DOI 10.4010/2016.1338
ISSN 2321 3361 © 2016 IJESC
Research Article
Volume 6 Issue No. 5
Analysis of Effects of Air Pollution on Chlorophyll, Water,
Carotenoid and Anthocyanin Content of Tree Leaves Using Spectral
Indices
Archana R. Mate1, Dr. Ratnadeep R. Deshmukh 2
PG Scholar1, Professor and Head2
Department of CS & IT
Dr. Babasaheb Ambedkar Marathwada University Aurangabad, Maharashtra, India
[email protected], [email protected]
Abstract:
This research aims to examine effects of Air pollution on chlorophyll content, water content, carotenoid content, anthocyanin
content of tree leaves using spectral indices. There are 4 trees selected for experiment namely Ashoka, Banyan, Neem and Peepal
tree. Samples are collected from University Campus (Control area) and Bus Stand area (Polluted area). ASD FieldSpec4
Spectroradiometer is used for collecting spectral reflectance measurement of tree leaves. Spectral indices are used for analysis of
spectral signature. Normalized Difference Vegetation Index (NDVI 680), Normalized Difference Vegetation Index (NDVI705), Ratio
Vegetation Index (RVI680), Simple Ratio (SR705), Chlorophyll index Red Edge (CIRed Edge) indices are used for estimate
chlorophyll content in which RVI680 index is more sensitive to chlorophyll content. Water index (WI), Normalized water index –
1(NWI900), Normalized water index – 2(NWI850), Normalized water index – 3(NWI880), Normalized water index –
4(NWI920)indices are used for estimate water content in which WI index is more sensitive to water content. Carotenoid
concentration index (CRI700), Photochemical reflectance index (PRI), Plant senescencing reflectance Index (PSRI), Carotenoid
concentration index (RNIR*CRI550), Carotenoid concentration index (RNIR*CRI700) ndices are used for estimate carotenoid
content in which RNIR*CRI700 index is more sensitive to carotenoid content. Anthocyanin Reflectance Index (ARI), Modified
Anthocyanin Reflectance Index (mARI), Red/Green indices are used for estimate anthocyanin content in which ARIindex is more
sensitive to anthocyanin content. Chlorophyll content seemed direct proportion with air pollution excluding Peepal tree. Water
content and Carotenoid Content are seemed inverse proportion with air pollution. Anthocyanin content area seemed direct
proportion with air pollution.
Key Words: Chlorophyll content, Water content, Carotenoid content, Anthocyanin content, Spectral Indices.
I.
INTRODUCTION
Micro Industrialization, monetary development, urbanization
and related increment in vitality requests have brought about a
significant decay of air quality in creating nations like India.
Oxides of nitrogen and sulfur and fly-slag constitute as the
significant extents for the vaporous and particulate emanations
from commercial enterprises and car. The presentation of
these contaminations to the leaves cause a diminishment in the
grouping of their photosynthetic colors viz., chlorophyll and
carotenoids, which influences the plant efficiency, ermination
of seeds, length of pedicles, and number of blooms
inflorescence.
Chlorophyll content of leaf is a good indicator of
photosynthesis activity, nutritional state and mutations, stress,
is of special significance to precision agriculture. All green
plants have unique spectral features, mainly because of the
chlorophyll, water content, carotenoid, Anthocyanin content
and other pigments and can together constitute the spectral
feature of a plant [1].
Knudson et al. propose that chlorophyll content could be a
helpful indicator for the assessment of injury prompted by
pollutants. Therefore, Chlorophyll content variation has been
utilized as a part of numerous studies with a specific end goal
to explore the effects of pollutants on plant [2] - [6]. Feng
Zhao 1 et al. work on Detection of Crop Injury from
Glyphosate on Soybeanand Cotton Using Plant Leaf
Hyperspectral Data. They use NDVI (Normalized Difference
International Journal of Engineering Science and Computing, May 2016
Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil
Adjusted Vegetation Index), and DVI (Difference Vegetation
Index) for detect crop injury [7].
Chlorophyll concentration of a leaf is strongly affected by
numerous external factors, for ex. Light and pollutants etc.
Mehmet saylmkaracan found chlorophyll content of
buxussempervirens and euonymus japonica leaves in different
localities and analyse the effect of air pollutions due to
vehicle traffic and rural have compared throught eight month
between October/2002 and May/2003 [8]. Chaoyang Wu a et
al. use different vegetation indices namely normalized
difference vegetation index (NDVI), modified simple ratio
(MSR) index and the modified chlorophyll absorption ratio
index (MCARI, TCARI) estimate chlorophyll content [9] .
