Remote sensing of plant functional types

Tansley review

Remote sensing of plant functionaltypes

Author for correspondence:Susan L. Ustin

Tel: +1 530 752 0621

Email: [email protected]

Received: 30 December 2009Accepted: 5 March 2010

Susan L. Ustin1 and John A. Gamon2

1Department of Land, Air, and Water Resources, University of California Davis, Davis, CA 95616,

USA; 2Departments of Earth & Atmospheric Sciences and Biological Sciences, University of Alberta,

Edmonton, AB, Canada T6G 2E3

New Phytologist (2010) 186: 795–816doi: 10.1111/j.1469-8137.2010.03284.x

Key words: continuous fields, discrete andcontinuous ecological traits, functionalgroups, imaging spectroscopy, LiDAR (lightdetection and ranging), optical types, plantfunctional types (PFT), remote sensing.

Summary

Conceptually, plant functional types represent a classification scheme between

species and broad vegetation types. Historically, these were based on physio-

logical, structural and ⁄ or phenological properties, whereas recently, they have

reflected plant responses to resources or environmental conditions. Often, an

underlying assumption, based on an economic analogy, is that the functional role

of vegetation can be identified by linked sets of morphological and physiological

traits constrained by resources, based on the hypothesis of functional convergence.

Using these concepts, ecologists have defined a variety of functional traits that are

often context dependent, and the diversity of proposed traits demonstrates the

lack of agreement on universal categories. Historically, remotely sensed data have

been interpreted in ways that parallel these observations, often focused on the

categorization of vegetation into discrete types, often dependent on the sampling

scale. At the same time, current thinking in both ecology and remote sensing has

moved towards viewing vegetation as a continuum rather than as discrete classes.

The capabilities of new remote sensing instruments have led us to propose a new

concept of optically distinguishable functional types (‘optical types’) as a unique

way to address the scale dependence of this problem. This would ensure more

direct relationships between ecological information and remote sensing observa-

tions.

Contents

Summary 795

I. Introduction 796

II. History of functional-type classifications of vegetation 796

III. History of remote sensing of vegetation 799

IV. New sensors and perspectives 802

V. Measuring detailed canopy structure 806

VI. The emerging hypothesis of ‘optical types’ 810

VII. Conclusions 811

Acknowledgements 811

References 811

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� The Authors (2010)

Journal compilation � New Phytologist Trust (2010)

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‘What’s the use of their having names,’ the Gnat said, ‘ifthey won’t answer to them?’ ‘No use to them,’ said Alice,‘but it’s useful to the people that name them, I suppose. Ifnot, why do they have names at all?’

Lewis Carroll: Through the Looking Glass

I. Introduction

Ecologists and plant geographers have long describednatural patterns and relationships between plants and theirenvironment by organizing groups of plant species intofunctional vegetation categories. Recently, ecologicalresearch has been driven by the need to develop a predictivecapability to understand how ecosystems will respond toincreasing anthropogenic impacts. Predicting changes incommunity composition and ecosystem dynamics requiresa better understanding of the ecological importance ofstructural, physiological and phenological characteristics oftoday’s vegetation (Chapin et al., 1997). Changes in thesekey properties affect site productivity and biogeochemicalcycling, and can cascade through communities and ecosys-tems, often with unpredictable consequences. A recurringtheme of ecosystem ecology is that a generalization oforganisms into functional groups is needed to simplify eco-system processes so that they can be understood and tested,in part to enable the prediction of environmental changes(Chapin et al., 1996, 1997; Smith et al., 1997). A func-tional classification system that provides sufficient detail tocapture component processes, but with sufficient abstrac-tion to develop regionally and globally useful models,would meet this need (Pacala & Kinzig, 2002).

The term functional type, often used synonymously withfunctional group, is similar to several others that have beenused in ecology in recent years, including life form(Raunkiaer, 1934), guild (Root, 1967) and strategy (Grime,2001). The concept of plant functional type (PFT) usesstructural, physiological and ⁄ or phenological features togroup species in response to environmental conditions oraccording to their impacts on ecosystems (Gitay & Nobel,1997; Lavorel et al., 1997; Shugart, 1997; Tilman et al.,1997). Implicit in this definition is the idea that functionaltypes provide a useful means of predicting the assemblageof species’ responses within an ecosystem to environmentalconditions or disturbance (Hooper & Vitousek, 1997).Despite this extended history of development, ecologistshave not agreed on a definition of functional types or thekey traits that define them. In addition, the characteristicsthat form the foundation for these groupings often are notfully understood, and there is no universal agreement onwhat traits should be included in a classifier and what traitsshould be omitted. As a result, the term has often beenapplied ad hoc depending on the purpose at hand. For exam-ple, some classifications emphasize clearly visible indicators

of phenology or life history (e.g. evergreen vs deciduous,annual vs perennial), whereas others focus on more hiddenphysiological traits, such as nitrogen fixation (Asner et al.,2008b) or alternative photosynthetic pathways (C3, C4 orcrassulacean acid metabolism) (Davidson & Csillag, 2003;Anderson et al., 2008). A central tenet in the concept ofPFT is that morphological and physiological adaptationsare linked in predictable ways by resource limitations,responses to disturbance, biotic factors or other aspects ofthe environment. The extent to which such linkages aregeneralized will determine our ability to detect functionaltypes with remote sensing.

In this review, we first consider the evolution of the eco-logical concept of functional groupings. We next considerthe history of remote sensing that has enabled this emergingtechnology to be used in the classification of vegetationfrom the local region to the globe. As remote sensing tech-nologies have evolved, so has our ability to detect greaterdetail in functional classes. In parallel with these technicaladvances, the terminology and concepts regarding func-tional types have also evolved, leading to the view that, atsome scales, vegetation properties exist along a continuumof traits and environmental conditions rather than as dis-tinct classes. In fact, the history of both vegetation classifica-tion and remote sensing illustrates the tension betweendiscrete classes and continuous traits.

Current remote measurement capabilities provide anopportunity for fundamentally new approaches to theclassification of functional types, which could even lead to aredefinition of the term itself. We initiate this discussion bysuggesting a new concept for approaching this problem bydeveloping an ‘optical-type’ definition built on basic eco-logical principles, and characterized by optical propertiesdetectable from remote measurements (Gamon, 2008). Byoptical properties, we mean the measurement of the absorp-tion and scattering properties of plant canopies based onprinciples of spectroscopy across the electromagneticspectrum from visible to microwave bands. We addressthe applications and limitations of such an approach andoutline remaining challenges to the concept of opticallydistinguishable functional types. Finally, we provide argu-ments for the greater integration between remote sensingand ecology (ecological remote sensing) that links ecologicaltheory with physical principles of spectroscopy and radiativetransfer. This could not only help to address the definitionof functional type and improve scaling from local to globaldata, but could also be applied to the challenge of monitor-ing biodiversity from remote sensing.

II. History of functional-type classifications ofvegetation

Meaningful descriptions of natural functional groupings ofplants were driven by the exploration of the New World.



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The explorations of Alexander von Humboldt (1807) in thelate 1700s in Europe, Africa and the Americas led to his rec-ognition of at least 16 species-based structural classes havingdifferent physiognomies or plant growth forms, which werenot dependent on taxonomic affiliation (von Humboldt,1849). Although von Humboldt did not explicitly definefunctional relationships, he did relate physiognomic formsto their physical environment, laying the foundation forlater functional classifications. Andreas F. W. Schimper(1903) developed the first detailed understanding of therelationships between environmental factors and the geo-graphical distributions of physiological functions, plantgrowth forms and life history traits. These ideas remainactive and classifications based on Schimper’s work are stillused in ecosystem (Prentice et al., 1992) and PFT (Box,1995) models. Relationships between functional growthforms and the environment were extended by EugenWarming (1909) and his student, Christen Raunkiaer, (seeRaunkiaer, 1934 for a collection of papers), who developedthese ideas into a formal growth form-based classificationsystem.

