©

Standard Article - doi: 10.3832/ifor0592-004

iForest – Biogeosciences and Forestry

Collection: COST Action FP0903 (2010) - Rome (Italy)“Research, monitoring and modelling in the study of climate change and air pollution
impacts on forest ecosystems”
Guest Editors: E Paoletti, J-P Tuovinen, N Clarke, G Matteucci, R Matyssek, G Wieser, R
Fischer, P Cudlin, N Potocic

Remote sensing of Japanese beech forest
decline using an improved Temperature
Vegetation Dryness Index (iTVDI)
Ishimura A, Shimizu Y, Rahimzadeh-Bajgiran P, Omasa K
The Tanzawa Mountains, which cover parts of Kanagawa, Yamanashi and
Shizuoka prefectures in Japan, are known for their natural beech forests.
Since the 1980s, decline of the beech forests, probably caused by air pollution,water stress and insect infestation, has become a serious problem. We esti­
mated the natural beech forest mortality rate in the mountains by using mul­ti-temporal 8-day composite data recorded at the MODIS instrument aboard the
Terra satellite, daily air temperature data at meteorological stations (AMeDAS)
in 2007, and a global digital elevation model obtained from ASTER aboard the
Terra satellite. For the estimation, we used a Normalized Difference Vegeta­tion Index (NDVI) indicating the vegetation density, a Temperature Vegetation
Dryness Index (TVDI), and an improved TVDI (iTVDI) indicating the differencesin transpiration rates between areas of similar vegetation density. We com­pared the NDVI, TVDI, and iTVDI maps with an existing mortality map of beech
forests in the study area to verify their accuracy. To produce iTVDI maps, we
calculated maps of air temperature by using ambient air temperature and ele­vation data. By interpolation using an environmental lapse rate, we calibrated
air temperature maps with good accuracy (RMSE = 0.49 °C). The iTVDI map
could detect mortality more accurately than the NDVI and TVDI maps in bothspring and summer. Use of iTVDI enabled us to detect forest decline caused byair pollution and water deficits, inducing a reduction in transpiration rates.
This index should be useful for monitoring vegetation decline.
Keywords: Decline, Forest, MODIS, Normalized Difference Vegetation Index(NDVI), Improved Temperature Vegetation Dryness Index (iTVDI)

Introduction

Surveys and monitoring of plant responses
to environmental stress are important for us
to understand plant functions and preserve
ecosystems. However, in mountainous areas,
it is difficult to conduct regular field surveys
and more efficient monitoring methods areneeded. Remote-sensing data obtained fromsensors aboard satellites or aircraft can beused for surveys in such areas. Thermal sen­sing is useful for detecting vegetation re­sponses such as stomatal closure, spectral
sensing is useful for detecting declines inchlorophyll content, and lidar is useful for
detecting changes in growth (Omasa et al.
2006, Jones & Vaughan 2010). In examples
of studies that have used remote-sensing data
to estimate vegetation conditions, Omasa etal. (1993) and Omasa (2002) examined theeffect of temperature differences on transpir­
ation rate and stomatal conductance in areas
near urban environments affected by air pol­© SISEF http://www.sisef.it/iforest/

lution. Rahimzadeh-Bajgiran et al. (2009)used MOderate Resolution Imaging Spec­
troradiometer (MODIS) satellite data to exa­mine drought in semi-arid regions. The spa­tial resolution of MODIS is only moderate(250, 500, or 1000 m), but it can observe
land surfaces frequently and over wide areas.
Thus, MODIS data are useful for studying
and analyzing wide ranges of land cover andtime series changes.
These studies focused not only on land
cover classification, but also analyzing in­formation on the physiological and ecologi­
cal aspects of vegetation by calculating in­dices correlated with vegetation density orevapotranspiration rate. For example, theNormalized Difference Vegetation Index(NDVI - Rouse et al. 1973) evaluates thedensity of vegetation. The Temperature Ve­
getation Dryness Index (TVDI - Sandholt etal. 2002) and the improved Temperature Ve­
getation Dryness Index (iTVDI - Rahimza­

