ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

Contents lists available at ScienceDirect

ISPRS Journal of Photogrammetry and Remote Sensing

journal homepage: www.elsevier .com/ locate/ isprs jprs

Spectroscopic remote sensing of plant stress at leaf and canopy levelsusing the chlorophyll 680 nm absorption feature with continuumremoval

http://dx.doi.org/10.1016/j.isprsjprs.2014.08.0150924-2716/� 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

⇑ Corresponding author at: Remote Sensing Division (DSR), Brazilian NationalInstitute for Space Research (INPE), Av dos Astronautas, 1758, Jd da Granja, CEP12227-010, São José dos Campos, SP, Brazil.

E-mail addresses: ieda@dsr.inpe.br (I.Del’Arco Sanches), beto@ige.unicamp.br (C.R. Souza Filho), raymond@usgs.gov (R.F. Kokaly).

Ieda Del’Arco Sanches a,b,⇑, Carlos Roberto Souza Filho b, Raymond Floyd Kokaly c

a Remote Sensing Division (DSR), Brazilian National Institute for Space Research (INPE), Av dos Astronautas, 1758, Jd da Granja, CEP 12227-010 São José dos Campos, SP, Brazilb Institute of Geosciences, University of Campinas (UNICAMP), P.O. Box 6152, 13083-970 Campinas, SP, Brazilc U.S. Geological Survey, MS 973 Box 25046, Denver Federal Center, Denver, CO 80225, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 June 2014Received in revised form 19 August 2014Accepted 26 August 2014

Keywords:HyperspectralAirborne sensorChlorophyll absorption featureContinuum removalSpectral feature analysisVegetation index

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infraredwavelengths. A time series of close range leaf and canopy reflectance data of two plant species grownin hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS air-borne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over thefield experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, andarea) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detec-tion Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre(680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDIand a narrow-band normalised difference vegetation index were evaluated for their ability to detectstressed plants. The objective was to analyse how the parameters/indices were affected by increasingdegrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level(e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage(67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress inhydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. Thiswas particularly true using thresholds based on the ASD canopy data and considering the combinationof higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.� 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier

B.V. All rights reserved.

1. Introduction

Plant stress can be defined as any disturbance that adverselyinfluences plant growth (Jackson, 1986). It is caused by unfavour-able environmental conditions such as lack of nutrients, waterdeficiency, diseases, insect damage, and pollution. It would beextremely helpful for farmers, foresters and ecologists worldwideto be able to detect unhealthy plants as early as possible. In thepast decades, remote sensing has proved its utility for monitoringvegetation, and with the recent advances in airborne imaging spec-trometers, the capability to characterise plant canopies, including

stress condition, should improve significantly (Carter, 1994;Thenkabail et al., 2011).

Mapping and monitoring vegetation stress is useful for severalapplications. The early detection of plant stress is critical to avoidyield losses in crops (e.g. Zarco-Tejada et al., 2009) and in forests(e.g. Eitel et al., 2011). Vegetation stress along with other factorssuch as species composition and the spatial distribution of carbonstocks can improve the understanding of ecosystem processes(Swatantran et al., 2011). Metal induced vegetation stress can beused to monitor phytoremediation of contaminated soils (e.g.Rathod et al., 2013). Plant stress can be applied as an explorationmethod for gas and oil hydrocarbon seepage (Noomen et al.,2012; Lammoglia and Souza Filho, 2013). Detection of oil-inducedvegetation stress can be used to localize the space distribution ofoil spills on land (e.g. Li et al., 2005 and Khanna et al., 2013). Veg-etation status can be used as an indirect indicator of undergroundpipeline leakages (e.g. Sanches et al., 2013a,b).

Fig. 1. True colour composition image produced from ProSpecTIR-VS data acquiredover the area used in the experiment (Paulinia, Brazil). The three planting slots(60 m � 5 m), corresponding to the control (CTR) and plants stressed by theapplication of diesel (DSL) and gasoline (GSL) into the soil, are outlined on theimage in blue rectangles. The gray lines represent the thresholds between fourrepetitions/parcels of the trial (P1–P4). In each parcel, the plots (3 m � 5 m)corresponding to the brachiaria (BR) and perennial soybean (PS) plants arehighlighted in red and yellow dashed rectangles, correspondingly.

112 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

In general, all plants seem to respond to stress in a similar way(e.g. decline in growth rate and in the rate of acquisition of allresources) regardless of the source of the stress (Chapin, 1991).Carter (1993) examined eight stress agents among six plant speciesand the results indicated that physiological responses to stress donot vary much for different stressors. De Jong et al. (2012) analysedthe spectral response of Mediterranean species submitted to fivedifferent types of stress, and the spectral signs observed were thesame independently of the stressor. Sanches et al. (2013b) foundthat three different plant species grown over contaminated soilshad similar spectral behavior. They found that the red edge (spec-tral region of rapid change in reflectance of vegetation between thevisible red and the near infrared range – 680–740 nm) showed ashift of its highest peak towards shorter wavelengths, followedby a decrease in its gradient. Although similar physiological andspectral trends are in general observed for vegetation under stress,recent study found different physiological responses in plantsexposed to explosives (grown over soil contaminated with TNTor RDX) compared to those experiencing natural stress (i.e. droughtand salinity) (Zinnert et al., 2013).

The increase of reflectance in visible wavelengths is the mostconsistent expression in leaf reflectance to plant stress (Carter,1993, 1994; Carter and Knapp, 2001), being commonly a conse-quence of the reduction of leaf chlorophyll concentration triggeredby the stress (Knipling, 1970). In plant stress studies, the red edgeis a common focus (e.g., Carter and Miller, 1994; Lamb et al., 2002;Zarco-Tejada et al., 2003). Plants under stress normally present ablue shift of the red edge position (e.g. Filella and Penuelas,1994; Jago et al., 1999; Smith et al., 2004; Sanches et al., 2013b).The 680 nm corresponds to the photosystem II light reaction cen-tre, known as P680. Several vegetation indices explore the680 nm feature, such as the Normalized Pigment Chlorophyll Index(NPCI) (Penuelas et al., 1994), the Plant Senescence ReflectanceIndex (PSRI) (Merzlyak et al., 1999) and the Simple Ratio PigmentIndex (SRPI) (Penuelas et al., 1995). P680, along with the photosys-tem I light reaction centre (known as P700; also named for itsabsorption peak), are key pigments in the photochemical reactionsof plant photosynthesis (Hoefnagels, 2011). For remote sensingapplications (i.e. airborne or spaceborne) the advantages to explorethe visible/near infrared part of the electromagnetic spectrum arethat this region is less affected by atmospheric absorption andmeasurements, typically, have greater signal-to-noise ratio thanother parts of the reflected solar wavelength region.

