Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation

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Evaluating variations of physiology-based hyperspectral features alonga soil water gradient in a Eucalyptusgrandis plantationMoses Azong Cho a , Jan van Aardt b , Russell Main a & BonganiMajeke aa Council for Scientific and Industrial Research (CSIR) – Ecosystem,Earth Observation Unit, PO Box 395 , Pretoria , 0001 , SouthAfricab RIT: Center for Imaging Science, Laboratory for ImagingAlgorithms and Systems, 54 Lomb Memorial Drive, Building17-3173 , Rochester , NY , 14623 , USAPublished online: 20 Jul 2010.

To cite this article: Moses Azong Cho , Jan van Aardt , Russell Main & Bongani Majeke (2010)Evaluating variations of physiology-based hyperspectral features along a soil water gradient in aEucalyptus grandis plantation, International Journal of Remote Sensing, 31:12, 3143-3159, DOI:10.1080/01431160903154390

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Evaluating variations of physiology-based hyperspectral features along asoil water gradient in a Eucalyptus grandis plantation

MOSES AZONG CHO*†, JAN VAN AARDT‡, RUSSELL MAIN†

and BONGANI MAJEKE†

†Council for Scientific and Industrial Research (CSIR) – Ecosystem, Earth Observation

Unit, PO Box 395, Pretoria 0001, South Africa

‡RIT: Center for Imaging Science, Laboratory for Imaging Algorithms and Systems,

54 Lomb Memorial Drive, Building 17-3173, Rochester NY 14623, USA

(Received 1 February 2008; in final form 12 November 2008)

Remote sensing is viewed as a cost-effective alternative to intensive field surveys in

assessing site factors that affect growth of Eucalyptus grandis over broad areas.

The objective of this study was to assess the utility of hyperspectral remote sensing

to discriminate between site qualities in E. grandis plantation in KwaZulu-Natal,

South Africa. The relationships between physiology-based hyperspectral indica-

tors and site quality, as defined by total available water (TAW), were assessed for

E. grandis plantations through one-way analysis of variance (ANOVA). Canopy

reflectance spectra for 68 trees (25 good, 25 medium and 18 poor sites) were

collected on clear-sky days using an Analytical Spectral Device (ASD) spectro-

radiometer (350–2500 nm) from a raised platform. Foliar macronutrient concen-

trations for N, P, K, S, Ca, Mg and Na and their corresponding spectral features

were also evaluated. The spectral signals for leaf water – normalized difference

water index (NDWI), water band index (WBI) and moisture stress index (MSI) –

exhibited significant differences (p , 0.05) between sites. The magnitudes of these

indices showed distinct gradients from the poor to the good sites. Similar results

were observed for chlorophyll indices. These results show that differences in site

quality based on TAW could be detected via imaging spectroscopy of canopy

water or chlorophyll content. Among the macronutrients, only K and Ca exhibited

significant differences between sites. However, a Tukey post-hoc test showed

differences between the good and medium or medium and poor sites, a trend not

consistent with the TAW gradient. The study also revealed the capability of

continuum-removed spectral features to provide information on the physiological

state of vegetation. The normalized band depth index (NBDI), derived from

continuum-removed spectra in the region of the red-edge, showed the highest

potential to differentiate between sites in this study. The study thus demonstrated

the capability of hyperspectral remote sensing of vegetation canopies in identifying

the site factors that affect growth of E. grandis in KwaZulu Natal, South Africa.

1. Introduction

Water and nutrient supplies are the main abiotic factors affecting plantation growth in

the tropics (Stape et al. 2004) and evaluation of these supplies is relevant for zoning

plantation potential and for establishing silvicultural methods for site preparation,

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160903154390

International Journal of Remote Sensing

Vol. 31, No. 12, 20 June 2010, 3143–3159

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mailto:[email protected]

http://www.tandf.co.uk/journals

fertilization and control of competition (Attiwill and Adams 1996). Zoning of Eucalyptus

grandis potential in KwaZulu Natal, South Africa is based on the total available water

(TAW) (Smith et al. 2005) as water availability is the main limiting resource affecting

growth of E. grandis in this region (Little and Rolando 2002). Large variations in water

availability exist in the KwaZulu Natal region because of large gradients in temperature(due to altitude), rainfall, parent material, terrain position, soils and water availability

(du Toit 2006). Du Toit (2006) argued that stand growth and wood properties can be

optimized by manipulating soil water and nutrient availability on a site-specific basis.

