Land Vegetation and Cryosphere: ADEOS Vegetation
Biology, ADEOS-Cryosphere & Hydrology
GLI-Land, GLI Vegetation and
GLE Cryosphere Group Joint Workshop.
-ADEOS-Cryosphere & Hydrology
and GLI-Cryosphere Group Joint Session-
Room H December 7 1999
Remarks:Dr. F. Nishio
Program and some results for ADEOS/ADEOS -II field campaign (F.
of Coastal Zone of Antarctica using ADEOS / AVNIR and microwave
sensors (Dr. H. Enomoto and Dr. F. Nishio)
between spectral reflectance and evaporation from vegetated surface
(A. Kondo and A. Higuchi)
applications of ADEOS / AVNIR images (K. Takara and Y.
anticipation for the ADEOS- II mission over Sea Ice, in view of ADEOS /
NSCAT results and the first SeaWind/QuikSCAT data (R. Ezraty and A.
reflection properties of flat snow surface at AEOS-II/GLI channels
mask over Snow/Ice surface and Snow Grain Size and Impurity Retrieval
using ADEOS-II/GLI measurements (K. Stamnes)
of Snow Grain size and Soot Concentration based on Neural Network
Land Vegetation and Cryosphere: ADEOS Vegetation Biology,
ADEOS-Cryosphere & Hydrology
GLI-Land, GLI Vegetation and GLE
Cryosphere Group Joint Workshop.
-ADEOS-Cryosphere & Hydrology and GLI-Cryosphere Group
Joint Session-Morning Session
Room H December 7 1999
1. Opening Remarks:Dr. F. NishioExplained contents
of presentation - each 20 minutes - and structure of Q&A session.
Introduced each speaker.
2. Validation Program and some results for ADEOS/ADEOS-II field
campaign (F. Nishio)First news about plenary session of
6.12.99, and that delay of ADEOS may be up to one year.
(Hokkaido University of Education) is working on ADEOS studies, and
algorithm for sea ice in both Arctic regions. Professor Nishio's
presentation covered the following topics:
(Dr. James Simpson, Scripps Oceanography U.C.S. San Diego) Is
retrieval for parameters standard satellite retrieval or model?
- Sea Ice and Climate in Okhotsk Sea
- Sea Ice Concentration - key mission of ADEOS-II, using AMSR. Will
use bootstrap algorithm.
- Data flow from brightness temperatures from AMSR, plus temperature
of sea ice and depth of snow on ice.
- Study of distribution of snow on sea ice (AMR and AMSS data) and
presentation of results so far.
- Model for the spectral albedo of pure snow
- Snow depth distribution algorithm - various data (e.g. snow grain
- AMR, AMSS actual results for snow depth and grain size distribution
- Wish to continue these studies with ADEOS-II using GLI
(Dr. Nishio) Algorithm using GLI data, also including other data, plus
Confirmed by BRDF correction for the snow being carried
3. Observation of Coastal Zone of Antarctica using ADEOS / AVNIR
and microwave sensors (Dr. H. Enomoto and Dr. F.
Nishio)Dr. Enomoto's (Kitami Institute of Technology)
presentation covered the following:
In conclusion, concluded that further observation was necessary,
using present and future data for break up of fast ice. See presentation
- Antarctic research for Japanese Group - East Antarctica
- Antarctic research station in Lutzow Holm Bay
- fast ice area break up studies
- data from ADEOS / AVNIR and Nimbus-7
- Speed of flow and fractation of Shirase (over interval of a few
- NDJF Mean Temperature (Showa Station)
- Surface conditions - breaking up/melting signs
- distribution of puddles on fast sea ice and open water
- hydrological process on coastal slope - melting features (using
ADEOS / AVNIR image)
- Meteorological data for 2 observations (31 Jan and 4 Feb) Warmer
weather showed rapid changes in
- retreat/break up of fast ice
- Data points from Luzthow Holm Bay, and microwave data for brightness
temperature gradient ratio and
- meteorological data
- 10 year melting index for Showa Station (1987 - 1998)
- Cracks near the edge of glacier - may indicate end point of break-up
Q: (Dr. Simpson): Microwave data can give false signature
for water on ice, light break up - how is this compensated
A: Compared meteorological data and field data (i.e. local
observation also used)
Q: (Dr. Nishio) : What is difference
between gradient ratio for melting index and gradient ratio of sea ice?
How about polarization?
A: 0 (zero) is whole ice cover, and
positive gradient measure means water is open. - (minus) 37 GHz was used.
Similar signs can be observed when ice is thick - but starting point could
not be used in 1998.
4. Relationship between spectral reflectance and evaporation from
vegetated surface (A. Kondo and A. Higuchi)Chiba
University - group of hydrologists. Presentation covered the above topic.