Spectral transformations and vegetation indices have been usedintensively in an effort to eliminate or reduce effects not related tothe target’s properties of interest and to enhance spectral features.The continuum removal transformation consists of estimating theabsorptions not due to the chemical bond of interest and removingtheir effects (Clark and Roush, 1984). The continuum removalmethod was initially used in geological remote sensing and wasfirst applied to studies of leaf biochemistry by Kokaly and Clark(1999). Continuum-removed spectra of plants have been furthernormalised to relate changes in shapes of absorption features tothe abundance of a specific biochemical, for example, nitrogen(Kokaly, 2001), by ratioing of the depths within absorption featuresto the depth at the centre of the feature. This procedure minimisesthe influence of factors such as atmosphere absorptions, soil back-ground and leaf water, improving spectral feature analysis of veg-etation canopies. Later, Mutanga et al. (2004) develop other twoindices based on feature depths, which were denominated the Nor-malised Band Depth Index (NBDI) and the Continuum-RemovedDerivative Reflectance (CRDR). Parameters derived from contin-uum removal of plant absorption feature have been used to esti-mate biochemicals of dry leaves (e.g. Kokaly and Clark, 1999;Curran et al., 2001), to discriminate vegetation types (e.g. Kokalyet al., 2003; Schmidt and Skidmore, 2003), to predict canopy

pasture nutrients (e.g. Mutanga et al., 2004; Sanches et al.,2013c) and to estimate pasture biomass (e.g. Mutanga andSkidmore, 2004). Noomen et al. (2008) have tested 51 hyperspec-tral indices for detecting changes in maize and wheat canopyreflectance as a result of underground natural gas leakage. Mostindices approached were ratios using visible and near-infraredbands. Only a few indices were based on shortwave infrared bands.The continuum removal technique was not explored in their work.

Following the same reasoning, but with the purpose to detectplant stress, a new index, the Plant Stress Detection Index (PSDI),is introduced here. The PSDI is calculated based on a ratio of valueson the green edge of the chlorophyll 680 nm feature relative to thecentre of the feature. The objective of this work is to assess thecapability of spectral feature analysis of the chlorophyll absorptionfeature, in general, and the PSDI, specifically, to detect plant stress.Absorption feature parameters (depth, width, and area) and PSDIvalues of healthy and stressed plants are calculated and comparedfor leaf and canopy spectra measured in areas of vegetationaffected by increasing amounts of hydrocarbon contamination.Comparisons with a hyperspectral vegetation index (HVI) (i.e. nar-row band NDVI) are also presented. For that purpose, leaf and can-opy field data and ProSpecTIR-VS imaging spectroscopy dataacquired from a field trial are used here to analyse the spectra ofa grass species and a leguminous species.

2. Materials and methods

The spectral data analysed in this study were acquired from afield trial conducted in an experimental area of the University ofCampinas (CPQBA-UNICAMP), in the locality of Paulinia, Sao PauloState, Brazil, in 2010. The experiment consisted of three plantingslots (Fig. 1) with dimensions of 60 m (length) � 5 m (width), inwhich five vegetal species were grown, including the forages

I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122 113

brachiaria grass (Brachiaria brizantha H.S.), hereafter referred to asBR, and perennial soybean (Neonotonia wightii Am.), from nowreferred to as PS. The soil used in the experiment is a eutrofericred latosol with 65% of clay. During the test, forced flows of diesel(DSL) and gasoline (GSL) were applied to the soil of two of the slots,triggering plant stress. The plants in the third slot were not submit-ted to any type of stress, and they were used as the control (CTR)treatment. All plots were watered equally throughout the trial.The experiment was conducted over a period of 6 weeks, from19th April to 29th May 2010 (refer to Sanches et al. (2013b) formore details about the experiment). In each slot, four plots(parcels) of the abovementioned species were planted (P1–P4)(Fig. 1). The first contamination was produced when all plant spe-cies were well developed (i.e., the canopy was fully developed). Theexperiment was ceased when the majority of the plants in the DSLand GSL plots were visually highly stressed.

Leaf and canopy spectra of healthy and stressed brachiariaand perennial soybean plants were obtained from field and air-borne platforms. Three other cultures were planted during theexperiment but were not analysed in this study for divergentreasons. The sugarcane plants grew taller than the height ofthe mobile platform used to acquire the canopy data, compro-mising the spectral measurements of this specie. The beans werehighly affected by the contaminations and almost no plantsurvived to be measured simultaneously to the others. Maizewas also discarded based on previous analysis (Sanches etal., 2013b); this issue is further mentioned in the manuscript(see Section 4).

2.1. Acquisition of in situ vegetation spectra

Field (leaf and canopy) spectral measurements were obtainedwith a portable FieldSpec� 3 High-Resolution spectrometer, whichdetects electromagnetic radiation in the spectral range between350 nm and 2500 nm (ASD, 2011a). In this study, the spectral anal-ysis was focused on the chlorophyll absorption feature centerednear 680 nm. In this region, the FieldSpec� 3 Hi-Res has a spectralresolution of 3 nm (@ 700 nm) and a sampling interval of 1.4 nm. ASpectralon� panel (Labsphere Inc.) was used as the reflectancestandard. The spectrometer was configured to average 25 measure-ments for each record. This study focus on the chlorophyllabsorption feature centered near 680 nm, because previousanalysis (Sanches et al., 2013a) of this dataset showed that plantstress induced by hydrocarbon contamination was first detectedin this absorption feature compared to other vegetation spectralfeatures.

To acquire spectra of the vegetation canopy in situ, a mobileplatform was used. During measurements, the spectrometer waspositioned on the platform at a distance of 3.65 m from the soil,and its optical fiber operated with a 5� foreoptics. With thisarrangement, each measurement comprised a ground samplingarea of 30 cm diameter. For each spectral measurement (desig-nated M1 to M9), 10 spectra were acquired from contiguous andnon-overlapping areas in the central portion of each of the fourparcels (replicates), plant species and treatments (CTR, DSL andGSL). To ensure that throughout the field campaign the exactlysame plot area would be measured, ten marks were painted inthe top of the mobile platform, marking the right spot where theFieldSpec should be placed.

To measure plant leaves (adaxial surface) spectra, an ASD plantprobe (contact probe) and an ASD leaf clip accessory (ASD, 2011b)were coupled to the spectrometer. The sampling area measuredwith these accessories corresponded to 10 mm of diameter.

The nine spectral measurements (M1 to M9) were acquiredalong a period time of 40 days. This protocol allowed the spectralcharacterisation a, at leaf and canopy level, of healthy plants

(CTR) and plants grown under increasing volumes of stress factors(DSL and GSL) (Fig. 2).

2.2. ProSpecTIR-VS airborne imaging spectrometer

An image of the experimental area (Fig. 1) was acquired usingthe ProSpecTIR-VS imaging spectrometer (SPECTIR, 2012), whichoperates with Specim’s AISA dual sensors covering visible/nearinfrared (VNIR) (400–970 nm; AISA Eagle) and shortwave infrared(SWIR) (970–2500 nm; AISA Hawk) wavelengths. The image wasacquired on 15th May 2010, which is the corresponding date tothe canopy spectral measurement M8. The FotoTerra/SpecTIR com-panies operated the sensor in Brazil. The imaging data were pro-vided with 357 channels distributed among the VNIR (122channels) and the SWIR (235 channels) at a spatial resolution of0.6 m. In this study, only data from the VNIR sensor were used.The spectral resolution ranged from 5 to 6 nm across the wave-length range of the VNIR sensor. The data was available both atradiance and 1A processing level (i.e. reflectance). In order to con-vert the calibrated radiance data to surface reflectance values, thesoftware package ATCOR4 was employed using MODTRAN4 atmo-spheric lookup tables to correct for atmospheric absorption andscattering components (Richter and Schlapfer, 2012). Polishing ofthe reflectance was achieved using a SpecTIR proprietary programbased on a Savitsky–Golay algorithm.

