growing season, enhancing photosynthesis and increasing primary production (increasing sink strength). Indirect evidence for increased photosynthetic activity in northern latitudes has been provided by satellite data (Myneni 1997) and atmospheric studies (Keeling et al. 1996). The resulting increase in carbon uptake by the biosphere associated with this "northern greening" could act as a powerful negative feedback to global warming and slow the atmospheric increase of carbon.

2) On the other hand, warmer temperatures could also increase plant and soil respiration, causing increased release of stored carbon to the atmosphere(transition to a carbon source). Direct evidence for this second effect has been provided by flux measurements in the tundra (e.g. Oechel et al. 1993. If sustained, this stimulated respiration could have a powerful "feedforward" effect, enhancing global warming.

Collectively, tundra and boreal forest ecosystems represent over 18 percent of the Earth’s land surface (DeFries and Townshend 1994). Because of the large amount of carbon stored in these systems (Billings et al. 1982, Trumbore and Harden (1997), the net outcome of contrasting predictions will exert a large influence on future global climate patterns.

Recent analyses of long-term data sets suggest that the situation is considerably more complex than these two simple predictions allow, and tundra ecosystems may be reverting towards a net carbon sink during the growing season, but remaining a net source overall when winter fluxes are considered (Oechel et al, in review). The reason may be that the net carbon balance is determined by the interaction of several dynamic processes, each with its own time constant. For example, in the short-term (e.g. several weeks to years) enhanced temperature and drying can stimulate respiration in the short run, leading to a transient "burst" in CO2 release from the biosphere to the atmosphere, and supporting increased source activity (prediction #2) in the short run (Oechel et al. 1993). On the other hand, enhanced nitrogen availability (Oechel and Vourlitis 1994) or direct CO2 fertilization effects (Grulke et al. 1990) may stimulate photosynthetic carbon uptake over a slightly longer (e.g. several year) time scale, leading to enhanced sink strength (prediction #1). In the longer run (e.g. many decades), changing species composition and population structure could lead to system adjustments that dampen shorter-term perturbations. Thus, to make reliable predictions about future carbon balance in northern latitudes, it will be necessary to consider multiple processes operating at several temporal scales. Improved monitoring tools are now needed to fully characterize these processes.

The Utility of Remote Sensing

Remote sensing has the potential to contribute enormously to our understanding – or mis-understanding – of the carbon cycle in northern ecosystems. The benefits of remote sensing include its consistent format and large area coverage, enabling comparisons over large regions. Most vegetation studies employing existing satellite sensors (e.g.NOAA AVHRR or Landsat sensors) are based on indices derived from broad-band signals at very coarse spatial scales (pixel size or "instantaneous field of view" on the order of 400 m2 to 1 km2). However, the difficulty of applying remote sensing in an experimental context (with clear "treatments" and "controls") is a potentially serious weakness of most satellite systems. This problem is further enhanced by the broad bandwidth and large pixel sizes of existing satellite sensors, which lead to "fuzzy" conclusions. Together, these weaknesses prevent conventional remote sensing approaches from pinpointing subtle, but important, changes in ecosystem function.

One widely applied index is the Normalized Difference Vegetation Index (NDVI), which is sensitive to the amount of green vegetation cover within a given area (Swain and Davis 1978, Jackson et al. 1983, Jensen 1983).However, NDVI per se does not readily distinguish between species or functionally distinct vegetation classes. Because arctic vegetation is highly variable, this is likely to be a limitation in northern latitudes. In any given location, the dominant cover may consist of vascular plants, moss, or lichens. Even within a cover type there can be considerable variability in physiological activity (Chapin and Shaver 1985b) and spectral signatures (figure 1) due to variations in structure, pigmentation, and other factors influencing physiological activity. For example, vascular plants may be herbaceous or woody, and plant architecture may be erect, prostrate, or growing in tufts, and plants may be deciduous or evergreen. Vegetation type and growth form vary dramatically along short topographic gradients as well as regionally and latitudinally (Chapin and Shaver 1985a). These different in cover type and associated structure can significantly affect the exchange of gases and energy between the biosphere and the atmosphere. For example, moss insulates the soil, and this affects soil processes such as the formation of ground ice and the rate of microbial decomposition, both of which are temperature dependent (Bubier et al. 1997). Thus, the type of cover may be an indicator of the respiratory contribution to total CO2 exhange. Because NDVI is often unable to distinguish fine cover types (species or functional types), it cannot reveal the changing component fluxes associated with contrasting cover classes.

Another potential application of NDVI is to monitor temporal and spatial patterns in potential photosynthetic activity (e.g. Tucker et al. 1986a, Myneni et al 1997). To the extent that changes in NDVI accurately reflect changes in green vegetation cover across regions, and to the extent that these cover changes reflect real changes in photosynthetic activity, NDVI may provide a useful measure of terrestrial photosynthesis and productivity. However, for several reasons, these links are not always reliable, as discussed below, and other methods may be needed to supplement NDVI (Gamon and Qiu 1999).

The link between NDVI and CO2 exchange is due to the correlation between NDVI and the fraction of photosynthetically active radiation (PAR) absorbed by green vegetation (fAPAR). For most vegetation, this relationship is approximately linear (Baret and Guyot1991, Goward and Huemmrich 1992, Gamonet al. 1995), ensuring that NDVI can provide a useful measure of potential photosynthetic activity (e.g. Myneni et al. 1995). A simple light-use efficiency model describes this relationship between Absorbed Photosynthetically Active Radiation (APAR, which is estimated by NDVI) and Gross Primary Production (G):

(equation 1)

where G is the production between times t1and t2, and these times may represent the start and end of the growing season or some subset of the growing season. PARo(t) is the incoming PAR at time t, fAPAR(t) is the fraction of PAR absorbed by green vegetation at time t, and ε(t) is the dry matter : radiation quotient (sometimes referred to as the light-use efficiency or radiation-use efficiency), which is a measure of the ability of the plant to convert energy into biomass (Monteith 1977; Russell et al. 1989).

Based largely on studies of crop systems, the underlying assumption of several authors (e.g. Monteith 1977, Russell et al. 1989) is that factors such as water and nutrient availability generally limit the canopy structure so that the primary effect of stress is on fAPAR. Consequently, several studies have erroneously assumed that efficiency (ε) term is sufficiently invariant across ecosystems and vegetation types that it can be treated as a constant in the model (Monteith 1977, Heimann and Keeling 1989, Russell et al. 1989, Myneni et al. 1995). However, an abundance of studies have demonstrated that this is not the case (Running and Nemani 1988, Hunt and Running 1992, Running and Hunt 1993, Runyon et al. 1994, Gamon et al. 1995, Joel et al. 1997). This is because a number of stresses, including temperature, soil type, water availability, disease, plant type, and plant age can severely reduce light-use efficiency (Prince1991). This reduced efficiency is often manifested as photosynthetic downregulation (a short-term, reversible decline in photosynthetic activity) often detectable as a reduction in stomatal aperture, carboxylation, and photosystem II activity(Sellers et al. 1996, Gamon et al. 1997). In some ecosystems, reduced efficiency caused by stress-induced downregulation can result in as much as a five-fold variation in realized photosynthetic flux rates with no detectable change in NDVI (Gamon et al. 1995). Because modeling efforts linking a global circulation model (GCM) to a surface-atmosphere transport and physiological model (SiB2) have indicated that downregulation can significantly impact climate predictions, the effect of overlooking downregulation may lead to erroneous conclusions regarding carbon balance and global climate (Sellers et al. 1996). However, due to a shortage of appropriate field measurements, particularly for remote ecosystems, the exact significance of physiological control on these larger earth system processes remains uncertain (Hall et al. 1995, Prince and Goward 1995, Sellers et al. 1996).