But, first, timely information on the availability of soil water and nutrients over

broad areas must be determined. Direct field methods to determine water or nutrient

availability are time-consuming and labour-intensive. The use of remote sensing data is

viewed as an alternative means of acquiring much of this information over broad areas.

The amount of water or nutrients held within the vegetation canopy is often a function ofwater or nutrient availability. The implication of this for remote sensing is that any

changes in vegetation water or nutrient content (physiological state) are likely to influence

spectral reflectance properties (Gamon et al. 1992, Asner et al. 2000).

The advent of hyperspectral remote sensing has provided new opportunities for

ecologists to improve on the detection, mapping and monitoring of changes in the

physiological state and productivity of terrestrial ecosystems. Subtle changes in the

condition of the vegetation, underpinned by changes in foliage biochemical contents,

have been shown to influence vegetation reflectance at specific wavelengths (Aber andFederer 1992, Gamon et al. 1992, Curran 1994, Blackburn 1998, Asner et al. 2000). The

application of hyperspectral remote sensing for modelling the physiological status of

vegetation has shown great potential for yield forecasting or proactive fertilizer man-

agement in precision agriculture and forestry (Haboudane et al. 2002, Goel et al. 2003).

The enhanced capability of hyperspectral sensors for predicting vegetation vitality

compared to multi-spectral sensors is explained by their ability to measure spectral

reflectance in contiguous narrow bands (often less than 10 nm) in the visible to the

shortwave infrared (SWIR) spectral range (Rock et al. 1988, Vane and Goetz 1988,Kokaly and Clark 1999). Although the high spectral resolution of hyperspectral data is

essential for quantifying subtle changes in vegetation condition, these over-sampled

data also contain redundant information at the band level (Bajwa et al. 2004). Linking

wavelengths to specific compounds is further complicated by the fact that reflectance,

absorption, or transmittance at each wavelength is usually affected by more than one

factor of the vegetation, including foliar biochemical content, leaf and canopy struc-

ture/architecture, background (soil and/or litter) reflectance, and the illumination/view-

ing geometry (Knipling 1970, Huete and Jackson 1988, Qi et al. 1995, Asner et al. 2000).Spectral data transformations, notably, vegetation indices, derivative analysis and

continuum-removal, were applied to minimize the effects of data redundancy, and to

enhance spectral signals for specific biochemicals (table 1). For example, the moisture

stress index (Hunt and Rock 1989, Harris et al. 2006), water band index (Penuelas

et al. 1994) and normalized difference water index (Gao 1996) were used to detect leaf

water stress. Other studies utilized the relationship between vegetation moisture and

chlorophyll to determine canopy moisture status, e.g. the chlorophyll-sensitive

(Horler et al. 1983, Curran et al. 1990, Jago et al. 1999) red-edge position (Filellaand Penuelas 1994). Continuum-removed spectra were used to provide accurate

estimates of foliar nutrient concentrations (Kokaly and Clark 1999, Curran et al.

2001, Mutanga et al. 2003). Most studies relating leaf water content to spectral

reflectance were conducted under controlled conditions either as laboratory or field

3144 M. A. Cho et al.

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experiments with agricultural crops (e.g. Danson et al. 1992, Penuelas et al. 1993,

Filella and Penuelas 1994, Strachan et al. 2002, Liu et al. 2004, Sun et al. 2008). There

are no studies directly linking soil water or nutrient availability to canopy reflectance

for tree stands growing under natural conditions. The determination of site factors

that affect growth and development of trees growing under natural conditions have

proved to be particularly challenging, mainly due to the possible interaction among a

host of environmental factors, e.g. soil nutrients, wind and temperature (Ares andFownes 2000). The following questions were therefore addressed in this study:

(1) could the gradient in site quality as defined by the TAW be associated with canopy

water content or moisture stress as detected via hyperspectral remote sensing of the

canopy and (2) does the variability in site quality explain the variance in other canopy

biochemicals (macronutrients), as expressed through their spectral indicators?

The broad objective, therefore, was to assess if site qualities could be discriminated

via hyperspectral remote sensing of leaf water, chlorophyll and nutrient contents.

