Pugnaire F.I. Valladares F. Functional Plant Ecology

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temporal scales (Daughtry et al. 1983, Goward et al. 1985, Bartlett et al. 1990, Steinmetz et al.

1990, Gamon et al. 1995, Joel et al. 1997, Sims et al. 2006). Due to the strong links between

vegetation indices and radiation absorbed by photosynthetic canopy elements, a number

of models explicitly or implicitly use some form of Equation 22.2 and Equation 22.3 to derive

photosynthetic fluxes or primary production. These models vary widely in complexity

and detail, but can be roughly divided as follows: (1) models that assume a uniform efficiency

(fixed coefficient) for all vegetation types (Heimann and Keeling 1989, Myneni et al. 1995)

and (2) models that allow efficiency to vary, either according to biome type (Ruimy et al.

1994) or according to dynamic environmental conditions, notably temperature and

water availability (Potter et al. 1993, Field et al. 1995, Prince and Goward 1995, Running

et al. 2004).

The assumption of a constant efficiency has been carefully examined at many scales

using a combination of approaches that include remote sensing, modeling, biomass harvesting,

APAR sampling, and flux measurements (Bartlett et al. 1990, Gamon et al. 1993, 1995,

Running and Hunt 1993, Runyon et al. 1994, Valentini et al. 1995, Joel et al. 1997, Landsberg

et al. 1997). The general conclusion has been that the assumption of a constant efficiency

may work well in certain cases, but not in others. For example, in certain annual grasslands,

well-managed crops, and possibly deciduous forests, where physiological activity closely

tracks canopy greenness and light absorption, it is often possible to model photosynthesis

or NPP accurately from APAR alone by assuming a constant efficiency (Monteith 1977,

Russell et al. 1989, Gamon et al. 1993). By contrast, in water- or nutrient-stressed canopies

(Joel et al. 1997, Landsberg et al. 1997) or in evergreens exposed to periodically unfavorable

environmental conditions (Running and Nemani 1988, Hunt and Running 1992, Running

and Hunt 1993, Runyon et al. 1994, Gamon et al. 1995), it is inappropriate to assume

a constant efficiency, particularly over short periods (hours to months). In these cases,

photosynthetic downregulation can be significant, making it difficult to accurately

predict fluxes from APAR alone. Because photosynthetic downregulation and the associated

reductions in light-use efficiency can exert a significant feedback on atmospheric processes

(Sellers et al. 1996a), efficiency should be considered a variable in surface-atmosphere

flux models.

There are a number of approaches to defining a variable light-use efficiency term in

terrestrial photosynthetic or production models. Some models incorporate different values

for efficiency according to biome type (Ruimy et al. 1994) and others link efficiency to

temporally varying environmental conditions and physiological status (Potter et al. 1993,

Prince and Goward 1995, Running et al. 2004). In their current formulation, most of these

models suffer from the fact that some of the information needed to run or validate

these models is not readily available at the same scale as the remotely sensed inputs;

consequently, some critical model values must be assumed or derived indirectly from meas-

urements often made at inappropriate scales (Hall et al. 1995, Sellers et al. 1995). For

example, many models derive the efficiency value from weather station inputs, which are

not available at the necessary density for all regions of the Earth (Running et al. 2004). The

challenge is to develop sensors and algorithms that provide all the necessary model inputs at

the appropriate scale, without reference to external assumptions or excessive model tuning.

The wide variety of current and emerging sensors (Table 22.1) provides a range of

tools for evaluating radiation-use efficiency. Some of these methods directly assess light

regulation at the level of the photosynthetic reaction center. For example, for terrestrial

vegetation, both chlorophyll fluorescence (at approximately 685 and 735 nm) and reflectance

at 531 nm provide indicators of fundamental photoregulatory processes linked to carboxyla-

tion (Figure 22.8 and Figure 22.9). The fluorescence index DF=Fm’ (Genty et al. 1989) is a

widely used measure of photosystem II radiation-use efficiency. However, this index is best

applied at very close range (mm to cm), in part because of a requirement for saturating light

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670 Functional Plant Ecology

pulses (Bolhar-Nordenkampf et al. 1989). New algorithms and advances in fluorescence

technology, including laser-induced fluorescence, are beginning to relax this limitation

(Gu

nther et al. 1994). However, at this time, quantitative applications of chlorophyll fluore-

scence to efficiency estimation are most easily applied at a very close range (e.g., leaf scales).