While data are few, the existing studies suggest that light-use efficiency may not be constant for tundra sites. Whiting et al. (1992) measured CO2 fluxes and NDVI for a variety of tundra habitats from very dry lichen covered sites to wetter sites dominated by gramineous vegetation and wet meadows with varying mixtures of herbaceous vegetation and mosses. They found a strong correlation between the ratio of net CO2 exchange to incident PAR with NDVI. However, this relationship was different from ones derived from global studies (Tucker and Sellers 1986), suggesting that tundra vegetation has different light use efficiencies from other vegetation types. In a later study, Whiting (1994) found that the NDVI-CO2 relationship was degraded when mosses were present in the scene, again suggesting differences in physiological responses and light-use efficiencies for different cover types within tundra ecosystems. One possible reason for the differences in NDVI response for mosses versus vascular plants is that mosses become light saturated at low levels of irradiance compared with vascular plants (Murray et al. 1993), however, the impact of this behavior on the NDVI-CO₂ relationship remains to be demonstrated.

In conclusion, while NDVI is related to one component (fAPAR) of CO2 flux, it cannot fully explain variation in CO2 flux among ecosystems or within a single ecosystem. Consequently, it is unlikely to track subtle but functionally significant changes in cover predicted for northern latitudes. Similar limitations of NDVI have already been demonstrated for a wide variety of ecosytems (Running and Nemani 1988, Hunt and Running 1992, Running and Hunt 1993, Runyon et al. 1994, Gamon et al. 1995, Joel et al. 1997), illustrating that additional information is needed when applying remote sensing to studies of carbon flux. Because warming is likely to affect physiological function at many levels (Oechel et al, in review), a more sophisticated remote sensing approach is clearly needed.

Recent Advances in Remote Sensing

Recent advances in remote sensing offer to overcome many of the limitations described above. These include the advent of "hyperspectral" sensors, which sample reflectance in many narrow spectral bands, typically 2-10 nm apart. By supplying detailed spectra composed of many contiguous spectral bands (figure 1), these sensors provide a much richer level of information than is possible with conventional, broadband sensors. The additional information provided by this spectral detail allows the characterization of "spectral signatures," i.e. spectral features associated with specific cover types or physiological states. A number of potentially useful interpretations of hyperspectral sensors are now being applied (e.g. table 1), and many of these could be useful in addressing functional changes in arctic ecosystems.

One application of hyperspectral data is in improved classification of the variety of different tundra land cover types based on their spectral signatures.. Petzold and Goward (1988) showed clear differences in the spectral reflectance of subarctic lichens and vascular plants. Bubier et al. (1997) found distinct reflectance spectra not only for lichens, mosses, broadleaf, and needle leaf plants, but also between Sphagnum mosses, feather mosses, and brown mosses. Differences between the spectral reflectance of moss species were also observed by Vogelmann and Moss (1993). Mulhern (1995) identified spectral reflectance characteristics for subarctic vegetation types. In particular, she observed differences between several species of lichens. Hyperspectral data can also distinguish between living and senesced vegetation (Roberts et al. 1993, Asner et al. 1998), or between different cover types and bare ground or rocks (Mulhern 1995). In our preliminary studies at Barrow, Alaska, contrasts among species and cover classes are readily detectable with hyperspectral sensors (figure 1). A primary goal of this project will be to further characterize these spectral signatures, and to link them to cover types and physiological function.

Table 1. Mathematical interpretations or "indices" derived from spectral reflectance and their significance. Many of these will be explored in this study for their influence on system carbon flux.

Index	Meaning or significance	Relevant references
Normalized Difference Vegetation Index (NDVI) Derivative indices Shade fraction Pigment indices (chlorophyll, carotenoids, & anthocyanins) Photochemical reflectance index (PRI) Surface temperature Water band index (WBI) Various SWIR absorption bands Albedo Combined indices (NDVI combined with temperature, PRI, or WBI)	Green vegetation cover, fAPAR Vegetation cover, fAPAR (corrected for soil background) Canopy structure, surface roughness Pigment content, absorbed PAR, and possibly light-use efficiency Index of xanthophyll cycle pigment activity and relative photosynthetic rate With NDVI, can be used to determine vegetation type, or partitioning between latent and sensible heat flux Index of canopy water content, leaf area index, and possibly productivity Indices of soil type, mineral composition, or presence of lignin/cellulose(dead canopy material) Indicates spring/thaw, affects energy balance, climate system and surface temperatures Indicator of altered physiological properties, and changing light-use efficiency	Goward et al. 1985) Sellers et al. (1992) Hall et al. 1990,Demetriades-Shah et al. 1990, 1992 Roberts et al. 1993 Gamon and Surfus 1999 Gamon et al. 1997 Nemani and Running (1997) Penuelas (1997), Gamon and Qiu (1999) Clark et al. (1998), Curran and Kupiec (1995) Sellers et al. 1997 Nemani and Running 1997, Gamon et al 1998, Gamon and Qiu 1999

Figure 1

Sample spectra of three arctic tundra cover types (vascular plants, mosses, and lichens) sampled at Barrow, Alaska on September 26, 1999. Each panel represents several individual species within a category (dashed lines) as well as a mean spectrum for that category (thick, solid lines). Note the wide variation in spectral shapes associated with varying pigment content, structure, and other functional traits. For example, plants and lichens contain different sets of pigments that influence their reflectance signatures. In this case, several plant species showing high anthocyanin content, visible as reflectance peaks around 650 nm (top panel), and this pigment appears to be is lacking in lichens (bottom panel). On the other hand, several lichens indicate strong absorbance features visible as dips in the reflectance plots around 420 nm (bottom panel), which appear to be absent in plants (top two panels). Among cover types, the height of the average spectra (thick lines) reveal that leaf spectra appear to be intermediate in brightness between moss (dark ) and lichens (bright), which would affect overall surface albedo, and thus energy balance and system flux rates. These characteristics can be further defined using spectral indices (table 1), and will be compared to pigment extracts, photosynthetic rates, and other physiological assays for different cover types. In this way, we will link spectral features to cover types and their varying physiological states.

One way to link cover types to function is to build an understanding of links between cover types, their biophysical properties influencing reflectance, and their related functional roles in the ecosystem. For example, Bubier et al. (1997) indicated that the Sphagnum mosses found in hummocks differed spectrally from those found in hollows in boreal peatlands. These distinctions relate to carbon uptake as the hollow species is more prone to desiccation than hummock species and therefore more sensitive to water table depth. During wet periods the hollow species are more productive than the hummock mosses, while the opposite is true during dry times.