In parallel with the geographical approach, Clausen et al.(1948) demonstrated the interactions between climatic andgenetic controls on the distribution of plant growth formsor ecotypes in transplant gardens at Stanford University andother sites in California (USA). These groundbreakingexperiments formed a foundation for further ecophysiologi-cal research, which improved our understanding of physio-logical responses of plants to environmental conditionsand how these relate to plant distribution (e.g. Berry &Bjorkman, 1980; Pearcy & Ehleringer, 1984; Sage et al.,1999).

The study of form and function further expanded inthe 1970s (Wiens, 1976) and 1980s (Urban et al.,1987) in parallel with advances in the understanding ofthe physiological characteristics of plant species and rela-tionships to plant form (Givnish, 1986). During thisperiod, the realization of the scope of environmentalchanges at the global scale from human impacts on landuse and land conversion coincided with emerging con-cerns about environmental sustainability, global habitabil-ity and climate change, which led to increasing emphasison ecosystem science and the emergence of global ecol-ogy in the 1990s.

The convergence of structure, physiology and phenologyis thought to be driven by adaptations to similar climatic,topographic and edaphic conditions arising in differentregions of the earth (e.g. Walter, 1931, 1979; Whittaker,1956, 1962, 1975). A more recent restatement of the con-cept of functional convergence explains how resource con-straints define plant traits related to resource acquisition. Abody of ecological theory, grounded in economic arguments(Mooney & Gulmon, 1979; Bloom et al., 1985), states thatplant form and function are constrained by resource limita-

tions (e.g. Field et al., 1992; West et al., 1997; Wrightet al., 2004). One consequence of this is that a plant’s allo-cation of energy to structural and physiological componentsshould reflect the integration of multiple stresses andresource availability in predictable ways (Fig. 1).

Examples of structural responses to resource limitations(or ‘stress’) include patterns of above-ground growth orfoliar mass, root to shoot ratios, specific leaf area (SLA), leaflongevity, leaf orientation and elemental composition(Bloom et al., 1985; Field & Mooney, 1986; Ehleringer &Werk, 1986; Givnish, 1986). Similarly, physiological invest-ments in photosynthesis, light harvesting and water ornutrient acquisition should be resource constrained,and should also be tied to structural investments (Fieldet al., 1992). This framework has sometimes been calledthe ‘functional convergence hypothesis’ (Field, 1991)Although, in its original expression, it primarily addressedthe biochemical capacity for photosynthesis, this hypothesiscan also be extended to canopy structure (Field, 1991).Here, we apply this concept in a broad sense to indicate theconvergence of form and function defined by resourcelimitations and evolutionary constraints. This hypothesisprovides a useful paradigm for understanding PFTs,because it tells us that readily apparent morphologicaland phenological traits should be linked to underlyingphysiological traits.

At the leaf scale, functional convergence is seen in the sig-nificant relationship between maximum assimilation (Amax)and leaf nitrogen content (Field & Mooney, 1986), or inthe tendency of SLA to modulate this relationship (Reichet al., 1998). In an analysis of 2548 species from 175 sitesaround the world, Wright et al. (2004) demonstrated that

Res

ourc

e B

Resource A

Fig. 1 Illustration of functional groups as localized regions along acontinuum (line) of physiological and structural traits defined byresource constraints (here represented by two axes). Resource spaceis more realistically defined by multiple axes (n-dimensional spacedefined by resources A, B, C, … N). Symbols are illustrative of plantfunctional types only and do not indicate specific taxa.





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c. 74% of the variability in leaf traits can be explained bya single axis (principal component) derived from leaflongevity, leaf mass per area, leaf nitrogen content, leafphosphorus content, photosynthetic capacity and dark res-piration. For the majority of species, representing differentgrowth forms, biomes, climates and functional types, pho-tosynthesis and respiration increase proportionally with leafnitrogen, leaf thinness and shortness of leaf lifespan. Theconvergence of so many plant species along a single axis oflinked physiological and structural traits, named the ‘world-wide leaf economics spectrum’ (Wright et al., 2004), is apowerful restatement of the functional convergence hypo-thesis. Much of the variation along major and minor axesrepresents co-existing species that could be represented inprincipal components space as higher dimensional axes(such as shown in Fig. 1), and which could express differentresource constraints.

At stand scales, functional convergence argues that theinvestment in canopy structure and physiology matchesresource availability; consequently, there should be predict-able relationships between vegetation structural attributesdetectable by remote sensing and physiological behaviorcharacteristic of functional types (Field, 1991; Field et al.,1992). Fig. 1 (see also Fig. 1 in Wright et al., 2005) illus-trates how functional traits can be distributed amongspecies along a continuum of two limiting resources. Vari-ability along the line indicates that additional resource axesmay exist, which, in ecological terms, would represent nichedifferentiation among co-occurring species. At the localscale of communities or plant stands, where alpha diversityis not high, each individual species is located in a discreteregion or trait space, and is therefore distinguishable,whereas, at a larger scale, distributions become more contin-uous because of the greater diversity. This allows us toexplore the possibility of regionally distinct functional cate-gories (hence spreading away from the main axis) within acontinuous spectrum of traits.

There is a significant difference between all of the func-tional types in an ecosystem contributing to its biodiver-sity and the functional types that exert the greatestinfluence on ecosystem properties such as productivity,carbon and water budgets, and biogeochemical cycling.These properties are primarily affected by the dominantplant species, which tend to be the most abundant over-story species with the largest biomass, as expressed in themass ratio hypothesis of Grime (1998) and widely demon-strated in the ecological literature (e.g. Diaz et al., 2004;Garnier et al., 2004; Hooper et al., 2005). In this view, atthe ecosystem level, biodiversity is represented as havingcomplementary functional traits (Loreau et al., 2001;Hooper et al., 2005). This distinction is important asmost remote sensing measurements, with the exception oflight detection and ranging (LiDAR) and radio detectionand ranging (RADAR) systems, primarily extract informa-

tion about the upper canopy and not the vegetation in theunderstory, thus much of the heterogeneity in functionaltraits for understory species is not readily observed.

An example of convergence among traits of dominantspecies at the canopy scale is the consistent relationshipbetween the absorbed radiation (APAR, measured byremote sensing) and plant productivity (e.g. net primaryproduction, NPP), which has been observed for much ofthe world’s vegetation (Goward et al., 1985). Similarly, awide range of remotely sensed data have been used to estab-lish close links to total canopy leaf area and NPP. Ollingeret al. (2008) found strong correlations in a globally exten-sive dataset, between canopy nitrogen, canopy structure andcanopy near-infrared (near-IR) reflectance, which is readilydetected by remote sensing.

Ecological theory, including the concept of functionalconvergence, also presents many challenges to the conceptof discrete functional types. At first sight, these demonstra-tions that plant species or vegetation types fall on a contin-uum described by structural and physiologicalcharacteristics linked to resources tend to call into questionthe concept of distinct categories embodied in the term‘functional type’. However, it need not be inconsistentbecause, from this perspective, ‘functional types’ devolvefrom universally applicable relationships represented in n-dimensional resource space (as illustrated in Fig. 1) tolocally defined regions, in much the same way that biomescan be defined as local regions in climate space (Whittaker,1975) or that niches are locally defined regions inn-dimensional resource space (Hutchinson, 1958). In thiscontext, functional types are clearly context dependent,according to the resources used to define the n-dimensionalspace (often a consequence of the tools at hand). A goodexample of this apparent contradiction is found in arcticvegetation, which can be characterized either as discretepatches with contrasting functional characteristics, or by acontinuous approach based on a few key resources, includ-ing water, nitrogen and temperature (Shaver et al., 2007).

A long history of ecophysiological research illustrates thatdifferences in environmental tolerances often exist withindifferent ecotypes of the same species (Clausen et al., 1948;Clausen & Heisey, 1958), and so, even at this level, theexact meaning of functional type becomes unclear. In addi-tion, we are unable to define a universal functional-typeclassification system when species with intermediate traitsexist, as happens when extending classifiers to largegeographical areas (e.g. see Lavorel et al., 1997) or whenbiodiversity is high, as in the tropics, and the variability iscontinuous (Westby & Leishman, 1997). As classificationsystems expand and become more inclusive, more specieswith intermediate characteristics accumulate, confoundingthe classifier. Gitay & Nobel (1997), among others, ques-tion whether consistent ecologically interpretable groupsactually exist, and doubt that a universal functional classifi-






cation can be defined because of the lack of agreement ontraits in space and ⁄ or time. Nonetheless, the concept offunctional type has emerged as a useful tool to providebalance between abstraction and detail to address large-scale ecosystem processes.