Alex S. Olpenda1 and Enrico C. Paringit also use
vegetation indices RVI, NDVI, DVI including Red Edge
Parameter (REP) to assess the effects of air pollution on
potted bougainvillea plants [10]. Jan-Chang CHEN1 et al.
seemed index mNDVI705 is more sensitive to detecting
chlorophyll content in a wide range of tree species across a
terrain. They are shown that among the indices tested,
mNDVI detects the best in different terrain vegetation
reflection [11]. C. Lin
et al. evaluate chlorophyll
concentrations of fresh and water-stressed leaves . They show
that total concentration of chlorophyll a and b decreases
because of Water deficits and Red edge characteristics, such
as position and reflectance are sensitive to water stress [12].
5465
http://ijesc.org/
Researches [13] – [18] use indices for chlorophyll
estimation based on reflectance at 550 nm and/or around 700
nm and showed good result in Chlorophyll estimation.
DrissHaboudane et al. was usedseveral combined indices to
test and evaluate chlorophyllcontent using hyperspectral
imagery [19].Qiu-xiang Yi et al. performed two band
combinations of ratio type of vegetation index (RVI) and the
normalized difference type of vegetation index (NDVI) on
cotton leaf using spectral reflectance. The results shown that
the new index DR1647/DR1133 good for EWT estimation
and DR1653/DR1687 good for FMC estimation which is
developed using two-band combinations [20].
Penuelas et al. use Water Index WI (R900/R970) for
estimation of plant water concentration (PWC) by groundbased, reflectance measurements. When normalizing WI by
NDVI the correlations increased. They show that plant
reflectance at 680, 900, and 970 nm could speed up the
measurement of PWC [21]. Penuelas and Inoue show that the
ratio of WI/NDVI (WI=R900/R970, NDVI=(R900-R680) /
(R900+R680) also used to predict the water content of plant
[22]. E. Raymond Hunt determine leafRelative Water content
(RWC) of different specieswith different leaf morphologies by
testing the ability of the LeafWater Content Index (LWCI)
[23].
M. A. Babar et al. develop two new indices normalized
water index-1 [NWI-1] and normalized water index-2 [NWI2]) for estimating water status in crop leaf. The NIR-based
index (NWI-2) showed a very high efficiency in selecting
superior genotypes in different experiments [24]. B. Prasad et
al. develop two more normalized water indices (NWI-3 and
NWI-4) and were shown to detect a significant proportion of
the highest yielding genotypes and an equal, or higher,
correlated response than direct response for grain yield in
winter wheat rain fed environments [25].
Several researchers have successfully estimated
Carotenoid in vegetation using visible ratios [26], visible/NIR
ratios [27] - [29], red edge reflectance-ratio indices [21], [28],
[30] –[32], spectral and derivative red edge indices [33] and
for forest canopies, scaling-up and model inversion methods
with narrow bands are used [34]. Daniel A. Sims and John A.
Gamon proved that carotenoid/chlorophyll ratios was related
to the photochemical reflectance index (PRI) which is
developed for estimate xanthophyll cycle pigment changes in
green leaves. They also used PSRI index for estimate
carotenoid content [35].
The anthocyanin is common pigments in higher plants.
Gamon and Surfus suggested using a red/green index, a ratio
of reflectance in the red and the green spectral bands, to
estimate Anthocyanin content [15]. The ρred /ρgreen ratio
estimates Anthocyanin content by comparing reflectance in
the red region of the spectrum to reflectance in the green
region where both Chlorophyll and Anthocyanin absorb. ARI
index was used to estimate Anthocyanin content [28].
For determining leaf Anthocyanin content nondestructively, several vegetation indices were designed. These
indices are based on reflectance in a few spectral bands with
varying levels of sensitivity to changes in Anthocyanin
content as well as to the content of other pigments. Anatoly A.
Gitelson et al. estimate anthocyanin contents using ARI,
mARI and Red/Green index [36].Gitelson,A. A et al. develop
modified anthocyanin reflectance index (mARI) presented as
the conceptual model for Anthocyanin estimation [37].
II. MATERIALS AND METHODOLOGY
2.1 Study Area
The leaf samples of four species given in table 1 used in this
study were collected from Dr. Babasaheb Ambedkar
Marathwada University and Bus Stand Area of Aurangabad
city which is one district of Maharashtra state in western India.
Aurangabad is located at 19° 53' N and 75° 23' E. The annual
mean temperature at the study area range from 17 to 33 °C and
average annual rainfall is 710 mm.
Aurangabad is connected by roads with various cities of
Maharashtra and other states. From Dhule to Solapur,
211National Highway passes through the city. Aurangabad
has road connectivity to Pune, Ahmednagar, Nagpur, Jalna,
Beed, Mumbai and the path are presently four lane street.