Another way to link cover types to function is to detect specific spectral features (e.g through the use of indices illustrated in table 1) and clearly link them to specific traits (pigment, nitrogen or water content) affecting physiological function. For example, the Water Band Index (WBI) is strongly linked to vegetation water content (Penuelas 1997, Gamon et al 1999), which, in turn, influences water and CO₂ flux rates in many ecosystems (Gamon and Qiu 1999). Mosses may have water content as low as 5-10% of their dry weight, with optimum water contents for photosynthesis of 600-1300%. Thus, variations in reflectance features can identify physiologically significant differences in vegetation water content, and assist in detecting different functional states related to flux rates. Additionally, a variety of pigment indices are now able to detect relative levels of different pigment classes (chlorophylls, carotenoids and anthocyanins) (Penuelas and Filella 1998, Gamon and Qiu 1999, Gamon and Surfus 1999). These indices vary among species, or even within a species across time and space, and can be strongly related to photosynthetic function (Gamon et al. 1997, Gamon and Surfus 1999). Further, the combination of physiological indices (indicating light-use efficiency, or the ε term of equation 1) with structural indices (affecting fAPAR in equation 1) may be particularly potent way of separating contrasting cover types and functional states (Gamon and Qiu 1999). Thus, hyperspectral sensors offer a multitude of ways to assess physiological status, many of which are proving successful in other ecosystems, but remain to be tested in arctic systems.

We predict that a combination of approaches incorporating hyperspectral sensors will significantly enhance our ability to detect functional change in arctic ecosystems. Our initial objective will be to fully characterize the spectral "signatures" of tundra cover types at several spatial scales. The result will be a web-based spectral library of arctic cover types that will facilitate interpretation of other remote sensing efforts at larger scales of aggregation (e.g. aircraft and satellite data). Additionally, by linking features in these spectral signatures to ongoing ecosystem manipulations and flux monitoring studies at several scales, we will provide a basis for improving our understanding of controls on carbon flux. The ultimate objective will be to make use of this additional information in process models to refine our understanding of carbon balance for the region.

Methods

We propose to collect hyperspectral reflectance data and link it to the type and biophysical state of tundra vegetation. Observations will be collected at a range of spatial scales to develop an understanding of how the reflectance information varies with scale for this region. This will provide the information allowing for future analysis of satellite data. The reflectance data will be combined with carbon flux data from experiment sites to develop models of carbon exchange driven by remotely sensed data. In addition, we will observe sites undergoing experimental manipulations to help refine carbon models and to see how the spectral reflectance of the landscape may change in response to climate change. Our project involves collaboration with the ongoing flux studies of Dr. Walter Oechel and colleagues (see letter of support from Dr. Oechel). An objective of year one will be to develop a web-based spectral library that will make our spectral data fully available. In this way, the measurements obtained in this study will assist in the interpretation of other remote sensing efforts, and will add value to other funded studies in the region. By the end of year two, our goal is to develop refined models of carbon flux that will assist in understanding factors controlling ecosystem fluxes in the region.

Field sites

In May, 2000 (year 1), we will begin field work in Barrow, Alaska, at the tundra site monitored by Dr. Walter Oechel and colleagues. We will equip that site with a tram system for automated monitoring of spectral reflectance within the flux tower footprint (sampling region). This tram system will enable automated data acquisition throughout the 2000 growing season. In three intensive field campaigns (early-, mid-, and late-season), we will mount the spectrometer on the aircraft (San Diego State University’s Sky Arrow 650 Environmental Research Aircraft) and sample continuous transects between Barrow and Atqasuk, Alaska, in conjunction with aircraft flux measurements. This transect sampling will allow us to expand our local-scale (Barrow tower) measurements to the larger, regional scale, and will facilitate comparison of tower and aircraft flux data. Finally, at the Barrow site, we will conduct detailed sampling at the fine (e.g. leaf ) scale of all major cover types and at the plot (approx. 1 m²) scale of experimental treatments.

Based on the outcome of year one, and pending further funding, in May 2001 (year 2) we will set up a second tram system and monitoring regime in Atqasuk, Alaska, based on the experience in Barrow. The instrumentation and sampling scales are further described below.

Instrumentation

This project takes advantage of a new class of hyperspectral (narrow-band) spectrometers that can be readily configured for sampling at a range of spatial scales. By providing information on surface reflectance at several scales of aggregation, this approach can directly link an understanding of species and physiological processes (often best understood at a fine scale) to patterns observed at larger scales by remote sensing (Gamon et al. 1998, Gamon and Qiu 1999). Two types of instruments will be applied at different spatial scales, and these applications are described in detail, below.

Fine-scale sampling - linking reflectance to cover type and physiological function at leaf and plot scales.

When fitted with appropriate foreoptics, the leaf reflectometer (UniSpec, PP Systems, Haverhill MA) is ideally suited for sampling of small (e.g. 1 mm to 1 cm diameter) tissue regions (Gamon and Surfus 1999). At this scale, fine features in spectral signatures, expressed as "reflectance indices" (table 1) can be readily compared to physiologically relevant measures of pigment composition, leaf structure, nitrogen or water content, and photosynthetic activity. In this way, strong linkages can be developed between species and functional types, their spectral signatures, and their specific physiological state at a particular a point in time (e.g., Gamon et al. 1997, Gamon and Surfus 1999), providing a basis for interpretation at larger scales (Gamon and Qiu, 1999). Preliminary measurements in the tundra (figure 1) indicate that this instrument works well on a variety of cover types, including lichens, mosses, and vascular plants.

When modified with a straight fiber optic, the leaf reflectometer is ideally suited for "remote" sampling in the field, enabling the measurement at scales ranging from 1 cm to approximately 1 m in diameter (e.g. Gamon et al 1998, Gamon et al. 1999). This upper scale is ideal for sampling small regions of uniform cover (e.g. patches of moss or lichen or vascular plants, or patches of mixed cover types). Thus, it can be readily applied to experimental manipulations and flux measurements at the plot scale. Our preliminary measurements in the tundra indicate that this sampling approach readily distinguishes contrasting experimental treatments and cover types having contrasting effects on overall system fluxes (figures 2 and 3). Additionally, by coupling sampling at this scale with fine (e.g. leaf) scale sampling (described above), we can test models of spectral mixture analysis (e.g. Roberts 1993, Gamon et al. 1993), allowing a formal way to link remote measurements of regions with mixed composition to component cover types and their associated physiological properties.

To test the physiological significance of spectral features (see table 1), several physiological assays will be conducted in close coordination with reflectance measurements. Some of these assays (e.g. pigment content and water content - Gamon and Surfus 1999, Gamon et al. 1999) will be completed as part of this study. Other assays (e.g. photosynthetic and respiratory fluxes) will be collected by Dr. Oechel’s group, and we will work closely with his field team to coordinate our reflectance sampling with these measurements.

Landscape sampling – characterizing spatial and temporal patterns within a flux "footprint"

In our study, we plan to employ a novel, dual-detector spectrometer (UniSpec 2, PP Systems, Haverhill, MA) for automated tram and aircraft sampling at the landscape level. This instrument is based on preliminary studies at the one m² scale ("pixel" or "grain size") with single-detector units along systematic transects (e.g. Gamon et al. 1999). Preliminary studies in the arctic tundra demonstrate that this sampling approach can readily resolve individual landscape patches likely to have contrasting impacts on overall system fluxes (figure 3). This system utilizes two matched, synchronized detectors, one looking down at the target (e.g. tundra surface) and one measuring downwelling irradiance (to calculated PARo in equation 1 and correct for changing light conditions). The two detectors will be cross-calibrated using a 99% reflective standard (Spectralon, Labsphere, North Sutton, NH). Unlike conventional optical sensors that work poorly under cloudy skies, this sampling approach will enable data collection under virtually any sky condition (including days with overcast skies or rapidly changing light conditions, which are common in the arctic). This dual-detector system will be mounted on an automated tram for routine, repeatable sampling within the flux footprint, providing a rich data source for comparing changing fluxes with changing optical properties across the season.

Figure 2.

