Barton 2012 Advances in Remote Sensing of Plant Stress

COMMENTARY Advances in remote sensing of plant stress Craig V. M. Barton Received: 19 October 2011 / Accepted: 26 October 2011 / Published online: 24 November 2011 # Springer Science+Business Media B.V. 2011 Keywords Remote sensing . Plant stress . PRI . Photochemical reflectance index Since we first began to actively cultivate plants, we have been using remote sensing to assess the health and vigour of our crops and ornamentals. By looking at plants and observing chang
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  COMMENTARY Advances in remote sensing of plant stress Craig V. M. Barton Received: 19 October 2011 /Accepted: 26 October 2011 /Published online: 24 November 2011 # Springer Science+Business Media B.V. 2011 Keywords  Remotesensing.Plantstress.PRI.PhotochemicalreflectanceindexSince we first began to actively cultivate plants, wehave been using remote sensing to assess the healthand vigour of our crops and ornamentals. By lookingat plants and observing changes in the angle of theleaves over time we can detect water stress, the colour of the leaves has informed us of nutrient limitationsand imbalances, the patchiness of leaf colour andform often relates to pest and disease attack. Our ability to assess the health of plants and vegetationquickly and accurately simply by  “ looking ”  at them is being raised to new levels with the advent of newsensors and instruments that can  “ see ”  across a wider range of wavelengths than our eyes, and improvedunderstanding of the physics and biochemistry under-lying the relationships between vegetation status andits  “ appearance ” .When light strikes a leaf, part of the light spectrumis reflected towards the observer. This reflectance isgoverned by leaf surface properties, internal structureand the concentration and distribution of biochemicalcomponents within the leaf (e.g. nitrogen, lignin,cellulose). Thus there is information in the reflectedlight that relates to the physical and biochemical properties of the leaf. At the canopy scale, factorssuch as leaf angle distribution, leaf area index, litter and soil properties and the view and illuminationgeometry all influence the reflectance properties of the scene. The interpretation of this complex radiation pattern is the major challenge of remote sensing.Vegetation reflectance can be detected usingnarrow-bandwidth spectroradiometers that measurein the visible and near-infrared parts of the spectrum.In the visible spectrum (400  –  700 nm), leaf reflectanceis low because of absorption by photosynthetic pigments (mainly chlorophyll and carotenoids). Inthe near-infrared (700  –  1,300 nm), on the other hand,the reflectance is influenced by structural properties inthe leaf. Variation in reflectance in the middle infraredregion (1,300  –  3,000 nm) is related to absorptioncharacteristics of water and other compounds. Bothabsolute and relative differences in the reflectanceamong these various wavebands have been used toderive indices that correlate with vegetation condition.For example, a change in the chlorophyll content willinfluence the reflectance in the red part of thespectrum but not the near-infra red; an index basedon the ratio of these two wave bands (the simple ratio, NIR/R) has been shown to relate to green biomass Plant Soil (2012) 354:41  –  44DOI 10.1007/s11104-011-1051-0Responsible Editor: John McPherson Cheeseman.C. V. M. Barton ( * ) NSW DPI,PO box 100, Beecroft, NSW 2119, Australia e-mail:  (Gamon et al. 1995). An alternative formulation of these two wavebands, the normalised differencevegetation index, NDVI (NIR-R/NIR+R), takes theratio of the difference between the red and infraredreflectance ’ s relative to the sum of the reflectance ’ s.This has been shown to relate well to green biomasswhile being less sensitive to variation in soilreflectance properties (Gamon et al. 1995). Suchsemi-empirical indices have been widely used toassess broad changes in vegetation properties bothspatially and temporally, and are suited to the types of data that have been available from airborne andsatellite instruments for the last 20 years.Advances in the spectral resolution and fallingcosts of instruments have resulted in considerablymore information being available to use to developindices and probe vegetation remotely. This vast troveof information is being used to generate new or refined empirical indices using more wavebands andmore complex formulations, e.g. red edge to assesschlorophyll content (Filella and Peñuelas 1994), thenormalised phaeophytinization index to assess chlo-rophyll degradation (Peñuelas et al. 1995a ), and thestructural independent pigment index to assess carot-enoid: chlorophyll a ratio (Peñuelas et al. 1995a ).These empirical and semi-empirical indices arevaluable tools; however, we can also make use of our mechanistic understanding of the influence of  biochemistry and optical physics on the reflectance of the vegetation to develop indices based on theunderlying physiological