Table 1. Published physiology-based vegetation indices applied in this study.

Index FormulaPhysiologicalsignificance References

Soil-adjusted andatmosphericallyresistant vegetationindex (SARVI)

SARVI ¼ (RNIR – Rrb)/(RNIR þ Rrb þ L),where Rrb ¼ Rred –�(Rblue – Rred), � ¼atmospheric aerosolcorrection function;L ¼ soil-adjustmentfactor.� ¼ 1, L ¼ 0.5

SARVI minimizesatmosphere and soil-induced variationsand is related to leafarea index

Haboudaneet al. (2004),Myneni andAsrar(1994)

Normalizeddifferencevegetation index(NDVI)

(R800 nm - R680 nm) /(R800 nm þ R680 nm)

Leaf area index,canopy greenness

Tucker (1979)

Normalizeddifference waterindex (NDWI)

(R860 nm - R1240 nm) /(R860 nm þ R1240 nm)

Sensitive to changes inliquid water contentof vegetation canopies

Gao (1996)

Moisture stressindex (MSI)

R1600 nm / R817 nm Sensitive to leafwater stress

Hunt andRock (1989)

Water band index(WBI)

R970 nm / R900 nm Sensitive to leaf water Penuelas et al.(1994)

Photochemicalreflectance index(PRI)

(R531 nm – R570 nm) /(R531 nm þ R570 nm)

related toxanthophylls,pigments,photosynthetic light-use efficiency

Gamon et al.(1992)

Carter index R760/R695 Chlorophyll Carter (1994)Red-edge position

(REP)Linear extrapolation

methodrelated to nitrogen and

chlorophyllCho and

Skidmore(2006)

Normalized banddepth differenceindex (NBDI)

(max. BD – BD740 nm) /(max. BD þBD740 nm)BD ¼ banddepth derived fromcontinuum-removedspectrum

Nitrogen, chlorophyll Mutanga et al.(2004)

Discriminating site qualities in E. grandis 3145

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Foliar chemistry is applied in this context as a proxy for describing vegetation system

state. Research outcomes potentially could lead to increased operational application

of spectral data to site and systems state assessment, based on hyperspectral remote

sensing proof-of-concept studies.

2. Material and methods

2.1 Study site

The study area (figure 1) is located in the Richmond area of the KwaZulu-NatalMidlands of South Africa (29� 490 S, 30� 170 E) and consists of an even-aged (4–7 years

old) closed canopy Eucalyptus grandis plantation. Plantation forestry is a major land

use in the study area due to suitable climate and soils. Three main genera are grown

for commercial forestry, these being Pines (Pinus spp.), Eucalypts (Eucalyptus spp.)

and Wattle (Acacia spp.). The main product is pulpwood for the pulp and paper

industry. Average monthly rainfall ranges from 820 mm to 1300 mm, but averages

1000 mm per annum, mostly falling between October and April. Average monthly

midday temperature varies between 28� C to 33� C in summer and between 12� C and20� C in winter. The terrain generally consists of undulating plains, but incised with

steep river valleys, with altitude rising from 800 m to 1400 m above mean sea level. The

geology consists of sandstone and clay formations, which have resulted in sandy clay,

to sandy clay loam soils. The experimental site is classified into three site qualities,

namely, good, medium and poor. Site quality is based on TAW as estimated from the

soil form (type) and effective rooting depth (ERD), in conjunction with rainfall and

temperature classes (Smith et al. 2005).

2.2 Measurements of canopy spectral reflectance

Canopy reflectance spectra for 68 trees (25 good, 25 medium and 18 poor sites) were

collected on clear-sky days using an Analytical Spectral Device (ASD) spectroradi-

ometer (FieldSpec3 Pro FR, Analytical Spectral Device, Inc., USA). The measure-

ments were made during the late winter season (August) from a raised platform(crane). The trees were selected at irregular intervals along narrow roads within the

plantations, given limited access due to the size and manoeuvrability of the crane. The

ASD spectroradiometer covers the range from 350 nm to 2500 nm. The sampling

interval over the 350–1050 nm range is 1.4 nm with a spectral resolution (full

bandwidth at half maximum) of 3 nm. Over the 1050–2500 nm range, the sampling

interval is 2 nm and the spectral resolution is between 10 nm and 12 nm. The results

are then interpolated by the ASD software to produce readings at every 1 nm. Four

evenly distributed reflectance measurements were collected at approximately 1 m to1.5 m above the canopy of each tree using the 25� field-of-view (FOV) fibre optic of

the ASD. Thus, each measurement covered an instantaneous FOV (IFOV) of about

14–20 cm, resulting in a total IFOV of about 56–80 cm for each tree. The trees were

about 10–15 m tall, with crown diameters of about 1–1.5 m. It was not possible to

capture the full canopy in a single FOV because of the height limitation of the crane.