Mean spectra (+SEM, indicated by varying line thickness) of experimental treatment plots at Barrow, Alaska, sampled on September 26, 1999. Water and temperature treatments have been provided by Dr. Oechel and colleagues (San Diego State University), and are indicated on each panel. Note the dramatic effect of heating on surface reflectance (right panels), which can be largely overcome by reducing water levels (middle right panel), indicating interactive effects of temperature and moisture on this ecosystem. A goal of this study will be to utilize experimental treatments to explicitly link spectral signatures at the plot scale to cover types and flux measurements at this plot scale (see letter of support from Dr. Oechel).

Figure 3.

Transects illustrating variability of two reflectance indices (NDVI and WBI – see table 1) within a flux tower "footprint" (sampling region) in Barrow, Alaska, on September 26, 1999. Note marked variation along this transect, and extreme index values associated with particular microtopographical features and cover types. This kind of transect sampling from automated trams and aircraft will be used to characterize average properties within a flux sampling region, as well as the fine-scale (1-m) spatial structure of variation in those properties. Because this finer scale of sampling can be linked to experimental treatments and observations of cover types (e.g. figures 1 and 2), this approach will expose factors influencing component system fluxes. Because this method can be aggregated to the scale of the flux sampling footprint, it also allows direct comparison to system fluxes measured from towers and aircraft. Analyzing these data at multiple spatial scales will allow evaluation of "detectibility" of specific features at progressively larger scales, facilitating a more comprehensive understanding of what signals should be visible from satellite platforms.

Additionally, during three periods a year – early season (e.g. Mid June), mid-season (e.g. mid-July) and late season (e.g. mid-August) – we will mount the dual-detector system on a light aircraft that is already equipped for eddy covariance sampling (San Diego State University’s Sky Arrow 650 Environmental Research Aircraft). This instrument will readily fit in the instrument bay, and can be readily modified for aircraft sampling. In this application, the spectrometer will be used to obtain contiguous spectral signatures at approximately 1m distances (approx. 1 m² pixel size) along continuous transects between Barrow and Atqasuk, allowing us to extend our local-scale observations to the larger region. Because this aircraft will also be collecting continuous flux measurements, this dataset will facilitate comparison between spectral signatures and changing flux rates across the Barrow-Atqasuk region. This will allow us to examine the influence of sampling scale (pixel or grain size) on predicted fluxes, and should assist in interpreting a variety of remotely sensed data sets in the arctic. Additionally, because we will also have a rich data set obtained at a finer scale, we will have a strong basis for linking changing spectral patterns to varying cover types (e.g. species or functional type composition) and altered flux rates. As with the 1 m² plot scale sampling (see above), a primary tool for interpreting complex, mixed spectral signatures at this larger scale will be spectral mixture analysis (e.g. Roberts et al. 1993, Gamon et al. 1993), based on the spectral library developed at a finer spatial scale.

By matching the temporal and spatial scale of sampling with that of eddy covariance measured both from tower (e.g.Vourlitis and Oechel 1999) and aircraft (Oechel et al in press) platforms, we will allow direct comparison, across both time and space, of changing spectral properties and changing fluxes. This comparison in both temporal and spatial domains, will be critical to developing more robust models linking remotely sensed signatures to changing fluxes.

Refining carbon flux models

An ultimate goal of this project will be to improve upon existing carbon flux models, that are largely driven by broad-band spectral measurements (e.g. NDVI). Because our approach is compatible with these existing models, it will provide a finer level of information regarding cover type and light-use efficiency that will allow us to refine existing models, rather than replace them. In this regard, we will work closely with Dr. Oechel and colleagues (see letter of support) in developing a modeling approach that is consistent with all available data (including NDVI) and meets the needs of a wide research group.

The primary approach will be to further refine light-use efficiency models defined in equation 1 above by clearly linking changing light use efficiency (detected with flux sampling) to changing optical signatures. Several expressions of hyperspectral data listed in Table 1, including the photochemical reflectance index, the water band index, the red/green reflectance ratio, and other indices of physiological content and activity (e.g. Penuelas and Filella 1998, Gamon and Surfus 1999, Gamon and Qiu 1999) will be examined by comparison with physiological and flux sampling for their utility in tracking changing light use efficiency across species, functional types, growth stage, and season. Additionally, because we will be collecting thermal (surface temperature) data in conjunction with our automated optical sampling, we will be able to explore linked thermal-optical indices that have proven to be very useful at distinguishing cover types with contrasting function (Nemani and Running 1997). Although some of these approaches have proved promising in other ecosystems (Gamon and Qiu 1999), their full application in tundra ecosystems have yet to be tested.

Expected Results

In year one, the achievable objective of this work will be an archived spectral data set ("spectral library") of cover types and related functional states at several spatial scales and seasons. Because this library will be web-based, it will be fully available to other investigators (see Data Plan, below), and can be widely used in teaching and research. In this way, the measurements obtained in this study will assist in the interpretation of other remote sensing efforts, and will add value to other funded studies that are trying to understand functional change in this region. By developing links between reflectance signatures, cover types, and physiological function, the result of this effort will be a better understanding of physiological and structural factors influencing flux rates.

A final objective, to be reached by the end of year two, will be to develop refined models of carbon flux that will assist in understanding factors controlling ecosystem fluxes in the region. This model will be flexible in that it can be applied at a coarse level with existing "NDVI-sensors" (e.g. AVHRR, Myneni et al. 1997), but can also be applied at a finer level by utilizing the rich level of information available with an emerging class of sensors. For example, we anticipate that our approach will be applicable to the Hyperion instrument on the Earth Observer (EO)-1 satellite and MODIS instrument on the Terra satellite to study changes in vegetation and carbon balance for the tundra ecosystem.

IV. Data Plan

The primary product in year one will be a web-based spectral library of different arctic cover types (vegetation classes, ice, soil, etc.) sampled at different spatial scales (leaf to landscape). This library will be used to explore spectral mixture analysis, and develop radiation–use efficiency models of carbon flux, and will be made fully available to other investigators through the PI’s home page [http://vcsars.calstatela.edu]. Web pages will provide general descriptions of the experiment and an index of the available data. The documentation and data will be able to be downloaded from the site from anywhere in the world. By the end of year one, our first season’s data will be made freely and widely available through this system. The combination of the tundra spectral reflectance data with data collected in other biomes will be a significant aid to the analysis of global remotely sensed data.

Based on our experience with similar research projects in the recent past (FIFE and BOREAS) we anticipate that our spectral library will be useful to future investigators. The rich information provided by hyperspectral signatures of tundra land cover types will be useful for interpretation of a variety of remote sensing observations from different platforms. The reflectance phenology collected at the flux tower site provides baseline data to see if there are future changes in landcover type, growing season length, or physiological activity that can be detected using remote sensing. The reflectance measurements from the aircraft will also provide data useful for the analysis of present day satellite observations, as well as baseline data set for comparisons with future conditions. Therefore it is very important to store these data in a format that we can expect to be able to be read years after the end of this study.

The documentation format will be consistent with the format of the NASA Distributed Active Archive Centers (DAAC). This storage format is conservative, in that it is very likely that ASCII text files will be readable for the foreseeable future (Strebel et al. 1998). This format of data storage and documentation has been used in the First International Satellite Land Surface Climotology Project Field Experiment (FIFE) and the BOReal Ecosystem and Atmosphere Study (BOREAS). Over 10 years after the end of FIFE new papers continue to be published using their data, indicating that both the data and documentation are usable by users unfamiliar with the experiment. Additionally, we will make the library available to IARC and support any format required by IARC.