Another
Nagpur–Aurangabad–Mumbai
highway
is
additionally being created. The city is presented with two bus
stations specifically Central Bus stand and CIDCO bus stand,
situated around 6 km far from each other. Aurangabad is 17th
most polluted city in India.
Table 1: Tree species used in this study
Tree Species
Common Names
Family
Botanical Names
Ashoka
Leguminosae
SaracaIndica
Banyan
Moraceae
FicusBenghalensis
Neem
Meliaceae
AzadirachtaIndica
Peepal
Moraceae
FicusReligiosa
International Journal of Engineering Science and Computing, May 2016
5466
2.2 Plant Material and Reflectance Measurement
There are 4 species, namely Ashoka, Banyan, Neem,
Peepalare used in this study. Tree leaf samples are collected
from the two types of Area University Area (Control Area)
and Bus Stand Area (Polluted Area). From each area, 5 leaves
of each tree are for reflectance measurement. The ASD
Fieldspec 4 Spectroradiometer is used for spectral reflectance
measurement. Each leaf from polluted area is cleaned with wet
cotton and then reflectance measurements are taken. The
sampling interval at the spectral range (350-1000nm) is 1.4nm
and at the spectral range (1000-2500nm) is 1.1nm.
The height of the light source is 44.5 cm, Gun height is 5 cm
and distance between light source and the gun is 50cm.
Samples are collected on 22/04/2016 at 10.30 and reflectance
measurement are taken between 11.30 Am to 1.30Pm. 8o
Field Of View (FOV) is used and room temperature is
maintained 23oC when taking spectral reflectance. Length of
Ashoka tree leaf is 15-18 cm and width is 2.30-3 cm. Length
of Banyan tree leaf is 13-18 cm and width is 7-12 cm. Length
of Neem tree leaf is 6-8 cm and width is 2-3 cm. Length of
Peepal tree leaf is 15-18 cm and width is 12-15 cm. Spectral
reflectance of samples are captured using RS3 Software.
2.3 Spectral Indices
ViewSpecPro Software is used for Data Analysis. Spectral
reflectance captured by RS3 gives input to ViewSpecPro
Software. Mean value calculated using 5 leaf of every tree
from each area. Table2 shows the spectral indices used in this
study. Spectral indices applied on Mean value.
III. RESULT AND DISCUSSION
Figure 1 shows the Mean reflectance of all trees. The blue line
shows reflectance of leaf collected from university area and
pink line shows the reflectance of leaf collected from Bus
Stand area.
http://ijesc.org/
Table 2: Spectral indices used in this study
Content
Index Name
Normalized Difference
Vegetation Index
Chlorophyll
Water
Carotenoid
Anthocyanin
Index
Abbreviation
NDVI680
Index calculation
Reference
[7]
Normalized Difference
Vegetation Index
Ratio Vegetation Index
NDVI705
[38]
RVI680
[7]
Simple Ratio
SR705
[38]
Chlorophyll index Red
Edge
Water index
CI red edge
[29]
WI
[21], [39]
Normalized water index - 1
NWI-1
[24], [40]
Normalized water index - 2
NWI-2
[24], [40]
Normalized water index - 3
NWI-3
[40], [41]
Normalized water index - 4
NWI-4
[40], [41]
Carotenoid concentration
index
CRI 700
[27,28]
Photochemical reflectance
index
Plant senescencing
reflectance Index
Carotenoid concentration
index
Carotenoid concentration
index
Anthocyanin Reflectance
Index
PRI
[35]
PSRI
[35]
[27;28]
[27,28]
ARI
[37]
Modified Anthocyanin
Reflectance Index
mARI
[37]
Red / Green
Red / Green
[37]
Ashoka
International Journal of Engineering Science and Computing, May 2016
Banyan
5467
http://ijesc.org/
Neem
Peepal
Figure 1: Mean reflectance of Trees.
3.1 Statistical Analysis:
In Ashoka and Banyan leaf reflectance of university area is
higher in all regions than leaf reflectance of Bus Stand Area.
Table 3 shows statistical calculation of different band for
trees.For Ashoka, Banyan, Neem tree in all band Control area
has higher reflectance of Min, Max, Mean than Polluted area.
For Ashoka tree and Banyan tree in Red, Green and NIR band
Control area has high reflectance of STDEV than polluted
area. In Blue band Ashoka tree has high reflectance of STDEV
in Control area and low reflectance in Polluted area but for
Banyan tree reflectance of STDEV reflectance is equal in both
areas. In Blue band Neem tree reflectance of STDEV
reflectance is equal in both area but for Red, Green, NIR band
has low reflectance of STDEV in Control area and high
reflectance in Polluted area.
