Botanical Studies (2007) 48: 71-77.

*

Corresponding author: E-mail: cct@gisfore.npust.edu.tw;

Tel: +886-87740301; Fax: +886-87740134.

INTRODUCTION

In recent decades, the development of remote sensing

methods to measure leaf chlorophyll content and surface

spectral reflectance has received much attention, since

variations in leaf chlorophyll content can provide

information concerning the physiological state of a leaf or

plant. Several vegetation indices estimated from remote

sensing data have been considered for assessing the status

of leaf chlorophyll content, plant biomass, production,

and vegetation health status. Destructive methods of leaf

chlorophyll content quantification include traditional

methods using extraction and spectrophotometric or HPLC

measurement, but they are considered time consuming and

expensive. In contrast, spectral reflectance measurements

are nondestructive, rapid, and can be applied across spatial

scales (Gamon and Qiu, 1999). Many theoretical models

have been developed for predicting leaf reflectance from

leaf chlorophyll, plant water content, and vegetation

structure variables (Dawson et al., 1998; Jacquemoud

et al., 1996). Without such extrapolation procedures, it

would be impossible to make landscale and ecosystem

assessments from leaf level analysis. Most large scale

research projects are now using remotely sensed data to

estimate the condition of ecosystems. However, most

relationships between leaf reflectance and chlorophyll

contents have been derived empirically derived.

Sims and Gamon (2002) have reported that spectral

indices provide relatively poor correlations with leaf

chlorophyll content when applied across a wide range

of species and plant functional types. They, therefore,

modified some vegetation indices to demonstrate the

application of spectral indices on species with widely

varying leaf structure. This strategy was applied in this

study and served as an impetus to further analyze leaf

chlorophyll content and surface spectral reflectance in

a wider range of vegetation, using modify vegetation

indices mNDVI (modify normalized difference) and

mSR (modify simple ratio) as test parameters. Normal

Leaf chlorophyll content and surface spectral reflectance

of tree species along a terrain gradient in Taiwan¡¦s

Kenting National Park

Jan-Chang CHEN

1

, Chi-Ming YANG

2

,

Shou-Tsung Wu

3

, Yuh-Lurng CHUNG

4

, Albert Linton

CHARLES

1

,

and Chaur-Tzuhn CHEN

4,

*

1

Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and

Technology, Neipu, Pingtung, Taiwan

2

Research Center for Biodiversity, Academia Sinica, Taipei, Taiwan

3

Department of Tourism Management, Shih Chien University, Kaohsiung, Taiwan

4

Department of Forestry, National Pingtung University of Science and Technology, Neipu, Pingtung, Taiwan

(Received October 21, 2005; Accepted June 27, 2006)

ABSTRACT.

This study was conducted to investigate variations of leaf chlorophyll content and surface

spectral reflectance of different tree species across contrasting terrain in the Nanjenshan Reserve of Kenting

National Park, southern Taiwan. Tree species composition and forest types vary because of intense northeast

monsoons that frequent this area. In this study, we used several remote sensing technique indices¡Xnormalized

difference vegetation index (NDVI), modified normalized difference vegetation index (mNDVI), simple ratio

(SR), and modified simple ratio (mSR)¡Xto analyze the spectral reflectance data collected from portable

spectroradiometers and the GER 1500 and CM1000 chlorophyll meters to estimate leaf chlorophyll content.

The results showed that significant differences (P<0.01) arose only among the modified indices mSR 705 nm

and mNDVI 705 nm. The index mNDVI705 seemed more sensitive to detecting chlorophyll content in a wide

range of tree species across a terrain. Among the indices tested, the mNDVI consistently deviated from the

general relationship between chlorophyll content and spectral reflection in different vegetation. The findings

indicated that the modified indices were better at studying different tree species than normalized indices

across terrain.

Keywords: Leaf chlorophyll; mNDVI; Portable spectroradiometers; Spectral reflectance; Vegetation index.

PHYSIOLOGY

72

Botanical Studies, Vol. 48, 2007

vegetation indices are normally applied when investigating

vegetative differences among similar species, whereas

modified indices are specially designed to investigate

different species (Sims and Gamon, 2002).

Theoretically, terrain difference affects the growth of

vegetation and determines the chlorophyll content and

surface spectral reflectance. Our objective in this study

was to demonstrate how different leaf chlorophyll content

and surface spectral reflectance characteristics are affected

by terrain divergence in Kenting National Park, Taiwan.