After the first field season (2000), we will work closely with our collaborators (Dr. Walter Oechel and colleagues, San Diego State University) to explore ways of archiving both the spectral library and the flux data in a way that can be most effectively used by other investigators for modeling purposes (see letter of support from Dr. Oechel). In this effort, we will follow the format of the NASA Distributed Active Archive Centers (DAAC), mentioned above. A final objective by the end of year two, will be to complete a web page combining the spectral library with sample flux data, photographs, and other ancillary data for general use in teaching and research.

V. References Cited

Asner, G. P., Wessman, C. A. and Schimel, D. S. (1998), Heterogeneity of savanna canopy structure and function from imaging spectrometry and inverse modeling, Ecological Applications 8 (4):1022-1036.

Baret, F. and Guyot, G. (1991), Potential and limits of vegetation indices for LAI and APAR assessment, Remote Sensing of Environment 35 (2&3): 161-173.

Billings WD, Luken JO, Mortensen DA, Peterson KM (1982) Arctic tundra: A source or sink for atmospheric carbon dioxide in a changing environment? Oecologia 52:7-11.

Bubier, J. L., Rock, B. N. and Crill, P. M. (1997), Spectral reflectance measurements of boreal wetland and forest mosses, J. Geophys. Res. 102 (D24): 29,483-29,494.

Chapin FS III, Shaver, GR (1985a), Arctic. Physiological Ecology of North American Plant Communities. (B. F. Chabot and H. A. Mooney, Ed.), New York, Chapman and Hall, pp.16-40.

Chapin FS III, Shaver GR (1985b) Individualistic growth response of tundra plant species to environmental manipulations in the field. Ecology 66:564-576.

Chapman, W. L. and Walsh, J. E. (1993), Recent variations of sea ice and air temperature in high latitudes, Bull. Am. Meteorol. Soc. 74 : 33-47.

Clark, R.N., Vance, J.S., Livo, K.E., and Green, R.O. (1998) Mineral mapping with imaging spectroscopy: the Ray Mine, AZ. In Green, R.O. (Ed.) Summaries of the Seventh JPL Airborne Science Workshop, January12-16, 1998, Jet Propulsion Laboratory, Pasadena, CA. [http://makalu.jpl.nasa.gov/docs/workshops/98_docs/10.pdf].

Curran ,P.J. and Kupiec, J.A. (1995) Imaging spectrometry: a new tool for ecology. In: Danson, F.M. and Plummer, S.E. (Eds.) Advances in Environmental Remote Sensing. Wiley, Chichester.

DeFries, R. S. and Townshend, J. R. G. (1994), NDVI-derived land cover classifications at a global scale, Int. J. Remote Sensing 15 (17): 3567-3586.

Demetriades-Shah, T. H., Kanemasu, E. T. and Flitcroft, I. D. (1992) Comparison of ground- and satellite-based measurements of the fraction of photosynthetically active radiation intercepted by tallgrass prairie, J. Geophys. Res. 97 (D17): 18947-18950.

Demetriades-Shah, T. H., Steven, M. D. and Clark, J. A. (1990), High resolution derivative spectra in remote sensing, Remote Sens. Environ. 33 : 55-64.

Denning, A. S., Fung, I. and Randall, D. A. (1995), Strong simulated meridional gradient of atmospheric CO2 due to seasonal exchange with the terrestrial biota, Nature 376 : 240-243.

Ehleringer, J.R. and Field, C.B. (Eds.) Scaling Physiological Processes: Leaf to Globe. Academic Press, San Diego.

Gamon, J.A., Field, C.B., Goulden, M., Griffin, K., Hartley, A., Joel, G., Penuelas, J., and Valentini, R. (1995) Relationships between NDVI, canopy structure, and photosynthetic activity in three Californian vegetation types. Ecological Applications. 5(1):28-41.

Gamon JA, Field CB, Roberts DA, Ustin SL, Valentini R (1993) Functional patterns in an annual grassland during an AVIRIS overflight. Remote Sensing of Environment. 44:239-253.

Gamon, J.A., Lee, L-F., Qiu, H-L., Davis, S., Roberts, D.A., and Ustin, S.L. (1998) A multi-scale sampling strategy for detecting physiologically significant signals in AVIRIS imagery. In: Summaries of the Seventh Annual JPL Earth Science Workshop, January 12-16, 1998, Pasadena, CA[http://makalu.jpl.nasa.gov/docs/workshops/98_docs/toc.htm].

Gamon, J.A. and Qiu, H-L. (1999) Ecological applications of remote sensing at multiple scales. pp. 805-846. In: Pugnaire FI, Valladares F (Eds) Handbook of Functional Plant Ecology. Marcel Dekker, New York.

Gamon JA, Qiu H-L, Roberts DA, Ustin SL, Fuentes DA, Rahman A, Sims D, Stylinski C (1999) Water expressions from hyperspectral reflectance: implications for ecosystem flux modeling. In: Green RO (ed) Summaries of the Eighth JPL Airborne Earth Science Workshop. Feb 9-11, 1999, Jet Propulsion Laboratory, Pasadena, CA

Gamon, J.A., Serrano, L., and Surfus, J.S. (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient levels. Oecologia 112:492-501.

Gamon, J.A. and Surfus, J.S. (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytologist. 143:105-117.

Goward, S. N. and Huemmrich, K. F. (1992), Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model, Remote Sensing of Environment 39 : 119-140.

Goward, S. N., Tucker, C.J., and Dye, D.G. (1985) North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio64:3-14.

Grulke, N.E., et al. (1990) Carbon balance in tussock tundra under ambient and elevated atmospheric CO2. Oecologia83:485-494.

Hall, F. G., Huemmrich, K. F. and Goward, S. N. (1990), Use of narrow band spectra to estimate fraction of absorbed photosynthetically active radiation, Remote Sens. Environ. 32 :47-54.

Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment. 51:138-156.

Heimann M, Keeling CD (1989) A three-dimensional model of atmospheric CO₂ transport based on observed winds: 2. Model description and simulated tracer experiments. pp 237-275 In: Peterson DH (ed) Aspects of Climate Variability in the Pacific and the Western Americas. AGU Monograph, 55. Washington, D.C.: American Geophysical Union.

Huemmrich, K. F. and Goward, S. N. (1997), Vegetation canopy PAR absorptance and NDVI: an assessment for ten tree species with the SAIL model, Remote Sens. Environ. 61 (2):254-269.

Hunt, E.R. and Running, S.W. (1992) Simulated dry matter yield for aspen and spruce stands in the North American boreal forest. Canadian Journal of Remote Sensing. 18:126-133.

Jackson, R. D., Slater, P. N. and Pinter, P. J., Jr. (1983), Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres, Remote Sens. Environ. 13 : 187-208.

Jensen, J. R. (1983), Biophysical remote sensing, Annals Assoc. Amer. Geog. 73 : 111-132.

Joel, G., Gamon, J.A., and Field, C.B. (1997) Production efficiency in sunflower: the role of water and nitrogen stress. Remote Sensing of Environment. 62:176-188.

Kattenberg et al. (1995) Climate models – projections of future climate. Pp. 285-357. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Harris, N., Kattenberg, A., and Maskell, K. (eds.). Climate Change 1995: The Science of Climate Change. Cambridge University Press, NY.

Keeling, C. D., Chin, J. F. S. and Whorf, T. P. (1996), Increased activity of northern vegetation inferred from atmospheric CO2 measurements, Nature 382 : 146-149.