For Peepal tree in Blue band Min, Max, Mean reflectance are
high in Control area and low in polluted area but STDEV
reflectance is low in Control Area and high in Polluted area. In
Red band Min reflectance is high in Control area and low in
Polluted area but Max, Mean, STDEV reflectance are low in
Control area and high in Polluted area. In Green band all Min,
Max, Mean and STDEV reflectance are low in Control area
Tree
Name
Ashoka
Banyan
Neem
Peepal
Band
Blue
Red
Green
NIR
Blue
Red
Green
NIR
Blue
Red
Green
NIR
Blue
Red
Green
NIR
Min
0.151
0.156
0.157
0.244
0.149
0.164
0.162
0.315
0.104
0.118
0.116
0.170
0.126
0.133
0.125
0.249
and high in Polluted area. In NIR bad Min reflectance is high
in Control area and low in Polluted area but Max, Mean,
STDEV reflectance are high in Control area and low in
Polluted area.
3.2 Analysis using Spectral Indices
 Chlorophyll Content Analysis
Table 4shows chlorophyll content estimation using spectral
indices. For Ashoka, Banyan and Neem tree indices values are
low in control area and high in polluted area. But in Peepal
tree indices values are high in control area and low in polluted
area. RVI680 index have maximum values and NDVI705 index
have minimum values[11].
For Ashoka tree using NDVI680 ,NDVI705, RVI680, SR705, CI Red
Edge indices, it is found that Polluted area has 0.076, 0.073,
1.497, 0.419, 0.285 more chlorophyll content than Control
area respectively. Using same indices, Polluted area has more
chlorophyll content for Banyan tree are 0.065, 0.081, 1.141,
0.348, 0.243 and for Neem tree 0.051, 0.021, 1.148, 0.130,
0.059 more chlorophyll content than Control area respectively.
Table 3: Statistical Analysis of this Study
Control Area
Max
Mean
STDEV
Min
0.156
0.152
0.001
0.090
0.232
0.202
0.023
0.092
0.244
0.173
0.016
0.090
0.743
0.708
0.077
0.147
0.164
0.153
0.003
0.082
0.280
0.238
0.035
0.096
0.315
0.195
0.030
0.096
0.744
0.702
0.060
0.197
0.118
0.113
0.005
0.080
0.170
0.147
0.016
0.090
0.170
0.125
0.010
0.089
0.629
0.594
0.075
0.149
0.133
0.127
0.001
0.090
0.252
0.206
0.036
0.129
0.249
0.154
0.025
0.138
0.693
0.653
0.063
0.321
Polluted Area
Max
Mean
0.092
0.090
0.139
0.120
0.147
0.101
0.602
0.565
0.096
0.087
0.181
0.150
0.197
0.117
0.580
0.543
0.090
0.086
0.141
0.119
0.149
0.098
0.618
0.580
0.129
0.105
0.272
0.231
0.321
0.193
0.645
0.611
STDEV
0.000
0.014
0.010
0.076
0.003
0.025
0.020
0.055
0.003
0.016
0.011
0.077
0.009
0.042
0.040
0.046
*Ranges of Bands- Blue Band :400 nm-500 nm, Red Band : 500 nm-600 nm, Green Band 600 nm-700 nm, NIR(Near Infrared) Band 700 nm-1300 nm.
International Journal of Engineering Science and Computing, May 2016
5468
http://ijesc.org/
But for Peepal tree using same indices, found that Polluted
area has 0.066, 0.136, 1.101, 0.514, 0.343 less chlorophyll
content than Control area respectively. Figure 2shows
graphical representation of chlorophyll content using spectral
indices.
Table 4: Chlorophyll content of trees
Tree
Spectral
Control Area Polluted Area
Name
Indices
(University
(Bus Stand
Campus)
Area )
NDVI680
0.640
0.716
NDVI705
0.373
0.446
Ashoka
RVI680
4.557
6.054
SR705
2.189
2.608
CI Red Edge
1.831
2.116
NDVI680
0.630
0.695
NDVI705
0.277
0.358
Banyan
RVI680
4.409
5.550
SR705
1.765
2.113
CI Red Edge
1.521
1.764
NDVI680
0.675
0.726
NDVI705
0.429
0.450
Neem
RVI680
5.145
6.293
SR705
2.505
2.635
CI Red Edge
2.058
2.117
NDVI680
0.685
0.619
NDVI705
0.335
0.199
Peepal
RVI680
5.349
4.248
SR705
2.010
1.496
CI Red Edge
1.686
1.343
 Water Content Analysis
Table 5shows water content estimation using spectral indices.