2.3. Spectral data processing and data analysis

The leaf and canopy spectra collected with the ASD spectrome-ter were processed using the USGS PRISM software (Kokaly, 2011).The data for each treatment (CTR, DSL, and GSL), species (BR andPS), and measurement date (M1 to M9) were averaged. Each aver-age spectrum was converted to absolute reflectance by removingthe absorption features of the Spectralon reference panel (Kokaly,2011). Continuum removal (Clark and Roush, 1984) of the chloro-phyll absorption feature centered near 680 nm was calculatedusing adjusted continuum end points. In contrast, Kokaly et al.(2003) used fixed continuum endpoints when analyzing the chlo-rophyll feature for species discrimination. In the latest version ofthe USGS PRISM software (available at ftp://ftpext.cr.usgs.gov/cr/co/denver/kokaly/usgsprism/), endpoints used in continuumremoval can be tailored for each spectrum analysed.

Once the plots were visually inspected on the ProSpecTIR-VSreflectance data (Figs. 1 and 3a), ten pixels were selected fromthe middle portion of each plot (Fig. 3b) and reflectance spectraextracted from them (Fig. 3c) and averaged. Twenty-four averagedspectra were analysed, corresponding to three spectra per treat-ment (CTR, DSL and GSL) in four repetitions (P1 to P4) for eachplant species (BR and PS). Then, the continuum-removed reflec-tance spectra for the chlorophyll absorption feature were obtained.

2.3.1. Absorption feature parametersThe continuum-removed spectrum (R0) is calculated by dividing

the original reflectance values within the spectral feature by thecorresponding values of a continuum line (Fig. 4a) establishedbetween the two endpoints (left and right) of the feature. Theresultant absorption feature is characterised by the depth at thefeature centre (Dc) (i.e., the maximum feature depth), the featurewidth (the full width at half maximum – FWHM) and the featurearea (Fig. 4b; see also Kokaly, 2011). Variations of these parametersdescribe, in simple terms, the variation in the shape of an absorp-tion feature. Here, the Dc, FWHM and area of the 680 nm chloro-phyll absorption feature of healthy and stressed plants areexamined.

Fig. 2. Spectra were obtained on nine dates of the field experiment. Measurements were accomplished for each plant group under investigation; i.e., plants not submitted to astress factor (CTR) and plants stressed by the application of increasing amounts of diesel (DSL) and gasoline (GSL) into the soil where they were grown.

Fig. 3. (a) Subset of the ProSpecTIR-VS true colour image shown in Fig. 1; (b) zoomin a perennial soybean plot screening the location of the selected 10 pixels; (c)example of perennial soybean spectra (n = 10) extracted from the imagery.

Fig. 4. (a) Example of a brachiaria spectrum extracted from the ProSpecTIR-VSimage showing the continuum line over the chlorophyll absorption feature centrednear 680 nm and (b) the continuum-removed 680 nm feature.

114 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

2.3.2. Plant Stress Detection Index (PSDI)A new index based on the continuum-removed 680 nm

chlorophyll absorption feature is presented in this study. In thecontinuum-removed reflectance spectra (Fig. 4b), the continuum-removed reflectance at the feature centre (R0c) is the minimum R0

value in the continuum-removed absorption feature (Fig. 4b). Thisparameter is used in the PSDI formula (Eq. (1)), as follows:

PSDIðiÞ ¼ ðDðiÞ � R0cÞ=ðDðiÞ þ R0cÞ ð1Þ

where the feature depth (D) (Fig. 5a–c) of each channel i in theabsorption feature is computed by (Eq. (2)):

DðiÞ ¼ 1� R0ðiÞ ð2Þ

PSDIs calculated for the chlorophyll 680 nm absorption featuresof brachiaria leaf and canopy spectra are illustrated in Fig. 5b and d,respectively. In Fig. 5, the arrows in the plots (arrows of the samecolour have the same length) show how the PSDI enhances the dif-ferences between healthy and unhealthy plants. In PDSI producedfrom leaf spectra this difference is doubled in the wavelengthsaround 575 nm and in PSDI derived from canopy spectra the differ-ence is even more substantial around 560 nm. Accordingly, forplant stress detection it is necessary to calculate the PSDI for onlyone wavelength, depending if a leaf (Eq. (3)) or canopy spectrum isused (Eq. (4)):

Leaf PSDI575 ¼ ðDð575nmÞ � R0cÞ=ðDð575nmÞ þ R0cÞ ð3Þ

Canopy PSDI560 ¼ ðDð560nmÞ � R0cÞ=ðDð560nmÞ þ R0cÞ ð4Þ

These wavelengths (575 nm, 560 nm) were specified based onthe PSDI data of healthy plants (control) analysed.

The performance of the proposed index was assessed by com-paring the results of plant stress detection based on the parametersof the 680 nm chlorophyll absorption feature (Dc, FWHM and Area)with the PSDI results. In addition, a comparison was also madewith the most commonly used vegetation index, the NormalizedDifference Vegetation Index (NDVI) (Rouse et al., 1973) using nar-row bands, which will be referred as HVI800–670 (Eq. (5)):

HVI800—670 ¼ ðRð800nmÞ � Rð670nmÞÞ=ðRð800nmÞ þ Rð670nmÞÞ ð5Þ

3. Results

3.1. Plant stress symptoms observed

Physiological (e.g. leaf colour) and morphological (e.g. leafdroop) changes in plant can occur because of stress (Jackson,

Fig. 5. Examples of spectra extracted from healthy and stressed brachiaria. (a) Depth of the 680 nm chlorophyll absorption feature of an ASD leaf spectra and (b) thecorresponding PSDI. (c) Depth of the 680 nm chlorophyll absorption feature extracted from a ProSpecTIR-VS pixel and (d) the corresponding PSDI. Arrows of the same colourhave the same length.

I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122 115

1986). The stress caused to the brachiaria and the perennial soy-bean plants, which were grown over soils contaminated with die-sel or gasoline in this study, were leaf-yellowing, decrease of greenbiomass, increase of dry matter and sparsening of the canopy cover(Fig. 6). Weak growth, changes in leaves structure accompanied bychlorophyll degradation and consequently colour variation, fallingof leaves, lower density of plants and in extreme cases vegetationdeath are effects previously observed on plants grown in soils con-taminated by hydrocarbons (Smith et al., 2004; Souza Filho et al.(2008); Lammoglia and Souza Filho, 2013).