Monteith, J. L. (1977), Climate and efficiency of crop production in Britain, Phil. Trans. R. Soc. Lond. B 281 :277-294.

Mulhern, T. (1995), Spectral contrasts of subarctic vegetation: basis for mapping lichens with satellite data. University of Maryland, Ph. D.

Murray, K. J., Tenhunen, J. D. and Nowak, R. S. (1993), Photoinhibition as a control on photosynthesis and production of Sphagnum mosses, Oecologia 96 : 200-207.

Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. and Nemani, R. R.(1997), Increased plant growth in the northern high latitudes from 1981 to1991, Nature 386 (6626): 698-702.

Myneni RB, Los SO, Asrar G (1985) Potential gross primary productivity of terrestrial vegetation from 1982-1990. Geophysical Research Letters. 22:2617-2620.

Oechel WC, Vourlitis GL (1994) The effects of climate change on arctic tundra ecosystems. TREE 9:324-329.

Oechel, W.C., Vourlitis, G.L., Hastings, S.J., Zulueta, R.C., Hinzman, L., and Kane, D. (in review) Long-term ecosystem CO2 flux measurements in the arctics how acclimation to decadal climate warming. Nature.

Oechel WC, Vourlitis GL, Verfaillie J Jr, Crawford T, Brooks S, Dumas E, Hope A, Stow D, Boynton B, Nosov V, Zulueta R (in press) A scaling approach for quantifying the net CO2 flux of the Kuparuk river basin, Alaska. Global Change Biology.

Penuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3:151-156.

Penuelas, J., Pinol, J., Ogaya, R., and Filella, I.(1997) Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing. 18:2869-2875.

Petzold, D. E. and Goward, S. N. (1988), Reflectance spectra of Subarctic lichens, Remote Sens. Environ. 24 :481-492.

Prince, S. D. (1991), Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988, Int. J. Remote Sens. 12 (6): 1301-1312.

Prince SD, Goward SN (1995) Global primary production: a remote sensing approach. Journal of Biogeography 22:815-835.

Roberts, D.A., Smith, M.O., and Adams, J.B. (1993)Green vegetation, non-photosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment. 44:255-269.

Running, S.W. and Hunt, E.R., Jr (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. Pp. 141-158 In:

Running, S.W. and Nemani, R.R. (1988) Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sensing of Environment. 24:347-367.

Runyon, J., Waring, R.H., Goward, S.N., and Welles, J.M.(1994) Environmental limits on net primary production and light-use efficiency across the Oregon Transect. Ecological Applications. 4:226-237.

Russell, G., Jarvis, P. G. and Monteith, J. L. (1989), Absorption of radiation by canopies and stand growth. Plant Canopies: Their Growth, Form and Function. (G. Russell, B. Marshall and P. G. Jarvis, Ed.), Cambridge, Cambridge University Press, pp. 21-40.

Schlesinger, M. E. and Mitchell, J. F. B. (1987), Climate model calculations of the equilibrium climatic response to increased carbon dioxide, Rev. Geophys. 25 (4): 760-798.

Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., and Hall, F.G.(1992) Canopy reflectance, photosynthesis, and transpiration. III. An analysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment. 42:187-216.

Sellers, P. J., et al. (1996), Comparison of radiative and physiological effects of doubled atmospheric CO2 on continental climate, Science 271 : 1402-1406.

Sellers PJ et al. (1997) BOREAS in 1997: Experiment overview, scientific results, and future directions. In Journal of Geophysical Research, 102 (D24):28,731-28,769.

Strebel, D. E., Landis, D. R., Huemmrich, K. F., Newcomer, J. A. and Meeson, B. W. (1998), The FIFE data publication experiment, J. Atmos. Sci. 55 (7): 1277-1283.

Swain, P. H. and Davis, S. M. (1978), Remote Sensing: The Quantitative Approach, McGraw-Hill Book Company, New York.

Tans, P. P., Fung, I. Y. and Takahashi, T. (1990), Observational constraints on the global atmospheric CO2 budget, Science (247): 1431-1438.

Trumbore, S.E. and Harden, J.W. (1997) Accumulation and turnover of carbonin organic and mineral soils of the BOREAS northern study area. Journal of Geophysical Research, 102 (D24):28,817-28,830.

Tucker CY, Fung IY, Keeling CD, Gammon RH (1986a) Relationship between atmospheric CO₂ variations and a satellite-derived vegetation index. Nature 319:195-199.

Tucker, C. J. and Sellers, P. J. (1986b), Satellite remote sensing of primary production, Int. J. Remote Sens. 7(11): 1395-1416.

Valentini, R., Gamon, J.A., and Field, C.B. (1995) Ecosystem gas exchange in a California serpentine grassland: seasonal patterns and implications for scaling. Ecology.76(6):1940-1952

Vogelmann, J. E. and Moss, D. M. (1993), Spectral reflectance measurements in the genus Sphagnum, Remote Sens. Environ. 45 : 273-279.

Vourlitis GL, Oechel WC (1999) Eddy covariance measurements of net CO2 flux and energy balance of an Alaskan moist-tussock tundra ecosystem. Ecology 80:686-701.

Whiting, G. J. (1994), CO2 exchange in the Hudson Bay lowland: Community characteristics and multispectral reflectance properties, J. Geophys. Res. 99 : 1519-1528.

Whiting, G. J., Bartlett, D. S., Fan, S., Bawkin, P. S. and Wofsy, S. C.(1992), Biospheric/atmospheric CO2 exchange in tundra ecosystems: Community characteristics and relationship with multispectral surface reflectance, J. Geophys. Res. 97 : 16,671-16,680.

VI. Milestone Chart
2000

January 1, 2000 - Procure equipment (tram and spectrometers) and begin testing of tram and aircraft applications in southern California with collaborators (Dr. Oechel and colleagues).

May 15, 2000 – Travel to Barrow, Alaska to set up tram system in the arctic for continuous sampling within flux tower footprint. Begin fine-scale reflectance sampling (leaf and plot scale) to begin collection of spectral library. Graduate student will stay through the summer season with Dr. Oechel’s group. Consultant and PI will visit on a periodic basis (three trips per season for the consultant, and 2 trips per season for the PI).

June 15, 2000 – Collect early season reflectance and flux data from aircraft.

July 15, 2000 – Collect mid-season reflectance and flux data from aircraft.

August 15, 2000 – Collect end-of-season reflectance and flux data from aircraft.

October, 2000 - graduate student or technician leaves Barrow for southern California.

December 1, 2000 - spectral library data available on WWW. Begin testing of model integrating optical sensors with flux data. Complete manuscript relating spectral features to cover types and function.

2001

January 2001 – Procure equipment for year two (tram and spectrometer)

May 15, 2001 – Travel to Atqasuk to set up second automated tram system and begin data collection. Graduate student to stay through the growing season with Dr. Oechel’s group. Consultant and PI will visit on a periodic basis (three trips per season for the consultant, and 2 trips per season for the PI).

June 15, 2001 – Collect early season reflectance and flux data from aircraft.

July 15, 2001 – Collect mid-season reflectance and flux data from aircraft.

August 15, 2001 – Collect end-of-season reflectance and flux data from aircraft.

October, 2001 - graduate student leaves second flux site.

December 1, 2001 – Full spectral library data available on WWW. Complete initial tests of refined light-use efficiency model using data from years one and two. Complete manuscript evaluating light-use efficiency model for prediction of carbon fluxes.