III. History of remote sensing of vegetation

The history of remote sensing mirrors the evolution of eco-logical concepts described above. Advances in technologyhave progressively expanded the capability for distinguish-ing the structure, phenology and physiology of vegetation,yielding new insights into the concept of PFTs. As thesedata have become more widely accepted by the ecologicalcommunity, there has been renewed interest in developingfunctional classification schemes driven by remote measure-ments.

To understand how the development of remote sensingtechnologies has shaped our thinking about functional vege-tation categories, it is necessary to briefly review this history.The heritage of many vegetation mapping methods stemsfrom their photogrammetry origins. Because different typesof plants have characteristic spatial distributions, densityand canopy architectures, the color and texture patternsassociated with them become recognizable to a photo-inter-pretation analyst.

By World War II, color-infrared (CIR) film began to beused to detect the state of health of the vegetation. It waspossible to detect severe plant stress because, as leavessenesce and foliar density declines, reflectance, i.e. thebrightness or the amount of reflected sunlight in thenear-IR wavelength band of CIR photography (displayed orprinted in red), declines and reflectance in the true redwavelength band increases (displayed or printed in green).This causes the color balance to change from bright red forhealthy plants to brown for unhealthy ones (Jackson,1986). However, apart from detecting obvious stress, aerialphotography has limited ability to detect physiological state,thus limiting its utility for PFTs.

The development of digital sensors, concurrent withadvances in informatics, has accelerated computer-basedimage analysis in the last two decades, and producedmethods that are rapid, repeatable, objective and can beglobally applied. Digital remote sensing technology hasmade it possible to routinely and systematically monitorecosystems at a range of spatial scales from submeter tokilometer resolutions, which has facilitated further develop-ment of the functional classification of vegetation. The datafrom these systems are spatially explicit and extensive ratherthan extrapolated from limited subsamples, as would be thecase in any field measurement campaign. Compared withfilm, digital imagery increases the spectral range that can bemeasured, particularly in the solar-IR and in the region ofthermal emissions. Digital sensors allow greater automation

in data processing relative to film-based methods and canbe rigorously statistically analyzed. When repeated in time,digital measurements provide detailed information aboutchanges in form, state and abundance of vegetation. All ofthese features have facilitated the development of the func-tional classification of vegetation.

Satellite and airborne observations of solar reflectance inthe visible and IR regions primarily measure the surface ornear-surface conditions. For vegetation communities, thisprimarily limits observations to the dominant species in thecanopy overstory, and thus emphasizes the abundance anddistribution of land cover types and ecosystem-based classi-fications rather than species (e.g. conifer forests rather thanindividual species). Until recently, the ability of remotesensing to resolve PFTs has been limited by the relativelycoarse pixel resolution, few and broad spectral bands, andinfrequent temporal coverage of satellite and airborneobserving systems. Fig. 2 illustrates how the percentagereflectance of a typical plant spectrum appears in the differ-ent spectral resolutions of three current satellites and oneairborne system, which impacts their ability to discriminatePFTs. In this figure, the measured reflectance is seen in thedark lines, the width of which illustrates the resolution ofthe instrument; connecting gray lines are gaps between thelocation of spectral bands. Gaps in the spectrum at c. 1.25–1.375 lm and 1.75–2.0 lm are regions in which atmo-spheric water vapor absorbs energy, and therefore the landsurface is not detected. The progressive improvements insensor capabilities over the past 40 yr, including theimproved spatial and spectral resolution, and their implica-tions for assessing PFTs, are further outlined below.

The era of global digital imagery began in 1972 when,for the first time, repeated opportunities for synoptic viewsof entire continents became possible. Since that time, threecategories of satellite instruments have had widespread usefor land cover mapping (National Research Council of theNational Academies, 2008). The first group includes the‘environmental monitoring’ satellites, such as Landsat The-matic Mapper, which make measurements twice monthly,but at resolutions of tens of meters, allowing the determina-tion of physiognomy, cover type and fractional canopycover. These have had extensive use for land cover classifica-tion at regional scales (Ustin et al., 1986; Smith et al.,1990a,b). The second group includes the global weathersatellites (e.g. AVHRR and MODIS) which monitor vege-tation structure and phenology at daily or near-daily inter-vals at the kilometer scale designed for monitoring climateresponses (Tucker et al., 1985). Lastly, the third group rep-resents recent additions to the suite of civilian satellites (e.g.GeoEye, Ikonos, Quickbird, TerraSAR-X) and provide veryhigh spatial resolution measurements, from submeter to afew meters. These latter sensors provide vegetation measure-ments at scales that overlap traditional field experiments,but the images cover very limited spatial extents.






In addition to these satellite sensors, a wide range ofpowerful aircraft and field sensors now facilitate the inter-pretation of satellite data. As further discussed below, air-borne imaging spectrometers and field spectrometers aremaking novel contributions to our understanding offunctional vegetation categories (Gamon et al., 2004; Ustinet al., 2004), and LiDAR instruments are contributing newforms of explicit three-dimensional structural information(Lefsky et al., 1999; Gillespie et al., 2004; Vierling et al.,2008). However, because these sensors represent newertechnologies not yet fully deployed to collect systematic glo-bal data, most land cover analyses have been restricted toone of the first three types of instruments operating at coar-ser spectral and spatial scales. Consequently, most remotelysensed global vegetation classifications to date have beenlimited to general categories of growth form, fractionalcover and phenology.

The first land observing satellite, the Earth ResourcesTechnology Satellite (ERTS 1), later renamed Landsat, ledto the development of a land cover classification system forNorth America that is still in wide use and based on plantphysiognomic types (Anderson et al., 1976) that generallycorrespond to eight of the 16 growth forms defined by vonHumboldt (1807). Although this level of class resolutionis coarse, land cover classes were accurately mapped(Anderson et al., 1976). These early classifications oftensucceeded in distinguishing broad functional types, in partbecause the biochemical composition and morphologicalstructure of foliage are accentuated by differences in growthform, combined to produce characteristic reflectance pat-

terns (e.g. very low reflectance in the near-IR in conifer andwetland ecosystems compared with grasslands and crops).To put this in an ecological context, the convergencebetween form and function makes it possible to infer physi-ology from physiognomy and phenology.

Beginning in the mid-1980s, the focus of researchswitched from local scale mapping to the development ofpredictive models to assess the consequences of land usechange and climate change over the whole globe, whichrenewed interest in the development and refining of theconcepts of vegetation functional types applicable to thisscale. Until this time, most global vegetation maps werebased on potential vegetation predicted from climate space(Holdridge, 1947; Walter, 1973), and not on actually mea-sured land cover maps. For the first time, the global distri-bution of land cover types could be developed by compilingmonthly AVHRR weather satellite data to classify globalland cover. Initially performed for the African continent,Tucker et al. (1985) showed that it was possible to use a‘greenness’ index, specifically the Normalized DifferenceVegetation Index (NDVI), from weather satellite data todevelop a consistent continental-scale map of actual vegeta-tion, rather than a potential vegetation map based onclimate. Continuing this approach, DeFries et al. (1999)used AVHRR to produce a consistent land cover map with12 vegetation categories based on life form (herbaceous,woody, open or closed canopy), leaf type (needleleaf orbroadleaf) and leaf duration (evergreen or deciduous). Thefact that this many cover types could be identified from thetwo AVHRR bands (red and near-IR) testifies to the strong

Fig. 2 Information content from plant spectrum as measured by different satellites: from the low spectral (and spatial) resolution of theAdvanced Very High Resolution Radiometer (AVHRR) (a), Landsat Thematic Mapper (Landsat TM) (b) and Moderate Resolution ImagingSpectrometer (MODIS) (c) to the higher spectral and spatial resolution of a land cover mapping instrument, such as a full spectral resolutionimaging spectrometer like NASA’s Airborne Visible Infrared Imaging Spectrometer (AVIRIS) (d). Solid black lines show the spectral band widthsof each sensor’s bands and connecting gray lines provide a visual aid that shows the spectral detail for each sensor.






correlation between light absorption by photosyntheticpigments and structural features indicative of functionallydistinct types, a further demonstration of functional conver-gence measurable by remote sensing systems.