195

Department of Biological andEnvironmental Engineering, Graduate Schoolof Agricultural and Life Sciences, Universityof Tokyo, 1-1-1 Yayoi, Bunkyo-ku, 113-8657
Tokyo (Japan)@ Kenji Omasa (aomasa@mail.ecc.utokyo.ac.jp)
Received: Nov 30, 2010 - Accepted: Jul 20,
2011
Citation: Ishimura A, Shimizu Y,
Rahimzadeh-Bajgiran P, Omasa K, 2011.
Remote sensing of Japanese beech forest
decline using an improved Temperature
Vegetation Dryness Index (iTVDI). iForest 4:
195-199 [online 2011-11-03] URL:
http://www.sisef.it/iforest/show.php?
id=592

deh-Bajgiran et al. 2011) evaluate the evapo­transpiration rate. TVDI is calculated byusing a vegetation index (e.g., the NDVI)
and the surface temperature. In contrast,
iTVDI is calculated by using a vegetation in­dex and the difference between surface andair temperatures. In fully covered vegetation
areas in which the leaf surface is not wet,
TVDI and iTVDI can be used to evaluate
differences in the rates of evapotranspiration
over areas of the same vegetation density.
iTVDI corrects for the effect of elevation on
surface temperature by using air temperature.
In the Tanzawa Mountains, which were thefocus of this study, the existence of deadtrees and trees with discolored leaves andlow biomass has been reported (Yamane etal. 2007a). By using iTVDI, we can expect
to detect trees that have normal quantities ofleaves but depressed evapotranspiration rates
because of stomatal closure caused by lackof water or air pollution. To detect the on­going forest decline (Yamane et al. 2007b)that is occurring in the Tanzawa Mountains,
we need to evaluate such information on thephysiological and ecological condition of ve­getation.
Here, we made NDVI, TVDI, and iTVDI
maps from MODIS data retrieved frequently
in 2007 and compared them with an existing
mortality map of beech forests in the study
area (Koshiji et al. 1996). We examined thepossibility of estimating the beech forest
mortality rate and differences in evapotran­
spiration related to stomatal closure.

Materials and methods
Study area
The study area covered part of the Tanzawa
Mountains of Japan (Fig. 1 - about 6300
km2: 138.62-139.40°E, 35.33-36.00 °N). The
Tanzawa Mountains include part of
Kanagawa, Yamanashi and Shizuoka prefec­tures. Natural beech forests are preserved in

iForest (2011) 4: 195-199

Ishimura A et al. - iForest 4: 195-199

NDVI maps

Fig. 1 - Location of the study
area. Dotted line in the lower
figure indicates Tanzawa-Oy­
ama Quasi-National Park
where serious beech forest de­cline is occurring.

NDVI, which uses the reflectance in thenear-infrared and red bands, is a popular in­dex for vegetation monitoring. NDVI re­flects both chlorophyll content and vegeta­tion amount. MODIS observes these wave­
lengths as band 2 (841 to 876 nm - near in­frared) and band 1 (620 to 670 nm - red).
NDVI is calculated as follows (eqn. 1):
NDVI =

NIR− RED Band 2−Band 1=
NIR+ RED Band 2+Band 1

where RED and NIR represent the spectralreflectance measurements acquired in the red
and near-infrared regions, respectively.

Maps of surface temperature and of sur­face temperature minus air temperature

the areas, which has been designated a
Quasi-National Park. However, decline of
the beech forests has become a serious pro­blem since the 1980s and continues tospread. Koshiji et al. (1996) reported that theextent of forest containing more than 201
dead trees per 1 km2 was about 55 km2 in
1985. Air pollution (mainly from ozone),water stress, and insect infestation arethought to be factors in this decline, but themain factors have not yet been disclosed yet
and the mechanism of the decline remains
unknown (Yamane et al. 2007b). Our study
area included Tanzawa National Park wheremost of the decline has been reported (Fig.
1).

Data
We used remote-sensing data obtained
from the MODIS sensor aboard the Terra sa­
tellite. MODIS has 36 spectral bands (wave­
length ranging from 0.40 to 14.4 μm) andsupplies many kinds of data products. We
used MOD09A1 data, which provide imagesof reflectance (8 bands, 459 to 2155 nm,with 500 m pixel resolution) and MOD11A2
data, which provide images of land surface
temperature (with 1000 m pixel resolution).
Each dataset is a composite of 8 days’ data
to reduce the effects of clouds and noise. We
used 46 data sets from 2007. In the study
area, the number of pixels (with 500 m pixel
resolution) was 24 820.

iForest (2011) 4: 195-199

We used daily air temperature and eleva­
tion data observed at 10 meteorological sta­tions (Automated Meteorological Data Ac­
quisition System - AMeDAS) in the study
area and a Global Digital Elevation Model(GDEM - with 30 m pixel resolution) ob­
tained from the ASTER sensor abroad the
Terra to produce air temperature maps.

Methods
The methodology is illustrated in the flow
chart in Fig. 2. We made maps of NDVI,TVDI and iTVDI and compared them with aforest mortality map (Koshiji et al. 1996).
For image processing, we reprojected theseremote-sensing images and masked them toreduce the effect of cloud and noise. The
mortality map showed the number of deadtrees per 1 km2. Koshiji et al. (1996) usedaerial photography (with 25 cm pixel resolu­tion) from 1985 and counted the number ofdead trees by visual observation. The extent
of the mortality was classified into threelevels, low, moderate, or high. Low meansthat there were fewer than 201 dead trees per
1 km2, moderate means 201 to 400, and highmeans more than 401. Yamane et al. (2007a)
conducted a ground survey in the Tanzawa
Mountains in 2007 and suggested that thearea where there had been more dead trees in1985 represented stronger decline in 2007.
Therefore, we used the 1985 mortality map
as ground truth data for 2007.

196

Generally, surface temperature (Ts) andsurface temperature minus air temperature(Ts – Ta) reflect evapotranspiration rates, be­cause the Ts value is reduced by latent heat.
Therefore, we can use Ts and (Ts – Ta) as
indices of the evapotranspiration rates. (Ts –Ta) is the surface temperature corrected by
Ta. We consider that this value works well inmountainous areas, where the effect of ele­vation on Ts cannot be ignored.
We made (Ts – Ta) maps to produce
iTVDI maps, calculating the differences
between the Ts and Ta maps. We obtained
Ts maps from MODIS data and made the Tamaps from AMeDAS data and the GDEM.
We calculated the daily air temperature value
at each pixel by weighting the nearest four
AMeDAS stations’ temperature values using
the inverse distance-weighted algorithm
between each pixel and each of the four sta­tions, and we corrected the air temperature
values by using the environmental lapse rate.
We used 6.49 (°C / 1000 m) as the lapse
rate, as defined by the International Standard
Atmosphere model. Because the land surface
temperature values obtained by MODIS are
8-day composites, we calculated 8-day mean
air temperatures. We obtained 46 (Ts – Ta)
map scenes and calculated their differences
between them.

TVDI and iTVDI maps
TVDI and iTVDI are indices developed toestimate the evapotranspiration per each ve­getation amount. These indices were de­veloped from the water deficit index (WDI Moran et al. 1994). WDI quantifies the rateof evapotranspiration from partial vegetation
cover. In a vegetation environment where thepotential maximum evapotranspiration rate
is LEp and the actual one is LE, WDI is cal­culated by using eqn. 2. When the evapo­transpiration rate is relatively small, the WDI
value approaches 1 (eqn. 2):
WDI =1−(Ts−Ta )obs −(Ts−Ta )dry
LE
=1−
LE p
(Ts−Ta )wet −(Ts−Ta )dry

© SISEF http://www.sisef.it/iforest/

An improved index of beech forest decline
Fig. 2 - Flow chart of the image
processing procedure. MO­
D09A1 and MOD11A2 are
MODIS data. AMeDAS data
have daily temperature values
and altitudes. GDEM is the
Global Digital Elevation Modelobtained by using ASTER. Ts issurface (soil and canopy) tem­
perature, Ta is air temperature.
Ground-truth data are GIS data
on beech forest mortality
in the Tanzawa Mountains(Koshiji et al. 1996).

where (Ts – Ta)obs is the surface temperatureminus air temperature at the observation
time, and (Ts – Ta)wet and (Ts – Ta)dry are (re­spectively) the minimum and maximum (Ts– Ta) for the same vegetation index value(e.g., NDVI) in the trapezoidal shape of thevegetation index vs. (Ts – Ta). WDI needs
calibration using Penman-Monteith com­bined with energy balance equation (Men­dez-Barroso et al. 2008). Therefore, in the
WDI model, some of the inputs required toestimate the vertices of the trapezoid are dif­ficult to assess (e.g., aerodynamic resistance,
maximum and minimum canopy resistance,and soil heat flux).
TVDI and iTVDI were developed to apply
the WDI principle to natural ecosystem stu­
dies on a large scale. These indices are re­trieved by using satellite and meteorological
data and by calculating the positions of twolines (Fig. 3, segments 1-2 and 3-4) thatdefine the top and bottom of the plots of the
Ts vs. NDVI space (for TVDI) and the (Ts –Ta) vs. NDVI space (for iTVDI). The theo­
retical shape resulting from (Ts – Ta) vs.
NDVI that is used to derive iTVDI is presen­
ted in Fig. 3. The n vertices (n = 1, 2, 3, 4) inFig. 3 indicate well-watered full-cover vege­
tation, full-cover vegetation suffering highwater stress, saturated bare soil, and dry baresoil, respectively. Both TVDI and iTVDI in­
dicate the relative position of Ts or (Ts – Ta)on a line on which NDVI remains constant.
iTVDI at point C in Fig. 3 is calculated byusing segments AB and BC as in eqn. 3 andTVDI is calculated by using the same prin­ciple as in eqn. 4 (eqn. 3, eqn. 4):
iTVDI =1−

Fig. 3 - Definition of iTVDI. Plots are calculated using the data from 1 to 9 May 2007. This
trapezoidal shape shows the differences in potential evapotranspiration under each vegeta­tion density. The iTVDI at point C is given by 1-BC/AB, as shown in eqn. 3.
Fig. 4 - Ta map (avera­
ge of 28 July to 4 Au­
gust 2007) calculated
from AMeDAS data
and GDEM data. The
region is the sameas in Fig. 1.

© SISEF http://www.sisef.it/iforest/

197(Ts−Ta)obs−(Ts−Ta)max
BC=1−(Ts−Ta)min−(Ts−Ta)max
AB

TVDI =1−(Ts)obs−(Ts)max(Ts)min−(Ts)max

We selected a study area that included many
characteristic environments, including forest,
soil, and cropland. We positioned the rightand left lines of the trapezoid to include 95%of all plots (mean of NDVI ± 2 standard de­viations [SD]) and the top and bottom lines
to delineate 10 plots including 99% of all theoriginal study plots (mean of (Ts – Ta) ± 3
SD) at each step (by dividing the difference
between the right and left edges into 10 parts- Fig. 3).
Both TVDI and iTVDI vary between 0 and
1. Values increased in environments where
evapotranspiration rates are low. These va­
lues can be used to estimate differences in
evapotranspiration rates between areas withthe same vegetation amount. In mountainous
areas such as the Tanzawa Mountains, envi­
ronmental lapse rate correction should work
well on iTVDI.

iForest (2011) 4: 195-199

Ishimura A et al. - iForest 4: 195-199

Results and Discussion
Ta map
We made Ta maps from the data from the
10 AMeDAS stations and the GDEM data
(example for the period July 28 to August 4,
2007 - Fig. 4). To verify the accuracy of themethod, we made maps by using the datafrom nine of the stations and compared theestimated data with the observations at the
10th, excluded, station. The method cali­brated air temperature with good accuracy(RMSE = 0.49 °C).

Spatial comprehension between NDVI,TVDI and iTVDI
We focused on two temporal datasets,spring (May 1 to May 8, 2007), when theleaves emerge, and summer (July 28 to Au­
gust 4, 2007) when the amount of leaf coverpeaks. We show NDVI, TVDI, and iTVDI
maps for summer (Fig. 5). The black pixels
are areas where there are no data because ofcloud masking. The NDVI values at almost
all pixels were greater than 0.8. This sugge­
sted that the NDVI was unable to detect
dead trees. In contrast, the TVDI and iTVDI
maps had spatially varied values. The fin­ding that the NDVI map had uniform values
suggested that there were areas where theevapotranspiration rates were different des­pite the same vegetation amounts.

Statistical evaluation of NDVI, TVDI
and iTVDI
We verified the NDVI, TVDI, and iTVDI
values at the pixels as Low, Moderate, orHigh on the beech forest mortality map(Koshiji et al. 1996 - Fig. 6). The data forboth spring and summer showed that iTVDI
had a better relationship with mortality rate
than did NDVI or TVDI. As was found in
the comparison of summer data among thethree indices, NDVI values in summer were

Fig. 5 - NDVI, TVDI, and iTVDI maps calculated from MODIS composite data obtained
from 28 July to 4 August 2007. Black pixels are areas where there are no data because ofcloud masking. The dotted region is a magnification of that in Fig. 1.
so high (near 0.9) that NDVI did not detect
dead trees. TVDI values were unreliable insummer, because areas with low evapotran­
spiration rates should have had relatively
low values.
NDVI was accurate only for the spring

data. TVDI did not work well in either sea­son, but iTVDI worked well in both seasons.
iTVDI detects differences in evapotranspira­tion in areas with the same vegetation
amount. Thus, iTVDI detected well-covered
areas where transpiration rates were low;
Fig. 6 - Values of NDVI, TVDI and iTVDI inareas classified as Low, Moderate, and Highin the mortality map in spring and summer.
Columns marked by different letters differ
significantly at P < 0.05 according one-way
ANOVA followed by LSD (least significant
difference) tests. Error bars show the standard
error of the mean.

iForest (2011) 4: 195-199

198© SISEF http://www.sisef.it/iforest/

An improved index of beech forest decline
these areas tended to have dead trees. The
difference between TVDI and iTVDI
showed that the effect of an elevation in Ts
could not be ignored. Reduction in transpira­tion rates is likely attributable to stomatalclosure caused by air pollution and water
stress. iTVDI suggested the existence ofareas on the map where transpiration rates
were reduced by stomatal closure. Judging
from the above, we found that indices calcu­
lated from the MODIS data and the meteoro­
logical data, especially iTVDI, could be used
to estimate forest mortality rate in bothspring and summer. iTVDI can thus be used
to detect areas that are suffering a reduction
in transpiration rates and are at risk of future
decline. It should therefore be useful as anindex for monitoring vegetation decline.
Comparison with other remote-sensing stu­
dies of forests reveals that vegetation indicestend to be saturated in the high biomass re­gion, and there has been hope for a vegeta­tion index that sensitively reflects the vege­
tation status in such regions (Huete et al.
2002). We found that iTVDI worked well inhigh biomass regions such as those in the
Tanzawa Mountains.

Conclusions

Diagnosis and monitoring of plant re­sponses to environmental stress are impor­tant to our understanding and preservation ofplant function. We tried to detect forest mor­tality rate in the Tanzawa Mountains by cal­culating NDVI, TVDI and iTVDI maps
using MODIS data. NDVI was saturated andTVDI was unreliable in summer. In contrast,
iTVDI worked well in both spring and sum­
mer and showed a good relationship with
forest mortality rate.
iTVDI detects areas where evapotranspira­tion rates are relatively reduced and the ro­bustness of this index makes it useful formonitoring mountainous areas. We therefore© SISEF http://www.sisef.it/iforest/

expect that it will be a good index for moni­
toring vegetation decline.

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