VII. Project Responsibilities

Dr. Gamon (PI) will coordinate the project. A primary activity will be the development of instrumentation, and initial testing on cable and aircraft platforms. In collaboration with Dr. Oechel and colleagues, the PI will test these instrument applications in winter and spring of 2000, when the aircraft will be in southern California. In consultation with Dr. Oechel (who has a long record of supporting student research in the arctic), the PI will also recruit a graduate student assistant, most likely from California State University, Los Angeles, or San Diego State University. Additionally, the PI will visit field sites early in each field season to oversee initial testing of equipment, and will oversee development of the spectral library and associated web pages.

Dr. Huemmrich (consultant) will oversee fieldwork on a more continuous basis, with periodic (3) visits during the each field season. During these visits, he will work closely with the student research assistant and Dr. Oechel’s field team to ensure proper coordination with the flux sampling efforts. Primary tasks will also include assurance of instrument calibration and data integrity in the field.

The graduate student will provide 5 months of continuous field support during the Arctic summer. During this period, the student will collect periodic (e.g. weekly) data from the automated tram systems, and will complete fine-scale sampling. Some samples (e.g. pigments) will be shipped to Los Angeles for analyses in the PI’s lab. During the off-season, this student will complete data analyses and work closely with the PI, consultant, and Dr. Oechel’s team on data analysis and manuscript preparation.

VIII Budget Justification

Personnel

PI – The budget includes funds for teaching release (one month in 2000, and 2 months in 2001) to enable the PI to participate in this project during the academic year. Two weeks of summer support for the PI are also requested to cover two field trips per year to Alaska.

Graduate Student – The budget includes $18,000 per year to cover salary for a graduate student, who will be expected to participate in 5 months of fieldwork, plus data analysis during the academic year.

Fringe Benefits

Fringe benefits are the normal campus rates (31% for the PI during the academic year, and 12% for the student and the PI during the summer).

Consultant

The budget includes one-half the annual salary for the consultant (Dr. F. Huemmrich), who will participate in field work with three field campaigns during each field season, and will ensure instrument and data integrity during this time.

Travel

Travel covers the cost of 6 annual round trip airfares between California and Barrow, Alaska, and 3 round trip airfares between Maryland and Barrow, Alaska (Consultant). Additionally, three annual round trips to Fairbanks (annual IARC meeting) are budgeted to allow all project personnel to attend the meeting. Total airfare is $9150 per year. Travel also includes the rental of a vehicle (28 days) to allow setup of the tram system ($3200). The rental of this vehicle will most likely be shared with Dr. Oechel’s group (collaborators).

Travel includes $21,312 per year for room and board, which is based on 192 person-days in the arctic, at a daily lodging rate of $69 (NARL Hotel, Barrow) and a daily board rate of $42.

Equipment

The annual equipment budget includes $7100 for an automated tram system (based on the Oak Ridge design of Drs. Steve Brooks and Tilden Meyers), and $25,000 for a dual-detector spectrometer (UniSpecII, PP Systems, Haverhill, MA). Additionally, in year one, the budget includes a portable field spectrometer for fine-scale field sampling (UniSpec, PP Systems, Haverhill, MA).

Supplies

The annual supply budget includes funding for computer and datalogger supplies ($3000, including a laptop or datalogger for field data acquisition), a balance ($1000, for measuring tissue dry weight and water content), spectrometer foreoptics ($1000), spectrometer calibration panel ($1000), laser altimeter ($4000, for mapping microtopography along the tram system), digital camera ($1000 for recording cover types and obtaining imagery for the spectral library to go on the web page), and pigment sampling supplies ($2000).

Other Direct Costs

The budget includes publication costs ($1000 per year) and shipping costs ($700 per year, to send equipment and supplies between Alaska and California).

Indirect Costs

The indirect rate is $26%, which is the negotiated University rate for off-campus research.

IX Biographical sketches

Biographical Sketch - John A. Gamon
A) Vitae

John A. Gamon

Associate Professor, Center for Environmental Analysis

& Department of Biology and Microbiology phone: (213)343-2066 or -4224

California State University, Los Angeles FAX: (213) 343-6451

5151 State University Drive email:jgamon@calstatela.edu

Los Angeles, CA 90032-8201 http://vcsars.calstatela.edu

Recent Employment

Sept. 96- Associate Professor, Department of Biology and Microbiology

present California State University, Los Angeles, California.

Sept. 91- Assistant Professor, Department of Biology and Microbiology

Sept. 96 California State University, Los Angeles, California.

April 89 - Postdoctoral Research Fellow, with Dr. Christopher Field,
Sept. 91 Carnegie Institution of Washington, Stanford, California.

Education

1986-1989 University of California, Davis, California
Ph.D. degree in Botany (June 1989). Advisor: Dr. Robert Pearcy

1984-1986 University of California, Davis, California
M.S. degree in Botany (June 1986). Advisor: Dr. Robert Pearcy

1975-1979 Yale University, New Haven, Connecticut
B.S. degree in Biology (May 1979)

Current & Recent Grant Support (1992-1999)

1998-2003 PI: "Monitoring changing ecosystem productivity and functional diversity in evergreen-dominated ecosystems using multi-scale remote sensing"(NSF CREST program)

1998-2000 PI: "Integrating models with multi-scale remotely sensed data for improved assessment of photosynthetic fluxes" (NASA BOREAS Program)

1997-1998 PI: "CSARS: A Regional Center for Interdisciplinary Environmental Research, Education, and Outreach" (NASA Centers of Excellence in Applications of Remote Sensing to Regional and Global Integrated Environmental Assessments)

1997-2001 Co-PI: "Impacts of Urban Smog on Physiological Responses of Arabidopsis thaliana" (NSF, Collaborative RUI, PI: Dr. R. Vellanoweth).

1993-1998 PI: "Predicting Photosynthetic Fluxes from Spectral Reflectance of Leaves and Canopies" (National Science Foundation)

1993-1997 Co-PI: "Measurement and Prediction of CO2 and H2O Exchange from Boreal Forest Tree Species." (NASA BOREAS program, PI: Dr. Joseph Berry, Carnegie Institution of Washington.)

Winter 1994 Visiting Scientist: Travel grant to Smithsonian Tropical Research Institution

(Smithsonian Institution)

1993-1996 PI: "Predicting Photosynthetic Fluxes from Spectral Reflectance of Leaves and Canopies" (NASA, Terrestrial Ecology)

1992-1996 PI: "Culturally Diverse Education Assistance, Training Cooperative Agreement" Student Training Grant (U.S. Environmental Protection Agency)

B) Selected Publications

Serrano L, Gamon JA, Penuelas J (in review) Estimation of canopy photosynthetic and non-photosynthetic components from spectral transmittance measurements in Mediterranean vegetation. Ecology.

Gamon JA, Surfus JS (1999)Assessing leaf pigment content and activity with a reflectometer. New Phytologist. 143:105-117.

Gamon JA, Qiu H-L (1999)Ecological applications of remote sensing at multiple scales. Pages 805-846 In: Pugnaire FI, Valladares F (Eds) Handbook of Functional Plant Ecology. Marcel Dekker, Inc. New York.

Gamon JA, Serrano L, Surfus JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient levels. Oecologia112:492-501.

Joel G, Gamon JA, Field CB (1997) Production efficiency in sunflower: the role of water and nitrogen stress. Remote Sensing of Environment.62:176-188.

Gamon JA, Field CB, Goulden M, Griffin K, Hartley A, Joel G, PenuelasJ., Valentini, R (1995) Relationships between NDVI, canopy structure, and photosynthetic activity in three Californian vegetation types. Ecological Applications. 5(1):28-41.