In conclusion, good relationship and evapotranspiration
rate, spectral brightness in VNIR wavelength and NDVI. More research is
needed in relationships for different vegetation.
- measuring surface state variables and evapotranspiration - empirical
relationship between measured flux
- and satellite data. Intend to use ADEOS / AVNIR.
- study area of Tsukuba Science City and flux stations (routine data
- satellite data from LANDSAT/TM and SPOT/XS data (NASA)
- Seasonal changes in Et and NDVI - relationship to evapotranspiration
- Red and Infrared Brightness - relation to daily evapotranspiration
- Upward short wave radiation/PAR and NIR components
- Seasonal changes in Albedo, IE/Qn and H/Qn
- Seasonal Variation of Spectral brightness (Red and NIR) and NDVI
- Volumetric water content of surface soil layer
- Relationship of NDVI with IE and IE/Qn
- Relationship of IE with NDVI and red band brightness (in growing
Q: (Dr. Andrew Tronin,
EORC, JAXA) I believe that NDVI is not the best parameter for forest,
only grassland/paddy field. Different parameters are needed (e.g.
different short wave bands)
- PGLIERC 1999 - preliminary global imager experiment
- North China Plain Project
A: (Dr. Kondo) I agree, correlation
obtained from forest data is not so good. Also, the forest used is small
and mixed, as it is difficult to obtain large forest areas in Japan - this
may be the main reason for poor data.
5. Hydrological applications of ADEOS / AVNIR images (K. Takara
and Y. Tachikawa)Dr. Takara of Kyoto University, (Disaster
Prevention Research Institute)
Started with outline of work carried out
by Disaster Prevention Research Institute and explained that he would
present hydrological applications of ADEOS / AVNIR images.
In conclusion - hydrological models are useful for large basins.
Land cover classification accuracy for run-off needs to be verified.
ADEOS-II /GLI images have more bands, so look forward to
- GEWEX - GAME/HUBEX
- Hydrological model structure (using AVNIR data)
- image examples from ADEOS / AVNIR
- Image processing system images
- Channel network dataset for Shiguang River and procedure for making
channel network dataset
- Model structure of routing model
- Structure of subsystem models and structure of total system models
- Results from Shiguang River basin (2 locations)
- Land cover classification application (with degraded resolution
- Image class cross plot graphs (unsupervised classification ->
assigned land cover class)
- Results of land cover classification (13 classes, best for the
- No of pixels for land cover classification (16-m and 2000-m)
Q: (Dr. Wang Zhihua, Aero Geophysical and Remote Sensing
Control, Ministry of Land and Resources, China) Do you use topographical
maps, is geometrical correction done before making measurements?
(Dr. Takara) Use processing system with ground control points (from
Q: (Dr. Wang Zhihua) How many ground control
A: (Dr. Y. Tachikawa) - not ground control points in
fact. Used four corners of location of image to rectify the
A: (Dr. Takara) For this analysis it doesn't matter -
compared results of classification on same image, so geometric location is
not so important for this analysis. This is an only unsupervised
classification result, if confirmed on ground, then geometrical correction
will be necessary.
Q: (Dr. Wing Zhihua) I understand, but for
land cover classification and formation there may still be problems.
6. An anticipation for the ADEOS II mission over Sea Ice, in view
of ADEOS / NSCAT results and the first SeaWind/QuikSCAT data (R.
Ezraty and A. Cavani)Presenters were unable to attend, so
presentation made by Prof. Nishio on their behalf. Professor Nishio
apologized that the topic was reverting to the imaging of sea ice.
SeaWinds very well suited for discriminating isotropic surfaces such as
sea ice. (e.g. discrimination of sea ice/water areas). Professor Nishio
able to make copies of this presentation as required for further
- Important for describing global changes, and this research is very
much applied (previously SSMI
- microwave data), and now using ADEOS, AMSR and SeaWinds data.
- NSCAT and SSMI data over 20 years
- ODDEN NSCAT data
- Empirical backscatter model over arctic sea ice
- QuikSCAT simulation from NSCAT data
- QuikSCAT backscatter maps over arctic ocean, normalized standard
deviation maps of arctic region, pixel
- normalized standard deviation maps
- QuikSCAT sea ice/open water discrimination images
- backscatter and normalized standard deviation maps for Antarctic
region, pixel values over Antarctic Ocean
Dr. Nishio announced coffee break until 11 am. (20
7. Bidirectional reflection properties of flat snow surface at
ADEOS-II/GLI channels (T. Aoki)Dr. Aoki presented his
studies on result of validation, as follows.
(Dr. Nishio) What is reason for difference between theoretical and
observation results for spectral albedo?