In contrast to fluorescence, spectral reflectance is now applied at several spatial scales to

monitor the activity of xanthophyll cycle pigments. One common expression of xanthophyll

cycle pigment activity is the photochemical reflectance index (PRI):

PRI ¼

531

 R

REF

)

531

þ R

REF

)

where R

531

represents reflectance at 531 nm (the xanthophyll cycle band) and R

REF

represents

reflectance at a reference wavelength (typically 570 nm, Pen

uelas et al. 1995b, Gamon et al.

1997, 2001). This index requires a narrow-band detector with bandwidths of approximately

10 nm full-width half maximum (FWHM) or finer, which includes the CASI, AVIRIS

sensors, and any of the personal spectrometers listed in Table 22.1.

Because the xanthophyll cycle pigments function in photosynthetic light regulation

(Pfu

ndel and Bilger 1994, Demmig-Adams and Adams 1996, Demmig-Adams et al. 1996),

and because interconversion of these pigments is detectable with spectral reflectance (Gamon

Wavelen

th (nm)

Xanthophyll

Fluorescence

quenching

0 min illum.

10 min illum.

∆ Reflectance (10

−

0 min)

Reflectance

500

−0.02

−0.01

0.00

0.01

0.0

0.1

0.2

0.3

0.4

0.5

(a)

(b)

600 700 800 900

FIGURE 22.9 (a) Reflectance of a single Douglas fir (Pseudotsuga menziesii) needle sampled

immediately on illumination (0 min illum.) and 10 minutes after illumination (10 min illum.) with

irradiance equivalent to full sun. (b) When plotted as a difference spectrum (10 min minus 0 min),

subtle changes in apparent reflectance appear that can be attributed to xanthophylls pigment

conversion (feature near 531 nm) and chlorophyll fluorescence quenching (double feature near 685 and

735 nm). Optical indices of xanthophylls pigment activity and chlorophyll fluorescence can be used to

monitor changing photosynthetic light-use efficiency (Gamon et al. 1997). This Douglas fir needle

spectrum was sampled from the Wind River Canopy Crane with a leaf reflectometer. (From Gamon

and Surfus, New Phytol., 143, 105, 1999. With permission.)

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Ecological Applications of Remote Sensing at Multiple Scales 671

et al. 1990), PRI provides a measure of radiation-use efficiency at the level of the fundamental

photosynthetic light reactions. Because these reactions are closely linked to photosynthetic

carbon uptake, they can also provide a near-direct index of photosynthetic light-use efficiency

(Gamon et al. 1992, 1997, 2001, Penuelas et al. 1995b, 1997a, Filella et al. 1996).

Recently, the advent of new satellite sensors with suitable bands has enabled space-

borne measurement of PRI, with some promising results. Hyperion, a prototype hyper-

spectral satellite sensor, has allowed calculation of PRI over limited terrestrial regions

(Asner et al. 2004). The MODIS sensor has a band at 530 nm and one at 550 nm that

were designed for ocean color, so were not originally designed for terrestrial sampling. These

bands which approximate the wavelengths used for leaf- and canopy-scale PRI measurements

are now evaluated over terrestrial regions to explore the utility of satellite-based PRI meas-

urements. Recent findings indicate that these satellite PRI measurements track seasonal

patterns of photosynthetic light-use efficiency remarkably well, and manage to capture

periods of reduced photosynthetic activity due to drought (Asner et al. 2004, Rahman et al.

2004). These studies suggest the derivation of terrestrial carbon fluxes entirely from remote

sensing remains a reasonable goal. However, further validation is needed to understand

whether light-use efficiency models developed at finer spatial scales and tested over short

time periods properly capture the same processes over larger time spans and spatial scales.

Consequently, it remains unclear whether satellite PRI measurements are really tracking

physiological regulatory processes linked to radiation-use efficiency, or whether the signifi-

cant relationships between satellite PRI measurements and radiation-use efficiency are simply

fortuitous. Thus, the ability to develop predictive models using this index remains a central

challenge of terrestrial light-use efficiency models. Addressing this challenge necessarily

requires experimental approaches and multiscale sampling that is often not done with satellite

studies alone.

Models of marine photosynthesis or primary production differ from terrestrial models in

detail, but are remarkably similar in concept to those defined earlier (for a recent review, see

Behrenfeld and Falkowski 1997). Satellite-driven production models generally derive surface

chlorophyll concentration from ocean color measurements, and typically include a term for

the maximum photosynthetic rate for a give temperature, scaled by a term (or terms) that

accounts for depth-resolved distribution of light and phytoplankton. For example, one

version of these models describes marine primary production as:

NPP ¼ C

sat

 Z

 f (PAR)  P

opt

(T),

where C

sat

is the phytoplankton chlorophyll concentration (similar to APAR), Z

refers to

the depth to which positive NPP occurs, f(PAR) is the fraction of this depth where photo-

synthesis is light-saturated (analogous to an efficiency term), and P

opt

(T ) refers to the

maximum carbon fixation rate as a function of temperature (Field et al. 1998).

Due to the different albedos over land and water, and due to different research histories

in terrestrial and marine science communities, satellite sensors are generally designed separ-

ately for ocean and terrestrial sampling. With very few exceptions (e.g., Field et al. 1998,

Behrenfeld et al. 2001), ocean and terrestrial productivity patterns have been generally

reported separately, with very little consideration for how they may influence each other

or both be influenced by larger patterns. The advent of the SeaWiFS and MODIS

sensors with their global NPP products (Figure 22.7) now provides a basis for a more uniform

earth-system approach to understanding primary productivity. For example, the impacts of

periodic climate events (such as El Nino-Southern Oscillation) that affect both ocean

and terrestrial ecosystems can be more readily understood with the advent of truly global

productivity products (Behrenfeld et al. 2001).

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672 Functional Plant Ecology

FUTURE RECOMMENDATIONS

Although remote sensing has considerable power and promise, there remains substantial

barriers to the use of remote sensing for ecological studies. These limitations are both

technical and cultural. On the technical side, many processes and properties of interest to

ecologists are currently inaccessible or poorly described by remote sensing. For example,

remote sensing is particularly useful for depicting the production and energy input end of

ecosystem fluxes, but it does not readily depict many important processes associated with

allocation, respiration, or belowground activities. Similarly, largely due to the coarse spatial

and spectral scales of most satellite sensors, they cannot readily describe species composition,

diversity, distribution, and individual demographic variables at the scales traditionally studied

by population ecologists. Because remote sensing alone is insufficient to fully characterize

many critical ecological states and processes, it is likely that examination of critical ecological

question will continue to require multiple approaches.

Because not all variables can be directly derived from remote sensing, we must often

evaluate whether it is possible to do without information that was previously considered

essential. Furthermore, because remote sensing measures differently from most field sampling

methods, it often forces us to rethink how we structure our concepts and models. One

example is the concept of LAI, which is generally thought to be an important parameter

for terrestrial photosynthesis models (Running and Hunt 1993, Sellers et al. 1996b). This view

derives, in part, from the fact that leaf-level gas exchange is typically expressed on a leaf area

basis (Field et al. 1989). Furthermore, leaf area is readily estimated for single canopies or

vegetation stands, and ecologists, foresters, and agronomists now have a wide array of tools

and procedures for determining leaf area (Norman and Campbell 1989, Daughtry 1990,

Welles 1990, Chen and Cihlar 1995). Many remote sensing studies attempt to translate

vegetation indices into LAI, as if they were necessarily closely related, and this often requires

varying calibrations and assumptions for different species or vegetation types (Sellers et al.

1996b). However, as described earlier, remote vegetation indices actual sample light absorp-

tion, or effective leaf are index, which incorporates clumping at the leaf and branch scales

and can differ substantially from LAI determined by traditional methods (Chen 1996).

Consequently, when using remote sensing, we must reconsider the functional significance

and relevance of LAI as it has traditionally been applied. One solution is to define a new

parameter, effective LAI, which is more closely linked to remote vegetation indices, light

absorption, and photosynthetic production at the ecosystem level, precluding the need for

individual species calibrations.

Similarly, ecologists have long considered the relative importance of species versus

functional types in ecosystem function, and this topic has received considerable attention

recently as new experimental evidence continues to emerge linking functional categories to

ecosystem performance (Grime 1997, Hooper and Vitousek 1997, Tilman et al. 1997, Wardle

et al. 1997). Although species continue to be a fundamental unit for many branches of

ecology, recent studies suggest that the concept of functional types will gradually emerge

as a more accessible unit of ecosystem function (Chapin 1993, Schulze and Mooney 1994,

Chapin et al. 1996, 1997, Lavorel et al. 1997, Smith et al. 1997). In many cases, remote sensing

may be more able to depict functional types than species, and it is likely that the use of remote

sensing further spurs the development of the concept and application of functional types.