DeFries et al. (1995, 1999) also developed an alternativeto the classification of distinct classes that describes vegeta-tion properties in terms of ‘continuous fields’ of functionalattributes, such as growth form (tree, shrub, herbaceous),life history (annual, perennial), seasonality (evergreen,deciduous) and photosynthetic pathway (C3, C4, crassula-cean acid metabolism). This, as in the previous example,was performed by capturing the ‘greenness’ of the landcover at monthly or bimonthly intervals from the globalcoverage of the AVHRR weather satellite, explicitly intro-ducing phenology into the classification scheme. The ideafor defining proportional estimates of plant properties on apixel basis, rather than assigning the pixel to a ‘type’, was anextension of the mixture analysis methods that producemaps of the fractional composition of the scene compo-nents. These methods were developed to address the classifi-cation problems that arise when more than one vegetationtype occurs in a pixel. The technique, derived from analyti-cal chemistry and widely used in many environmental

applications, was initially developed for Landsat data in the1980s (e.g. Ustin et al., 1986; Smith et al., 1990a,b). Theearly spectral mixture analysis methods applied to single-date Landsat and other mid-resolution data used multiplespectral bands to identify different land cover components,such as the fraction of plant cover, the proportion of foliage,plant litter (including woody debris), soil, water, etc. ineach pixel (discussed further in Section IV ‘New sensorsand perspectives’).

Because the AVHRR has only two spectral bands (red,near-IR) at coarse spatial resolution (8 km · 8 km pixels),it was not directly able to differentiate vegetation types fromremote sensing data alone, but had to include informationon geographical location and temporal pattern (Fig. 3). InFig. 3, differences in the maps come from assigning a PFTto a biome type (top) vs estimating the PFT from AVHRRdata (bottom), which is then proportionally consolidatedwith up to four PFTs per grid cell. Biomes that includemultiple PFTs are poorly represented in the biome-basedmap. Bonan et al. (2002) concluded that the use of spatiallycontinuous distributions of coexisting PFTs, as shown inthe bottom panel, is necessary to link climate and ecosystemmodels.

(a)

(b)

Fig. 3 Contrasting vegetation maps from the National Center for Atmospheric Research (NCAR) Land Surface Model. (a) The climate-basedbiome type (28 biomes, e.g. tundra, savanna, etc.) of each grid cell is from a model by Olson et al. (1983) in which one of the 12 plantfunctional types (PFTs; e.g. C3 or C4 grasses, deciduous forest, etc.) shown in the legend is assigned to the grid cell. (b) Map produced from anestimate of the actual vegetation composition, using temporal and spatial (1 km) data from the Advanced Very High Resolution Radiometer(AVHRR) satellite to classify the PFT for each 1 km pixel, which is then averaged to the grid cell by proportionally representing up to four PFTsfor each full resolution cell (3� · 3�). Each of the PFTs had a unique spectral–temporal signature in the AVHRR data. The two methods producedifferent maps when more than one PFT is present in a biome. (Figure reprinted with permission from Bonan et al., 2002).






The global mapping methods developed with AVHRRhave since been extended to other satellite sensors.Launched in 2000, MODIS was expected to produce a newgeneration of maps based on actual vegetation types. MO-DIS has seven bands (Fig. 1c), with six located in wave-length positions similar to the Landsat Thematic Mapper,plus one extra band in the shortwave infrared (SWIR)region, that are measured at 500 m · 500 m pixels. Thisincrease in information content compared with the coarseresolution of AVHRR suggests that plant properties similarto those derived from Landsat data could be measured atthe global scale by MODIS.

Currently, the methods to retrieve more detailed mapsare still under active research, which reveals that challengesstill remain to achieve consistent vegetation mapping at theglobal scale. Different sensors often yield different classes;consequently, there is no universal agreement between theclassifications from different sensors and the comparison ofresults is difficult because of spectral and spatial resolutiondifferences. For example, the 16-class map of Friedl et al.(2002) showed significant disagreement with a global mapbased primarily on the Satellite Pour l’Observation de laTerre (SPOT-4) vegetation satellite, after data were aggre-gated to eight classes (Fritz et al., 2003). Several issues makeit difficult to unravel the sources of this misclassification,including technical differences in sensor design and calibra-tion, differences in analytical methods, misregistration ofthe pixels and mapping errors, such as misclassification ofedge pixels, inconsistencies in the base map classifications,classification criteria and errors in the assumed ‘true’ groundvalidation data. The results from different instrumentscould be more rigorously compared by simulating eachinstrument’s performance using data from a spaceborneimaging spectrometer (also called a hyperspectral imager), anew technology which several countries are proposing to flywithin the next decade.

Despite the availability of new instruments measuringmore spectral bands over a wider range of the electromag-netic spectrum, it has been, and continues to be, difficult toidentify more than these few growth form types in individ-ual remote sensing datasets, because of the seemingly inher-ent high levels of variance within and between classes. Thisproblem relates to the spatial extent of the analysis and thenumber of statistically distinct spectral classes, which isgreatest at the finest spatial scales and decreases as the pixelsize increases (Woodcock & Strahler, 1987). Given theincreasing ecological interest in large-scale ecosystem pro-cesses, approaches that define the continuous variation ofrelevant functional properties may be a more practical alter-native than defining a large number of discrete types(DeFries et al., 1995; Shaver et al., 2007). Obtainingmeasurements of plant biophysical characteristics that areindependent of taxonomic or phylogenetic relationships hascreated opportunities to explore plant functional properties

from entirely new perspectives. The emergence of time-ser-ies satellite products has enabled a consistent analysis of sea-sonal patterns (phenology) that is continuing to expand theconcept of vegetation functional types.

IV. New sensors and perspectives

Historically, our ability to apply remote sensing to mappingPFTs has been limited by the spatial, temporal and spectralscales of the technology at hand. As described above, instru-ments with broad spectral bands yield general vegetationclasses, and the addition of more bands in other parts of theelectromagnetic spectrum, more viewing angles or thefusion of data types can reveal finer distinctions in vegeta-tion function. Fortunately, the newer remote sensingapproaches have the potential to revolutionize observations,particularly imaging spectrometers and LiDAR imagers,which are already providing ‘sharper tools’ for assessingphysiology and structure. Essentially, these new tools areexpanding the range of detectable structural and physiologi-cal properties and, by extension, expanding the resourcevolume that can be observed (the number of n-dimensionalaxes, as illustrated for two resource axes in Fig. 1). Thiseffectively increases the number of classes of vegetation thatcan be distinguished remotely, and can in turn yield newinsights into the study of functional types.

Fig. 4 shows several biochemical and structural differ-ences that are observed in plant and soil spectra from thevisible and reflected solar-IR spectrum. In the visible region(400–700 nm), the overall shape of the reflectancespectrum of foliage is determined by photosynthetic pig-ments but, at wavelengths longer than 1500 nm, theabsorption of energy by water dominates vegetation reflec-tance. In the near-IR (between 700 and 1500 nm), reflec-tance is controlled by scattering between air–water

Fig. 4 Spectra of different live and dry plant foliage and soil fromthe Jasper Ridge Biological Preserve, Stanford University (USA) andelsewhere (from ENVI veg and veg2 spectral libraries identifiedas provided by Dr Chris Elvidge (Elvidge, 1990; CD Elvidge,unpublished).






interfaces in the leaf and between leaves in the canopy, thuscapturing a component of leaf tissue structure and leaf areaindex and leaf angle distribution at the canopy scale. Specieswith more compact leaves and thicker cell walls (lower leafmass per area) have fewer air spaces, less water and reducedscattering from air–water interfaces, and consequently havelower near-IR reflectance than thin leaves (Lichtenthaleret al., 1981; Abrams & Kubiske, 1990). Typically, theremote sensing of biochemical content is stronger on anarea basis (g cm)2) than on a mass basis (g g)1) (e.g. seeGrossman et al., 1996; Jacquemoud et al., 1996). Poorteret al. (2009) have reported that leaf mass area varies by afactor of 100 among species (between 30 and 330 g m)2

for most species), with c. 35% attributed to functionalgroups. Leaf mass area is a key species’ property propor-tional to the relative growth rate (Poorter et al., 2009) andis a measure of the life history strategy (Westoby, 1998;Grime, 2001; Westoby et al., 2002). Recent reports haveindicated that spectral differences related to specific leafmass can be measured at both leaf and canopy scales intropical forest vegetation (Asner & Martin, 2008; Sanchez-Azofeifa et al., 2009).