Table 5: Water content of trees
Tree
Species
Ashoka
Banyan
Neem
Peepal
Indices
Control Area
(University
Campus)
WI
NWI900
NWI850
NWI880
NWI920
WI
NWI900
NWI850
NWI880
NWI920
WI
NWI900
NWI850
NWI880
NWI920
WI
NWI900
NWI850
NWI880
NWI920
1.007
0.003
0.001
-0.003
-0.004
1.035
-0.017
-0.013
-0.016
-0.016
1.008
-0.004
0.002
-0.002
-0.006
1.032
-0.016
-0.013
-0.016
-0.016
Polluted
Area
(Bus Stand
Area)
0.997
0.003
0.012
0.005
0.000
1.031
-0.015
-0.011
-0.014
-0.015
1.005
-0.002
0.005
0.000
-0.004
1.003
-0.002
0.006
0.001
-0.003
Figure 2: Graphical representation of Chlorophyll Content of Ashoka, Banyan, Neem, Peepaltrees
International Journal of Engineering Science and Computing, May 2016
5469
http://ijesc.org/
Figure 3: Graphical representation of Water Content of Ashoka, Banyan, Neem, Peepaltree
For all trees WI index values are high in Control area and low
in Polluted area. But NWI900, NWI850, NWI880, NWI920 indices
have low values in Control area and high value in Polluted
area. WI index high value indicates high water content in tree
leaf and low value indicates low water content in tree leaf [24]
but lowest values for NWI900, NWI850, NWI880, NWI920 indices
indicates high water content and high value indicates low
water content in tree leaf [25] .
For Ashoka tree using WI, NWI900, NWI850, NWI880, NWI920
indices, it is found that Polluted area has 0.01, 0, 0.011, 0.008,
0.004 less water content than Control area respectively. Using
same indices, Polluted area has 0.004, 0.002, 0.002, 0.002,
0.001 for Banyan tree, for Neem tree 0.003, 0.002, 0.003,
0.002, 0.002 and for Peepal tree 0.029, 0.014, 0.019, 0.017,
0.013 less water content than Control area respectively.Figure
3shows graphical representation of water content using
spectral indices.
 Carotenoid content
Table 6shows water content estimation using spectral indices.
For all trees, CRI700, PRI, PSRI, RNIR*CRI550,RNIR*CRI700
indicesvalue are low in Control area and high in Polluted area.
For all indices low values indicates high carotenoid content
and high value indicates low carotenoid content in tree
leaf[25].
International Journal of Engineering Science and Computing, May 2016
Table 6: Carotenoid content of trees
Tree
Species
Ashoka
Banyan
Neem
Peepal
5470
Indices
CRI700
PRI
PSRI
RNIR*CRI550
RNIR*CRI700
CRI700
PRI
PSRI
RNIR*CRI550
RNIR*CRI700
CRI700
PRI
PSRI
RNIR*CRI550
RNIR*CRI700
CRI700
PRI
PSRI
RNIR*CRI550
RNIR*CRI700
Control
Area
(University
Campus)
1.716
0.008
0.000
3.072
3.233
2.033
-0.028
0.000
3.044
3.342
1.931
0.010
-0.002
4.740
4.760
2.195
-0.011
-0.011
4.189
4.169
Polluted Area
(Bus Stand
Area)
2.812
0.009
0.003
6.349
6.585
3.327
-0.003
0.008
6.248
6.508
3.190
0.016
0.004
6.640
6.848
2.665
0.008
0.021
4.290
4.639
http://ijesc.org/
For Ashoka tree CRI700, PRI, PSRI,
RNIR*CRI550,
RNIR*CRI700 using indices, it is found that Polluted area has
1.096, 0.001, 0.003, 3.277, 3.352 less carotenoid content than
Control area respectively. Using same indices, Polluted has
1.294, 0.025, 0.008, 3.204, 3.166 for Banyan tree, for Neem
tree 1.259, 0.006, 0.006, 1.9, 2.088 and for Peepal tree 0.47,
0.019, 0.032, 0.101, 0.47 less carotenoid content than Control
area respectively.Figure 4shows graphical representation of
carotenoid content using spectral indices.
 Anthocyanin content Analysis:
Table 7shows anthocyanin content estimation using spectral
indices. ARI, and mARI, Red/Green, were calculated using
average reflectance values in following bands Green = 540 –
560 nm, Red = 660 – 680 nm, Red Edge = 690 – 710 nm, NIR
= 760 – 800 nm. For all trees ARI and mARI indices values
are low in Control area and high in Polluted area. But
Red/Green index have high values in Control area and low
value in Polluted area. Higher values for ARI and mARI
indices indicates high anthocyanin content and low
valuesindicates low anthocyanin content in tree leaf but for
Red/ Green index high value indicates low Anthocyanin
content in tree leaf and low value indicates high anthocyanin
content in tree leaf [36].