processes. The photochem-ical reflectance index (PRI) is one such index that isreceiving growing interest due to its potential tomonitor photosynthetic performance and plant stressin real time (Gamon et al. 1992; Peñuelas et al.1995b, c). The PRI is a narrow waveband reflectance indexdefined as (R531-R570)/(R531+R570) where R531and R570 are the reflectance at 531 and 570 nmrespectively (Gamon et al. 1992). It is based on thesubtle changes in leaf reflectance at 531 nm as a result of the operation of the xanthophyll cycle. When morelight falls on a leaf than can be used for photosyn-thesis, some of the energy must be dissipated to avoiddamage to the photosynthetic apparatus. There is a conversion of the xanthophyll pigments from theepoxidised (violaxanthin) state to the de-epoxidized(antheraxanthin and zeaxanthin) state, which results indissipation of excess energy in the pigment bedassociated with photosystem II (Demmig-Adams andAdams 1994; Pfundel and Bilger  1994). This change in pigment composition leads to a decrease inreflectance at 531 nm but has little effect at 570 nm,hence the derivation of the PRI as a normaliseddifference index.PRI has been shown to correlate with photosyn-thetic radiation use efficiency (RUE) of both leaves(Gamon et al. 1997; Peñuelas et al. 1995b) and canopies (Filella et al. 1996). Gamon et al. (1997) showed a consistent relationship between PRI andRUE across a number of species, functional types andnutrient treatments. A recent review of PRI and RUEat a range of spatial and temporal scales reported that PRI accounted for 42%, 59% and 62% of thevariability of RUE at the leaf, canopy and ecosystemlevels respectively (Garbulsky et al. 2011). Theyfound more than 80 articles published between 1992and 2009 that reported results on the PRI and itsrelationship with one or more variables related to photosynthetic performance. More than a third of these studies were published during 2008  –  09, reflect-ing an increasing interest in the use of PRI across a range of species and vegetation types.When a plant becomes stressed, it is less able toutilise light effectively. This leads to a decrease inRUE and a change in PRI. Hence, vegetation stressmay be detected remotely by temporal or spatialvariation in PRI. Such an ability to reliably detect vegetation stress using remote sensing has manyapplications  —  from precision farming where scanninga crop for stress could result in better targeting of fertiliser, biocides and pesticides, to detection of industrial pollution and assessing remediation meas-ures, to assessing environmental stress in naturalvegetation.PRI has recently been used to assess the degree of stress induced by salt exposure in coastal shrubs bothunder controlled laboratory conditions (Zinnert et al.2011) and in natural vegetation (Naumann et al.2008). Zinnert et al. (2011) found a good correlation  between a number of leaf level measures of photo-synthetic performance which can be used to assessstress (e.g. non-photochemical quenching and fluo-rescence) and the leaf chloride concentration as it varied over time in response to a range of salinitytreatments in two coastal shrub species. Correspondingmeasurements of canopy PRI also correlated well withleaf chloride concentrations, suggesting that PRI can be 42 Plant Soil (2012) 354:41  –  44  usedtoremotelydetectsalinitystressinthesespecies.Ina separate study using one of the same species (  Myricacerifera ) in a natural ecosystem, PRI, assessed fromairborne hyperspectral imagery, correlated well withsalinity stress which varied depending on the degreeof exposure to salt spray (Naumann et al. 2008).Other reflectance indices such as NDVI and thechlorophyll index did not vary in response to salinitystress in this study, suggesting that PRI is independent of these other indices and does provide uniqueinformation that relates to vegetation stress.Such studies provide an indication of the promiseof PRI as a tool to detect plant stress, but while thereis a good correlation between stress and PRI withineach study, it is not clear if the relationships aretransportable from one study to the next. At the scaleof canopies and whole ecosystems PRI can beaffected by canopy and stand structure (Barton and North 2001; Hall et al. 2008; Hilker et al. 2008). A large number of factors influence the strength of thereflectance in the two wavebands used to calculatePRI received by a remote sensor, including illumina-tion and view geometry, leaf area index, leaf angledistribution, the percent of woody or dead material,soil reflectance properties and atmospheric properties.These variables will change from site to site andthrough time at a single site and may result in changesto the PRI that are not related to changes in stresslevels. Furthermore, the relative proportion of infor-mation from sunlit or shaded leaves depends on theillumination and view geometry, and in a typicalscene the majority of the reflected light is comingfrom the most brightly lit elements. Thus, reflectanceindices tend to be dominated by