Leaf level differences can be amplified at the canopy levelby the three-dimensional canopy architecture (Horn, 1971,1975) as a result of multiple scattering. Consequently,needleleaf (e.g. pine, Pinus spp.) and schlerophyllous leafspecies (California Coast Live Oak, Quercus agrifolia) aremuch darker (i.e. lower reflectance) in the near-IR than arebroadleaf deciduous species such as arroyo willow (Salixlasiolepis) (Fig. 4). As a result, both the leaf and canopyspectra contain information relevant for PFT classification.

A number of functionally important compounds havebeen demonstrated to be identifiable and quantifiable inplant spectra, for example pigments (Gamon et al., 1997;Fuentes et al., 2001; Sims & Gamon, 2002; Gitelson et al.,2005; Feret et al., 2008), water (Roberts et al., 1997; Ustinet al., 1998b; Serrano et al., 2000; Sims & Gamon, 2003;Trombetti et al., 2007) and nitrogen-containing com-pounds (Kokaly & Clark, 1999; Kokaly, 2001; Ollinger &Smith, 2005; Asner & Vitousek, 2005). Of these, leaf pig-ments are clearly the most widely studied, and the influenceof pigments on leaf optics is an active area of research (Jac-quemoud & Ustin, 2008; Ustin et al., 2009). In recentyears, several methods have been developed to quantifychlorophyll, carotenoid and anthocyanin pigments (Gitel-son et al., 2006; Feret et al., 2008; Ustin et al., 2009). Theevolution of imaging spectrometers that can quantify thesecompounds, singly or in combination, from their spectralabsorption features, whilst also characterizing structural orphenological patterns, has led to new opportunities for thedetailed assessment of physiological activity and state fromremote sensing. By combining information on pigmentsand water, it is possible to generate detailed functional mapsof vegetation (e.g. Fuentes et al., 2001) and related spatial

patterns of carbon or water vapor flux (Rahman et al.,2001; Giambelluca et al., 2009). Together, these studiesand others have demonstrated the power of using fineabsorption features in imaging spectrometer data to distin-guish functionally distinct species, at least at the level ofdominant species.

Other plant biochemicals provide spectral informationon the functional status and characteristics of an ecosystem.Dry plant litter has absorption features caused by cell wallconstituents, sugars and nitrogen compounds that becomeobservable after water is removed (Curran, 1989; Elvidge,1990; Wessman, 1990), for example as seen in Fig. 4 at1750, 2150 and 2300 nm. The dry needles from a conifer,Sequoia sempervirens (coast redwood) and dry grass foliagein Fig. 4 show the characteristic cellulose absorption in the2000–2200 nm region. Lastly, a soil spectrum from JasperRidge Biological Preserve, Stanford, California shows thetypical monotonically increasing soil reflectance from 400to c. 1800 nm and slightly lower reflectance to 2500 nm,but with some spectral structure between 2300 and2500 nm.

Fortuitously, the ability to detect different levels of func-tionally important compounds is often facilitated by thestructural properties of vegetation. Absorption features pres-ent at leaf scales are accentuated by the multiple scatteringof plant canopies, which enhance their delectability at can-opy scales. Roberts et al. (2004) have provided examples ofhow the spectra of different species change across spatialscales from leaf, branch and canopy, for a large number ofspecies common to the Douglas Fir–western hemlock for-ests of the Pacific Northwest. Their study illustrates howweak absorption features at the leaf scale are compoundedat the branch and canopy scales, enhancing these featuresand strengthening species’ differences. In addition, differentlevels of nitrogen and chlorophyll tend to be associated withdifferent patterns of canopy structure, often contributing todistinguish functionally distinct species within a landscape,as recently shown for nitrogen concentration by Ollingeret al. (2008). In another example, Zygielbaum et al. (2009)found that they could detect the early onset of water stress,not directly from changes in the IR absorption features ofwater, but from systematic changes in the albedo of photo-synthetically active radiation.

The spectra in Fig. 4 could represent end members (com-ponents that can be spectrally identified in images: soils,water, dry plant materials, different vegetation types) andused in a spectral mixture analysis, as introduced above. Amixture analysis computes the fractional composition ofeach end member on a pixel-by-pixel basis for all pixels inthe image, based on a best-fit criterion. The assumption isthat the pixel spectrum is produced by adding the fractionalcomposition of each end member in the pixel times the endmember reflectance spectrum for all end members. This isachieved by solving for the spectral composition that






matches the measured pixel spectrum (e.g. described inSmith et al., 1990a,b). Fig. 5 shows an example of a mix-ture analysis for Stanford University’s Jasper Ridge Biologi-cal Preserve (Ustin et al., 1998a). In this example, we showan independently produced vegetation map for the preserveand three images from spring, summer and fall that captureseasonal patterns in this ecosystem. The images werederived using a healthy green vegetation spectrum (such asthe arroyo willow), a dry grass and a soil spectrum, as shownin Fig. 4. Here, the evolving colors and tones representchanging fractions of the end members in the pixels, yield-ing accurate vegetation maps. There is an obvious corre-spondence to the land cover types shown in the vegetationmap, and this correspondence becomes most evident bymulti-temporal analysis revealing contrasting phenologicalpatterns for different vegetation types. This example illus-trates how combining the structural and physiological sig-nals present in imaging spectrometry with phenologicalinformation can improve the differentiation of contrastingvegetation types.

As recently as 1994, Price (1994) published an influentialpaper concluding that it was impossible to map individualplant species using remote sensing. However, with instru-ments capturing a high level of spectral detail, combinedwith sufficiently small pixels, it becomes possible to use theinformation provided by many absorbing compoundscombined with structural features to differentiate dominant

species and ⁄ or community types, and their related func-tional properties. For example, Okin et al. (2001), Lasset al. (2002), Williams & Hunt (2002), Mundt et al.(2005), Underwood et al. (2003, 2006), Hunt (2007) andNoujdina & Ustin (2008) mapped several different invasivespecies in rangeland and savanna habitats, Hamada et al.(2007) mapped Tamarix spp. (tamarisk), Ramsey et al.(2005) mapped Triadica sebifera (Chinese tallow), andNarumalini et al. (2009) mapped other floodplain invaders,whereas Underwood et al. (2006), Hestir et al. (2008) andAndrew & Ustin (2008, 2009) mapped individual aquaticand wetland species, and Ustin & Xiao (2001) mapped bor-eal forest. The key to success in species’ mapping is relatedto the presence of biochemical and ⁄ or structural differencesbetween invaders and native species, such as C3 and C4

photosynthetic pathways, succulence vs sclerophyllousleaves and differences in physiognomic form, for examplegrass vs shrub. Asner & Vitousek (2005) mapped canopynitrogen content in Hawaii and related spatial variation toan invasive nitrogen-fixing species.

Using a modified form of spectral mixture analysis (mul-tiple end member spectral mixture analysis, or MESMA),Dennison & Roberts (2003) mapped the dominant speciesin chaparral vegetation in the Coast Range of Central Cali-fornia, as shown in Fig. 6. These species have differentreproductive and wildfire response strategies, providing anexample of the power of high spectral resolution approachesto distinguish between functionally distinct species. Forexample, the dominant species, Adenostoma fasciculatum(chamise), are evergreen with reduced needle-like leaves; theshrub has the capability to stump sprout after wildfire(McMurray, 1990). Ceanothus megacarpus (bigpod cea-nothus) has larger evergreen leaves compared withAdenostoma; after fire, the species regenerates from seed(Schlesinger & Gill, 1978). Arctostaphylos glandulosa (east-wood Manzanita) is also evergreen with sclerophyllousleaves; the shrub regrows from seed and stump sprouts(Howard, 1992). In addition to shrubs, this communityincludes evergreen, sclerophyllous oaks (e.g. Quercusagrifolia) and grasslands that are dominated by winter-activeannual grasses (e.g. Bromus spp.).