2.3 Measurement of foliar nutrient concentrations

One hundred sun leaves were collected for nutrient analyses from each tree. Leaf

samples were immediately stored in a cooled container after which they were analysed


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for a variety of foliar macronutrients: N, P, K, Ca, Mg, S and Na. The leaves wereoven-dried at 60� C until constant weight and milled before nutrient analyses.

Nitrogen concentration was analysed using a Leco FP528 nitrogen analyser

(Horneck and Miller 1998). The mechanism of the FP528 nitrogen analyser is based

on rapid combustion of the dry sample (0.1500 g � 0.05) and measurement of the

thermal conductivity of resulting nitrogen oxides (NOx) and N2 gases for nitrogen.

The analysis of the other mineral elements (P, K, Ca, Mg, S and Na) was performed by

inductively coupled plasma (ICP) (Madejon et al. 2003).

Legend

Towns

Study site

Study site

Richmond

Pietermaritzburg

Location of KwaZulu-Natal, South Africa

South Africa

Greenhill plantations

Roads0

N

3 6 12 km

Figure 1. Study site in the Richmond area of the KwaZulu-Natal Midlands, South Africa.


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2.4 Data analyses

One-way analysis of variance (ANOVA) was used as the main statistical tool to determine

whether significant relationships exist between foliar biochemicals or their spectral proxies

and site quality. The Tukey honestly significant difference (HSD) post-hoc test was used to

assess the pairwise differences between sites. The original ASD spectra were re-sampled to

match the wavelength specification of the airborne HyMap spectrometer. The HyMap

sensor comprises 126 wavebands, covering the wavelength range from 436–2485 nm, with

average spectral resolutions of 15 nm (436–1313 nm), 13 nm (1409–1800 nm) and 17 nm

(1953–2485 nm). The HyMap sensor was selected since this imaging spectrometer is theone most frequently available in South Africa and a HyMap airborne flight campaign is

slated for the study area to extend the research to the airborne level. In addition, the

spectral re-sampling reduced the noise in the canopy reflectance data. The spectral re-

sampling of the ASD data to the HyMap band setting was carried using a Gaussian fitting

algorithm in ENVI (Environment for Visualising Images, Research System, Inc.; V 4.3).

The data analysis followed a heuristic approach. First, we assessed the ability of

foliar nutrients in discriminating between sites using individual nutrients and latent

variables, i.e. principal component scores derived by principal component analysis(PCA). PCA was applied with the assumption that differences between sites may be

explained by a combination of all the foliar nutrients. PCA decomposes the nutrient

data into a few uncorrelated or latent variables that best explain the nutrient data.

Secondly, we evaluated the capability of the spectral features of the measured nutri-

ents and those of water and chlorophyll in discriminating between site qualities.

The spectral features for the various macronutrients were determined by correlat-

ing continuum-removed features and the nutrient concentrations. The continua were

removed for three absorption features centred at 679 nm, 1186 nm and 1730 nm,associated with chlorophyll, water and foliar nutrients (nitrogen, protein, starch,

cellulose and lignin), respectively (Curran et al. 2001) (figure 2). The continuum is

removed by dividing the original reflectance values in an absorption trough by the

corresponding values of the continuum line (Kokaly and Clark 1999). The output

curves have values between zero and one in which the absorption troughs are

enhanced (Schmidt and Skidmore 2001). The band depths (BD) of each point in the

absorption feature were computed using equation (1):

BD ¼ 1� R0 (1)

where R0 is the continuum-removed reflectance value.