A related issue is the concept of biodiversity, here defined as species diversity (analogous

to alpha diversity, Whittaker 1972). Most satellite sensors are simply too coarse spectrally and

spatially to distinguish individual organisms, an essential prerequisite to counting individuals.

Furthermore, remote sensing is generally limited to the canopy surface visible from above, and

may miss the diversity associated with understory or belowground vegetation. However,

recent work, often using hyperspectral field sensors or high-resolution (small pixel) sensors,

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Ecological Applications of Remote Sensing at Multiple Scales 673

have demonstrated a remarkable ability to distinguish different functional types and, in some

cases, species (Roberts et al. 1998). These methods can become particularly powerful when

time series are employed to distinguish contrasting phenological patterns (Kalacska et al.

in press). So although classical measures of species biodiversity may remain elusive with

current remote sensing tools, it is time to define a new concept of optical diversity that can be

directly determined from remote sensing. Given the synoptic and rapid sampling capabilities

of many spectrometers, and the urgent need to identify biodiversity hotspots and identify

areas for conservation priority, the application of new remote sensing tools to biodiversity

assessment is emerging as a critical research need.

Clearly, remote sensing presents a number of challenges to traditional views and practices

in ecology, and this leads to the issue of culture, and how we perform science. The scientific

method, with experimental treatments, controls, and independent replicates, is not easily

followed with the tools of remote sensing, particularly at the larger scales. One solution to

this dilemma lies in the use of natural experiments, in which temporal or spatial dimensions

can be substituted for individual treatments. Another approach lies in the linking of remotely

sensed parameters to process models that can be run as a series of virtual experiments, which

is a common practice in global modeling (Sellers et al. 1996a). In either case, remote sensing

frequently requires that we redefine what is considered an acceptable level of evidence, and

forces us to continue to reevaluate the process of science itself, leading to new paradigms.

Reluctance to adopt remote sensing among the ecological community is often due to the

fuzzy nature of the data derived from great distances; we are often more comfortable

with the exactness of readily measurable or directly observable quantities that match the

scales best perceived by our senses. However, fine-scale observations may not be directly

relevant to critical, large-scale processes. Moreover, there is great benefit in examining

patterns and processes across scales. This concern on how information transcends (or fails

to transcend) scales can yield valuable insight into complex ecological issues, and remote

sensing will undoubtedly continue to provide a principle tool for exploring the influence

of scale on these processes (Wessman 1992, Ehleringer and Field 1993, Quattrochi and

Goodchild 1997). It is likely that ecologists gain greater comfort with remote sensing as

they gain familiarity with issues of scaling. The redefinition of traditional ecological questions

to more closely match scales appropriate for remote sensing undoubtedly leads to further

progress in ecology.

Historically, remote sensing has been an engineering-driven field disproportionately

influenced by military, intelligence, and commercial extractive (e.g., mining) industries;

consequently, many of the instruments of remote sensing have been designed in ignorance

of the needs of ecology. As a community, ecologists have inherited the tools designed for

other purposes and have been struggling to adapt these tools to new uses. This tradition has

been changing, most notably with the advent of NASA’s Earth Observation System and

with new biospheric sensors such as the MODIS sensor (Running et al. 2004; Table 22.1,

Figure 22.7). There is a great need for ecologists to continue to influence the design and

application of remote sensing tools so that the instrumentation more closely reflects the

evolving needs of the ecological community. This can most effectively be done if the larger

scientific community works across disciplinary boundaries, and if ecologists gain familiarity

with the view from above.

ACKNOWLEDGMENTS

Thanks to D. Roberts and M. Gardner for atmospherically corrected AVIRIS imagery shown

in Figure 22.5. The spectra in Figure 22.9 were collected with assistance from personnel at the

Wind River Canopy Crane Research Facility located within the T.T. Munger Research

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674 Functional Plant Ecology

Natural Area in Washington State, USA. This facility is a cooperative scientific venture

among the University of Washington, the USFS Pacific Northwest Research Station, and

the USFS Gifford Pinchot National Forest. C.J. Fotheringham, S.D. Prince, J. Randerson,

D. Roberts, and A. Ruimy provided valuable suggestions in early drafts of the manuscript.

Many of the research findings presented here were supported by grants from NASA, NSF,

and the US EPA to J.A.G. Additional support was provided in the form of an iCORE

Fellowship through the University of Alberta to J.A.G.

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