31Development and Validation of New Glacial Lake Inventory
in the Bhutan Himalayas Using ALOS ‘DAICHI’
Takeo TADONO1*, Sachi KAWAMOTO2, Chiyuki NARAMA3, Tsutomu YAMANOKUCHI2,
Jinro UKITA4, Nobuhiro TOMIYAMA2 and Hironori YABUKI5
1

Earth Observation Research Center, Japan Aerospace Exploration Agency
2-1-1, Sengen, Tsukuba, Ibaraki 305-8505, Japan
2
Remote Sensing Technology Center of Japan, Tsukuba, Ibaraki 305-8505, Japan
3
Research Institute for Humanity and Nature, Kyoto 603-8047, Japan
4
Faculty of Science, Niigata University, Niigata 950-2181, Japan
5
Japan Agency for Marine-Earth Science and Technology, Yokosuka, Kanagawa 237-0061, Japan* e-mail: tadono.takeo@jaxa.jp

Abstract
This study aims to develop and validate a new glacial lake inventory in order to gain an understanding ofexisting conditions. This is being done using optical imageries acquired by the Panchromatic Remote-sensing
Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type 2(AVNIR-2) onboard the Advanced Land Observing Satellite (ALOS, nicknamed ‘Daichi’). Glacial lakes cancause outburst floods when natural dams break, and they represent a serious hazard to downstream regions. A
major challenge associated with glacial lake outburst floods (GLOFs) is the inability to predict when they will
occur or how much damage they will cause. In order to mitigate GLOF hazard risks, a new glacial lake inventory of the Bhutan Himalayas using ALOS imageries is currently being developed, combining PRISM and
AVNIR-2 results in imaging with a fine spatial resolution of 2.5 m and multi-spectral precise geolocation. In
addition, PRISM can derive precise digital terrain information, i.e., digital surface model (DSM) using its
along-track stereo capabilities. We describe the methodology used in the development of the ALOS-based
glacial lake inventory, including image processing procedures, glacial lake extraction, accuracy assessmentsof the generated PRISM DSM for the entire country of Bhutan, and validation of the inventory.
Key words: ALOS, AVNIR-2, DSM, glacial lake, GLOF, PRISM

1. Introduction
Failure of glacial lakes which are dammed bymoraines causes outburst floods and poses a serious
hazard to downstream regions. Some researchers believethat global warming has caused the development andexpansion of glacial lakes. On the other hand, otherresearchers state that there is no clear evidence foraccelerated expansion from recent warming, especially inthe 2000s (e.g., Fujita et al., 2009). Be that as it may,people living in such regions are exposed to the risk ofglacial lake outburst floods (GLOFs). This danger exists
not only in the Bhutan Himalayan region but also in othercold mountainous regions around the world. To mitigate
the damage caused by GLOFs, it is necessary to take acomprehensive approach, including investigations into
mass balance, especially melting and shrinking processesof glaciers, evolution processes of glacial lakes, thepotential of glacial lakes for outbursts, GLOF trigger
mechanisms, and preparations for risk management, such
Global Environmental Research
16/2012: 31-40
printed in Japanas operational monitoring, hazard mapping and early
warning systems.
An international research project entitled “Study on
Glacial Lake Outburst Floods in the Bhutan Himalayas”
has been implemented since 2009 as one of the “Scienceand Technology Research Partnership for Sustainable
Development” (SATREPS) programs supported by theJapan Science and Technology Agency (JST) and theJapan International Cooperation Agency (JICA). The
project aims to achieve comprehensive adaptations for
GLOF risks in Bhutan (Fujita et al., 2012). One of thetasks of the project is the development of a new glaciallake inventory using satellite imagery in order to gain anunderstanding of existing conditions and the expansion
history of glacial lakes in Bhutan.
Recently, various types of satellite data with different
resolutions, recurrences, numbers of spectral bands,
functional capabilities and costs have become available.
The accuracy of satellite data has also significantly
improved. They are effective in monitoring surface©2012 AIRIES32