A band depth normalization procedure was used to minimize the effects of perturb-

ing factors: leaf water, soil reflectance and atmospheric effects (Kokaly and Clark

1999). The normalized band depths (BNC, equation (2)) within the continuum-

removed absorption bands were calculated by dividing the band depth of each

channel by the band depth at the band centre (BDc,)

BNC ¼ BDð ÞBDð ÞC

(2)

The spectral regions between 1800–1990 nm and 2391–2485 nm were not included

in the analysis because of noise in the reflectance data, caused by atmospheric water

absorption and attenuation of spectral energy at the longer wavelengths, respectively.

Lastly, we assessed the capability of physiology-based vegetation indices (table 1),

including those derived from the raw reflectance, first derivative spectra and


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continuum-removed spectra. The soil-adjusted and atmospherically resistant vegeta-

tion index (SARVI) and Normalized Difference Vegetation Index (NDVI), all proxies

for canopy greenness and leaf area index (LAI) (Haboudane et al. 2004), were

included in the analysis to determine whether LAI could play an important role inspectral differentiation between site qualities.

3. Results

3.1 Explaining differences between site qualities using nutrient concentrations

Among the foliar macronutrients, only K and Ca were significantly different between

sites qualities (table 2), with the Tukey’s HSD post-hoc test showing significant

differences between the good and medium sites. There was no clear increasing or

decreasing gradient of K or Ca concentration from the poor to the good sites. The

highest variance in nutrient concentration accounted for by site quality was observed

for Ca (R2 ¼ 0.12, p , 0.05).

Wavelength (nm)

0.0

0.2

0.4

0.6

0.8

1.0

1

2

3

Ref

lect

ance

Ref

lect

ance

(a)

400 800 1200 1600 2000 2400

Wavelength (nm)400 800 1200 1600 2000 2400

0.0

0.2

0.4

0.6

0.8

1.0

1.2

679 nm

1186 nm

1730 nm

(b)

Figure 2. Continuum removal applied to (a) three absorption features and (b) the resultingcontinuum-removed curve.


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Although principal component 5 (PC5) explained only 8.5% of the total variance in

the nutrient data, it was the only principal component that exhibited significant differ-ences (p , 0.01) between site qualities. Tukey’s HSD post-hoc tests revealed significant

differences between the good and the medium site qualities, a result consistent with those

for K and Ca. N, P and S were not significantly different between site qualities.

3.2 Explaining differences between site qualities using spectral features ofbiochemicals

3.2.1 Spectral features of macronutrients. Table 3 shows the spectral features for

the various macronutrients as determined from their correlations with BNC bands.

Most of the significant BNC bands were located in the 1680–1790 nm spectral range.

The highest correlations were observed for P, N and S. The use of BNC in order tominimize the effect of atmospheric absorption and enhance the spectral signal for

nutrients is illustrated for phosphorus in figure 3. The BNC feature at 1703 nm was

Table 3. Spectral features for various macronutrients as determined from their correla-tions with band depths normalized to band centre (BNC).

Nutrients BNC bands (nm)Pearson correlation

coefficient (r)

N 1740, 1715, 1703, 1776,1690, 1752

-0.55***, -0.53***, -0.52***, 0.43***,-0.42***, -0.30*

P 1703, 1715, 1776, 1740, 1690,1752

-0.61***, -0.54***, 0.52***, -0.51***,-0.44***, -0.30*

K 1715, 1776, 1703 -0.37**, 0.34**, -0.31*S 1740, 1715, 1703, 1776, 1690 -0.42 ***, -0.41**, -0.38**, 0.36**, -0.26*Ca 1752, 1740, 1271 -0.37**, -0.32*, 30*Mg 541, 1128 -0.26*, 25*Na 1157 -0.25*

*p , 0.05, **p , 0.01, ***p , 0.001.

Table 2. One-way analysis of variance (ANOVA) for detection of differences between sitequalities using foliar nutrient concentrations.

Site quality means

NutrientsGood

(n ¼ 25)Medium(n ¼ 25)

Poor(n¼18)

Overallmean

CV(%)

Significanceof differencesbetween sites

(p-values)

Varianceexplained by

site quality (R2)

Tukeypost-

hoc test

N (%) 1.97 2.01 2.02 2.00 9 0.60 0.02P (%) 0.10 0.11 0.11 0.11 15 0.55 0.02K (%) 0.61 0.73 0.70 0.68 23 0.02* 0.11* GvsM*Ca (%) 0.74 0.60 0.76 0.69 29 0.01* 0.12* GvsM*

MvsP*S (%) 0.13 0.13 0.13 0.13 9 0.76 0.01Mg (%) 0.28 0.29 0.30 0.29 20 0.62 0.01Na (mg

g-1)1906 2267 2202 2117 40 0.28 0.04

CV, coefficient of variance; G, good; M, medium; P, poor sites.*p , 0.05.