MATERIALS AND METHODS

Study area

The experiment was

conducted on site at the

Nanjenshan Reserve of Kenting National Park, southern

Taiwan (Figure 1). In this area, forest vegetation

distribution is influenced by three major topographic

positions or terrains; windward slopes, valley and leeward

slopes (Hsieh and Hsieh, 1990). In the study area, the

winter precipitation and the intensity of northeast winds

Figure 1. Location of the study sites of Nanjenshan Reserve of Kenting National Park, southern Taiwan.

CHEN et al. ¡X Leaf chlorophyll and surface spectral reflectance

73

were found to correlate with the differentiation of forest

types. Those in the northeast district are evergreen

since the wind is rather, whereas, the thorny scrubs and

deciduous scrubs appear in the southwest district due to

severe dry winds. The high species diversity of Kenting

National Park is largely attributable to the heterogeneous

environment (Su and Su, 1988).

Leaf sampling

Measurement of leaf spectral reflectance was obtained

by randomly harvesting leaves from 20 tree species from

the three types of terrain. From each terrain type, one

sampling site from the leeward area and two sampling

sites from the windward and valley areas, respectively,

were selected. Fifty sample leaves taken from three to

nine dominant species were randomly collected from

each sampling site. Sampling was done at the top of the

canopies of each species and was carried out in late April

2005. Leaf samples were stored in plastic bags and kept

cool for further analysis. The leaf samples collected are

listed in Table 1.

Reflectance measurements

All spectral measurements were made with a field

portable spectrometer (GER-1500, SVC, Poughkeepsie,

NY, USA). GER-1500 has a nominal spectral range from

350 to 1050 nm with approximately 1.5 nm nominal

bandwidth. Leaf reflectance was measured with a

bifurcated fiber optic cable and the spectrometer collected

both leaf reflect once and reference reflect once data. Leaf

samples were illuminated by sunlight. All measurements

were carried out in triplicate and all tests in this study

were scaled to leaf level.

Quantification of chlorophyll

Leaf chlorophyll content was estimated by using a

portable chlorophyll meter (CM-1000, Spectrum Tech.,

Plainfield, Ill. USA). CM-1000 readings were made in

situ on the plants designated for harvesting, midway

along the youngest fully expanded leaf. The Minolta

SPAD 502 chlorophyll meter has become recognized as

a reliable substitute for total chlorophyll (Ommen et al.,

1999; Daughtry et al., 2000; Bauerle et al., 2004; Van den

Berg and Perkins, 2004; Netto et al., 2005). However, the

CM1000 chlorophyll meter was utilized and investigated

as a potential substitute for the SPAD meter in applications

with small leaves and where measurements were difficult

to make (i.e., turf). Four tree species (Michelia compressa;

Psychotria rubra; Daphniphyllum glaucescens; Gordonia

axillaris) were selected as test examples of the relationship

between the CM1000 chlorophyll meter and chlorophyll

content measurements, and the method for monitoring

chlorophyll content followed that described in Yang et

al. (1998). As the test results indicated (Figure 2), the

relationship between the CM1000 chlorophyll meter

and chlorophyll content is strong, in that the larger the

indices of the chlorophyll meter are, the higher is the total

chlorophyll content. Therefore, the CM1000 was selected

as the chlorophyll meter for use in this study.

Vegetation indices

The normalized difference vegetation index (NDVI)

(Eqn. 1) (Tucker, 1979) and simple ratio (SR) (Eqn. 2)

were used to calculate the vegetative indices obtained

from spectral reflectance measurements:

(1)

NDVI

705

=

R

750

¡VR

705

R

750

+R

705

(2)

SR

705

=

R

750

R

705

where the wavelengths for NDVI and SR were 705 and

750 nm, respectively, and are based on the chlorophyll

index developed by Gitelson and Merzlyak (1994). R

705

and R

750

are the leaf sample spectral reflectance from

the GER-1500. Based on the results of Sims and Gamon

(2002), SR and NDVI indices may be affected by different

species leaf surfaces. They modified these two indices to

compensate for high leaf surface reflectance, which tends

to increase reflectance across the whole visible spectrum

of a wide range of species. Adding a constant to all

reflectance values reduces both SR and NDVI even when

there is no change in the absorptance of tissues below

Table 1. List of all the species used in this study

a

.

Species

Terrain

Aucuba chinensis Benth.