- Site of field experiment: Kitami city, Hokkaido
- Photographs of experiment instruments
- Results of snow depth work; snow type, snow grain size, density,
- Snow model
- Spectral albedo; correlation between snow surface albedo and
- Calculated results of BRDF
- Scattering angle model and phase function
- Observation results of BRDF; rainbows are not observed on
- Conclusion on Snow BRDF Observation
- Effects of aerosols; rural aerosol model
- Rayleigh Atmosphere and Atmosphere with rural aerosols
- BRDF rural - BRDF Rayleigh
- GLI viewing angle
- Conclusion for the Effect of Aerosols on Snow BRDF
A: (Dr. Aoki) At the
present time there is no explanation for the difference in this region
(indicating graph). One possible reason could be problems with refractive
index data or another possible reason could be a problem in the model. We
are not able to explain the difference at this stage. For example, the
snow surface albedo shows the same difference in data.
Nishio) Sometimes, there is consistent data.
A: (Dr. Aoki) Yes,
some areas are consistent. We have checked many snow models, but we can't
get agreement in this region (indicating graph)
8. Cloud mask over Snow/Ice surface and Snow Grain Size and
Impurity Retrieval using ADEOS-II/GLI measurements (K.
Stamnes)Dr. Stamnes presented his studies on cloud mask over
(Dr. Aoki) You say that snow size may change according to wavelength. If
you think inversely, can you estimate profile of snow grain size by using
- Why difficult; insufficient albedo and thermal contrast
- To arrive at a better confidence level, combine a set of tests
- Surface classification
- Snow/sea-ice discrimination
- Flow chart for processing one pixel
- Application cloud mask algorithm to satellite data
- Cloudy / clear discriminator image
- Summary of cloud mask algorithm
- Snow grain size and impurity retrieval
- Penetration depth of radiation
- Application of snow grain size and impurity retrieval algorithm to
satellite and simulated data
- Selection of aerosol model
- Flow chart for aerosol model discrimination
- Figure 3.4: the structure of the simulated GLI image
- Summary of snow grain size and impurity retrieval
A: (Dr. Stamnes) In principle it can be
done, and a 2/3 layer model is possible, but the question is how easy that
may be. We have tried the same concept for chlorophyll retrieval from the
ocean, but an operational algorithm would take some work.
(Dr. Tronin) Is retrieval valid for snow on land or on sea ice?
A: (Dr.Stamnes) Both. We only wish to distinguish between snow
over sea ice and bare sea ice.
Q: (Dr. Simpson) Do you eliminate
weeds in sea ice?
A: (Dr. Stamnes) We need to distinguish
between open water and snow covered sea ice, so we check that first. The
pixel can be a mixture of everything.
Q: (Dr. Simpson) I agree
that a mixed signal is possible. I just wanted to clarify. Thank
Q: (Dr. Tronin) When snow is wet during melting, is snow
A: (Dr. Stamnes) Firstly, there is a great
increase in grain size. There will be a point where it is so wet that it
doesn't look like snow any more to the wavelengths. Not sure how that
would influence reflectance. There is no water index in the model, so that
needs to be investigated.
9. Determination of Snow Grain size and Soot Concentration based on
Neural Network (T.Oishi)Contributors: Tomohiko Oishi
Teruo Aoki (MRI)
Akihiko Tanaka (Tokai Uni)
Q: (Dr. Simpson) Topographical structure can
be extremely sensitive; sensitivity tests are necessary. Have you carried
out sensitivity tests?
- Main purpose
- BRDF wavelength patterns
- General Procedure of Neural Network; Radiative Transfer Computation
Doubling and Adding Method
- (Aoki's model)
- Conditions of Computation
- Two Step Neural Network Mode (First and Second Step) and Equations
- Single Step Neural Network Mode
- Results of Two Step Mode; snow grain size and soot concentration:
estimate and true values
- Results of Single Step Mode (as above)
A: Yes, it is necessary. From results, we
can see that the Single Mode seems to be better. We have tried it many
times and results seem to be OK but sensitivity test must be carried out.
Q: (Dr. Simpson) Did you look at details of output probability
A: (Dr. Aoki) That is difficult to
Q: (Dr. Simpson) OK we can talk about it later.
(Dr. Nishio) Do you have validation criteria for this
A: We have carried out experiments since 1995/6 and
have experienced how to collect data. We need to continue to get more
reliable data and methods.
Dr. Nishio concluded the morning session
with a reminder to participants that JAXA is calling for manuscripts and
images to help with ADEOS and rocket launch problems. Please submit any
papers by March 31st 2000.
GLI-Land, GLI Vegetation and GLI
Cryosphere Group Joint Workshop.