The capability of imaging spectrometers to distinguishbetween functionally distinct types has led to powerful newmapping approaches that offer novel ways of categorizingvegetation function. For example, Xiao et al. (2004) usedhigh spatial resolution imaging spectroscopy to map streettrees by PFTs (Fig. 7) and by species (Fig. 8). As thesedifferent species have contrasting effects on energy balanceand water usage, high-resolution spectrometry providesa new tool for the classification of PFTs in the urbanenvironment.

The spectral and spatial resolution required to map PFTsat the scales relevant for the discrimination of processes andpatterns is not fully understood, but is starting to be realized

(a) (b)

(c) (d)

Fig. 5 Seasonal changes in vegetation types at Jasper RidgeBiological Preserve measured from NASA’s Airborne Visible InfraredImaging Spectrometer (AVIRIS) and analyzed using a spectralmixture analysis. (a) A pre-existing map of the vegetation types; thelines on the three image panels are the land cover class boundariesof this map. The image panels show the relative distribution of greenvegetation (displayed in green), bare soil (blue) and dry vegetation(red) for June 2, 1992 (b), September 4, 1992 (c) and October 6,1992 (d). (From Ustin et al., 1998a).






through scaling studies. In Fig. 9, the study of Underwoodet al. (2007) is illustrated, in which four map versions werecompared of two plant communities along the central Cali-fornia coast with invasive plant species. They compared theclassification accuracy from high spectral resolution imagingspectroscopy data (174 bands) acquired at 4 m · 4 mspatial resolution (pixel size) and used these data to simulate

the resolution of the six Landsat Thematic Mapper bands at4 m and 30 m pixels. They found that the map classifica-tion accuracy was best (75%, with a kappa statistic of 0.7,or excellent agreement) using the full spectral and spatialresolution data (Fig. 9a). In this panel, the class composi-tion shows little pixelation and classes have mostly continu-ous spatial patterns. When the spatial resolution wasdegraded to 30 m but the spectral resolution was retained,the accuracy declined to 58% with a kappa statistic of 0.5,or moderate agreement (Fig. 9b), whereas the spectrallydegraded data matching the six Landsat bands only had anaccuracy of 42% (kappa statistic of 0.3, poor agreement),even though the spatial resolution was still 4 m. This accu-racy declined slightly more, to 37% (kappa statistic of 0.3),for images matching the 30 m spatial and spectral resolu-tion of Landsat. Fig. 9(b–d) all show increasing pixelationof the spatial patterns, indicating that the classification istenuous and detection is inconsistent. The improved dis-crimination obtained with higher spatial and spectral reso-lution is consistent with previous findings (e.g. Gamonet al., 2004), and demonstrates the importance of higherspectral resolution in mapping PFTs, and also shows, forthis community, that a spatial resolution better than 30 mis not always required to achieve good classification results.

The improved precision of today’s measurements isa reflection of both improved measurement technology(better sensors) and progress in quantitative analysis. Forexample, the most widely used leaf radiative transfer model,PROSPECT (Jacquemoud & Baret, 1990), has had 10 yrof development (Jacquemoud et al., 2009). The currentversion of the model, linked with one of the several radiativetransfer canopy models, most commonly a version of SAIL

Fig. 6 A map of the distribution of dominant species in the SantaYnez Mountains, along the southern central coast of California(USA). The map was created using a multiple end member spectralmixture analysis on imaging spectroscopy data from NASA’s AirborneVisible Infrared Imaging Spectrometer (AVIRIS), with an estimatedoverall map accuracy of 89% (from Dennison & Roberts, 2003).

(a) (b) (c) (d)

Fig. 7 Color infrared (IR) image from NASA’s Airborne Visible Infrared Imaging Spectrometer (AVIRIS) showing part of the city of Modesto,California (USA), indicating the extent of vegetation in red. Images to the right are based on spectral matching algorithms and show thelocations of conifer trees, broadleaf evergreen trees and broadleaf deciduous trees (from Xiao et al., 2004).






(Verhoef, 1984), can invert reflectance in imaging spec-trometry images to predict, on a pixel basis, concentrationsof chlorophyll a and b, carotenoids, water, SLA and drymatter (Feret et al., 2008). This capability to accuratelymeasure and model the full reflectance spectrum in the solardomain permits model predictions of the biochemistryfrom the reflectance of any band combination or bandwidths. Thus, through radiative transfer modeling, a path-way exists to directly relate the detailed site reflectance dataand functional type descriptions (such as leaf area index,foliage biomass, pigment composition, woody debris frac-tion, etc.) to the continuous fields extracted from spectralattributes measured at the global scale. The ability todirectly scale between optical measurements at fine spatialand spectral scales using aircraft or moderate resolutionsatellites makes it possible to simultaneously measure local

variation in functional types and link these to globalobservations of continuous traits from instruments with fewspectral bands and large pixels.

V. Measuring detailed canopy structure

Information about vegetation structure, from the leaf tothe entire stand, is an essential component for assessingPFTs. Instruments such as imaging spectrometers do notmeasure three-dimensional structural properties of vegeta-tion directly, although they can be used to infer structuralproperties, in part through the shadows cast by plant struc-ture (Greenberg et al., 2005; Leboeuf et al., 2007). Strati-fying images by shadow fraction allows some canopystructure to be estimated indirectly. Richardson et al.(1975) first reported the impact of shadows on crop

(a)

(c)

(b)

Fig. 8 Modesto, California (USA): (a) color-infrared (CIR) film for the map section; (b) incompletely sampled field data for this area; (c) detailedspecies-level street tree map. The map used a hierarchical classification method employing the spectral signatures of these species (from Xiaoet al., 2004).






reflectance. This information was incorporated into spectralmixture models (e.g. Smith et al., 1990a) that define a‘shade fraction’ and used in classifications of land cover,and additionally, when inverted, as a surrogate for quanti-fying albedo (eg. Roberts et al., 2004). Geometric-opticalmodels (e.g. Li & Strahler, 1986) classify vegetation,particularly woodlands and forests, in terms of the propor-tion of sunlit and shaded canopies. Recently, multipleview-angle instruments have attracted interest because of

their potential to quantify structural information though amore rigorous analysis of the bidirectional reflectance dis-tribution function (BRDF). Multiple view-angle datatypically use only one or a few spectral bands, but probeeach pixel with measurements from multiple directions.When combined, these data can be modeled to provideinformation about the three-dimensional surface structure.However, the large pixel sizes of satellite data and inade-quate access to higher spatial resolution multi-angle

N

0 0.5 1 1.5 2Kilometers

N


N


N


(a) (b)

(c) (d)

Fig. 9 Three invasive species, iceplant (Carpobrotus edulis), Jubata grass (Cortaderia jubata) and Blue gum (Eucalyptus globulus), invadingtwo native shrub communities, Coastal Sage Scrub and Mixed Chaparral, at Vandenberg Air Force Base, California (USA). Maps show NASA’sAirborne Visible Infrared Imaging Spectrometer (AVIRIS) data with 174 wavebands at 4 m pixel resolution (a), 174 bands at 30 m pixels (b),six bands simulating Landsat Thematic Mapper bands at 4 m pixel resolution(c) and simulated Landsat Thematic Mapper data with six bandsand 30 m pixel resolution (d) (from Underwood et al., 2007).






airborne or satellite data have limited the adoption of thesemethods (Su et al., 2007).