3.2. Results for ASD leaf and canopy data analysis

The PSDI, HVI800–670, Dc, FWHM and area values per leaf andcanopy measurements (Fig. 2) are plotted in Figs. 7 and 8, respec-tively. It is observed that for healthy plants (CTR) the indices/parameters values do not present much variation, whereas forstressed plants (DSL and GSL) there is a decrease in these valuesas the duration and volume of the stress factor increases. Asexpected, the trend observed for all indices and feature parametersis overall very similar.

There is a high positive correlation among all the parameters/indices analysed (Table 1). The highest correlation (1.00) wasobserved between the PSDI and feature area and betweenHVI800–670 and Dc. The feature area is also correlated to the featurewidth (FWHM) and depth (Dc), whereas the FWHM and the Dc areless inter-correlated.

The descriptive statistics of the parameters/indices are pre-sented in Table 2. The canopy means were higher than the leafmeans, whereas the data variation (CV) was higher in leaf spectracompared to canopy spectra.

Based on the variation observed in the brachiaria and perennialsoybean healthy plants, thresholds were set for each index/param-eter to assess their capability to detect plant stress based on leafand canopy data, separately (Table 2). The most liberal threshold

tested was based on the minimum value. If the index/parameterfor a sample is less than the minimum (Min) index/parametervalue of the control group (CTR), then the sample refers to astressed plant. The other thresholds evaluated were the meanvalue minus twice the standard deviation (Mean � (2 * SD)), themean value minus three times the SD (Mean � (3 * SD)) and theminimum value minus the SD (Min � SD).

The percentages of stressed plants (i.e. number of samples inthe entire dataset identified as stressed plant), for time periodsM2–M9, detected by each index/parameter using the four thresh-olds (Table 2) are presented in Table 3. For the ASD leaf data, con-sidering the performance along all thresholds, the best results wereobtained with the PSDI575 and feature area (detection of 67–70% ofstressed plants). For the canopy data, the highest percentage ofstressed plants were detected with the FWHM (52%) when usingthe most liberals thresholds (Min and Mean � (2 * SD)) and withthe PSDI560, HVI800–670 and Dc (35%) for the most conservativesthresholds (Mean � (3 * SD) and Min-SD). Considering leaf andcanopy data results combined, the PSDI, followed by the FWHM,had the best performance in detecting plant stress along thethresholds.

The similar results obtained with the HVI800–670 and the Dc is inaccordance with the high inter-correlation of these two variables(Table 1). The same was observed for PSDI and feature Area. How-ever, the performance of the PSDI in detecting plant stress in can-opy data was better than the area (Table 3). This is an indicationthat with the PSDI the detection of the changes caused by thestress in the chlorophyll absorption feature is more efficient.

The results for plant stress detection using the PSDI and theMin-SD threshold are presented in Fig. 9. Brachiaria plant stresswas detected at leaf and canopy scales independent of the stressfactor type (diesel or gasoline). The same was noticed for leafperennial soybean. However only the plant stress caused by soilcontaminated with gasoline (GSL) was detected using perennialsoybean canopy data.

Fig. 6. Examples of brachiaria and perennial soybean plots showing healthy and stressed plants.

Fig. 7. Leaf level PSDI, HVI800–670, Dc, FWHM and area trends for brachiaria and perennial soybean spectral measurements of healthy (CTR) and stressed plants (DSL and GSL)using the ASD spectrometer.

116 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

Fig. 8. Canopy level PSDI, HVI800–670, Dc, FWHM and area trends for brachiaria and perennial soybean spectral measurements of healthy (CTR) and stressed plants (DSL andGSL) using the ASD spectrometer.

Table 1R-squares between absorption features parameters and indices for brachiaria andperennial soybean leaf and canopy data. The highest and lowest correlations arehighlighted in light and dark gray, respectively.

I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122 117

3.3. ProSpecTIR-VS image

The PSDI, HVI800–670, Dc, FWHM and area calculated based onthe imaging spectrometer data are plotted in Fig. 10 along withthe M8 ASD canopy data (previously shown in Fig. 8). The M8ASD spectral measurements were acquired in a date close to theProSpecTIR image acquisition. There is a good compatibilitybetween ASD and image canopy data. Again, the trend observed

for all indices and feature parameters is similar. Overall, thehealthy plants (CTR) indices/parameters values tend to be higherthan the stressed plants (DSL and GSL) values.

The thresholds specified based on the ASD canopy data (Table 2)were applied to the ProSpecTIR-VS image data. The results are pre-sented in Table 4. For the most liberal thresholds (Min andMean � (2 * SD)), false-positives (control plants classified asstressed plants) were observed for all indices/parameters. For themost conservative threshold (Min-SD), only the FWHM shows falsepositives. Overall, the PSDI and Dc display the lowest number offalse-positives across thresholds. Between these two, the PSDIyields the highest percentage of stressed plants detected.

The plots where plant stress was detected with the PSDI usingthe most conservative threshold tested (Min-SD) are shown inFig. 11. The stress was detected in all GSL parcels independent ofplant species. For the DSL stress factor, the result was better forbrachiaria. Only one perennial soybean parcel was identified asstressed. Field observations showed that plant stress caused bythe contamination of the soil with gasoline was more severe thanthe stress caused by the contamination with diesel, mainly for theperennial soybean canopy (Fig. 12).

4. Discussion

At first, observing the similar trends presented for all of the fea-tures parameters and indices analysed (Figs. 7 and 8), it seemedthat the plant stress detection could be done with the same degreeof success independently of the parameter/index used. As men-tioned earlier, this similarity results from the high correlationamong these parameters/indices (Table 1). It has been shown inthis work that the feature area, width and depth at feature centrewere reduced all together (proportionally), as the chlorophyllabsorption feature shrank by the decrease in light absorptioncaused by the stress. Similarly, the PSDI and HVI800–670 values fol-lowed this reduction, demonstrating their sensibility to theseparameters, in particularly to the feature area and maximum fea-ture depth, respectively. However, when testing the parameters/indices under different scenarios (i.e. thresholds and data type –leaf or canopy, ASD or ProSpecTIR-VS), their efficiency as plant

Table 2Statistics for the ASD leaf and canopy control treatment (healthy brachiaria and perennial soybean plants) and thresholds(highlighted in gray) for plant stress detection: minimum value (Min), mean value minus twice the standard deviation(Mean � (2*SD)), mean value minus three times the SD (Mean � (3*SD)) and minimum value minus the SD (Min � SD) (leaf:n = 46; canopy: n = 50).

LEAFMin Max Range Mean SD CV Mean-(2*SD) Mean-(3*SD) Min-SD

PSDI575 0.06 0.50 0.44 0.26 0.17 0.66 -0.09 -0.26 -0.11HVI800-670 0.65 0.80 0.16 0.72 0.06 0.08 0.60 0.55 0.59Dc 0.72 0.85 0.13 0.79 0.05 0.06 0.69 0.65 0.67FWHM 117 137 20 130 6 0.05 119 113 111Area 83 109 26 96 10 0.10 77 67 74

CANOPYMin Max Range Mean SD CV Mean-(2*SD) Mean-(3*SD) Min-SD

PSDI560 0.19 0.74 0.55 0.54 0.16 0.30 0.22 0.06 0.02HVI800-670 0.79 0.92 0.13 0.87 0.04 0.04 0.80 0.76 0.75Dc 0.82 0.94 0.11 0.90 0.03 0.04 0.83 0.80 0.79FWHM 141 156 15 150 4 0.03 141 137 137Area 108 140 32 127 9 0.07 109 99 98

Table 3ASD data. Percentage of stressed plants detected by each indices/parameters for thethresholds tested: minimum value (Min), mean value minus twice the standarddeviation (Mean � (2*SD)), mean value minus three times the SD (Mean � (3*SD))and minimum value minus the SD (Min � SD). The best result for each threshold ishighlighted in gray (leaf: n = 46; canopy: n = 50).

Min Mean-(2*SD) Mean-(3*SD) Min-SD

LEA

F

PSDI575 70% 67% 67% 67%HVI800-670 70% 67% 50% 67%Dc 70% 67% 53% 67%FWHM 67% 67% 67% 67%Area 70% 67% 67% 67%

CA

NO

PY

PSDI560 48% 48% 35% 35%HVI800-670 45% 45% 35% 35%Dc 42% 45% 35% 35%FWHM 52% 52% 32% 32%Area 48% 48% 32% 29%

LEA

F +C

AN

. PSDI 59% 57% 51% 51%HVI800-670 57% 56% 43% 51%Dc 56% 56% 44% 51%FWHM 59% 59% 49% 49%Area 59% 57% 48% 49%

118 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

stress indicators varies, especially for canopy data. For the mostsevere levels of plant stress, all indices/parameters performedcorrespondingly (i.e. detected the stress), whereas for low andintermediate levels of stress the performance can be diverse,depending on data type (leaf or canopy), plant species and thresh-old adopted. Overall, the performance of the PSDI was more robustacross the different situations and thresholds evaluated.

In this study, thresholds developed from the canopy reflectancespectra measured with the ASD spectrometer were directly appliedto the ProSpecTIR-VS data. These spectrometers have similar char-acteristics (sampling interval and bandpass) in the VNIR range,allowing this direct application. To test this, the ASD measure-ments were convolved to the sampling and bandpass characteris-tics of the imaging spectrometer. The absorption featureparameters and PSDI values computed from the convolved datawere within 1% of the values computed from original ASD spectra.If imaging spectrometers with coarser sampling and resolution in

the VNIR range were used, such as AVIRIS-Classic (Green et al.,1998) or HyMap (Cocks et al., 1998), thresholds would need tobe established from the convolved spectra.

The idea to develop the PSDI was to have an objective and sim-ple methodology to detect plant stress. Both leaf and canopy spec-tra were explored, but it was noticed that the usefulness of thePSDI was more relevant to canopy data analysis. For all thresholdsevaluated, negative values of PSDI560 were related to plants understress. Thus, for this study, it can be stated that if a sample has acanopy PSDI560 6 0, the sample refers to a stressed plant. In thatcase there is no need to have control samples (healthy plants) tocompare to the unknown samples.

When spectral libraries are available, spectral feature analysiscan be applied using a fitting algorithm. Recently, Kokaly et al.(2013) have delineated oil contamination in marshes from theDeepwater Horizon spill in the Barataria Bay by comparing AVIRISdata to reference spectra of oiled marsh. The two hydrocarbonabsorption features centred near 1.7 lm and 2.3 lm were analysedusing the Material Identification and Characterization Algorithm(MICA), a module of PRISM (Kokaly, 2011). To detect the post-impact stress caused by such contamination, a simple approach,such as the PSDI presented here, would seem to be a reasonableway to examine ecosystem responses (similar to Khanna et al.,2013).

Different wavelengths were specified for PSDI analysis of leaf(575 nm) and canopy data (560 nm). Among the healthy plants(CTR) considered here, none presented negative values of PSDIfrom these bands until the feature centre at around 680 nm (exam-ple in Fig. 5b and d). The 560 nm and 575 nm appeared among thewavelengths ranges (535–640 nm 685–700 nm) most sensitive tostress when analysing leaf spectra of plants submitted to differenttypes of stress (Carter, 1993). As presented in Table 2, the length(FWHM) of the chlorophyll absorption feature is wider in canopyspectra (141–156 nm) than in leaf spectra (117–137 nm). This isthe reason why the canopy PSDI uses a shorter wavelength as akey. In fact, the PSDI575 could be used to detect stress at canopylevel as well, but the detection would not be as efficient as thatprovided by the PSDI560.

The HVI800–670 was the only parameter/index analysed that wasnot calculated based on the continuum-removed reflectance. Sim-ilar results were yielded with the HVI800–670 and the chlorophyllfeature depth at the feature centre (Dc). For the most conservativethreshold when analysing leaf data (Mean � (3 * SD)), the worst

Fig. 10. ProSpecTIR-VS-based PSDI, HVI800–670, Dc, FWHM and area for brachiaria and perennial soybean canopies for each plot (P1 to P4) of healthy (CTR) and stressed plants(DSL and GSL). Values plotted in red asterisks correspond to the ASD-based values for canopy data from M8, acquired on a date near the image acquisition.

Fig. 9. Results for plant stress detection using the PSDI and the threshold Min-SD for leaf and canopy brachiaria and perennial soybean data. If the PSDI value for a sample isless than the minimum index value of the control group (CTR), then the sample is stamped as a stressed plant. No stress – the plant is not stressed because it was not exposedto any type of contaminant (including DSL M1 and GSL M1 which were measured before the first contamination was done). Stress not detected or not stressed yet – the plantwas exposed to the contaminant (diesel or gasoline) but it does not show any sign of stress yet or the stress was not detected with the PSDI. Stress detected – the plant wasstressed because it was exposed to the contaminant and the stress was detected.

Table 4ProSpecTIR-VS data. Percentage of stressed plants (n = 24), including both BR and PS,detected (overall accuracy) by each indices/parameters for the thresholds determinedbased on ASD canopy data: minimum value (Min), mean value minus twice thestandard deviation (Mean � (2*SD)), mean value minus three times the SD(Mean � (3*SD)) and minimum value minus the SD (Min � SD). For each threshold,the results with smaller percentage of false-positives (commission error) arehighlighted in gray.

Percentage of stressed plants detected (percentage of false-posi�ves)

Min Mean-(2*SD) Mean-(3*SD) Min-SDPSDI563 81% (13%) 81% (13%) 81% (0%) 75% (0%)HVI800-670 81% (25%) 88% (25%) 81% (13%) 81% (0%)Dc 81% (13%) 81% (13%) 81% (0%) 69% (0%)FWHM 94% (25%) 100% (25%) 81% (13%) 81% (13%)Area 81% (25%) 88% (25%) 81% (0%) 75% (0%)

I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122 119

result in detecting plant stress were obtained with the HVI800–670

(Table 3). Although for the Min-SD threshold the lowest percentageof false-positive and highest percentage of stressed plants detectedin the ProSpecTIR-VS image was obtained with the HVI800–670, itwas also with this index that the highest percentages of false-pos-itives were reached for the other three thresholds tested.

It is relevant to mention that during the experiment only oneProSpecTIR-VS image could be acquired. Ideally, the availabilityof several images, from the beginning to the end of the experiment,would make it possible to effectively evaluate the PSDI to detectplant stress using temporal remote sensing imagery. But thatwas not possible due to budget constraints. The airborne surveytook place near the end of the experiment, when some plantsalready exhibited high degrees of stress. However, the fact thatthe stress on crop plots was detect using the PSDI (e.g. DSL-BRand GSL-BR in parcel 2 (P2) – Fig. 11), particularly where the stresssymptoms were not visually evident (e.g. DSL-BR and GSL-BR inparcel 2 (P2) – Fig. 11), demonstrates the efficacy of the proposedindex.

The findings of this work were obtained with the evaluation oftwo perennial plant species, the brachiaria grass (monocotyledon)and the perennial soybean leguminous (dicotyledon) (Figs. 6 and12). Mono and dicotyledons have distinct leaf structure (narrowversus broadleaves) and arrangement (vertical versus horizontal),which are very relevant under the remote sensing perspective.Despite their differences, the PSDI at 560 nm (563 nm in PRoSpec-TIR-VS image) could be used to detect plant stress in both plants.For annual plants (e.g. maize, beans), which goes through asequence of development phases in a rather short period (few

Fig. 11. ProSpecTIR-VS image (channel 563 nm) in grey scale. The blue rectanglesdelimit the planting slots (CTR healthy plants and DSL/GSL stressed plants). In eachparcel (separated by the gray lines), the plots corresponding to the brachiaria (BR)and perennial soybean (PS) plants are highlighted in red and yellow dashedrectangles, correspondingly. The ‘‘S’’ in red identifies the plots where plant stresswas detected with the PSDI using the most conservative threshold tested (Min-SD).

120 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

months), the detection of stress using the PSDI at 560 nm might beappropriate only for the first phases. When these plants reach theirphysiologic maturity they show symptoms of stressed plants. As aresult, the detection of stress caused by external agents can be

Fig. 12. Photos of perennial soybean and brachiaria plots taken on M8 – a date close to tand plants stressed by the application of diesel (DSL) and gasoline (GSL) into the soil ar

mistaken by natural plant development (Sanches et al., 2013b).Noomen et al. (2008) evaluated the correlation of different vegeta-tion index with oxygen concentration in maize and wheat. Is wasnot possible to find one reflectance index that could be used anany time during the growth cycle of the plants due to the constantchange of canopy characteristics. A more sophisticated approach,in which the threshold adjusts as a function of time and local tem-perature and precipitation patterns should be postulated.

Considering the PSDI, Dc and area parameters, two threshold(the most conservatives) predetermined based on ASD canopy datacould be successfully applied (i.e. no false-positives) to the imagingspectrometer data. Based on the results shown here, it is clear thata conservative constraint, such as the Min-SD, is necessary to min-imise the chances of false-positives when detecting plant stress inimage data. Comparable results are expected to be obtained forother airborne imaging spectrometers with resolution similar tothe ProSpecTIR-VS sensor.

4.1. Relevance of PSDI for hydrocarbon contamination detection andfuture application with upcoming orbital hyperspectral missions

The objective of this paper was to evaluate general plant stressdetection using spectroscopic remote sensing focused on the con-tinuum removal of the chlorophyll absorption feature. For thatpurpose, the data from a field experiment where plants weregrown over soil contaminated by liquid hydrocarbons (gasolineand diesel) were used. It is not the intention of the present docu-ment to discuss the results for hydrocarbon contaminationobtained with the mentioned trial. Those results were previouslypresented in Sanches et al. (2013a,b). However, since a newmethod is proposed in this study it is relevant to make a commentabout it. Considering the ASD data, with the PSDI it was possible todetect the plant stress in brachiaria and perennial soybean moreprecociously (i.e. when the plants were submitted to lower levelsof contaminants) compared to the red edge position (Sanches

he acquisition of the ProSpecTIR-VS image. Differences among healthy plants (CTR),e clearly portrayed in the pictures.

I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122 121

et al., 2013b). This improvement in plant stress detection using thePSDI was more significant when analysing the canopy data. Thisfinding is particularly interesting considering that the ultimategoal is to detect canopy plant stress over remote platforms (air-borne or spaceborne).

Although there is no orbital hyperspectral sensor with globaldata acquisition capacity currently in operation, different initia-tives are in progress for the planning, construction and launch ofthis type of sensors. The EnMAP sensor (Environmental Mappingand Analysis Program), under development by the German Aero-space Center (DLR) in partnership with the German Research Cen-tre for Geosciences (GFZ) (Kaufmann et al., 2012), should belaunched in 2018. NASA plans to launch HyspIRI (HyperspectralInfra-Red Imager) (Green et al., 2012) by 2023. There are also theHISUI from Japan (METI), the PRISMA from Italy (ASI), the MSMIfrom South Africa (SunSpace), the HYPXIM from France (CNES)and the SHALOM from Israel and Italy (ISA/ASI) (Staenz and Held,2012; Staenz et al., 2013). This constellation of satellites withhyperspectral sensors will provide enough spectral resolution forthe application of the PSDI at global level.

Overall, the use of hyperspectral technology offers great poten-tial for increasing knowledge on natural resources. Despite the rel-ative high cost for data acquisition, there is no doubt about theadvantages offered by this technology. The advances in this fieldwill be possible as long as more research is conducted by anincreasing number of scientists in different regions of the planetand this work represents a contribution to towards it.

5. Conclusion

For the management of natural environments or agriculturalareas, the detection of plant stress as early as possible has a greatvalue. It can allow an intervention to be made in time to control thestress factor and to restrain damages. In this context, the method-ology of exploring the chlorophyll absorption feature centred atapproximately 680 nm with the continuum removal techniquepresented in this work proved useful. It was observed a progressivedecrease in the chlorophyll absorption width, depth and area val-ues as the plants (DSL and GSL) were exposed to increasing levelsof contaminants (liquid hydrocarbons). Consequently, theseparameters have a potential to be used as plant stress indicators,as well as indices based on them.

Considering the two plant species analysed (brachiaria andperennial soybean), and all the thresholds tested, overall, the bestresults for detecting stress in this study were obtained with thePSDI and the feature area when analysing leaf data (67–70% ofstressed plants detected), and with the PSDI when exploring can-opy spectra extracted from field (35–48% of stressed plantsdetected) and airborne platforms (75–81% of stressed plantsdetected). The ProSpecTIR-VS image analysed in this study wasacquired in one single date (M8), whereas the ASD canopy datacorrespond to several dates (M1 to M9). That explains the higherpercentage of stressed plants detected based on the image. Also,the sample size of the imagery data analysed (n = 26 samples)was smaller than the sample size of the data collected in the field.

It would be advisable to apply the PSDI to other plant stress sit-uations, including different types of plants and stress factors in anexperimental setup with a larger sample size, to extend the indexperformance assessment. It would be also convenient to test themethodology for other hyperspectral images. Nevertheless, theresults presented in this study for two species of perennial forage(mono and dicotyledons) are an indication of the potential of spec-tral feature analysis of the chlorophyll absorption feature parame-ters (width, area and depth) and the PSDI as a plant stressindicator.