information fromsunlit leaves. By utilising information from multipleview angles of the same patch of vegetation as theviewing platform passes over, it has been demon-strated both from theory and field data that thefirst derivative of PRI with respect to the shadowfraction viewed by the sensor is proportional tolight use efficiency, and this relationship isinsensitive to the influence of vegetation structureand optical properties (Hall et al. 2008, 2011). This technique shows great promise where hyperspectraldata are available from multiple view angles.Despite the potential complications of vegetationstructure and illumination and view geometry, a meta-analysis of results from some 80 published studies of PRI showed a surprisingly consistent relationship between PRI and ecosystem carbon uptake efficiency(Garbulskyetal.2011). Garbulsky et al. concluded that ecosystems possess emergent properties that allow usto effectively explore their seemingly complex pho-tosynthetic behaviour using surprisingly simple opti-cal methods. However, understanding the basis for this convergence and unearthing the ecophysiologicalrules governing these responses remains a primarygoal of research in this area. Rapid development bothin instrumentation and analytical methodology is providing exciting opportunities to develop powerfultools for remotely determining the health and functionof vegetation from single plant to ecosystem scale.Carefully designed experiments such as that by Zinnert et al. (2011) are needed to explore the underlyingmechanisms and test the robustness of various remotesensingindicestoarangeofstressagentsacrossarangeof species. Their paper represents a valuable contribution tothis growing area of research. References Barton CVM, North PRJ (2001) Remote sensing of canopylight use efficiency using the photochemical reflectanceindex  —  model and sensitivity analysis. Remote SensEnviron 78:264  –  273Demmig-Adams B, Adams B (1994) The role of xanthophyllcycle carotenoids in the protection of photosynthesis.Trends Plant Sci 1:21  –  26Filella I, Peñuelas J (1994) The red edge position and shape asindicators of plant chlorophyll content, biomass andhydraulic status. Int J Remote Sens 15:1459  –  1470Filella I, Amaro T, Araus JL, Penuelas J (1996) Relationship between photosynthetic radiation-use efficiency of barleycanopies and the photochemical reflectance index (PRI).Physiol Plant 96:211  –  216Gamon JA, Peñuelas J, Field CB (1992) A narrow-wavebandspectral index that tracks diurnal changes in photosynthet-ic efficiency. Remote Sens Environ 41:35  –  44Gamon JA, Field CB, Goulden M, Griffin K, Hartley A, Joel Get al (1995) Relationships between NDVI, canopy struc-ture and photosynthesis in three Californian vegetationtypes. Ecol Appl 5:28  –  41Gamon JA, Serrano L, Surfus J (1997) The photochemicalreflectance index: an optical indicator of photosyntheticradiation use efficiency across species, functional typesand nutrient levels. Oecologia 112:492  –  501Garbulsky MF, Peñuelas J, Gamon JA, Inoue Y, Filella I (2011)The photochemical reflectance index (PRI) and the remotesensing of leaf, canopy and ecosystem radiation useefficiencies. A review and meta-analysis. Remote SensEnviron 115:281  –  297Hall FG, Hilker T, Coops NC, Lyapustin A, Huemmrich KF,Middleton E et al (2008) Multi-angle remote sensing of forest Plant Soil (2012) 354:41  –  44 43  light use efficiency by observing PRI variation with canopyshadow fraction. Remote Sens Environ 112:3201  –  3211Hall FG, Hilker T, Coops NC (2011) PHOTOSYNSAT, photosynthesis from space: theoretical foundations of a satellite concept and validation from tower and spacebornedata. Remote Sens Environ 115:1918  –  1925Hilker T, Coops NC, Coggins SB, Wulder MA, Brown M,Black TA (2008) Separating physiologically and direc-tionally induced changes in PRI using BRDF models.Remote Sens Environ 112:2777  –  2788 Naumann JC, Anderson JE, Young DR (2008) Linking physiological responses, chlorophyll fluorescence andhyperspectral imagery to detect salinity stress using the physiological reflectance index in the coastal shrub  Myricacerifera . Remote Sens Environ 112:3865  –  3875Peñuelas J, Filella I, Baret F (1995a) Semi-empirical indices toassess carotenoids/chlorophyll a ratio from leaf spectralreflectance. Photosynthetica 31:221  –  230Peñuelas J, Filella I, Gamon JA (1995b) Assessment of  photosynthetic radiation use efficiency with spectralreflectance. New Phytol 131:291  –  296Peñuelas JI, Filella I, Lloret L, Muñoz F, Vilajeliu M (1995c)Reflectance assessment of plant mite attack on apple trees.Int J Remote Sens 16:2727  –  2733Pfundel E, Bilger W (1994) Regulation and possible function of the violaxanthin cycle. Photosynth Res 42:89  –  109Zinnert JC, Nelson JD, Hoffman AM (2011) Effects of salinityon physiological responses and the photochemical reflec-tance index in two co-occurring coastal shrubs. Plant Soil(this issue). doi:10.1007/s11104-011-0955-z44 Plant Soil (2012) 354:41  –  44
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