Table 7: Anthocyanin content of trees
Tree
Species
Ashoka
Banyan
Neem
Peepal
Indices
ARI
mARI
Red/Green
ARI
mARI
Red/Green
ARI
mARI
Red/Green
ARI
mARI
Red/Green
Control Area
(University
Campus)
0.399
0.282
0.690
0.468
0.333
0.594
0.334
0.195
0.690
0.079
0.052
1.968
Polluted
Area (Bus
Stand Area)
0.794
0.430
0.659
0.612
0.335
0.542
0.774
0.433
0.643
0.546
0.321
0.537
For Ashoka tree using ARI, mARI, Red/Green indices, it is
found that Polluted area has 0.395, 0.148, 0.031 more
anthocyanin content than Control area respectively. Using
same indices, Polluted area has 0.144, 0.002, 0.052for Banyan
tree, for Neem tree 0.44, 0.238, 0.04, 0.047 and for Peepal tree
0.467, 0.269, 1.431 more anthocyanin content than Control
area respectively.Figure 5shows graphical representation of
anthocyanin content using spectral indices.
Figure 4: Graphical representation of Carotenoid of Ashoka, Banyan, Neem, Peepaltrees
International Journal of Engineering Science and Computing, May 2016
5471
http://ijesc.org/
Figure 5: Graphical representation of Anthocyanin Content of Ashoka, Banyan, Neem, Peepaltrees
[3] Kobriger, J. M. and T. W. Tibbitts, (1985). “Effects of
relative humidity prior to and during exposure on
IV. CONCLUSION
response of peas to ozone and sulfer dioxide.” Journal of
Chlorophyll content seemed direct proportion with air
American Society for Horticultural. Science Vol. 110,
pollution excluding Peepal tree. Water content and Carotenoid
pp. 21-24.
Content are seemed inverse proportion with air pollution.
[4]
Robinson, D.C. and A.R. Wellburn, (1991). “Seasonal
Anthocyanin content area seemed direct proportion with air
changes in the pigments of Norway spruce and
pollution. RVI680 index is more sensitive to chlorophyll
piceaabies Karst and the influence of summer ozone
content, for water content WI index, for carotenoid content
exposures.” Trans L. New Phytol., Vol. 119, pp. 251RNIR*CRI700 index and for anthocyanin content ARIindex is
259.
more sensitive.
[5] Pleijel, H., L. Skarby, K. Ojanpera and G.
Sellden,(1994). “Exposure of oats, Avena sativa L.to
ACKNOWLEDGEMENT
filtered and unfiltered air in open-top chambers: effects
This work is supported by Department of Science and
on grain yield and quality.” Environ. Pollut. Vol. 86, pp.
Technology under the Funds for Infrastructure under Science
129-134.
and Technology (DST-FIST) with sanction no. SR/FST/ETI[6] Della Torre G., F. Ferranti, M. Lupattelli, N. Pocceschi,
340/2013 to Department of Computer Science and Information
A. Figoli, C., (1998). “Nali and physiology of Hedera
Technology, Dr. Babasaheb Ambedkar Marathwada
helix.” Chemosphera , Vol. 36, pp. 651-656.
University, Aurangabad, Maharashtra, India. The authors
[7]
Feng Zhao, YanboHuan, YiqingGuo, Krishna N. Reddy,
would like to thank Department and University Authorities for
Matthew A. Lee,Reginald S. Fletcher and Steven
providing the infrastructure and necessary support for carrying
J.,(2014). ”Detection of Crop Injury from Glyphosate on
out the research.
Soybeanand Cotton Using Plant Leaf Hyperspectral
Data.” Remote Sensing. Vol.6, pp. 1538-1563.
REFERENCES
[8]
Mehmet SaylmKaracan ,(2006). ” Monitoring of
[1] Philip, H. S., Shirly, M. D., (1978). “Remote sensing: the
Changing Chlorophyll Content of Buxussempervirens L.
quantitative approach”, McGraw-Hill, New York. pp.
and Euonymus japonica L. fill leaves Affected with Air
226-227.
Pollution in Ankara.”,World Journal of Agricultural
[2] Knudson, L. L., T. W. Tibbitts and G.E. Edwards,(1977).
Sciences. Vol. 2(2), pp. 01-04 .
“Measuring of ozone injury by determination of leaf
[9]
Chaoyang Wu a,b, ZhengNiu a, Quan Tang a,b,
chlorophyll concentration.” Plant Physiol. ,Vol. 60, pp.
Wenjiang Huang c,(2008). ”Estimating chlorophyll
606-608.
content from hyperspectral vegetation indices: Modeling
International Journal of Engineering Science and Computing, May 2016
5472
http://ijesc.org/
and validation.” Agricultural and Forest Meteorology.
Vol. 148, pp. 1230 – 1241.
[10] Alex S. Olpenda1 and Enrico C. Paringit, (2011).
”Utilizing Spectral Reflectance And Vegetation Indices
OfBougainvillea SpectabilisFor Monitoring Particulate
AirPollution In Metro Manila.” In proceedings of the
32nd Asian Conference on Remote Sensing
(ACRS2011): Sensing for Green Asia, Taipei
International Convention Center, Taipei, Taiwan.
[11] Jan-Chang CHEN, Chi-Ming YANG, Shou-Tsung
Wu, Yuh-Lurng CHUNG, Albert Linton CHARLES,
and Chaur-Tzuhn CHEN, (2007). ”Leaf chlorophyll
content and surface spectral reflectanceof tree species
along a terrain gradient in Taiwan’sKenting National
Park.” Botanical Studies. Vol. 48, pp. 71-77.
[12] C. Lin1 , S. C. Popescu, S. C. Huang, P. T. Chang,
and H. L. Wen,(2015). ”A novel reflectance-based
model for evaluating chlorophyllconcentrations of fresh
and water-stressed leaves.”Biogeosciences. Vol. 12,
pp.49–66.
[13] Aoki M, Yabuki K, Totsuka T, Nishida M (1986).
“Remote sensing of chlorophyll content of leaf (I)
effective spectral reflection characteristics of leaf for the
evaluation of chlorophyll content in leaves of
dicotyledons.” Environment Control Biology. Vol. 24,
pp. 21– 26.
[14] Gitelson A, Merzlyak MN (1997).
“Remote
estimation of chlorophyll content in higher plant leaves.”
International Journal of Remote Sensing. Vol. 18, pp.
291–298.
[15] Gamon JA, Surfus JS (1999). “Assessing leaf
pigment content and activity with a reflectometer.” New
Phytol. Vol. 143, pp.105–117.
[16] Carter GA, Knapp AK (2001). “Leaf optical
properties in higher plants: linking spectral
characteristics to stress and chlorophyll concentration.”
American Journal of Botany. Vol. 84, pp.677–684.
[17] Richardson AD, Duigan SP, Berlyn GP (2002). ”An
evaluation of noninvasive methods to estimate foliar
chlorophyll content.” New Phytol. Vol. 153, pp. 185–
194.
[18] Sims DA, Gamon JA, (2002). “Relationship between
leaf pigment content and spectral reflectance across a
wide range species, leaf structures and development
stages.” Remote Sensing Environment. Vol. 81, pp. 337–
354.
[19] DrissHaboudane, Nicolas Tremblay, John R. Miller,
and Philippe Vigneault, (2008). ”Remote Estimation of
Crop Chlorophyll Content Using Spectral Indices
Derived From Hyperspectral Data.”, IEEE Transactions
On Geoscience and Remote Sensing. Vol. 46(2), pp.
423-437.
[20] Qiu-xiang Yi, An-mingBao, Qiang Wang, Jin Zhao,
(2013). ” Estimation of leaf water content in cotton by
means of hyperspectral indices.” Computers and
Electronics in Agriculture. Vol. 90, pp. 144–151.
International Journal of Engineering Science and Computing, May 2016
[21]
J. Penuelas, J. Pinol, R. Ogayaand I. Filella , (1997).
”Estimation of plant water concentration by the
reflectance WaterIndex WI (R900/R970).” International
journal of remote sensing. Vol.18(13), pp.2869-2875.
[22] Penuelas J., Inoue, Y.,(1999). “ Estimation of plant
water concentration by the reflectance WaterIndex WI
(R900/R970.”International Journal of Remote Sensing.
Vol.36(3), pp. 355–360.
[23] E. Raymond Hunt, Jr, (1989).“Detection of Changes
in Leaf Water Content Using Near- and Middle-Infrared
Reflectances.”Remote Sens. Environ. Vol. 30, pp. 43-54.
[24] M. A. Babar, M. P. Reynolds, M. van Ginkel, A. R.
Klatt, W. R. Raun, and M. L. Stone,(2006). ” Spectral
Reflectance Indices as a Potential Indirect Selection
Criteria for Wheat Yieldunder Irrigation.” Crop Science,
Vol. 46, pp. 578-588.
[25] B. Prasad, B. F. Carver, M. L. Stone, M. A. Babar,
W. R. Raun, and A. R. Klatt,(2007). “Genetic Analysis
of Indirect Selectionfor Winter Wheat Grain Yield using
SpectralReflectance Indices.”, Crop Science. Vol. 47, pp.
1416- 1425.
[26] Datt, B. (1998). “Remote sensing of chlorophyll a,
chlorophyll b, chlorophyll a+b, and total carotenoid
content in Eucalyptus leaves.” Remote Sensing of
Environment. Vol. 66, pp. 111–121.
[27] Gitelson, A. A., Gritz, U., &Merzlyak, M. N. (2003).
“Relationships between leaf chlorophyll content and
spectral reflectance and algorithms for non-destructive
chlorophyll assessment in higher plant leaves.” Journal
of Plant Physiology. Vol. 160, pp. 271 –282.
[28] Gitelson, A. A., Keydan, G. P., &Merzlyak, M. N.
(2006).” Three-band model for noninvasive estimation
of chlorophyll, carotenoids, and anthocyanin content in
higher plant leaves.” Geophysical Research Letters, 33,
L11402.
[29] Haboudane, D., Miller, J. R., Tremblay, N., ZarcoTejada, P. J., &Dextraze, L. (2002). “Integrated narrowband vegetation indices for prediction of crop
chlorophyll content for application to precision
agriculture.” Remote Sensing of Environment. Vol. 81,
pp. 416–426.
[30] Carter, G. A., &Spiering, B. A. (2002). “Optical
properties of intact leaves for estimating chlorophyll
content.” Journal of Environmental Quality. Vol. 31,
pp.1424–1432.
[31]
Le Maire, G., François, C., &Dufrene, E. (2004).
“Towards universal broad leaf chlorophyll indices using
PROSPECT simulated database and hyperspectral
reflectance measurements.” Remote Sensing of
Environment. Vol. 89, pp. 1–28.
[32] Richardson, A. D., &Berlyn, P. (2002). “Changes in
foliar spectral reflectance and chlorophyll fluorescence
of four temperate species following branch cutting.”
Tree Physiology, 22, 499–506.
[33] Miller, J. R., Hare, E. W., & Wu, J. (1990).
“Quantitative characterization of the vegetation red edge
5473
http://ijesc.org/
reflectance. An inverted-Gaussian reflectance model.”
International Journal of Remote Sensing. Vol. 11, pp.
1755–1773.
[34] Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H.,
Noland, T. L., & Sampson, P. H. (2001). “Scaling-up
and model inversion methods with narrow-band optical
indices for chlorophyll content estimation in closed
forest canopies with hyperspectral data.”
IEEE
Transactions on Geoscience and Remote Sensing. Vol.
39, pp. 1491–1507.
[35] Daniel A. Sims, John A. Gamon,(2002).”
Relationships between leaf pigment content and spectral
reflectance across a wide range of species, leaf structures
and developmental stages.” Remote Sensing of
Environment. Vol. 81, pp. 337– 354.
[36] Anatoly A. Gitelson, Olga B. Chivkunova, and Mark
N. Merzlyak, (2009). “Nondestructive Estimation of
Anthocyanins andChlorophylls in Anthocyanic Leaves.”
American Journal of Botany. Vol. 96(10), pp. 1861–
1868.
[37] Gitelson, A. A., M. N. Merzlyak, and O.
B.Chivkunova . (2001). “Optical properties and non-
International Journal of Engineering Science and Computing, May 2016
destructive estimation of anthocyanin content in plant
leaves.” Photochemistry and Photobiology. Vol. 74,
pp.38 – 45 .
[38] Jan-Chang Chen a, Chaur-Tzuhn Chen b, (2008).
“Correlation Analysis between Indices of Tree Leaf
SpectralReflectance And Chlorophyll content.” The
International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences. Vol.
XXXVII. Part B7.
[39] Penuelas, J., Filella, I., Biel, C., Serrano, L. and Savé,
R. (1993).” The reflectance at the 950–970 nm region as
an indicator of plant water status.” International Journal
of Remote Sensing. vol. 14, pp. 1887–1905.
[40] Prasad, B., Carver, BF., Stone, ML., Babar, MA.,
Raun, WR. And Klatt , AR. (2007). “Potential use of
spectral reflectance indices as a selection tool for grain
yield in winter wheat under Great Plains conditions.”
Crop Science. Vol. 47, pp.1426–1440.
[41] Gitelson AA, Kaufman YJ, Merzlyak MN (1996).”
Use of a green channel in remote sensing of global
vegetation from EOS-MODIS.”
Remote Sensing
Environment.
Vol.
58,
pp.
289–298.
5474
http://ijesc.org/