Penuelas J, Filella I, Gamon JA (1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance, New Phytologist131:291-296.

Valentini R, Gamon JA, Field CB (1995) Ecosystem gas exchange in a California serpentine grassland: seasonal patterns and implications for scaling. Ecology. 76(6):1940-1952

Penuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment.48:135-146.

Gamon JA, Field CB, Roberts DA, Ustin SL, Valentini R (1993) Functional patterns in an annual grassland during an AVIRIS overflight. Remote Sensing of Environment. 44:1-15

Gamon JA, Penuelas J, Field CB (1992) A Narrow-Waveband Spectral Index that Tracks Diurnal Changes in Photosynthetic Efficiency. Remote Sensing of Environment.41:35-44.

Gamon JA, Field CB, Bilger W, Bjorkman O, Fredeen A, Penuelas J(1990) Remote Sensing of the Xanthophyll Cycle and Chlorophyll Fluorescence in Sunflower Leaves and Canopies. Oecologia. 85:1-7.

C) Collaborators

Dr. Joseph Berry, Carnegie Institution

Dr. Barbara Bond, Oregon State University

Dr. Walt Oechel, San Diego State University

Dr. Stephen Mulkey, University of Florida

Dr. Dar Roberts, UC Santa Barbara

Dr. Chris Field, Carnegie Institution

D) Student Advisees (M.S. students 1991-present)

Lidia Ceballos Yoshida

Linda Flower

Miriam Schmidts

Eric Yi

Brian Zutta

Steve Hymowitz

37 undergraduates supported since 1991, largely through US EPA and NSF grants

2 visiting Ph.D. students supported since 1991 (Iolanda Filella and Lydia Serrano)

E) Graduate advisor: Dr. RobertPearcy, UC Davis

Postdoctoral Advisor: Dr. Christopher Field, Carnegie Institution, Stanford.

Curriculum Vitae - Karl Fred Huemmrich

BIOGRAPHICAL SKETCH

Dr. Huemmrich was involved in the operations and data analysis for the Boreal Ecosystem and Atmosphere Study (BOREAS) and the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) in Kansas. Dr. Huemmrich was the assistant Information Scientist on these experiments and has experience in the development and operation of interdisciplinary information systems in support of large field experiments. He has developed and used models of light interactions with vegetation, and has studied the use of remotely sensed data to collect information on biophysical variables using both computer models and field measurements. He also has experience in collecting data on biophysical variables in the field. He has used both broad band and hyperspectral radiometers to determine vegetation canopy spectral reflectance, measured leaf level reflectance and transmittance using an integration sphere, measured light transmittance of vegetation canopies and their leaf area index. He has also performed field work on the use of remote sensing in a variety of habitats, including the tundra in northern Quebec, forests in Vermont, Minnesota, Saskatchewan, Manitoba, and Oregon, grasslands in Kansas, agricultural fields in Maryland, and deserts in Oregon and New Mexico.

Office Address:

Code 923.4

NASA/GSFC

Greenbelt, MD 20771

Phone: (301) 286-4862

Fax: (301) 286-0239

E-mail: Karl.Huemmrich@gsfc.nasa.gov

EMPLOYMENT HISTORY

October 1997 to present - Assistant Research Scientist, Geography Department, University of Maryland, College Park, MD

October 1983 to October 1997 - Principal Scientist, Hughes STX Corporation, Lanham, MD

January 1978 - October 1983 - Programmer/Analyst, Computer Sciences Corporation, Silver Spring, MD

ACADEMIC BACKGROUND

Ph.D., Geography, University of Maryland, College Park, MD, 1984-1995

B.S., Physics, Carnegie-Mellon University, Pittsburgh, PA, 1974-1977

ACADEMIC AND PROFESSIONAL HONORS

NASA Group Achievement Award to BOREAS Team, 1994

Hughes STX Group Achievement Award to BOREAS Team, 1994

Hughes STX Peer Award, 1993

NASA Group Achievement Award to FIFE Science Team, 1990

PUBLICATIONS

Articles in Refereed Journals:

Goward, S. N. and Huemmrich, K. F. (1992), Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessmentusing the SAIL model, Remote Sensing of Environment 39 : 119-140.

Goward, S. N., Huemmrich, K. F. and Waring, R. H. (1994), Visible-near infrared spectral reflectance of landscape components in western Oregon, Remote Sens. Environ. 47 : 190-203.

Hall, F. G., Huemmrich, K. F. and Goward, S. N. (1990), Use of narrow band spectra to estimate fraction of absorbed photosynthetically active radiation, Remote Sens. Environ. 32 : 47-54.

Hall, F. G., Huemmrich, K. F., Goetz, S. J., Sellers, P. J. and Nickeson, J. E. (1992), Satellite remote sensing of surface energy balance: success, failures, and unresolved issues in FIFE, J. Geophys. Res. 97 (D17):19061-19090.

Hall, F. G., Knapp, D. E. and Huemmrich, K. F. (1997), Physically-based classification and satellite mapping of biophysical characteristics in the southern boreal forest, Journal of Geophysical Research 102 (D24):29567-29580.

Hall, F. G., Sellers, P. J., Strebel, D. E., Kanemasu, E. T., Kelly, R. D., Blad, B. L., Markham, B. J., Wang, J. R., and Huemmrich, F. (1991),Satellite Remote-Sensing of Surface-Energy and Mass Balance - Results from FIFE, Remote Sens. Environ. 35 (2-3): 187-199.

Hall, F. G., Shimabukuro, Y. E. and Huemmrich, K. F. (1995), Remote sensing of forest biophysical structure in boreal stands of Picea mariana using mixture decomposition and geometric reflectance models, Ecol. Appl. 5 (4):993-1013.

Huemmrich, K. F., Black, T. A., Jarvis, P. G., McCaughey, J. H., and Hall,F. G. (1999), High temporal resolution NDVI phenology from micrometeorological radiation sensors, Journal of Geophysical Research, in press.

Huemmrich, K. F. and Goward, S. N. (1997), Vegetation canopy PAR absorptance and NDVI: an assessment for ten tree species with the SAIL model, Remote Sens. Environ. 61 (2): 254-269.

Sellers, P. J., Meeson, B. W., Hall, F. G., Asrar, G., Murphy, R. E., Schiffer, R. A., Bretherton, F. P., Dickinson, R. E., Ellingson, R. G.,Field, C. B., Huemmrich, K. F., Justice, C. O., Melack, J. M., Roulet, N.T., Schimel, D. S. and Try, P. D. (1995), Remote sensing of the land surface for studies of global change: Models -algorithms - experiments, Remote Sensing of Environment 51 (1): 3-26.

Strebel, D.E., Landis, D.R., Huemmrich, K.F., Newcomer, J.A. and Meeson, B.W. (1998), The FIFE data publication experiment, J. Atmos. Sci. 55 (7):1277-1283

Monographs, Reports, and extension Publications:

An analysis of remote sensing of the fraction of absorbed photosynthetically active radiation in forest canopies. (1995) University of Maryland, Ph. D. thesis, (author).

Biophysical, Morphological, Canopy Optical Property, and Productivity Data>From the Superior National Forest, (1992), NASA, Technical MemorandumTM-104568, (co-author).

Collected Data of the First ISLSCP Field Experiment, Volume 1: Surface Observations and Non-Image Data Sets. (1994), Published on CD-ROM by NASA/GSFC, (co-author).