L

Bischofia javanica

L

Castanopsis carlesii

L

Champereia manillana

L

Cleyera japonica Thumb. var. japonica

L

Daphniphyllum glaucescens Blume subsp. var.

oldhamii (Hemsl.) Huang

L

Dendrocnide meyeniana

L

Gordonia axillaris

V

Ilex cochinchinensis

V

Illicium dunnianum

V

Machilus kusanoi

V

Michelia formosana

W

Neolitsea buisanensis

W

Neolitsea hiiranensis

W

Reevesia formosana Sprague

W

Schefflera octophylla

W

Syzygium euphlebium

W

a

The leaf samples measured for each species of the different

terrains (L= leeward; V= valley; W = windward).

74

Botanical Studies, Vol. 48, 2007

All statistical analyses were conducted using the

STATISTICA statistical software (Version 6.1, StatSoft

Inc. Tulsa, Oklahoma, USA, 2002). Coefficients of

determination (R

2

) were calculated for relationships

between various chlorophyll content values from

CM-1000 (independent variables) and mNDVI and mSR

values from GER-1500 (dependent variable). To test and

verify the relationship of chlorophyll content between

mNDVI and mSR, regression analyses were used in the

first data analysis step. For statistical reasons, we tried to

find out the best index to indicate the differences between

these four indices. The coefficient of variation (CV) was

chosen. To analyze the incidence of the two modified

indices, we used one way ANOVAs for each index,

comparing mNDVI and mSR indices with the CM-1000

index and different terrains separately.

RESULTS AND DISCUSSION

Vegetation indices and chlorophyll content

Results for the regression analysis showed that all of

the vegetation indices of this study positively correlated

with the CM1000 index. The coefficients of determination

(R

2

) calculated for relationships between this index

(independent variable) and the NDVI, SR, mNDVI and

mSR indices (dependent variables) for all samples (n =

117) showed that mSR (R

2

= 0.51) was slightly superior to

SR (R

2

= 0.34) in its correlation with the CM1000 index

the epidermis. They chose R445 as a measure of surface

reflectance and indicated that R

445

is a good reference for

all but the lowest chlorophyll content leaves. Besides, Le

Maire et al. (2004) also pointed out that the two modified

indices gave the best performance of universal broad leaf

chlorophyll indices. The modified indices of NDVI and

SR are as follows (Eqs. 3 and 4):

(3)

mND

705

=

R

750

¡VR

705

R

750

+R

705

¡V2R

445

(4)

mSR

705

=

R

750

¡VR

445

R

750

¡VR

445

Data analysis

The results of a Kolmogorov-Smirnov test (K-S test)

for normality of the CM1000 index were matched with the

normal distribution theory (Lilliefors, 1967). The indices

collected from the CM1000 meter, when fitted to the

normal distribution, all appeared to have the same index

value of 173 for the mean, mode and median statistical

parameters. Based on the above, we reclassified all the

samples (N=1136) into three groups (low, middle and

high) depending on the value of CM1000 indices. The

three groups were classified by the percentage of the total

samples 10%, 80% and 10%.

Figure 2. The relationship between leaf CM1000 meter readings and acetone extractable chlorophyll values measured from Michelia

compressa, Psychotria rubra, Gordonia axillaris and Daphniphyllum glaucescens.

CHEN et al. ¡X Leaf chlorophyll and surface spectral reflectance

75

(Figure 3), and mNDVI (R

2

= 0.59) was stronger than

NDVI (R

2

= 0.45).

Based on the analysis, our findings

were in agreement with Sims and Gamon (2002), in that

the modified indices mSR

705

and mNDVI

705

correlated

better with chlorophyll content. The results of CV showed

that the mNDVI had lowest CV (27%) of the four indices

(Table 2).

Terrain effect

Four indices in this study were computed for the

leaf samples in spectral reflectance measurement in the

three terrains. Each vegetation index (SR

705

, NDVI

705

,

mSR

705

and mNDVI

705

) was used in an analysis of

variance (ANOVA) to determine significant effects of

the three terrains (windward, valley, and leeward). The

results showed significant differences (P<0.01) among

the modified indices mSR

705

and mNDVI

705

, implying

the advantage of using modified vegetation indices over

normal indices on different terrains (Table 3).

The overall results showed that the mNDVI

705

was

successfully applied in this study. The confidence intervals

for leeward, windward, and valley were 0.084, 0.095,

and 0.105 respectively. These results may help us to

understand the variation of mND

705

on different terrains.