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Ref

lect

ance

at 1

452

nm

0.0

30.0

50.0

80.1

00.1

30.1

50.1

8

% r

efle

ctan

ce a

t 145

2 nm

–0.4

–0.2

0.0

0.2

0.4

Residuals

Cor

rela

tion:

r =

–0.

12

0.1

00.1

50.2

00.2

50.3

00.3

50.4

00.4

5–0.1

5

–0.1

0

–0.0

5

0.0

0

0.0

5

0.1

0

0.1

5

0.2

0

0.2

5

Residuals

Cor

rela

tion:

r =

0.9

99

0.0

60.0

70.0

80.0

90.1

00.1

10.1

20.1

30.1

40.1

50.1

6

0.0

60.0

70.0

80.0

90.1

00.1

10.1

20.1

30.1

40.1

50.1

6

Folia

r ph

osph

orus

con

cent

ratio

n (%

)

–0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BNC at 1703 nm

R2 =

0.3

7, p

= 0

.00,

y =

1.0

8−5.

56 x

(a)

(b)

Folia

r ph

osph

orus

con

cent

ratio

n (%

)

0.1

0

0.1

5

0.2

0

0.2

5

0.3

0

0.3

5

0.4

0

0.4

5

Reflectance at 1703

R2 =

0.0

02, p

= 0

.70,

y =

0.1

98−

0.18

x

Fig

ure

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linearly related to phosphorus concentration (R2 ¼ 0.37, p , 0.001) and the residuals

of the linear regression were not significantly correlated (Pearson’s r ¼ –0.12) with a

classic atmospheric absorption band at 1452 nm. On the contrary, the untransformed

reflectance at 1703 nm was not significantly correlated with phosphorus (p , 0.05)

and the residuals of the linear regression were highly correlated (Pearson’s r ¼ 0.99,p , 0.05) with the atmospheric absorption at 1452 nm.

3.2.2 Differences between site qualities. Among the spectral features related to the

various macronutrients, only BNC signals at 1740 nm and 1752 nm were significantly

different between site qualities. The post-hoc tests for these bands showed significant

differences between the poor and medium sites, a result similar to that of foliar Ca

concentration (table 4). In fact, among the macronutrients, BNC at 1752 nm was most

correlated with calcium. Furthermore, the highest variance in the spectral features of

the macronutrients, as explained by site quality, was observed for BNC at 1752 nm

(R2 ¼ 0.13, p , 0.05). The nutrient spectral features, in general, did not showconsistent gradients from the poor to the good sites.

All the water-related and chlorophyll red-edge indices exhibited significant differ-

ences between sites. The following gradients were observed (figure 4):

1. the NDWI decreased from the good to the poor sites;

2. the moisture stress index (MSI) and water band index (WBI) increased from the

good to the poor sites;

Table 4. Results of one-way ANOVA for differences between site qualities using spectralfeatures including (1) band depth normalized to band centre following continuum-removal

(BNC) and (2) vegetation indices.

Spectralfeature Biochemicals

Variance explained by sitequality (R2) p-value

Tukey’spost-hoc

BNC1690 P, N, S 0.01 0.70BNC1703 P, N, S, K 0.06 0.15BNC1715 P, N, S, K 0.04 0.26BNC1740 N, P, S, Ca 0.11* 0.02* MvsP*BNC1752 Ca, N, P 0.13* 0.01* MvsP*BNC1776 P, N, S, K 0.02 0.52BNC541 Mg 0.07 0.10BNC1157 Na 0.03 0.34NDWI Water 0.11* 0.02* GvsP*MSI Water 0.12* 0.01* GvsP*WBI Water 0.13* 0.01* GvsP*Carter

indexChlorophyll 0.09* 0.04* GvsP*

REP Chlorophyll 0.13* 0.01* GvsP*NBDI Chlorophyll 0.20** 0.00** GvsP**,

MvsP*PRI Xanthophylls 0.02 0.47NDVI Canopy

greenness, LAI0.05 0.21

SARVI Canopygreenness, LAI

0.06 0.13

G, good; M, medium; P, poor sites.*p , 0.05, **p , 0.01.


