Original Paper

Differences in NDVI and iTVDI Among Various Land CoverTypes in Kanto region, JapanTonglaga Tao, Yo Shimizu and Kenji Omasa＊
Department of Biological and Environmental Engineering, Graduate School of Agricultural and Life Sciences,
The University of Tokyo,
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113- 8657, Japan（Received May 15, 2015; Accepted June 19, 2015）

ABSTRACTThe differences in the improved Temperature Vegetation Dryness Index (iTVDI) and the Normalized Difference
Vegetation Index (NDVI) between eleven land cover types in northwestern part of the Kanto region of Japan were analyzed using Landsat5 TM data and a vegetation map. The results showed that there were significant differences in iTVDIand NDVI values among the land cover types. The NDVI values were highest in the forest regions including evergreen
forest, deciduous forest and mixed forest, while the iTVDI values were relatively lower in comparison to other land covertypes, except for water. The iTVDI values of the grassland, cropland and orchard were higher while their NDVI values
were not very low. And the iTVDI value of the paddy field was much higher than that of the water. In order to identify
the reason, these land cover types were further classified into two classes respectively, the paddy field was classified into
water-covered and not water-covered, and the other land cover types were classified into the full-covered and partialcovered. Consequently, the iTVDI values of the water-covered region of the paddy field and the well-vegetated region of
the other three types were reduced. These results suggest that different land cover types cause the differences of iTVDIvalues and NDVI values due to the different fraction of vegetation cover and transpiration rates.

Key words : iTVDI, land cover type, NDVI, transpiration

1. Introduction

about vegetation monitoring and environmental investigation (Tucker, 1979; Huete, 1988; Kogan, 1995; Motohka etVegetation is a very important part of the terrestrial ecosys-al., 2010), among which the NDVI (Normalized Differencetem, and its growth and reproduction are crucially dependent

Vegetation Index, Rouse et al., 1973) is a very common one.

on their physical environment. Meanwhile, the vegetation

It reflects plant biomass such as the chlorophyll content orcondition and dynamic changes reflect the environmental

Leaf Area Index (LAI). However, it was reported that there

state and existing problems, such as air pollution and drought

are some limitations in its application such as a delayed re-(Omasa et al., 2002; Omasa et al., 2005; Jones and Vaughan,sponse to the rainfall and little influence from significant

2010; Wan et al., 2014). Therefore, vegetation monitoring andprecipitation events to the NDVI in the later growing sea-investigation are essential.

son in arid or semi-arid region (Rundquist and Harrington,

In recent decades, remote sensing techniques have been

2000; Wang et al., 2001; Rahimzadeh-Bajgiran et al., 2008,

generally used in these field thanks to its lower cost and2009). Afterwards, the land surface temperature (Ts), which

time-saving advantages. Moreover, plenty of vegetation in-can indicate energy partitioning at the surface, was taken into

dices have been developed and applied in extensive studies

account and combined with the vegetation indices to make a*Corresponding author : Phone: +81-3-5841-5340, Fax: +81-3-5841-8175, E-mail: aomasa@mail.ecc.u-tokyo.ac.jp© The Society of Eco-Engineering（3）111

Ts/vegetation index space for monitoring of vegetation condi-on the values of the iTVDI and NDVI; and if there are sig-tion to avoid the aforesaid problems. The TVDI (Temperaturenificant differences in iTVDI and NDVI among land cover

Vegetation Dryness Index, Sandholt et al., 2002) was just de-types?

veloped on the basis of Ts/NDVI space, however it was not

In this study, for the first time, the differences in NDVI andsuitable for a heterogeneous area, as the air temperature wasiTVDI between various land cover types in Kanto region ofassumed to be a constant. The improved TVDI was developed

Japan were evaluated quantitatively.

(Rahimzadeh-Bajgiran, et al., 2012) on the basis of the TVDI,

2. Materials and methods

by replacing the Ts with Ts minus Ta (air temperature). And
it is relative to the transpiration rate and would respond to the2.1 Study area
The study area covered northwestern part of the Kantowater stress.
The iTVDI has also been used in some researches inregion of Japan (Fig. 1, 36.03-36.71 ºN, 137.97-139.57 ºE),

Japan. Ishimura et al. (2011) found the iTVDI was suitable forwhere there are various kinds of vegetation species and thedetection of Japanese beech forest decline caused by air pol-elevation of this area ranges from 0 to about 3000 meters.

lution and water deficit. Naito et al. (2012) compared the difference between NDVI and iTVDI among vegetation cover

2.2 Remote sensing datatypes in Lake Mashu located in Hokkaido, Japan and showed

The Landsat Thematic Mapper (TM) image, acquired onthat the different vegetation cover types could cause signifi-

the 28th of May 1993, was used. Firstly, it was converted fromcant differences in iTVDI values.

DN (Digital Number) to reflectance using FLAASH (FastHowever, the differences in iTVDI and NDVI among land

Line-of-sight Atmospheric Analysis of Spectral Hypercubes)

cover types have not been studied yet. And the following

atmospheric correction model in ENVI software. The band

points could be suggested: the influence of land cover types

4 (near infrared band: 0.76 - 0.90 μm) and band 3 (red band:0 2 4112（4）81216kilometers

Evergreen forest

Cropland

Paddy field

Deciduous forest

OrchardWater

Mixed forest

Bare land

Urban area

Grassland

Residential area

Unknown

Eco-Engineering0.63- 0.69 μm) with a spatial resolution of 30m, were thenand Reflection Radiometer Global Digital Elevation Model)

used to calculate the NDVI, and band 6 (thermal infrared

was used to calibrate the air temperature. Considering the to-band: 10.40-12.50 μm) with a spatial resolution of 120 m,

pography of the study area, the environmental lapse rate waswas used to retrieve the land surface temperature.

defined as 0.55 ℃/100 m (Yoshino, 1986). Fig. 2(B) presents
the interpolated Ta map after the DEM calibration.

2.3 Vegetation map
The vegetation map provided by the Biodiversity Center of

2.5 Masking

Japan was used to make the land cover classification map by

The pixels in the Ts-Ta map, with Ts-Ta values lower thanreclassifying it into 12 kinds of land cover types. The forest-5 ℃ were used to build a mask to eliminate the pixels cov-

was classified into evergreen, deciduous and mixed forests.

ered by thick clouds.

The residential area, of which over 30% are covered by vegetation, is also differentiated from the urban area. The other

2.6 Methods

kinds of land cover also include grassland, orchard, cropland,

2.6.1 NDVI

bare land, paddy field, water, and unknown which will not

The NDVI is the most common vegetation index of which

be discussed here. Each land cover type is made up of a mass

the value ranges from -1 to 1, reflecting the vegetationof pixels, among them bare land contains the minimum pixel

amount and chlorophyll content. It is calculated using Eq. (1)numbers of 57,849, and deciduous forest contains the maximum pixel numbers of 4,544,235. The land cover classification map is shown in Fig. 1.

NDVI ＝NIR−R

(1)

NIR＋R

where NIR is the reflectance value of the near infrared band
and R is the reflectance value of the red band.

2.4 Meteorological data
Air temperature data measured at 10 : 00 A.M. on the 28th

2.6.2 iTVDIof May 1993 and the elevation data from 84 meteorological

The iTVDI was developed for water stress detection,stations surrounding the study area were collected to interpo-and performed well in evapotranspiration estimation andlate the air temperature of the whole region using the inverse

water stress determination in the semi-arid region of Irandistance weighted method. To calculate the air temperature of(Rahimzadeh-Bajgiran et al., 2012). It also has a good re-a given pixel, the nearest 6 stations were used, as in this caselationship with the mortality rate of the beech forest in thethe Root Mean Square Error (RMSE) was the lowest. Also

Tanzawa mountain in Japan (Ishimura et al., 2011). The iT-the Digital Elevation Model (DEM) data (Fig. 2(A)) supplied

VDI is calculated using Eq. (2) and schematically shown inby ASTER GDEM (Advanced Spaceborne Thermal Emission

Fig. 3.
iTVDI ＝(Ts-Ta)obs − (Ts-Ta)min(Ts-Ta)max−(Ts-Ta)min

＝BCAB

(2)

where (Ts-Ta)obs is the difference between observed surfacetemperature and air temperature calibrated using DEM. (TsTa)max and (Ts-Ta)min are the maximum and minimum differences between Ts and Ta for the same NDVI value in thetrapezoidal shape of Ts-Ta vs. NDVI (Fig. 3), respectively.
Here the mean value of (Ts-Ta) ＋ S.D. × 2 was defined as
the dry edge and (Ts-Ta) − S.D. × 2 was defined as the wetedge. The iTVDI value ranges from 0 to 1, and the iTVDI
values of the pixels above the dry edge would result in higher
than 1 and the pixels nether the wet edge would lower than 0,
hence they were set to 1 and 0 respectively. The higher theiTVDI value is, the less transpiration it means and vice versa.

Eco-Engineering（5）113

relatively low, and those in the other regions were higher.

3. Results and discussion

The mean values of NDVI and iTVDI for each of the land

Spatial variations of the NDVI and iTVDI are presented incover types and S.D. are shown in Fig. 5. Although the stand-Fig. 4. From an overall perspective, it is shown that the NDVI

ard deviation of both iTVDI and NDVI for each land cov-of forest regions (Fig. 1) were higher in comparison to the oth-er type was high, the standard error ranged from 0.0001 ofer land cover types and in the mountainous area (Fig. 2(A)),

deciduous forest to 0.0011 of bare land for iTVDI and fromthe NDVI values of eastern part were higher than western

0.0001 of deciduous forest to 0.001 of bare land for NDVI.

part. On the other hand, the iTVDI in the forest region wereThe mean values of iTVDI in evergreen forest, deciduous
forest and mixed forest regions were comparatively low withvalues of 0.37, 0.42, and 0.43, respectively. The mean values of NDVI, however, were higher, with value of 0.76, 0.75
and 0.72, respectively. The mean values of iTVDI for grassland, cropland, orchard, were as high as 0.59, 0.64, and 0.66.
Especially, the paddy field, of which the iTVDI value wasexpected to be as low as water body, had a high iTVDI valueof 0.55, while that of the water was 0.26. In the case of theresidential area and the urban area, the mean values of iTVDI were 0.62 and 0.58, respectively, and the mean values
of NDVI were 0.45 and 0.35. The mean value of iTVDI andNDVI of the bare land were 0.50 and 0.46, and the mean valueof iTVDI of water was 0.26. According to the Tukey-Kramer
test ( p < 0.05) there are significant differences in both iTVDI
and NDVI values among land cover types.
The NDVI has a positive relationship with fraction of

0.2
0.0114（6）kWaterUrban area

jK

0.0

Paddy field

0.4

E

0.2

Residential area

0.6

NDVI

0.4

Bare land

0.8

0.6

Orchard

0.0
iTVDI
1.0

0.8

Cropland

0.2

aGrassland

0.4

J

Deciduous forest

0.6

iTVDI, NDVI0.8

iTVDIb c D B AGg C Fde fh i
I H

Evergreen forest1.0

Mixed forest

NDVI
1.0

Land cover type

Eco-Engineeringvegetation cover and LAI, and high LAI could increaseinterpret why the iTVDI value of paddy field was higher thanthe maximum transpiration rate of canopy (Mauser andexpectation. The mean values of NDVI and iTVDI of theSchädlich, 1998; Jones and Vaughan, 2010). Consequently,

two classes are shown in Fig. 6. The mean value of iTVDI ofthe forest regions which have high LAI, had higher NDVI

water-covered class reduced while that of not water-coveredvalues and lower iTVDI values in comparison to the otherclass increase and the mean values of NDVI for both classes

types except for water, and this is in accordance with the re-decreased little. In the same way, the grassland, cropland andsults of Naito et al. (2012). And under the influences of differ-the orchard were also classified into two classes (full-covered

ent dominant species and the season used in the analysis, theand partial-covered), respectively. The results are shown inNDVI values of each type of the forests and grassland wereFig. 6. In the case of the full-covered pixels of these threequite lower and iTVDI were higher than the results of Naito

types, the mean values of the iTVDI reduced in comparison toet al. (2012).

before classification while the NDVI increased. On the con-On the other hand, the iTVDI of the paddy field, grass-trary, in the case of the partial-vegetated pixels of them, theland, cropland and the orchard were higher while their NDVI

mean values of iTVDI increased while the NDVI decreased

were not very low. Therefore, in order to identify the reason,in comparison to the values before classification.

the paddy field was classified into water-covered and not

These results suggest that the different land cover types

water-covered based on the reflectance value of the Landsat

could cause the differences in iTVDI and NDVI values, more-image, and it was resulted in that the water-covered pixels

over, the regions with high LAI always result in low iTVDI

just account for 15.5% of the whole paddy field. This canvalues and the regions with low LAI always result in high
iTVDI values except for water. It means that the different land
cover types even the different plant communities of a land
cover type have different transpiration rate which, to an extent, can reflect the vegetation condition. According to Sagarand William (1988), air pollutant would injures the leaves
by entering the stomata and changing the integrity of cells.
Thereby it may influence the transpiration rate. Therefore,
the iTVDI may have potential to be used in detecting the vegetation damage caused by air pollutant.

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