The NASA BOREAS field campaign, spanning the bor-eal forests of Manitoba and Saskatchewan, Canada, was amulti-agency program that took place throughout most ofthe 1990s (Sellers et al., 1995). Multi-angle studies inBOREAS found that BRDF provided useful vegetationmapping tools (Lacaze & Roujean, 2001; Lacaze et al.,2002; Gamon et al., 2004). As structural information variessystematically between functional types (e.g. conifers vsdeciduous trees) that vary in their patterns of light capture,BRDF measurements provide novel approaches to func-tional vegetation classification. The SPOT satellite was thefirst to allow pointing to collect stereo views of particularsites. This satellite was followed by the Multi-angle ImagingSpectroRadiometer (MISR) instrument on the NASA’sTerra platform. Designed primarily for atmospheric mea-surements, it has also been used for land cover mapping,and collects four spectral bands (blue, green, red and near-IR) at nine view angles for 275 m pixels. The EuropeanSpace Agency has led the development of BRDF forecological applications by flying the demonstration multi-view-angle satellite CHRIS (Compact High ResolutionImaging Spectrometer) ⁄ Proba (Project for on-board auton-omy) since 2001 (Barnsley et al., 2004). Because of the nat-ure of this technology demonstration, the availability ofthese data has been strictly limited (Guanter et al., 2005).Nonetheless, Chopping et al. (2006) showed that, by usingfour viewing angles, they were able to accurately estimatethe fractional shrub cover against a grassland background (c.17 m pixel resolution) in the Jornada Experimental Rangein New Mexico. Although BRDF methods offer consider-able promise for understanding PFTs, these methods mustbe better integrated with ecological theory, and tools capa-ble of multi-angle observation must become more widelyavailable, before BRDF approaches can make a significantcontribution to our understanding of PFTs.

An alternative technology that can measure detailedstructural information about land cover and functionaltypes involves ‘active’ sensors that emit signals at specificfrequencies. Detailed structural information can beobtained from both RADAR and LiDAR technologies.RADAR emits radiation in the microwave region of thespectrum (c. 2 mm to c. 70 cm) and LiDAR emits radiationat 1064 nm or 1535 nm in the near-IR region for terrestrialapplications or in the green region at 550 nm for waterapplications. Because both systems use internal energysources, they can be operated at any time of the day ornight. Both systems precisely measure the time for the signal(the pulse) to return to the detector, thus locating the posi-tion of the scattering object in space with high precision.These instruments can emit and measure sufficient numbersof pulses to characterize the plant canopy surface and theground (topographic) surface. Typically, only the ‘first’ and

‘last’ LiDAR returns are used for canopy and ground struc-ture, mostly because of the large size of these files. Becausesome photons pass between leaves of the outer canopy sur-face, both the canopy depth and an understory, if present,can be mapped.

Fig. 10 shows the three-dimensional data derived froman advanced LiDAR system for which all returned pulsesare recorded and analyzed. This type of full waveform dataremains experimental, but demonstrates the potential toprovide detailed information on the three-dimensionalstructure of canopies. Such data are particularly valuable toobtain synoptic descriptions of multilayered forests. In theirstudy at this site, Asner et al. (2009b) found significantstructural changes in the three-dimensional savanna land-scape resulting from herbivory. One advantage of LiDARover RADAR for assessing structure is the fine horizontaland vertical spatial resolution that can be detected.However, the new commercial TerraSAR satellite has a 1 mresolution X-band RADAR system, so that this advantagemay not be an absolute limitation. An advantage ofRADAR is that the data can be acquired under all sky con-ditions. This makes RADAR particularly valuable for tropi-cal and polar research, where frequent cloud cover restrictsthe use of visible–IR sensors. Although RADARs have beenavailable longer than LiDARs and have been flown moreextensively in space [Ice, Cloud and Land Elevation Satellite(ICESat) carries the first spaceborne LiDAR, launched Jan-uary 2003], the compact size and reliability of airbornelasers, combined with their high spatial resolution, havecaused ecologists to prefer LiDAR technology for mappingvegetation structure at the local scale (e.g. Vierling et al.,2008).

Today’s airborne LiDARs can map the three-dimen-sional profile of the canopy and ground surface, quantify-ing the depth of the canopy, the height and spacing ofindividual trees and shrubs, the distribution of gap frac-tions, presence of a shrub or herbaceous forest understory,and presence of fuel ladders, among other vegetation char-acteristics (Lefsky et al., 1999; Riano et al., 2003, 2007).Airborne LiDARs are generally flown at low altitude(because of the limitations on the power of the laser) and,as a result, are usually acquired at very high spatial resolu-tion. Commercial airborne LiDAR instruments are flowntoday with horizontal resolutions of 0.5 m pixels and upto 10 cm vertical resolution. Among other examples rele-vant for defining PFTs, LiDAR has been used to estimateaboveground biomass (Nelson et al., 2003; Omasa et al.,2003; Patenaude et al., 2004), individual trees within astand (Popescu, 2007), leaf area density and crown bulkdensity (Riano et al., 2004a,b) and forest gap characteris-tics (Asner et al., 2008a,b).

Lefsky et al. (2005) showed that even coarse resolutionsatellite LiDAR data could be used to estimate tree heightover large areas employing the Geoscience Laser Altimeter






System (GLAS) instrument on ICESat. By processing theGLAS data with topographic information from theShuttle RADAR Topography Mission (SRTM), theymeasured the maximum forest height in three ecosystems(tropical broadleaf forests in Brazil, temperate broadleafforests in Tennessee, and temperate needleleaf forests inOregon), which were correlated with field estimates of thetree height, equivalent to 59%–68% of the variance inaboveground biomass estimates and equivalent to 73% ofthe variance for the Brazilian forests.

Clearly, these types of data provide unprecedented levelsof detail about canopy structure at a scale that cannot beobtained practically from ground measurements. To theextent that function can be inferred from structure, thesemethods can assist in functional vegetation mapping,although this potential remains relatively underutilized.Because they are largely limited to detecting the structural(as opposed to the functional) aspects of vegetation,RADAR and LiDAR have not yet been widely used to dis-tinguish functional types. However, the structural power ofthese sensors, particularly when combined with the abilityof imaging spectrometers to detect physiological details(discussed above), may provide innovative approaches toincrease our understanding of physiological functioning inways that transcend the measurement limits of physiologicalecology at regional and global scales.

Combined LiDAR and imaging spectrometer datasetsoutperform either type of individual dataset for map-ping dominant tree species and determining their abun-dance and distribution (Asner et al., 2008b). Hill &Thomson (2005), Sadro et al. (2007) and Andersonet al. (2008) have shown that combined high spatialresolution LiDAR and imaging spectroscopy can accu-rately map individual woodland, salt marsh and forestspecies, respectively. Combining data, especially fromthese two types of instruments, promises to increase ourability to measure PFTs because of the simultaneousacquisition of information about both ecosystem struc-ture and function.

By combining multi-angle imaging spectroscopy datawith LiDAR data to stratify vegetation reflectance byshadow fraction, Hilker et al. (2009) and Hall et al. (2008)were able to better isolate subtle features in forest canopyreflectance that were associated with the photoregulatoryxanthophyll pigments. The activity of these photoregulatorypigments and their associated spectral features vary betweenfunctional types (Gamon et al., 1997), and are often con-founded by vegetation structure (Barton & North, 2001;Middleton et al., 2009). Consequently, integrated analysis,made possible by the fusion of LiDAR and imaging spec-trometer data, can provide new insights into the spatialpatterns of physiological function.

Fig. 10 Image from Kruger National Park, South Africa using the Carnegie Airborne Observatory imaging spectrometer and LiDAR (lightdetection and ranging; Asner et al., 2007). The spectrometer and LiDAR data are spatially integrated and the results are displayed in a naturalcolor composite (red, 690 nm; green, 550 nm; blue, 460 nm). This spectral image is c. 1 km2 and contains a variety of plant functional typesspanning habitats from upland savanna to riparian and riverine communities. The red line indicates the location of the transect along which theLiDAR data are displayed as a profile at the top of the image, highlighting the diverse canopy structure among trees and shrub growth forms.Imagery courtesy of the Carnegie Airborne Observatory.






VI. The emerging hypothesis of ‘optical types’

Clearly, remote sensing today offers many innovative toolsfor assessing PFTs at a range of scales. To fully realize thepotential of these technologies, the data must be combinedwith ecological theory linking structural, physiological andphenological traits based on resource constraints. Remotesensing enables novel approaches to the assessment ofPFTs based on optical principles. This linkage leads usdirectly to the concept of ‘optical type’ and the relatedhypothesis that functional types can be distinguished lar-gely on the basis of optical properties detectable by remotesensing as conceptualized in Fig. 11. If the resource space(moisture, nutrient, light, temperature, etc.) can be definedin terms of n-multivariate axes, analogous to principalcomponents, then the goal is to orient the axes in the opti-cal space such that they map onto the axes of the resourcespace.

In essence, this concept builds on the ‘functional conver-gence’ hypothesis and begins to develop the theoretical basisto explain why remote sensing works as reliably as it does(Field, 1991). To put it another way, plants are essentiallysolar energy factories with their canopies structured to opti-mize the capture of light within existing resource constraints(Ehleringer & Werk, 1986). Consequently, by inverting theview and looking down from above, remote sensing directlyassesses key plant structural and physiological features thatreadily reveal resource constraints, and can be used toexplore the concept of functional type from a fresh perspec-tive. Because this concept of optical type is based on funda-mental physical principles (e.g. radiative transfer theory andprinciples of spectroscopy), which are linked to ecologicaltheory, it provides a potentially more rapid, uniform,scalable approach to the problem of measuring functionaltypes than is possible from field observations alone.

According to our hypothesis of optical types, the resourcespace axes that define optically distinguishable functionaltypes (Fig. 1) can be described by structural, biochemical orphysiologically significant constituents detectable with newremote sensing technologies. At the leaf level, these bio-chemical constituents include pigments (chlorophyll, carot-enoids and anthocyanins), plant water, nitrogen andstructural components (e.g. lignocellulose). At the canopyor stand level, the relevant signals also include leaf areaindex, leaf and branch clumping, leaf orientation, canopyheight, foliage volume and plant density, which are presum-ably linked to the expression of leaf-level constituents. Thedistribution of these features at increasingly larger scales(e.g. landscapes) and across time (phenology) provides aconsistent basis for scaling properties from local to globalscales.

Tests of novel remote sensing tools to assess functionaltypes remain limited, largely because the technology is new,but also because the full ecological framework for under-standing vegetation function has yet to mature or be real-ized by the respective ecological and remote sensingcommunities. Examples of recent attempts to formulatethese links include mapping of nitrogen-fixing invasives(Asner & Vitousek, 2005), linking optical properties associ-ated with pigments to photosynthetic capacity (Gamonet al., 1997), functional mapping of photosynthetic rate(Rahman et al., 2001; Fuentes et al., 2006) and evapotrans-piration (Fuentes et al., 2006), studies of functional conver-gence in arctic vegetation (Shaver et al., 2007) and recentattempts to distinguish trees from lianas in the tropics(Sanchez-Azofeifa et al., 2009). In addition, species-levelmapping is now possible, at least for dominant specieshaving distinct functional roles in ecosystems (Dennison &Roberts, 2003; Zomer et al., 2009; Santos et al., in press).

Based on multiple traits evident in vegetation opticalproperties, there have been a number of recent successfuldemonstrations of biodiversity assessment through opticaldiversity (Zutta, 2003; Carlson et al., 2007; Lucas &Carter, 2008; Asner et al., 2009a; Asner & Martin, 2009).Some of the studies argue that it is possible to characterizespectral types based on the underlying unique biochemicalsignatures, a concept termed ‘spectranomics’ (Asner & Mar-tin, 2009). It should be noted that these demonstrations ofconsistent linkages between biochemistry and optics have sofar been limited to the wet tropics, where seasonal contrastsare relatively small. Other studies in the seasonally dry tro-pics (Sanchez-Azofeifa et al., 2009) and in Mediterreaneanclimates (Zutta, 2003) have demonstrated that the ability todistinguish plant types based on underlying spectral featuresis strongly dependent on the phenological stages or environ-mental conditions. In addition, recent studies have demon-strated that biochemical features in reflectance signaturesare strongly influenced by the three-dimensional structureof vegetation stands (Barton & North, 2001; Roberts et al.,

Vegetationstructure

Biochemistry& Physiology Phenology

Optical type

Fig. 11 Proposed concept of ‘optical type’ based on the assessmentof vegetation structure, physiology and phenology – three variableshistorically contributing to ecological definitions of ‘plant functionaltype’. According to functional convergence theory, these variablesare related in predictable ways (double arrows). All three variablesaffect vegetation optical properties (single arrows) and contribute tothe definition of the concept of ‘optical type’, that is, functionalcategories accessible from remote sensing (from Gamon, 2008).






2004; Sims et al., 2006; Hilker et al., 2009). Thus, weargue that to have global application, any successful classifi-cation scheme based on optical types must consider all threeelements: biochemistry and physiology, structure, and phe-nology (Fig. 11). To be complete, this framework shouldconsider both ecological theory (e.g. functional convergenceand related economic concepts, Bloom et al., 1985; Field,1991) and physical theory (e.g. radiative transfer theory,Jacquemoud et al., 2009).

As ecologists and remote sensing scientists continue todevelop the concept of PFTs and understand how theseplant properties relate to remotely sensed variables,improved informatics and cyberinfrastructural capabilitieswill be needed. To assist in understanding the links betweenremote sensing and functional types, metadata standardscould be established for recording species’ composition,structure, phenology and physiological characteristics,together with GPS locations archived in web-based ecologi-cal databases. Existing efforts by FLUXNET, SpecNet,remote sensing and ecological communities represent animportant beginning (Michener & Brunt, 2000; Gamonet al., 2006; Agarwal et al., 2008). However, most existingarchival efforts lack the ability to easily integrate ecologicaldata with time series of remote sensing data, or to managethe sheer volumes or dimensions of data involved. In thiscontext, it is worth noting that NASA’s Earth ObservingSystem Data and Information System (EOS-DIS), whichdelivers and manages remote sensing data from earthobserving satellites, is the largest and most complex databaseeffort ever attempted (Marshall, 1993), and further integra-tion of these satellite data with field data or ecological datawill continue to require innovative computing scienceapproaches. Flexible ontological approaches are needed tolink spectral and ecological data and to search metadata inmeaningful ways that will advance the integration of ecol-ogy and remote sensing. Consequently, a full realization ofthe potential for monitoring functional types from remotesensing requires a new level of effort in informatics andcyberinfrastructure, such as described in NSF (2010) andreviewed in Gamon et al. (2010).

VII. Conclusions

Despite the long history of remote sensing measurements tomap vegetation, challenges remain to effectively link theobservations made in particular bands of the electromag-netic spectrum with plant traits and environmental condi-tions as ecologists and botanists understand them(Schaepman et al., 2009). This seems a formidable task,particularly given the lack of universal agreement on howbest to categorize groups of organisms. However, becauseremote sensing can detect fundamental vegetation proper-ties that link physical properties to ecological theory, andprovides spatial and temporal databases with consistent and

complete coverage, it offers the potential to create a univer-sal solution. Our ability to apply remote sensing for trulyglobal tests of the hypothesis of optical types will depend, inpart, on the capture of the essential plant optical characteris-tics which vary with the particular remote sensing method-ologies. Hence the need to fundamentally understand theutility of BRDF and combined LiDAR–imaging spectrom-eter approaches. The datasets produced by these instru-ments provide rich data sources, allowing us to expand theresource hypervolume and properly assess the structure,physiology and phenology in the context of resource space(Figs 1, 11). A full understanding of the links betweenremote sensing and functional types requires a more com-plete integration of physical remote sensing concepts with adeep understanding of ecological theory. Ultimately, thisdemands greater multidisciplinary training, requiringremote sensing scientists to study biology, ecologists tobecome similarly well versed in the power of newer remotesensing technologies, and everyone to achieve greater facilitywith analyses of large, complex datasets.

Acknowledgements

S.U. wishes to thank the Department of Biological Science,Victoria University, Wellington, New Zealand for providingspace and support during her sabbatical leave. We thank M.Andrew, D. Riano, M. J. Santos and M. J. Whiting for com-ments on an earlier draft of the manuscript. We especiallywish to thank the New Phytologist editors, Ms Jayne Young,for her exceptional patience with the many delays in com-pleting the manuscript, and Peter Curtis and David Ackerlyfor helping us with additional revisions and edits.

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Remote sensing of plant functional types