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3. the Carter index decreased from the good to the poor sites;4. the red-edge position (REP) shifted towards longer wavelengths from the poor

to the good sites;

5. the normalized band-depth index (NBDI) increased from the good to the poor

sites.

The Tukey’s HSD post-hoc tests for all the water and chlorophyll indices revealed

significant differences between the good and poor sites. NBDI further exhibited

significant differences between the medium and the poor. Moreover, the highest

variance in the vegetation indices, as explained by site quality, was observed for

NBDI (R2 ¼ 0.20, p , 0.01). Lastly, PRI and the canopy-structure indices (NDVI

and SARVI) were not significantly different between sites.

(a) Leaf water indices (b) Leaf chlorophyll indices

Site quality

0.925

0.930

0.935

0.940

0.945

0.950

0.955

Wat

er b

and

inde

x

Site quality

0.33

0.34

0.36

0.38

0.39

0.40

0.42

Site quality

7.5

8.0

8.5

9.0

9.5

10.0

10.5

Car

ter

inde

x

Site quality

0.82

0.83

0.84

0.85

0.86

0.87

0.88

0.89

Nor

mal

ised

Nor

mal

ised

Good Medium PoorGood Medium Poor

Good Medium Poor Good Medium Poor

Good Medium Poor Good Medium Poor

Site quality

714

715

716

717

718

719

720

721

722

723

724

Red

-edg

e po

sitio

n (n

m)

Moi

stur

e st

ress

Site quality

0.04

0.05

0.06

0.06

0.07

0.08

0.09 F(2, 65) = 4.143, p = 0.020

F(2, 65) = 4.0507, p = 0.015

F(2, 65) = 5.013, p = 0.009F(2, 65) = 8.37, p = 0.001

F(2, 65) = 3.296, p = 0.043

F(2, 65) = 5.09, p = 0.009

Figure 4. Relationship between (a) leaf water indices or (b) chlorophyll indices and E. grandissite quality. Vertical bars show 95% confidence intervals.


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4. Discussion

This study demonstrated the abilityofcanopy-level imaging spectroscopy to determinesite

factors that affect growth of E. grandis plantations. Differences in site quality based on

TAW for E. grandis plantations in KwaZulu Natal, South Africa could be detected via

imaging spectroscopy of canopy water content. The spectral signals for leaf water (NDWI,

WBI and MSI) exhibited significant differences between sites. The magnitudes of these

indices showed increasing or decreasing gradients from the poor to the good sites.

However, significant differences were only observed between the good and the poor.

There could be two possible reasons why significant differences were only observedbetween the good and the poor sites: (1) the differences between the good/medium and

medium/poor could be very subtle or (2) the spectral indices used in this study are not

sensitive enough to detect subtle differences between the good/medium or between med-

ium/poor sites. Similar results were observed for chlorophyll indices (Carter index, REP

and NBDI). For example, the red-edge position shifted towards longer wavelengths from

the poor to the good sites, indicating increasing chlorophyll content (Horler et al. 1983,

Curran et al. 1990). As a secondary effect, variations in water stress might have affected the

canopy chlorophyll content and chlorophyll fluorescence emission (Carter 1991, Flexaset al. 2000, Dobrowski et al. 2005, Calatayud et al. 2006, Harris et al. 2006). Variations in

chlorophyll content or fluorescence have been associated with the REP (Zarco-Tejada

et al. 2003, Cho et al. 2008). Rolando and Little (2003) demonstrated that water and light

stress in E. grandis seedlings can be detected using chlorophyll fluorescence data.

The variability in the soil water content could not be detected via canopy-structure

indices (NDVI and SARVI). This could be due partly to the fact that NDVI and

SARVI saturate at high canopy cover (Sellers 1985, Gao et al. 2000, Mutanga and

Skidmore 2004, Cho et al. 2007). The gradient in TAW did not induce significantdifferences in PRI, thus indicating a non-significant role of xanthophyll cycle pig-

ments in predicting site quality in E. grandis at the time of measurement. Gamon et al.