To be able not only to detect but also to identify the cause of thestress would be even more valuable. However this is not a simpletask as stressed plants present in general a similar reaction inde-pendently of the nature of the stress, as demonstrated by otherresearchers. The answer might be in the synergy of chlorophyll-based approaches with analysis of absorption features in theshortwave infrared wavelengths. For the time being, the remoteidentification of plant stress type remains a challenge and aimportant field to be researched.

Acknowledgements

I.D. Sanches is grateful to FAPESP for her post-doctorate schol-arship (Processo FAPESP n. 2011/03416-8). The authors are thank-ful to Wilson Oliveira (Petrobras) and Marcos Nopper Alves(CPQBA-UNICAMP) for their innovative ideas and contributionsduring the field experiments. We are greatly indebted to LuciolaMagalhaes and Giuliana Quiterio for their involvement with allphases and aspects of the spectral measurements in the field. Wealso acknowledge the researchers of the Technology Program forTransport (PROTRAN) of the Research Center of Petrobras (CEN-PES), especially to Pedro Altoe Ferreira, Lis Maria Leoni Rabacoand Renato Seixas da Rocha, for the financial and logistic supportdedicated to the Biomarkers Phase-1 Project developed in partner-ship with UNICAMP. C.R. Souza Filho acknowledges CNPq for theresearch grant.

References

Analytical Spectral Devices (ASD), 2011a. FieldSpec� Hi-Res portablespectroradiometer. <http://www.asdi.com/products/fieldspec-3-hi-res-portable-spectroradiometer> (accessed 04.02.11).

Analytical Spectral Devices (ASD), 2011b. Plant probe. <http://www.asdi.com/acessories/plant-probe> (accessed 08.02.11).

Carter, G.A., 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot.80 (3), 239–243.

Carter, G.A., 1994. Ratios of leaf reflectances in narrow wavebands as indicators ofplant stress. Int. J. Remote Sens. 15 (3), 697–703.

Carter, G.A., Knapp, A.K., 2001. Leaf optical properties in higher plants: linkingspectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88(4), 677–684.

Carter, G.A., Miller, R.L., 1994. Early detection of plant stress by digital imagingwithin narrow stress-sensitive wavebands. Remote Sens. Environ. 50, 295–302.

Chapin, F.S., 1991. Integrated responses of plants to stress. Bioscience 41 (1), 29–36.Clark, R.N., Roush, T.L., 1984. Reflectance spectroscopy: quantitative analysis

techniques for remote sensing applications. J. Geophys. Res. 89, 6329–6340.Cocks T., Jenssen, R. Stewart, A. Wilson, I., Shields, T., 1998. The HyMap airborne

hyperspectral sensor: The system, calibration and performance. In: Schaepman,M., Schlapfer, D., Itten, K.I. (Eds.), Proceedings of the 1st EARSeL Workshop onImaging Spectroscopy, 6–8 October 1998, Zurich, EARSeL, Paris, pp. 37–43.

Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemicalconcentration of leaves with reflectance spectrometry: testing the Kokaly andClark methodologies. Remote Sens. Environ. 76, 349–359.

De Jong, S.M., Addink, E.A., Hoogenboom, P., Nijland, W., 2012. The spectralresponse of Buxus sempervirens to different types of environmental stress – alaboratory experiment. ISPRS J. Photogrammetry Remote Sens. 74, 56–65.

Eitel, J.U.H., Vierling, L.A., Litvak, M.E., Long, D.S., Schulthess, U., Ager, A.A.,Krofcheck, D.J., Stoxcheck, L., 2011. Broadband, red-edge information fromsatellites improves early stress detection in a New Mexico conifer woodland.Remote Sens. Environ. 115, 3640–3646.

Filella, I., Penuelas, J., 1994. The red edge position and shape as indicators of plantchlorophyll content, biomass and hydric status. Int. J. Remote Sens. 15 (7),1459–1470.

Green, R.O., Eastwood, M.L., Sarture, C.M., Chrien, T.G., Aronsson, M., Chippendale,B.J., Faust, J.A., Pavri, B.E., Chovit, C.J., Solis, M., Olah, M.R., Williams, O., 1998.Imaging spectroscopy and the airborne visible/infrared imaging spectrometer(AVIRIS). Remote Sens. Environ. 65, 227–248.

Green, R.O., Hook, S.J., Middleton, E., Turner, W., Ungar, S., Knox, R., 2012. TheHyspIRI decadal survey mission: update on the mission concept and scienceobjectives for global imaging spectroscopy and multi-spectral thermalmeasurements. In: Proceedings of the International Geoscience and RemoteSensing Symposium (IGARSS 2012), Munich, Germany, 4 pages, 2012.

Hoefnagels, M., 2011. Biology: Concepts and Investigations, second ed. McGraw-Hill.

Jackson, R.D., 1986. Remote sensing of biotic and abiotic plant stress. Ann. Rev.Phytopathol. 24, 265–287.

122 I.D. Sanches et al. / ISPRS Journal of Photogrammetry and Remote Sensing 97 (2014) 111–122

Jago, R.A., Cutler, M.E.J., Curran, P.J., 1999. Estimating canopy chlorophyllconcentration from field and airborne spectra. Remote Sens. Environ. 68, 217–224.

Kaufmann, H., Segl, S., Duester, T., Rogass, C., Hofer, S., Mueller, A., Chlebek, C., 2012.The environmental mapping and analysis program (EnMAP) – presente status ofpreparatory phase. In: Proceedings of the International Geoscience and RemoteSensing Symposium (IGARSS 2012), Munich, Germany, 4 pages, 2012.

Khanna, S., Santos, M.J., Ustin, S.L., Koltunov, A., Kokaly, R.F., Roberts, D.A., 2013.Detection of salt marsh vegetation stress and recovery after the deepwaterhorizon oil spill in barataria bay, Gulf of Mexico using AVIRIS data. PLoS ONE 8(11), e78989. http://dx.doi.org/10.1371/journal.pone.0078989.

Knipling, E.B., 1970. Physical and physiological basis for the reflectance of visibleand near-infrared radiation from vegetation. Remote Sens. Environ. 1, 155–159.

Kokaly, R.F., 2001. Investigating a physical basis for spectroscopic estimates of leafnitrogen concentration. Remote Sens. Environ. 75, 153–161.

Kokaly, R.F., 2011. PRISM: Processing routines in IDL for spectroscopicmeasurements (installation manual and users guied, version 1.0). U.S.Geological Survey Open-File Report 2011–1155, 432p. <http://pubs.usgs.gov/of/2011/1155/>.

Kokaly, R.F., Clark, R.N., 1999. Spectroscopy determination of leaf biochemistryusing band-depth analysis of absorption features and stepwise multiple linearregression. Remote Sens. Environ. 67, 267–287.

Kokaly, R.F., Despain, D.G., Clark, R.N., Livo, K.E., 2003. Mapping vegetation inYellowstone National Park using spectral feature analysis of AVIRIS data.Remote Sens. Environ 84, 437–456.