T. TADONO et al.

conditions, including in remote regions where most of themountainous glaciers and glacial lakes in the world arelocated. In addition, data from a different set of satellites
make it possible to identify temporal changes and put
them into a historical context. Although satellite data aredefinitely not a panacea for monitoring everything, they
have become one of the most efficient and important
tools.
The objective of this study is to develop a new glaciallake inventory for the Bhutan Himalayan region by utilizing the Panchromatic Remote-sensing Instrument for
Stereo Mapping (PRISM) and the Advanced Visible andNear Infrared Radiometer type-2 (AVNIR-2) onboard theAdvanced Land Observing Satellite (ALOS, nicknamed‘Daichi’), which operated from January 2006 to April
2011. The latest glacial lake inventory for the Bhutan
Himalayas was published in 2001 by the International
Centre for Integrated Mountain Development (ICIMOD)
in Nepal (Mool et al., 2001). This inventory was based on
Landsat-5 TM data (30 m resolution) in 1999, andprovided the information for all glacial lakes in the
Bhutan Himalayas. It was reported that expansion ofsome glacial lakes was noted; however, changes in theconditions of glacial lakes in the Bhutan Himalayas since2000 were not incorporated. This study explains theprocedure for establishing the newly developed inventory in the following steps: image processing, lake
extraction, and screening of extracted lakes to identify
glacial lakes. The digital surface models (DSMs) weregenerated by PRISM for the entire country of Bhutan.
The generated DSMs along with the ALOS-based
inventory were validated by comparing them to theexisting digital elevation model (DEM) and groundbased global positioning system (GPS) measurements.

2. DataIn this study, our primary focus was on the dataprocessing carried out by PRISM and AVNIR-2 onboard
ALOS, which was launched on January 24, 2006, andcontinued to operate well even after the mission target
life of five years (Shimada et al., 2010). However, its
operation moved to the low load mode (LLM) on April
22, 2011 due to a power generation anomaly. This was
subsequently followed by an official termination of its
mission on May 12, 2011. Notwithstanding, approximately 6,500,000 scenes of archived data covering theentire globe are available to users. ALOS had three mission instruments: an L-band Synthetic Aperture Radarcalled PALSAR and two optical sensors. PRISM consisted of three radiometers: nadir-, forward-, and backward-looking, and had a 2.5 m spatial resolution with a
35 km swath width in triplet mode. It performed
along-track stereo observation to generate precise DSM.
AVNIR-2 had four radiometric bands ranging from visible to near infrared, and a 10 m resolution with a 70 km
swath width. They were both able to observe the same
area simultaneously.
Other satellite imageries such as KH-9/Hexagon,

SPOT-1 and 2, Landsat, JERS-1/OPS, and Terra/ASTER
were used primarily to investigate the expansion historyof the glacial lakes in the project. These results will be
included in the final version of the glacial lake inventory.
In cases where the above-mentioned instruments had
stereo capability, we attempted to extract the terrain
height information, i.e., DSM or DEM information,
because such information is beneficial for evaluating thepotential of GLOFs if the data were acquired over thelakes before a previous GLOF event.
We conducted field surveys in 2010 and 2011 in order
to collect ground control point (GCP) data, take continuous measurements while trekking and make lake size
measurements from portable GPS receivers. All groundbased GPS measurements were post-processed using
base-station GPS data. Some GCPs were used in processing the DSM and the ortho-rectified images (ORIs). The
measured glacial lake areas were used to validate theglacial lake inventory.

3. Methodology and Results
First, we established a procedure for the developmentof the glacial lake inventory by using satellite imageries
from image data processing that included ORI generation
for all satellite data used in our analyses, DSM generation
when possible, and the extraction of glacial lakes. It wasnecessary to correct the geometry of all satellite data inorder to compare them as they were acquired using
different satellite instruments with varying spatial resolutions, because one of the purposes was to investigate
the glacial lake expansion history. In this study, we
mainly describe the data processing procedure using
PRISM and AVNIR-2. Figure 1 shows the data processing flowchart of PRISM and AVNIR-2 aboard ALOS fordeveloping the glacial lake inventory in this study. Ukita
et al. (2011) and Tadono et al. (2011) reported onprogress in the processing and preliminary results of theglacial lake inventory.
The image data processing starts from ORI generation
for both PRISM and AVNIR-2 and DSM generation for
PRISM, for which our software, called the “DSM and
Ortho-image Generation Software for ALOS PRISM(DOGS-AP),” is used (Takaku & Tadono, 2009a).
DOGS-AP has the functions of data processing and

Fig. 1 The data processing flowchart of PRISM and AVNIR-2
for the development of the glacial lake inventory.

The ALOS-based glacial Lake Inventory Development in the Bhutan Himalayas

instrument calibration. Since the launch of ALOS, wehave carried out instrument calibrations of PRISM and
AVNIR-2 to improve the absolute accuracy of the standard products (Tadono et al., 2009). These results arealso reflected in the DOGS-AP software. As a result,
GCPs are not essential for PRISM. We validated thegenerated PRISM DSMs at several test sites with various
surface conditions (Takaku & Tadono, 2009b, 2010).
DSMs from the airborne Lidar and GCPs were used as
reference data. The results showed that the height
accuracies of PRISM DSMs fell in the range of 2.94 m to
7.15 m (root mean square error, RMSE), ~3.44 m (bias)
and ~6.44 m (standard deviation, STDEV). In addition,
we concluded in previous studies that the bias error
correction has only to consider the height of the DSM
even if GCPs were not used in processing them (Takaku& Tadono, 2011). The ORI of PRISM conducts simultaneous processing with DSM generation. To obtain theabove-mentioned height accuracy from the generated
PRISM DSM, ORI should also have precise planimetricaccuracy, except for low correlation regions, e.g., clouds,
oceans and saturated pixels. These pixels were maskedout before processing. We adopted a filter in the software
to derive height information in such masked areas fromsurrounding regions. PRISM DSM was generally
processed with 0.3 arcsec (approximately 10 m) pixel
spacing.
ORI generation was also carried out for AVNIR-2
using DOGS-AP with the existing DEM. The DEM
created by the Shuttle Radar Topography Mission(SRTM) was used for Bhutan. The absolute geometricaccuracy of AVNIR-2 was not as good as that of PRISM
due to hardware limitations (Tadono et al., 2009).

33

Therefore, GCPs were necessary for ORI processing.
These were collected from PRISM ORI and DSM if there
were no ground-based data. The AVNIR-2 images usedhere were acquired at zero degrees pointing angle, i.e.,
the nadir observation and the geolocation accuracy of the
ORI should therefore be basically within one pixel.
Pan-sharpened images were processed using both
ORI images from PRISM and AVNIR-2 to obtain 2.5 m
resolution color composite images. This is crucial for
identifying and interpreting detailed ground features as
required in making the glacial lake inventory. The
commercial software (ERDAS Imagine) was used in thisprocessing step. We tested a class of pan-sharpening
algorithms. Six methods were first selected from among
the available algorithms in the software: principal component analysis, multiplicative, the Brovey transformation, modified IHS, high-pass filtering, and subtractive
resolution merge (ERDAS Inc., 2008). The subtractive
method was finally selected for the image quality criterion, color balance, multiband usage and processing time.
Image tie points were collected between PRISM ORI as amaster image and AVNIR-2 ORI as a slave image, andthen the software automatically generated the pansharpened image. Figure 2 shows a pan-sharpened
mosaic image for the Bhutan Himalayas as of July 2011
with bands 3, 2 and 1 assigned as red, green, and blue,
respectively, and boundaries of river basins overlaid witha red line. The color dots show extracted glacial lakes.
The image covers almost the entire country of Bhutan,
except for some cloudy areas. Hence, it can be used not
only as a base image for the ALOS period but also as areference to other satellite imageries thanks to its fineresolution and precise geolocation.

Fig. 2 Pan-sharpened mosaic image by PRISM and AVNIR-2 in the Bhutan Himalayas as of July 2011
(red, green, blue = Band 3, 2, 1 as true color composite). The red lines show the boundaries of theriver basin and the colored dots show glacial lakes extracted in this study.

34

T. TADONO et al.

DSM mosaic processing was also carried out to create
another base image of Bhutan. There are three steps
involved in the processing: stacking, bias correction, andmosaicing. As mentioned above, the planimetric accuracy of PRISM ORI and DSM is basically high even if
processing is done without GCP. However, in this case abias height error may not be so negligible. Hence, DSM
stacking was done simply as a means of averaging theheights of all available scene-frame DSMs except for themasked areas. The bias offset of the DSM without GCP
was calculated by comparing height differences in overlapped areas with neighboring scene-frame DSMs, whichwere processed with GCPs. Figure 3 shows the mosaic
image of PRISM DSM in the Bhutan Himalayas as ofJuly 2011. The gray-scale corresponds to ellipsoid height,
which is defined as Geodetic Reference System 1980
(GRS 80) by the 1997 International Terrestrial Reference
Frame (ITRF 97) coordination, and black indicates areas
masked due to clouds. It has 0.3 arcsec (~10 m) pixel
spacing. Figure 4 gives a visual comparison between
PRISM DSM with 0.3 arcsec and SRTM with 3.0 arcsec(SRTM-3) as existing global DEMs. Details of the terrain
features can clearly be seen in PRISM DSM.
Before identifying glacial lakes in the image, all lakes
were extracted as much as possible so as to keep thequality consistent. Pan-sharpened images from PRISM
and AVNIR-2 were basically used as the base images for
extracting lakes. If an area of the pan-sharpened image
was not available due to cloud cover, AVNIR-2 was usedinstead to extract lakes. We tested automatic and semiautomatic methods of extracting water bodies from thepan-sharpened images. It turned out that these methods
were not feasible due to unstable effects caused by terrain,
shadows, clouds, frozen water and snow cover. Instead
manual digitizing was used to extract water bodies andthey were archieved as shape files. This was done using
commercial software (ERDAS Imagine and ArcGIS).
When a lake was clearly visible and distinguishable fromthe background, for example, when no ice was in lake, its
identification was rather straightforward. In order toensure that an object was a real lake, we consulted terrain
features from the PRISM DSM, e.g., contour lines,

Fig. 3 PRISM DSM mosaic in the Bhutan Himalayas as of July 2011. Black indicates areas masked due
to clouds, and red lines show the boundaries of the river basin. The yellow square represents themagnified area depicted in Fig. 5.

PRISM DSM

SRTM-3Fig. 4 Visual comparison between PRISM DSM (left) and SRTM-3 (right) in thesame region. The red on the right indicates void data areas in SRTM-3.

The ALOS-based glacial Lake Inventory Development in the Bhutan Himalayas

terrain slopes, elevation profiles and bird’s eye views.
This analysis provided additional terrain information
with regard to the location of ridges and divides, and to alarge extent eliminated false identification in shadow
zones.
Finally, glacial lakes were screened from the extracted lakes based on the following definition: a glaciallake is a body of water lying between the terminus of themother glacier and the Little Ice Age moraine that was
identified as a sparsely vegetated moraine surrounding
the lake (Iwata et al., 2002). Lakes located within 2 km of
the Little Ice Age moraine down-valley were also
included so as to take into account possible flooding
events with multiple lakes being involved. In addition,
supraglacial lakes on debris-covered glaciers were
included. We also set 0.01 km2 as the minimum lake sizein consideration of the fact that small lakes represent less
GLOF risk. Note that this is the definition which weadopted for our inventory. In fact it is different from theone for the existing glacial lake inventory by ICIMOD(Mool et al., 2001). The latter adopts the definition thatisolated lakes above 3,500 m a.s.l. are listed as theremnants of glacial lakes left over when the glaciers
retreated. The blue, yellow, orange, green, red, purple,
pink, light-green and sky-blue dots in Figure 2 represent
extracted ALOS-based glacial lakes across the Bhutan
Himalayan region as of July 2011. Those colors wereassigned for different river basins because our inventory
was also organized into nine separate river basins in
Bhutan: the Amo Chu, Wang Chu, Mo Chu, Pho Chu,
Mangde Chu, Chamkhar Chu, Kuri Chu and Dangme
Chu (‘Chu’ means river) and the Tibet side. The total
number of extracted lakes was 733. This figure is not
final as another round of quality control is currently inorder. The parameters of our inventory primarily followed the ICIMOD convention, including ID, coordinates of latitude and longitude of the lake center, area,
elevation, orientation, width and length of the lake, alongwith auxiliary information such as type of lake (e.g.,
moraine-dammed lake, ice-dammed lake, supraglaciallake). Each lake has its own ID based on the name of theriver basin and the latitude and longitude at the center ofthe lake. In addition, we provide a cross-reference withrespect to the ICIMOD code whenever available. Table 1
shows the contents of the ALOS-based glacial lakeinventory for the Mangde Chu basin as an example,
where 39 glacial lakes are listed. The inventory consistsof the shape files shown in Fig. 2, and the parametershown in Table 1.

4. Validations
In order to validate the generated PRISM DSM, the
ALOS-based glacial lake inventory was compared withreference data provided by ground-based GPS measurements and an existing DEM. First, the generated PRISM
DSM mosaic was compared to ground-based GPS measurements consisting of point measurements taken as
check points (CPs) in 2010 and 2011, and continuous

35

GPS measurements taken while trekking in 2010. These
measurements include some inaccurate data, which may
be caused by shadows from forests and steep terrain, as
well as conditions such as dilution of precision (DOP).
Therefore, a set of measurement points within +/–30 m oferrors with respect to the PRISM DSM were selected.
This set included a total of 3,268 GPS points consistingof the 3,198 continuously measured points in 2010,59 CPs in 2010, and 11 CPs in 2011. Figure 5 shows amagnified DSM mosaic of the area indicated by theyellow square in Fig. 3. The locations of the continuous
measurements are given by yellow dots, CPs in 2010 byred dots, and CPs in 2011 by blue dots. Figure 6 and theupper row of Table 2 summarize the validation results.
They confirm that the height accuracy of the PRISM
DSM mosaic was 2.3 m of bias error, 7.8 m of STDEV,
and 8.2 m of RMSE. These results are relatively consistent with our previous validation results as explained in
section 3 (Takaku & Tadono, 2009b, 2010).
Another validation effort for the DSM mosaic wascarried out using SRTM-3 as the existing DEM. This wasdone by simply overlaying both sets of data andcalculating height differences as PRISM DSM minus
SRTM-3 DEM. Figure 7 shows this height difference
image between the PRISM DSM and the SRTM-3 for theentire state of Bhutan. White represents areas in which
the height difference is around 0 m, while red and blue
graduations correspond to areas with around +/–30 m
differences. Note that black indicates areas masked in the
PRISM DSM, while green represents void data areas in
SRTM-3, which in many areas still remain void due tosteepness and other features of glacial terrain. Largeerrors shown by blues and reds in Fig. 7 are seen aroundthe north central section of the Bhutan Himalayas, whichis the highest and most mountainous region of Bhutan,
covered with glaciers and snow. These height errors may
have occurred in the PRISM DSM due to a lack ofimage-matching accuracy. However, regions of misinterpolation in SRTM-3 in northeastern Bhutan were notshaded in green as void data. Figure 8 is a histogram of
the height difference, and the lower row of Table 2
summarizes the statistical results of the validation. Morethan seven hundred million DSM heights were comparedin this validation, and the error clearly displayed characteristics of Gaussian distribution. The height accuracyof the PRISM DSM mosaic compared with SRTM-3 was– 0.4 m of bias and 20.7 m of STDEV and RMSE.
In these DSM validation exercises, it is difficult to becertain of the accuracy of the PRISM DSM mosaic
because both sets of reference data still remain uncertain.
The GPS measurements are limited in terms of area
coverage and accuracy, while SRTM-3 has void andinterpolation regions likely to have low accuracy.
Nonetheless, we can say that the height accuracy of the
PRISM DSM is around 8 to 20 m in RMSE. In addition, it
has a fine resolution with less than 20 m planimetricaccuracy and can provide detailed terrain features, as
shown in Fig. 4.

Table 1 Contents of the ALOS-based glacial lake inventory for the Mangde Chu basin.

36
T. TADONO et al.

The ALOS-based glacial Lake Inventory Development in the Bhutan Himalayas

Fig. 5 Magnified PRISM DSM mosaic in the northwestern region of the Bhutan Himalayas,
and location of GPS measurements points (yellow: continuous measurement in 2010;
red: CPs in 2010; and blue: CPs in 2011).

Fig. 6 Comparison of height between ground-based GPS measurements and
PRISM DSM (red: continuous measurement in 2010; green: CPs in2010; and blue: CPs in 2011).

Table 2 Summary of validation results for the PRISM DSM mosaic in
the Bhutan Himalayan region.
Reference
data

Total
number

Bias
error
(m)

Standard
deviation
(m)

RMSE
(m)

Ground-based
GPS

3,268

2.28

7.79

8.12

738,107,875

-0.44

20.71

20.72

SRTM-337

38

T. TADONO et al.

Fig. 7 Height difference image (i.e., PRISM DSM minus SRTM-3) in the Bhutan Himalayas.
Black indicates areas masked in the PRISM DSM, while the green represents the void data
areas in SRTM-3.

Fig. 8 Histogram of height differences between PRISM DSM
and SRTM-3.

Second, the ALOS-based glacial lake inventory wasvalidated by comparison with ground-based GPS measurements. Previous results on glacial lake inventories
have been reported by Ukita et al. (2011) and Tadono
et al. (2011). RMSE geolocation accuracy of 11.7 m andbias of 9.5 m were confirmed at the Metatshota Glacial
Lake in the Mangde Chu basin. This large bias error
included a gap between the actual shoreline and thatmeasured by walking with the GPS receiver around thelake. We also pointed out large location offsets between
our inventory and that of the ICIMOD (Mool et al., 2001).
Thus, a part of the difference in the lake size can be
attributed to the different observation times. However,this discrepancy appeared at other lakes, and the differences were not consistent. In order to look into thisdiscrepancy, we compared the inventory to ground-based
GPS measurements taken in 2011 at two other lakes in

the Wang Chu basin, located in western Bhutan. Figure 9shows a comparison of the polygons between theinventory (yellow) and the ground-based GPS measurements at those two lakes (green) that were overlaid onto
the pan-sharpened image. It can be clearly seen that theinventory more or less matches the ground measurements
for the westernmost lake. On the other hand, theeasternmost lake, called Karma Glacial Lake, has almost
the same location between the inventory and groundmeasurements. Yet the size of lake in the inventory islarger than that of the ground measurements. This size
difference is attributable to seasonal changes in water
depth, a phenomenon confirmed at Karma Lake during asurvey in 2011. Seasonal changes in the depth of water inglacial lakes, especially supraglacial lakes, remain acomplicated scientific research topic, and we will not
attempt to discuss the details in this paper. However,more careful considerations will be necessary whenanalyzing the expansion history of supraglacial lakes in
the future.

5. Conclusions
In this study, we have described and validated thedevelopment of a new glacial lake inventory for the
Bhutan Himalayan region using PRISM and AVNIR-2
onboard the ALOS satellite. We first processed orthorectified pan-sharpened images as well as PRISM DSMs
for the entire country of Bhutan. These images are
actually used not only for producing the glacial lakeinventory but also for other applications, such as providing base data, as there were no digital maps or terrain
models with fine enough spatial resolutions. The height
accuracy of the DSM mosaic was confirmed to be in therange of 8 m to 20 m RMSE using ground-based GPS

The ALOS-based glacial Lake Inventory Development in the Bhutan Himalayas

39

Fig. 9 Comparison of polygons between the ALOS-based inventory(yellow) and the ground-based GPS measurements (green) at twolakes in 2011. The background is the ALOS pan-sharpened image(red, green, blue = Band 3, 2, 1).

measurements and SRTM-3. Based on these base data,
the glacial lake inventory is in ongoing development, anda total of 733 lakes have so far been extracted for the
Bhutan Himalayan region. The geolocation accuracy of
the inventory was confirmed at two glacial lakes bycomparing the inventory data with actual ground measurements taken in 2011. Closer attention may be needed
for seasonal changes in water depth when considering the
supraglacial lake expansion history in the future. The
complete version of the ALOS-based glacial lake inventory will be available by March 2012 as planned and open
to the public via the web site at <http://www.eorc.jaxa.
jp/ALOS/en/bhutan_gli/index.htm>.

Acknowledgements
We would like to thank Mr. Junichi Takaku of theRemote Sensing Technology Center (RESTEC) of Japanfor assistance with the PRISM DSM generation. We
would also like to thank Prof. Nozomu Naito, Prof.
Takanobu Sawagaki, Dr. Satoru Yamaguchi, Prof. Jiro
Komori, and Prof. Koji Fujita for collecting andprocessing ground-based GPS data in 2010 and 2011.
This study was carried out under the project entitled“Study on Glacial Lake Outburst Floods in the Bhutan
Himalayas” supported by the Japan Science andTechnology Agency (JST) and the Japan International
Cooperation Agency (JICA), under the “Science andTechnology Research Partnership for Sustainable Development (SATREPS)”. We would also like to gratefully
acknowledge all our colleagues of the project for collaborating with us.

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Takeo TADONO
Takeo TADONO is an Associate Senior Researcher
at the Earth Observation Research Center(EORC), Japan Aerospace Exploration Agency(JAXA), Tsukuba, Ibaraki Pref., Japan, and is incharge of calibrating and validating optical
instruments on the Advanced Land Observing
Satellite (ALOS) and ALOS follow-on missions. He received his B.E.
degree in 1993, M.E. degree in 1995, and Ph.D. in 1998 in Civil
Engineering from Nagaoka University of Technology, Niigata Pref., Japan.
His research interests are calibration and validation of high-resolution
optical instruments and the development of algorithms for retrieving
geophysical and hydrological parameters from remote sensing data.

Sachi KAWAMOTO
Sachi KAWAMOTO works at the Remote Sensing
Technology Center of Japan (RESTEC), Tsukuba,
Ibaraki Pref., Japan. She is currently on loan fromthe Space Engineering Development (SED) Co.,
Ltd., Tokyo, Japan. She received her B.S. degreein 1993 in Marine Science from Tokai University,
Shizuoka Pref., Japan. She is in charge of geometric calibration andcharacterization of optical sensors on the Advanced Land Observing
Satellite (ALOS) including conducting field surveys.

Chiyuki NARAMA
Chiyuki NARAMA has been a Project Researcher
at the Research Institute for Humanity andNature (RIHN), Kyoto Pref., Japan since 2007,
and is working on reconstructing the relationshipbetween environmental changes and humanactivities in the Balkhash lake basin, Central
Eurasia during the last 1,000 years. His profession is physical geography,
and he received his Ph.D. in 2002 from Tokyo Metropolitan University,
Tokyo, Japan, with a focus on glacier changes in the Tien Shan andPamir-Alay mountains, Central Asia since the last glacial period. He has
been studying glacier changes, glacier hazards, and mountain environmental changes in Central Asia and the Indian Himalayas since 1996.

Tsutomu YAMANOKUCHI
Tsutomu YAMANOKUCHI joined the Remote
Sensing Technology Center of Japan (RESTEC)
in 1995. He is currently in charge of cryosphericapplications using ALOS data mainly at mountainglaciers and in Antarctica. He also dedicatesefforts to capacity building of satellite datautilization through hands-on training to users in developing countries. He
received his B.S. degree in Astrophysics from Nagoya University, Aichi
Pref., Japan in 1993, M.S. degree in Oceanography from Kyushu
University, Fukuoka Pref., Japan in 1995, and a Ph.D. in Science from the
Graduate University for Advanced Studies.

Jinro UKITA
Jinro UKITA has been a Professor of the Facultyof Science at Niigata University, Niigata Pref.,Japan since 2007. He received two M.S. degrees,
one in Oceanography from the University of
British Columbia and the other Atmospheric
Sciences from the University of Washington,
U.S., and his Ph.D. from Hokkaido University, Hokkaido Pref., Japan in
1998. From 1995 to 1996 he served as a lecturer at the University of Tokyo,
and then joined the National Space Development Agency of Japan(NASDA, the former JAXA), where he served as a research scientist anddeveloped a Japan-US joint science program for the International Arctic
Research Center (IARC) in Fairbanks Alaska, U.S. as a sub-leader. In 2000
he moved to the U.S. to join the cryosphere programme at NASA Goddard
Space Flight Center where he researched sea-ice. Between 1998 and 2004
he was an affiliated research scientist at the Lamont-Doherty Earth
Observatory of Columbia University, U.S.

Nobuhiro TOMIYAMA
Nobuhiro TOMIYAMA has worked at the Remote
Sensing Technology Center of Japan (RESTEC),Tokyo Pref., Japan since 2000 on various applications of remote sensing technologies forvolcanology, forestry, agriculture, and glaciology. He received his B.E. degree in 1998, andM.E. degree in 2000 from Kumamoto University, Kumamoto Pref., Japanfor research into analyses of crustal deformations and topographic changesof active volcanoes using the Interferometric SAR technique. He received
his Ph.D. in 2011 as an adult graduate student in the doctoral course at
Kumamoto University, Japan.

Hironori YABUKI
Hironori YABUKI is Team Leader of the
Terrestrial Environment Change Research Team,
Northern Hemisphere Cryosphere Program,Research Institute for Global Change of the Japan
Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Kanagawa Pref.,Japan. He received his M.S. degree from the Graduate School of Science,
Nagoya University, and Ph.D. from Nagoya University in 1998. He was a
researcher of the Frontier Research System for Global Change from 1998
to 2000, a research scientist at the Japan Marine Science and Technology
Center from 2000 to 2004, and a research scientist at JAMSTEC from 2004
to 2009.

(Received 25 October 2011, Accepted 13 February 2012)