(1992) showed that PRI correlates with the epoxidation state of the xanthophyll cycle

pigments and with the efficiency of photosynthesis in control and nitrogen stress

canopies, but not in water stress canopies undergoing midday wilting.

The macronutrients and their corresponding spectral features were, in general, not

significantly different among sites. K and Ca were the only foliar chemicals that

exhibited significant differences between sites. These constituents showed differencesbetween the good and the medium sites, a trend not consistent with TAW gradient. It

is difficult at this stage to determine if soil macronutrient concentrations accounted

for the variability in the canopy nutrients since they were not measured in this study.

N, P and S were not significantly different among sites. It should be noted that

although leaf nitrogen and chlorophyll are generally correlated, the relationship

depends on the physiological status of the plant (Mooney 1986, Boochs et al. 1990),

e.g. changing from low to high positive correlation with leaf age (Wenjiang et al. 2004)

and becoming inconsistent with leaf senescence (Yoder and Pettigrew-Crosby 1995).The seasonality of the physiology-based indices and the impact this might have on

their ability to discriminate between the sites qualities need to be established.

The study also revealed the capability of continuum-removed spectral features to

provide information on the physiological state of vegetation, thus confirming results

obtained in previous studies (Kokaly and Clark 1999, Curran et al. 2001, Mutanga et al.

2004). The NBDI derived from the red-edge showed the highest potential to differentiate

between sites in this study (table 4). Furthermore, continuum-removed features centred


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on 1730 nm (1680–1790 nm) showed a high potential for minimizing atmospheric

absorption and for enhancing the absorption features of nutrients, as illustrated in figure

3 for phosphorus. Among the measured nutrients, only the absorption features for

nitrogen have been directly linked to the 1680–1790 nm range in previous studies

e.g. at 1690 nm (Curran 1989). Other nutrients associated with the 1680–1790 nmspectral range include proteins, starch and lignin (Curran 1989, Yoder and Pettigrew-

Crosby 1995, Grossman et al. 1996). Thus, the high correlation exhibited between the

BNC at 1703 nm and phosphorus was attributed to the fact that phosphorus is an

important component of energy-rich molecules, adenosine triphosphate (Mooney 1986).

The significant correlation of the BNC band at 1740 nm with sulphur was attributed to

the fact that sulphur is a component of certain protein molecules (Mooney 1986).

Moreover, sulphur was highly correlated with nitrogen in this study (Pearson r¼ 0.80).

To summarize, this study supports growing evidence that hyperspectral remotesensing, accompanied by adequate spectral analysis, has significant potential to

increase our understanding of changes in physiological status and productivity of

ecosystems underpinned by site factors (Asner et al. 2000, Curran et al. 2001). The

integration of site information, derived from remote sensing, into growth models

might enhance the predictive capabilities of these models on a regional basis.

5. Conclusions

The results of this study showed that differences in E. grandis site quality, based on

TAW, could be detected via hyperspectral remote sensing of canopy water and

chlorophyll content. Foliar concentrations of K and Ca exhibited significant differ-

ences between sites, but did not show a consistent decreasing or increasing gradient

from the poor to the rich sites. The study also revealed the capability of continuum-

removed spectral features to provide information on the physiological state of vegeta-

tion. Continuum-removed features centred at 1730 nm (1680–1790 nm) showed

distinct potential for minimizing atmospheric absorption and for enhancing theabsorption features of nutrients such as N, P and S. Furthermore, the spectral

index, NBDI, derived from continuum-removed spectra in the region of the red-

edge, showed the highest potential to differentiate between sites in this study. This

study thus demonstrates the capability of canopy-level hyperspectral remote sensing

for determining site factors that affect growth of E. grandis in an even-aged plantation

in KwaZulu Natal, South Africa. The evaluation of site factors affecting E. grandis

growth is relevant for zoning plantation potential and for establishing silvicultural

methods for site preparation, fertilization and control of competition.

Acknowledgments

The authors would like to thank Mondi Business Paper and the Council for Scientific

and Industrial Research for respectively providing logistical and funding support for

this project. They would also like to thank Dr Mark Norris-Rogers and Mr Marius du

Plessis (Mondi BP) for their assistance during the field data collection.

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Documents

Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation