a new vegetation index based on the universal pattern ... new... · this study examined a new...

A new vegetation index based on the universal pattern decompositionmethod

LIFU ZHANG*{, S. FURUMI{, K. MURAMATSU{§, N. FUJIWARA""{,

M. DAIGO" and LIANGPEI ZHANG{{National Key Laboratory for Information Engineering in Surveying, Mapping, and

Remote Sensing, Wuhan University, Wuhan, 430079, China

{Laboratory of Nature Information Science, Department of Information and Computer

Sciences, Nara Women’s University, Nara, 630-8506, Japan

§KYOUSEI Science Center for Life and Nature, Nara Women’s University, Nara,

630-8506, Japan

"Department of Economics, Doshisha University, Kyoto, 602-8580, Japan

(Received 6 June 2005; in final form 11 October 2005 )

This study examined a new vegetation index, based on the universal pattern

decomposition method (VIUPD). The universal pattern decomposition method

(UPDM) allows for sensor-independent spectral analysis. Each pixel is expressed

as the linear sum of standard spectral patterns for water, vegetation and soil, with

supplementary patterns included when necessary. Pattern decomposition

coefficients for each pixel contain almost all the sensor-derived information,

while having the benefit of sensor independence. The VIUPD is expressed as a

linear sum of the pattern decomposition coefficients; thus, the VIUPD is a

sensor-independent index. Here, the VIUPD was used to examine vegetation

amounts and degree of terrestrial vegetation vigor; VIUPD results were

compared with results by the normalized difference vegetation index (NDVI),

an enhanced vegetation index (EVI) and a conventional vegetation index based

on pattern decomposition (VIPD). The results showed that the VIUPD reflects

vegetation and vegetation activity more sensitively than the NDVI and EVI.

Keywords: Vegetation index; Hyperspectral data; Sensor-independent; Sensitivity

analysis

1. Introduction

Vegetation indices (VIs) have the property of being sensitive to a variety of

biophysical vegetation canopy parameters, such as leaf area index (LAI), fraction

vegetation cover, leaf angle distribution and leaf chlorophyll concentration (Huete

et al. 1997). Researchers have used vegetation indices to quantify green-leafvegetation and to monitor major vegetation fluctuations and associated environ-

mental effects. Studies involving land-cover classifications, environmental monitor-

ing and deforestation, desertification, or drought brought on by climate change,

"" Present address: Laboratory of Nature Information Science, Department ofInformation and Computer Sciences, Nara Industrial University, Nara, Japan.

*Corresponding author. Email: [email protected]. Present address:Institute of Remote Sensing and Geographical Information System, Peking University,Beijing, 100871, China.

International Journal of Remote Sensing

Vol. 28, No. 1, 10 January 2007, 107–124

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160600857402

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

have employed VIs (Gonzalez-Alonso et al. 1997, Sannier et al., 2002, Goward et al.

2002). Various VIs have been developed for specific research objectives (Jordan

1969, Richardson et al. 1977, Tucker 1979, Huete et al. 1988, 1997 2002).

VIs are spectral transformations of two or more bands designed to enhance

vegetation properties and allow for reliable representations of photosynthetic

activity and structural canopy variations (Huete et al. 2002). A commonly used

index is the normalized difference vegetation index (NDVI), given as follows

NDVI~rNIR{rred

rNIRzrred

ð1Þ

The NDVI is an important component of local, regional and global vegetation

change studies; however, it uses only red and near infrared reflectance data (Nemani

et al. 1993). The enhanced vegetation index (EVI) uses the red and near infrared

bands, and also includes blue-band reflectance data to correct for aerosol influences

in the red band, and some other aerosol resistance coefficients (Huete et al. 2002).

The EVI is given as follows

EVI~GrNIR{rred

rNIRzC1|rred{C2|rbluezLð2Þ

where r values are atmospherically corrected or partially atmospherically corrected

(e.g., for Rayleigh and ozone absorption) surface reflectances; L is the canopy with

background adjustment addressing nonlinear, differential near infrared (NIR) - and

red (R) -band radiant transfer through the canopy; and C1, C2 are aerosol resistance

coefficients that use the blue band to correct for red-band aerosol influences. The

coefficients of the EVI algorithm are L51, C156, C257.5, and G52.5, where G is

the gain factor (Huete et al. 1994, 1997).

The above methods, namely broadband indices, use either two or three satellite-

observed wavelength bands, or require some additional coefficient inputs. The

broadband indices use average spectral information over broad bandwidths,

resulting in loss of critical information available in specific narrow bands

(Blackburn 1998). In addition, the broadband indices are known to be heavily

affected by soil background at low vegetation cover (Elvidge et al. 1985, Baret et al.

1991). Hyperspectral sensors measure reflectance in a large number of narrow

wavebands, generally with bandwidths of less than 10 nm. With these narrow bands,

reflectance and absorption features related to specific crop physical and chemical

characteristics can be detected (Strachan et al. 2002). The most effective band

wavelengths centred near 820, 1040, 1200, 1250, 1650, 2100 and 2260 nm with

bandwidths ranging from 10–300 nm (Gong et al. 2003). VIs derived from the R and

NIR bands is unsuitable for hyperspectral data analysis.

Hayashi et al. (1998) developed a new vegetation index based on the pattern

decomposition method (VIPD). The pattern decomposition method (PDM) is a type

of spectral mixing analysis (Adams et al. 1995) in which each pixel is expressed as the

linear sum of fixed standard spectral patterns for water, vegetation, and soil:

VIPD~

Cv{Cs{ Ss

�Pni~1

Ai

� �CwzSs

� �

SvzSsð Þ ð3Þ

where Cw, Cv, Cs are coefficients for water, vegetation, and soil, respectively; Sv, Ss

108 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

represent the total reflectance of vegetation and soil, respectively; and Ai refers to

the reflectance value of band i. (Hayashi et al. 1998).

Our analyses showed that the VIPD is more sensitive than the NDVI for

determining the vegetation area ratio, vertical vegetation thickness, and vegetation

type (e. g., broad leaf or needle leaf) when using Global Imager (GLI) data (Hayashiet al. 1998). However, since the PDM has sensor-dependent parameters, and the

constant values of Sv and Ss are sensor dependent, it is difficult directly to compare

results obtained using data from different sensors (Zhang et al. 2003).

Thus, we developed a sensor-independent universal pattern decomposition

method (UPDM) for hyper-multi-spectral data analysis (Zhang et al. 2006).

Analysis results demonstrated that the four coefficients (i.e. three standard pattern

decomposition coefficients and a supplementary coefficient) calculated by the

universal pattern decomposition method are sensor independent. We have now

developed a new vegetation index that is based on the universal pattern

decomposition method, which we call the VIUPD, and we publish it here for the

first time. This vegetation index based on the UPDM has many benefits over theconventional VIPD. The VIUPD is defined as a linear sum of the pattern

decomposition coefficients but is sensor independent. In the present paper, we

compare how our new vegetation index (VIUPD), the NDVI, the EVI, and the

VIPD represent the relationships between photosynthesis, the vegetation area ratio,

and the number of overlapping leaves.

2. A vegetation index (VIUPD) based on the UPDM and multi-spectral data

2.1 The universal pattern decomposition method (UPDM)

We developed a universal pattern decomposition method (Zhang et al. 2005) in

which reflectance (or brightness) data for each pixel observed by a sensor are

decomposed into standard spectral patterns of water, vegetation and soil as follows:

R ið Þ?Cw:Pw ið ÞzCv

:Pv ið ÞzCs:Ps ið Þ ð4Þ

where R(i) is the reflectance of band i measured on the ground (or by satellite sensor)

for any sample (or any pixel), and Cw, Cv and Cs are the decomposition coefficients.

For some studies, a UPDM with only three components is adequate; about 95.5%

of land-cover spectral reflectance information can be transformed into the three

decomposition coefficients and decomposed into the three standard patterns with

about 4.2% error per degree of freedom (Daigo et al. 2004). However, other studies

may require more detailed analysis of vegetation change, for example, to study

vegetation changes in more detail (Zhang et al. 2006). Thus, we added a yellow-leaf

coefficient as a supplementary spectral pattern, changing equation (4) to

R ið Þ?Cw:Pw ið ÞzCv

:Pv ið ÞzCs:Ps ið ÞzC4

:P4 ið Þ ð5Þ

where C4 represents the supplementary pattern coefficients of a yellow leaf, and

Pw(i), Pv(i), Ps(i), and P4(i) are the standard spectral patterns of water, vegetation,

soil, and the supplementary yellow-leaf pattern for band i (i represents the sensor

band numbers), intercepted from Pk(l) (k5w, v, s, 4) as follows:

Pk lð Þ~Ð

dlÐRk lð Þj jdl

Rk lð Þ ð6Þ

where the discrete band number i in equation (5) is changed into continuous

A new vegetation index 109

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

wavelength l; Pk(l) values are the spectral reflectance patterns of standard objects;Ðdl refers to integration of the total wavelength range The shapes and magnitudes

of the standard patterns Pk(l) are fixed for any sensor(Zhang et al. 2006). The P4(l)

value is defined using the yellow-leaf residual and the three components as follows

P4 lð Þ~ r4 lð ÞÐ

dlÐr4 lð Þj jdl

ð7Þ

where r4(l) is the residual value for a yellow leaf for the i band

r4 lð Þ~R4 lð Þ{ CwPw lð ÞzCvPv lð ÞzCsPs lð Þf g ð8Þ

R4(l) is the measured value of the sample yellow leaf, and r4(l) is the residual value.

The standard patterns are normalized as followsðPk lð Þj jdl~

ðdl k~w, v, s, 4ð Þ ð9Þ

We intercepted Pk(l) values for each sensor. The four standard patterns for each

sensor are defined by

Pk ið Þ~Ð le ið Þ

ls ið Þ Pk lð ÞdlÐ le ið Þls ið Þ dl

k~w, v, s, 4ð Þ ð10Þ

where ls(i) and le(i) are the start and end wavelengths of sensor band i, respectively,

andÐ le ið Þ

ls ið Þ dl is the wavelength width of band i.

2.2 Vegetation index VIUPD for multi-spectral data

Vegetation indices play an important role in monitoring global surface vegetation

change. Ideally, a vegetation index should reflect the amount of vegetation and the

degree of vegetation vigor. A vegetation index based on pattern decomposition

(VIPD) expresses the linear sum of the three pattern decomposition coefficients

(Hayashi et al. 1998) and is linear to the vegetation area ratio (Furumi et al. 1998).

However, the VIPD has sensor-dependent parameters. We thus developed a new

vegetation index that was based on four pattern decomposition coefficients (i.e. a

supplemental spectral pattern plus the three standard patterns) (Daigo et al. 2004).

The new vegetation index (or revised VIPD) was normalized by total reflectance or

total brightness to minimize shadow effects and obtain stable values. However, the

index still used conventional pattern decomposition coefficients.

We have now redefined the vegetation index by producing a vegetation index that

is based on a universal pattern decomposition method (VIUPD). The index is a

function of the linear combination of the pattern decomposition coefficients. The

formula is given as follows

VIUPD~Cv{a|Cs{C4ð Þ

CwzCvzCsð11Þ

where (Cw + Cv + Cs) represents the sum of total reflectance, even an supplementary

pattern is included, since the integrated value ofÐ

r4 lð Þdl equal zero (Zhang et al.

2006, Daigo et al. 2004), and a is the coefficient of standard soil pattern coefficients.

110 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

The Cs term in the numerator is a correction term for dead vegetation, because

the spectral pattern for dead vegetation contains a small portion of the

vegetation pattern. We determined parameter a so that the average VIUPD

value for dead vegetation equals zero. For standard vegetation, the VIUPD value

equals 1.

As mentioned above, a vegetation index should be sensitive to photosynthesis.

Plants transform sunlight to chemical energy by photosynthesis. During this

process, plants fix carbon dioxide and release oxygen while coping with water loss.

Photosynthesis measurements are necessary to understand productivity (biomass

accumulation) at leaf, plant, or community levels, as well as vegetative responses to

environmental stresses (Kelly and Lalzko 1995). Satellite remote sensors can

quantify the fraction of photosynthetically active radiation (PAR) absorbed by

vegetation. Research has found that net photosynthesis directly relates to the

amount of PAR absorbed by plants. In short, the more visible sunlight a plant

absorbs (during the growing season), the more the plant photosynthesizes, and the

more productive the plant is. Therefore, as a leaf turns from green to brown (i.e. as

the leaf dies) photosynthesis decreases until finally reaching zero. In response, CO2

absorption also decreases to zero (Taiz and Zeiger 1998).