Based on the discrepancies of the confidence intervals

in the three terrains, the northeast wind was affected more

than the other environmental factors in the study area.

The degree to which terrain influenced forest composition

Figure 3. The normalized difference index and SR index with index wavelengths of 705 nm (a, SR

705

and b, NDVI

705

) and the

modified indices (c, mSR

705

and d, mNDVI

705

) as a function of the leaf chlorophyll content. As expected from the index analyses, the (c)

mSR index and (d) mNDVI were largely sensitive to chlorophyll content.

Table 2. The coefficient of variation of four vegetation indices

a

.

CM1000 D1

CM1000 D2

CM1000 D3

Mean

Index

Mean ¡Ó S.D. CV %

Mean ¡Ó S.D. CV %

Mean ¡Ó S.D. CV %

CV %

SR

705

1.244 ¡Ó 0.278 22.3 2.189 ¡Ó 0.700 32.0 1.781 ¡Ó 0.571 32.1

28.8

mSR

705

0.099 ¡Ó 0.082 82.2 0.346 ¡Ó 0.129 37.2 0.255 ¡Ó 0.131 51.2

56.9

NDVI

705

2.317 ¡Ó 1.284 55.4 5.176 ¡Ó 1.038 20.1 4.179 ¡Ó 1.293 30.9

35.5

mNDVI

705

0.335 ¡Ó 0.179 53.5 0.666 ¡Ó 0.062 9.4

0.59 ¡Ó 0.101 17.1

26.7

a

N=117. Data shows mean ¡Ó S.D.

76

Botanical Studies, Vol. 48, 2007

was quite clear. A marked difference in forest composition

existed among species in the windward and leeward

forests. In the understory of the windward forest, saplings

and small trees are more abundant than in the leeward

forest (Hsieh et al., 2000). In the study area, winter

precipitation and the intensity of the Northeast wind

positively correlated with differentiation of forest types

(Su and Su, 1988). Monsoon rainforests receive sufficient

rains brought about by the Northeast wind during the

winter, and this supports evergreen trees. Large quantities

of deciduous trees occur in the leeward southwest district

with semi-deciduous forest physiognomy (Su and Su,

1988). Chen (1999) used NDVI to detect seasonal

variation in vegetation greenness with four SPOT XS

images (Systeme Pour l¡¦Observation de la Terre) in

the same study area and reported that leeward (0.438)

and windward (0.441) habitat had the highest degree of

vegetation greenness from June to September. However,

the NDVI between windward (0.241) and leeward (0.352)

from December to March was significantly different

(P>0.01). The study concluded that the northeastern

monsoon would decrease vegetation greenness. Similarly,

our analysis indicated that the northeast monsoon did

certainly affect the vegetation physiology in the study

area, and by using spectral characteristics detected the

differences of terrain variance and also spectral reflection

between vegetation species is possible.

CONCLUSION

We found a positive correlation between terrain and leaf

chlorophyll content, indicating that modified vegetation

indices, such as mNDVI, can help us to understand the

relationship between a native vegetation cover and its

terrain habitat. Among the indices tested, only the mNDVI

consistently deviated from the general relationships

between chlorophyll content and spectral reflection in

different vegetation. The ecological significance of this

study is based on the speed and efficiency by which

modified vegetation indices can detect chlorophyll content

of a complex forest vegetation. In this study, we have

shown that among the indices tested, mNDVI detects the

best in different terrain vegetation reflection. Nonetheless,

further investigative work remains to be carried out,

especially in leaf chlorophyll contents and remote sensing

applications.

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Table 3. The variance of analysis of four vegetation indices between three terrains.

Index

Terrain

Mean

sd

-95%

95%

Range

F

P

SR

705

Leeward

1.781 0.078

1.628

1.935

0.307

Valley

1.669 0.097

1.478

1.860

0.382

0.412 0.663

Windward 1.740 0.088

1.567

1.913

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NDVI

705

Leeward

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0.280

0.071

Valley

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0.275

0.088

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Windward 0.221 0.020

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0.261

0.08

mSR

705

Leeward

4.346

a

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3.968

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0.755

Valley

3.311

bc

0.238

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3.780

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Windward 3.790

ac

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4.217

0.853

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705

Leeward

0.587

a

0.021

0.545

0.629

0.084

Valley

0.477

b

0.026

0.425

0.530

0.105

6.304 0.002**

Windward 0.502

b

0.024

0.454

0.549

0.095

**Indicates significance different among the indices, P<0.01.

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