Kokaly, R.F., Couvillion, B.R., Holloway, J.M., Roberts, D.A., Ustin, S.L., Peterson, S.H.,Khanna, S., Piazza, S.C., 2013. Spectroscopy remote sensing of the distributionand persistence of oil from the deepwater horizon spill in barataria baymarshes. Remote Sens. Environ. 129, 210–230.

Lamb, D.W., Steyn-Ross, M., Schaare, P., Hanna, M.M., Silvester, W., Steyn-Ross, A.,2002. Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pastureusing the chlorophyll red-edge: theoretical modelling and experimentalobservations. Int. J. Remote Sens. 23, 3619–3648.

Lammoglia, T., Souza Filho, C.R., 2013. Unraveling hydrocarbon microseepages inonshore basins using spectral–spatial processing of advanced spacebornethermal emission and reflection radiometer (ASTER) data. Surv. Geophys. 34(3), 349–373.

Li, L., Ustin, S.L., Lay, M., 2005. Application of AVIRIS data in detection of oil-inducedvegetation stress and cover change at Jornada, New Mexico. Remote Sens.Environ 94, 1–16.

Merzlyak, J.R., Gitelson, A.A., Chivkunova, O.B., Rakitin, V.Y., 1999. Nondestructiveoptical detection of pigment changes during leaf senescence and fruit ripening.Physiol. Plant. 106, 135–141, Pipelove Accident Report NTSB/PAR-00/01.

Mutanga, O., Skidmore, A.K., 2004. Hyperspectral band depth analysis for a betterestimation of grass biomass (Cenchrus ciliares) measured under controlledlaboratory conditions. Int. J. Appl. Earth Obs. 5, 87–96.

Mutanga, O., Skidmore, A.K., Prins, H.H.T., 2004. Predicting in situ pasture quality inthe Kruger National Park, South Africa, using continuum-removed absorptionfeatures. Remote Sens. Environ. 89, 393–408.

Noomen, M.F., Smith, K.L., Colls, J.J., Steven, M.D., Skidmore, A.K., van der Meer, F.D.,2008. Hyperspectral indices for detecting changes in canopy reflectance as aresult of underground natural gas leakage. Int. J. Remote Sens. 29 (20), 5987–6008.

Noomen, M.F., Van der Werff, H.M.A., Van der Meer, F.D., 2012. Spectral and spatialindicators of botanical changes caused by long-term hydrocarbon seepage. Ecol.Infor. 8 (1), 55–64.

Penuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B., 1994. Reflectanceindices associated with physiological changes in nitrogen and water-limitedsunflower leaves. Remote Sens. Environ. 48, 135–146.

Penuelas, J., Filella, I., Lloret, P., Munoz, F., Vilajeliu, M., 1995. Reflectanceassessment of mite effects on apple trees. Int. J. Remote Sens. 16, 2727–2733.

Rathod, P.H., Rossiter, D.G., Noomen, M.F., Van der Meer, F.D., 2013. Proximalspectral sensing to monitor phytoremediation of metal – contaminated soils.Int. J. Phytorem. 15 (5), 405–426.

Richter, R., Schlapfer, D., 2012. Atmospheric/topographic correction for airborneimagery: ATCOR-4 User Guide, Version 6.3.0, December 2013, 225p. <http://www.rese.ch/pdf/atcor4_manual.pdf>.

Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetationsystems in the great plains with ERTS. In Proceedings of the Third EarthResources Technology Satellite-1 Symposium (ERTS-1), volume I, 1973, NASAGoddard Space Flight Center, Washington DC, pp. 309–317. (NASA, SP-351:Washington, DC).

Sanches, I.D., Souza Filho, C.R., Magalhães, L.A., Quitério, G.C.M., Alves, M.N.,Oliveira, W.J., 2013a. Assessing the impact of hydrocarbon leakages onvegetation using reflectance spectroscopy. ISPRS J. Photogrammetry RemoteSens. 78, 85–101.

Sanches, I.D., Souza Filho, C.R., Magalhães, L.A., Quitério, G.C.M., Alves, M.N.,Oliveira, W.J., 2013b. Unravelling remote sensing signatures of plantscontaminated with gasoline and diesel: an approach using the red edgespectral feature. Environ. Pollut. 174, 16–27.

Sanches, I.D., Tuohy, M.P., Hedley, M.J., Mackay, A.D., 2013c. Seasonal prediction ofin situ pasture macronutrients in New Zealand pastoral systems usinghyperspectral data. Int. J. Remote Sens. 31 (1), 276–302.

Schmidt, K.S., Skidmore, A.K., 2003. Spectral discrimination of vegetation types in acoastal wetland. Remote Sens. Environ. 85, 92–108.

Smith, K.L., Steven, M.D., Colls, J.J., 2004. Use of hyperspectral derivative ratios in thered-edge region to identify plant stress responses to gas leaks. Remote Sens.Environ. 92, 207–217.

Souza Filho, C.R., Augusto, V., Oliveira, W.J., Lammoglia, T., 2008. Detecção deexsudações de hidrocarbonetos por geobotânica e sensoriamento remoto multi-temporal: estudo de caso no Remanso do Fogo (MG). Revista Brasileira deGeociências 38 (2), 228–243.

SPECTIR. ProspecTIR products, 2012. <http://www.spectir.com/wp-content/uploads/2012/02/ProSpecTIR_VS_specs_2011.pdf> (accessed 05.11.12).

Staenz, K, Held, A., 2012. Summary of current and future terrestrial civilianhyperspectral spaceborne systems. In: Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS 2012). Munich, Germany,4 pages, 2012.

Staenz, K, Mueller, A., Heiden, U., 2013. Overview of terrestrial imagingspectroscopy missions. In: Proceedings of the IEEE International Geoscienceand Remote Sensing Symposium (IGARSS 2013). Melbourne, Australia, p. 3502–3505.

Swatantran, A., Dubayah, R., Roberts, D., Hofton, M., Blair, B., 2011. Mappingbiomass and stress in the Sierra Nevada using lidar and hyperspectral datafusion. Remote Sens. Environ. 115 (11), 2917–2930.

Thenkabail, P.S., Lyon, J.G., Huete, A., 2011. Hyperspectral Remote Sensing ofVegetation. CRC Press, Taylor & Francis Group, Boca Raton, FL, 704p.

Zarco-Tejada, P.J., Pushnik, J.C., Dobrowski, S., Ustin, S.L., 2003. Steady-statechlorophyll a fluorescence detection from canopy derivative reflectance anddouble-peak red edge effects. Remote Sens. Environ. 84, 283–294.

Zarco-Tejada, P.J., Berni, J.A.J., Suárez, L., Sepulcre-Cantó, F., Morales, F., Miller, J.R.,2009. Imaging chlorophyll fluorescence with an airborne narrow-bandmultispectral câmera for vegetation stress detection. Remote Sens. Environ.113 (6), 1262–1275.

Zinnert, J.C., Via, S.M., Young, D.R., 2013. Distinguishing natural from anthropogenicstress in plants: physiology, fluorescence and hyperspectral reflectance. PlantSoil 366, 133–141.