3. Data used in this study

3.1 Reflectance data

Sample reflectance data used in this analysis were measured outdoors under solar

light or indoors under a halogen lamp with a Field Spec FR (Analytical Spectral

Devices, Inc.) or MSR7000 (Opto Research Corp.) radio-spectrometer. Both radio-

spectrometers give raw spectral values every 1 nm for wavelengths of 350 to

2500 nm. Spectral resolution ranges from 3 to 10 nm with a 1-degree field of view.

After measuring spectra for each sample and for a standard white board, reflectance

was calculated from the raw spectral digital values of each sample divided by those

of the standard white board. We measured about 600 samples, including leaves, soil,

water, concrete and a sandy beach.

3.2 Photosynthesis data

We used a LI-6400 photosynthesis open system to measure light photosynthesis. In

this system, an air stream with a known CO2 concentration is constantly passed

through the leaf chamber. The air exiting the chamber will have a lower CO2

concentration than the air entering the chamber. We recorded changes in CO2 values

and the photosynthetic active radiation (PAR) using an LED light sensor. The

carbon dioxide concentration in the chamber was about 350 ppm to match the

surrounding air, and the air temperature in the chamber was about 28 uC (Furumi

et al. 2005). Figure 1 shows three typical results for absorbed CO2 and reflectance

for a green leaf (No. 1), yellow leaf (No. 2), and yellow–green leaf (No. 3) of Ginkgo

biloba tree.

Table 1 lists the photosynthesis data used in this study. In this table, the sample

leaves that we measured included Ginkgo biloba leaves and Magnolia praecocissima

leaves, the CO2 absorption values are correspond to Pmax, which is fitted by the least

square regression using the original measured data; the equation is as

follows(Furumi et al. 1998, 2005):


Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

P PARð Þ~ Pmax:b:PAR

Pmaxzb:PARð12Þ

where PAR represents the photosynthetic active radiation, and Pmax is the maximum

photosynthesis in a saturated region, b refers to the parameter that controls the

degree of the fitted curve. When PAR tends to ‘, the CO2 absorption values are

approximately equal to Pmax.

Figure 1. (a) Original reflectance patterns of three typical leaves: (b) the relationshipbetween PAR and CO2 absorption.

112 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

3.3 Other data

To verify the relationship between the VIs and the actual vegetation situation and

activity, we measured reflectance, CO2 absorption, and some other data, such as

vegetation area ratios (percent vegetation cover) and the number of overlappingleaves.

For the vegetation area ratio, the leaves that we used were obtained from

Cinnamonum camphora trees, and the soil was collected from the grounds of Nara

Women’s University of Japan and dried (Hayashi et al. 1998). We measured the

reflectance values with changes in the vegetation area ratio from 0% to 100%. LAI isdefined as the total one-sided area of all leaves in the canopy within a defined region

(m2/m2). Direct measure of canopy LAI is relatively accurate but extremely labour-

intensive and destructive (Gong et al. 2003). In the current study, we measured the

reflectance values with the changes of the number of overlapping leaves; we recorded

the reflectance of overlapping leaves obtained from Cinnamonum camphora trees using

1 to 10 overlapping leaves, and regard the value of overlapping number as LAI values,

then study the relationship between the numbers of overlapping leaves and VIs.

4. Results and discussion

4.1 Determination of VIUPD parameters

The spectral patterns of dead vegetation resemble soil patterns, but also contain a

small amount of the vegetation pattern. Parameter a in equation (11) compensates

for the vegetation pattern within the dead vegetation pattern. Parameter a was

determined so that the average VIUPD value for 46 samples of ground-measureddead leaves was nearly zero, in this case, the value of a was 0.10. For this result,

equation (11) is as follows:

VIUPD~Cv{0:10|Cs{C4ð Þ

CwzCvzCsð13Þ

4.2 Relationship between vegetation indices and the vegetation area ratio

A number of previous studies have shown that the VIPD is linear to the vegetationarea ratio (e.g., Furumi et al., 1998). Figure 2 shows the relationship between

various kinds of VI and the vegetation area ratio for the sample data listed in table 1.

Table 1. Sample leaves for photosynthesis.

Samplevumber

Sampleattribute

CO2 absorptionmmol/(m2?s)

Samplenumber

Sampleattribute

CO2 absorptionmmol/(m2?s)

532 Green leaf 2.05 552 Green leaf 1.62534 Yellow leaf 20.17 553 Green leaf 12.58536 Yellow green 1.30 554 Green leaf 14.59538 Green leaf 3.56 555 Green leaf 12.60540 Yellow green 0.82 556 Green leaf 10.47542 Yellow leaf 20.22 557 Green leaf 7.69544 Yellow leaf 20.28 558 Green leaf 10.78546 Green leaf 6.10 596 Green leaf 10.62548 Green leaf 4.69 598 Green leaf 10.45550 Yellow leaf 1.31


Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

In the figure, diagram A shows the relation between VIUPD and vegetation area

ratio, with the quadratic line fitted by least squares regression using the equation

f(x)5ax2 + bx + c; diagrams B, C, and D show relationships for the vegetation area

ratio and VIPD, EVI and NDVI , respectively.

Figure 2 shows that the quadratic coefficients of VIPD and VIUPD are smaller

than those of EVI and NDVI by one order of magnitude. Thus, VIPD and VIUPD

have greater linear correlation with the vegetation area ratio than EVI and NDVI.

That means the VIPD and VIUPD are more sensitive to the vegetation area ratio

(percent vegetation cover) than the other vegetation indices do.

4.3 Relationship between vegetation indices and gross photosynthetic production

When sunlight strikes objects, certain wavelengths are absorbed and other

wavelengths are reflected. Chlorophyll in plant leaves strongly absorbs visible light

(from 0.4 to 0.7 nm) for use in photosynthesis. The leaf cell structure, on the other

hand, strongly reflects near-infrared light (from 0.7 to 1.1 nm) (Carter 1991, Taiz

Figure 2. Relationships between VIs and the vegetation area ratio.

114 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

et al. 1998). The quantity of absorbed CO2 can reflect vegetation vigor. Plants with

higher CO2 absorption rates can be expected to have greater vegetation index values

(Hayashi et al. 1998).

Figure 3 shows the relationship between various kinds of vegetation indices and

gross photosynthetic production measured on the ground, as described in section 3.

The solid lines in the figures represent regression results by the equation

f(x)5ax2 + bx + c. The VIUPD showed a much smaller a coefficient value (6.25)

than did the VIPD, EVI and NDVI. These results suggest that the VIUPD has a

greater linear correlation with gross photosynthetic production than do the VIPD,

EVI, and NDVI. VIUPD has a more sensitivity to gross photosynthetic production

than the others do, which is a vital advantage while using it for NPP estimation, etc.

4.4 Relationship between vegetation indices and number of overlapping leaves

As described above, the more leaves a plant has, the more light will be reflected as a

function of wavelength. A vegetation index should be sensitive to the density or

Figure 2. (Continued.)


Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

thickness of surface vegetation (Huete 1988). In this study, we verified the relation

between the number of overlapping leaves and the VIs. Figure 4. shows the

relationships between the VIs and number of overlapping leaves. The solid lines

represent regression results using the formula f(x)5a(12e2bx), the horizontal axes

represent the number of overlapping leaves of Cinnamonum camphora trees. The

results show that the b coefficient values are smaller for the VIUPD than for the

VIPD, EVI and NDVI. Thus, the VIUPD is more sensitive than the other indices to

the number of overlapping leaves. The maximum overlapping leaves numbers before

VIs tend to saturation correspond to figure 4 are listed in table 2.

4.5 Relationships between vegetation indices

We compared the VIUPD with the EVI, NDVI and VIPD using various green-leaf,

yellow-leaf, dead-leaf, soil, and water samples for a CONTINUE sensor; to

calculate the VIs we used the blue (481.0–490.0 nm), red (651.0–660.0 nm) and NIR

(811.0–820.0 nm) bands. Figure 5 illustrates the relationships between selected VIs

Figure 3. Relationships between VIs and gross photosynthetic production (m mol CO2/m2/s).

116 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

and the VIUPD. Figure 5(a) shows the relationship between the EVI and VIUPD

for about 600 samples; diagram B shows the relationship between the NDVI and

VIUPD, and figure 5(b) shows the relationship between the VIPD and VIUPD.

Figure 5 demonstrates the sensitivity of all VIs to water, vegetation, and soil. For

one part of the yellow-leaf and dead-leaf patterns, the NDVI and EVI show

opposite values, that is, some dead-leaf values are bigger than those of yellow-leaves.

Since the NDVI and EVI use only two wavelengths (i.e., red and near infrared

bands), reflectance corresponding to the two bands is nearly the same for typical

yellow leaves, while for typical dead leaves, the reflectance for the red band is

smaller than that for the infrared band, as shown in figure 6. Thus, some of the VI

values for dead and yellow-leaves shown in figure 5 are reversed. For soil, the VIPD

has large positive values, which are compatible with yellow leaves.

Figure 5 show that among EVI, NDVI, VIPD, and VIUPD, only VIUPD gives

the expected order from green-leaf to water. That is, VIUPD is the best vegetation

index among EVI, NDVI, VIPD and VIUPD.



Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

5. Summary and conclusions

We developed a universal pattern decomposition method to obtain sensor-

independent pattern decomposition coefficients. In the current paper, a new

vegetation index, based on the UPDM, was examined. To investigate how well the

method measured vegetation change, we ground-measured about 600 samples,

including green-leaf, yellow-leaf, dead-leaf, soil, water, and concrete samples. In

addition to the three standard spectral patterns, we used a supplemental yellow-leaf

spectral pattern to study vegetation change in detail.

The vegetation index, based on the universal pattern decomposition index

(VIUPD), reflects the linear sum of the four pattern decomposition coefficients. The

VIUPD reflected vegetation concentrations, the amount of CO2 absorption, and the

degree of terrestrial vegetation vigor more sensitively than did the NDVI and EVI,

and was especially sensitive to CO2 absorption. The NDVI and EVI became

more rapidly saturated as a function of PAR. Two or three reflectance bands are

Figure 4. Relationships between the VIs and the number of overlapping leaves.

118 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

used to calculate EVI and NDVI, while the VIUPD and VIPD use multi-spectral

satellite- and ground-measured reflectance data. The VIUPD is computed using four

UPDM coefficients, that is, the VIUPD is an linear function of Cw, Cv, Cs, C4, and is

normalized with the total reflectance value (Zhang et al. 2006), the four coefficients

of UPDM are sensor-independent, thus the VIUPD take over the sensor-

independent characteristic of UPDM coefficients. The traditional broadband

vegetation indices usually constructed with near-infrared (NIR) and red (R) bands

Table 2. The overlapping leaves number before VIs tend to saturation.

Vegetation indicesThe overlapping leaves number before VIs tend to

saturation

VIUPD 4VIPD 3EVI 3NDVI 2



Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

(Blackburn 1998)., while the VIUPD is computed with the all observed wavelengths.

Thus the VIUPD is more suitable for multi-spectral analysis than the EVI, NDVI

and VIPD.

A wide range of satellite sensors has been used to construct vegetation indices,

from high resolution, narrow swath instruments such as Landsat-TM and

SPOT-HRV to lower resolution, wide swath instruments such as NOAAAVHRR

Figure 5. Relationships between selected VIs and the VIUPD.

120 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

and SPOT-VEGETATION (Steven et al. 2003). The value of the vegetation index is

critically dependent on the form of the data used to calculate it. Such dependencies

are extremely disadvantageous for global change research (Teillet et al. 1997). There

Figure 6. Spectral patterns of a typical vegetation sample.



Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

is therefore a strong incentive to combine data from more than one observing system

in order to fill the gaps in observation. For example, standardize vegetation indices

from different systems (Gallo and Daughtry 1987, Goetz 1997, Teillet et al. 1997).

Using hyper multi-spectral data, the VIUPD can provide sensor-independent

physical values and allow for direct comparisons using data from various sources.

Future VIUPD research will concentrate on applications using MODIS data and

ETM data acquired over the Three Gorges region of China, and will compare

VIUPD results with EVI and NDVI results.

Acknowledgements

This work was supported under the ADEOS-II/GLI Project of the Japan Aerospace

Exploration Agency (JAXA), the Academic Frontier Promotion Project of the

Ministry of Education, Science, Sports, and Culture of Japan, and the 973

Project of the People’s Republic of China (Project No. 2003CB415205). This paper

was partially funded by China Postdoctoral Science Foundation (Project

No. 2005038002).

ReferencesADAMS, J.B., SABOL, D.E., KAPOS, V., FILHO, R.A., ROBERTS, D.A., SMITH, M.O. and

GILLESPIE, A.R., 1995, Classification of multispectral images based on fractions of

endmembers: Application to land-cover change in the Brazilian Amazon. Remote

Sensing of Environment, 52, pp. 137–154.

BARET, F. and GUYOT, G., 1991, Potentials and limits of vegetation indices for LAI and

APAR assessment. Remote Sensing of Environment, 35, pp. 161–173.

BLACKBURN, G.A., 1998, Quantifying chlorophylls and caroteniods at leaf and canopy scales:

An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66,

pp. 273–285.

CARTER, G.A., 1991, Primary and secondary effects of water content of the spectral

reflectance of leaf. American Journal of Botany, 78, pp. 916–924.

DAIGO, M., ONO, A., FUJIWARA, N. and URABE, R., 2004, Pattern decomposition method for

hyper-multi-spectral data analysis. International Journal of Remote Sensing, 25, pp.

1153–1166.

ELVIDGE, C.D. and LYON, R.J.P., 1985, Influence of rock-soil spectral variation on the

assessment of green biomass. Remote Sensing of Environment, 17, pp. 265–269.

FUJIWARA, N., MURAMATSU, K., AWA, S., HAZUMI, A. and OCHIAI, F., 1996, Pattern

expansion method for satellite data analysis (in Japanese). Journal of Remote Sensing

Society of Japan, 17, pp. 17–37.

FURUMI, S., HAYASHI, A., MURUMATSU, K. and FUJIWARA, N., 1998, Relation between

vegetation vigor and new vegetation index based on pattern decomposition method.

Journal of Remote Sensing Society of Japan, 18, pp. 17–34.

FURUMI, S., XIONG, Y. and FUJIWARA, N., 2005, Establishment of an algorithm to estimate

canopy photosynthesis by pattern decomposition using multi-spectral data. Journal of

Remote Sensing Society of Japan, 25, pp. 47–59.

GALLO, K.P. and DAUGHTRY, C.S.T., 1987, Differences in vegetation indices for simulated

landsat-5 MSS and TM, NOAA-9 AVHRR and SPOT-1 sensor systems. Remote

Sensing of Environment, 23, pp. 439–452.

GONG, P., PU, R., BIGING, G.S. and LARRIEU, M.R., 2003, Estimation of forest leaf area

index using vegetation indices derived from hyperion hyperspectral data. IEEE

Transaction on Geoscience and Remote Sensing, 41, pp. 1355–1363.

GONZALEZ-ALONSO, F., CUEVAS, J.M., CASANOVA, J.L., CAILLE, A. and ILLERA, P., 1997, A

Forest fire risk assessment using NOAA-AVHRR images in the Valencia area, eastern

Spain. International Journal of Remote Sensing, 18, pp. 2201–2207.

122 L. Zhang et al.

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

GOWARD, S., XUE, Y. and CZAJKOWSKI, K.P., 2002, Evaluating land surface moisture

conditions from the remotely sensed temperature/vegetation index measurements. An

exploration with the simplified simple biosphere model. Remote Sensing of

Environment, 79, pp. 225–242.

GOETZ, S.J., 1997, Multi-sensor analysis of NDVI, surface temperature and biophysical

variables at a mixed grassland site. International Journal of Remote Sensing, 18, pp.

71–94.

HAYASHI, A., MURAMATSU, K., FURUMI, S., SHIONO, Y., FUJIWARA, N. and DAIGO, M., 1998,

An algorithm and a new vegetation index for ADEOS-II/GLI data analysis. Journal

of Remote Sensing Society of Japan, 18, pp. 28–50.

HUETE, A.R., 1988, A soil adjusted vegetation index (SAVI). Remote Sensing of Environment,

25, pp. 295–309.

HUETE, A., DIDAN, K., MIURA, T., RODRIGUEZ, E.P., GAO, X. and FERREIRA, L.G., 2002,

Overview of the radiometric and biophysical performance of the MODIS vegetation

indices. Remote Sensing of Environment, 83, pp. 195–213.

HUETE, A., JUSTICE, C. and LIU, H., 1994, Development of vegetation and soil indices for

MODIS-EOS. Remote Sensing of Environment, 49, pp. 224–234.

HUETE, A., LIU, H.Q., BATCHILY, K. and VAN LEEUWEN, W.J.D., 1997, A comparison of

vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing

of Environment, 59, pp. 440–451.

JORDAN, C.F., 1969, Derivation of leaf area index from quality of light on the forest floor.

Ecology, 50, pp. 663–666.

KELLY, G.J. and LALZKO, E., 1995, Photosynthesis (Berlin: Springer Verlag).

MURAMATSU, K., FURUMI, S., FUJIWARA, N., HAYASHI, A., DAIGO, M. and OCHIAI, F., 2000,

Pattern decomposition method in the albedo space for Landsat TM and MSS data

analysis. International Journal of Remote Sensing, 21, pp. 99–119.

NEMANI, R., PIERCE, L., RUNNING, S. and BAND, L., 1993, Forest ecosystem processes at the

watershed scale: Sensitivity to remotely-sensed leaf area index estimates. International

Journal of Remote Sensing, 14, pp. 2519–2534.

RICHARDSON, A.J. and WIEGAND, C.L., 1977, Distinguishing vegetation from soil background

information. Photogrammetric Engineering and Remote Sensing, 43, pp. 541–1552.

SANNIER, C.A.D., TAYLOR, J.C. and DU PLESSIS, W., 2002, Real-time monitoring of

vegetation biomass with NOAA-AVHRR in Etosha National Park, Namibia, for fire

risk assessment. International Journal of Remote Sensing, 23, pp. 71–89.

STRACHAN, I.B., PATTEY, E. and BOISVERT, J.B., 2002, Impact of nitrogen and environmental

conditions on corn as detected by hyperspectral reflectance. Remote Sensing of

Environment, 80, pp. 213–224.

STEVEN, M.D., MALTHUS, T.J., BARET, F., XU, H. and CHOPPING, M.J., 2003,

Intercalibration of vegetation indices from different sensor systems. Remote Sensing

of Environment, 88, pp. 412–422.

TAIZ, L. and ZEIGER, E., 1998, Plant Physioloy, 2nd edition (Sunderland: Sinauer

Assocoates).

TEILLET, P.M., STAENZ, K. and WILLIAMS, D.J., 1997, Effects of spectral, spatial, and

radiometric characteristics on remote sensing vegetation indices of forested regions.

Remote Sensing of Environment, 61, pp. 139–149.

TUCKER, C. and COMPTON, J., 1979, Red and photographic infrared linear combinations for

monitoring vegetation. Remote Sensing of Environment, 8, pp. 127–150.

ZHANG, L., MITSUSHITA, Y., FURUMI, S., MURAMATSU, K., FUJIWARA, N., DAIGO, M. and

ZHANG, L., 2003, Universality of modified pattern decomposition method for satellite

sensors. In Asia GIS Conference, Wuhan, 8–11 October 2003. Available online at:

www.hku.hk/cupem/asiagis/publications.htm (accessed 8 April 2005).

ZHANG, L., FURUMI, S., MURUMATSU, K., FUJIWARA, N., DAIGO, M. and ZHANG, L., 2006,

Sensor-independent analysis method for hyperspectral data based on the pattern


Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

decomposition method. International Journal of Remote Sensing, In production, 27,

pp. 4899–4910.

ZHANG, L., ZHANG, L., FUJIWARA, N. and MURUMATSU, K., 2005, The universal pattern

decomposition method based on the hyperspectral satellite remotely revised date (in

Chinese). Journal of Geomatics and Information Science of Wuhan University, China,

30, pp. 264–268.

124 A new vegetation index

Dow

nloa

ded

by [

Hon

g K

ong

Poly

tech

nic

Uni

vers

ity]

at 1

8:06

30

Dec

embe

r 20

11

a new vegetation index based on the universal pattern ... new... · this study examined a new...

Documents