-ADEOS-Cryosphere & Hydrology and
GLI-Cryosphere Group Joint Session-
Room H, December 7 1999
13:00 - 17:20
Site for ADEOS and ADEOS-II (Y. Honda)
land cover classification using ADEOS / AVNIR multi spectral
data(N.D. Duong) (Changed Title)
/ OCTS Mosaic data for vegetation monitoring (K. Kajiwara, Chiba
and Problems in NPP Estimation (changed title from Forest monitoring
using ADEOS and ADEOS-II) (Y. Awaya)
Monitoring with Oblique Viewing Remote Sensing (G Saito and T.
land surface parameters from ADEOS-II (Changed title, presented by
Geographical Position (Dr. T. Hashimoto)
Land Surface Radiation Budget Products from the ADEOS-II/GLI (Dr.
of Topographic Correction Algorithm for ADEOS-II GLI Data, for Use in
Forest Carbon Cycling and Primary Production Studies (C. Trotter)
of Spectral Indices Optimized for the GLI Instrument (M. M.
of New Vegetation Indices and Algorithms for Detecting Vegetation Change
Cover Classification by GLI 250m - Data (R. Tateishi)
Land Vegetation and Cryosphere: ADEOS Vegetation
Cryosphere & Hydrology
GLI-Land, GLI Vegetation
and GLI Cryosphere Group Joint Workshop.
Hydrology and GLI-Cryosphere Group Joint Session-
Room H, December 7 1999
13:00 - 17:20Dr. Honda opened the afternoon session
with an announcement that more ADEOS vegetation and GLI vegetation joint
sessions will be held on morning of 8.12.99, a free discussion session to
talk about harmonisation with GCOM. Also, there will be an evening PI
meeting on the same day.
1. Validation Site for ADEOS and ADEOS-II (Y.
Honda)Dr. Honda presented his studies on the topic above.
Q: (Dr. Michel Verstraete, Space
Applications Institute, Italy) How long does it take to make the
- City Light
- Vegetation map made by IGBP
- Validation site
- Explanation about sites
- Mobile system
- Distribution of Measurement points
- BRDF Measurement system
- Grass picking
- Measurement by 3D Scanning system; 3D laser scanner
- Monitoring using RCH(Radio-controlled helicopter); a helicopter
which has various measurement
- Programming flight; flight course
- Reflectance change by sensor zenith angle
- Digital Camera Images
- DEM (Digital Elevation Model)
- Other application; can be applied for forest and wetland
A: (Dr. Honda) It takes less than one second to
make the measurement, but the helicopter has to be stable for 8
Q: (Dr. Verstraete) When taking measurements, is the
helicopter moving or the sensor?
A: (Dr. Honda) The sensor is
2. Semi-automatic land cover classification using ADEOS / AVNIR
multi spectral data(N.D. Duong) (Changed Title)
Dr.Duong's presentation covered the following:
Q: (Dr. Honda) For your
time series data, how long an interval do you use for your
- Algorithm Development; improvement of GASC algorithm and 8 image
invariance for classification
- Program GASC_99.F90
- Computation flow chart
- Structure of CTRFILE
- Land Cover Definition by Image Invariant
- New Model for Color Composite of GLI Data
- Simulation of new color composite model compared with true color
composite and standard false color composite
- Classification Results
- Monthly Land Cover Monitoring
- Rice cultivation monitoring
- Secondary crop monitoring
- Conclusion and discussion
A: (Dr. Duong) A suitable interval is difficult to
achieve in tropical areas, with data being hard to obtain during the rainy
season. At the moment data interval is dependent on this problem, and
timing is not always applicable to the agricultural situation. Ideally
interval should be once a month, but this depends on GLI status, and we
hope to review and make suggestions after the results of this year’s
3. ADEOS / OCTS Mosaic data for vegetation monitoring (Dr. K.
Kajiwara ,Chiba UniversityQ: (Dr. Simpson) I have a
couple of comments to make. Using maximum composite MDI for cloud
detection can be problematical, especially in tropical region, where there
is no temperature contrast. Could you also please comment on the
following: using single ground control point file doesn't produce
coregistration of images, with different passes (and therefore different
tracks) of the satellite - is there any adjustment carried out to ensure
the same geographical location?
A: (Dr. Kajiwara) Do you mean an
overall large image?
Q: (Dr. Simpson) We can talk about it
Q: (Dr. Wang Zhihua) Which software are you using for
A: (Dr. Kajiwara) They are handmade and they are
not commercial. It has mostly been developed by Dr.
4. Progress and Problems in NPP Estimation (changed title from
Forest monitoring using ADEOS and ADEOS-II) (Dr. Y.
Q: (Dr. Verstraete) I would like to ask how you do your
sums, as NDVI is a different sum each day?
- Test mapping of NPP using satellite data
- Relationship between NPP and sum of NDVI
- NDVI of AVHRR data
- Seasonal changes of NDVI
- Methodology for NPP estimation
- flow of NPP estimation in forest
- Location map of the meteorological station
- NPP distribution estimation
- Sum of NDVI by solar radiation
- NPP estimation in forest
- Seasonal changes of SPAD values
- Relationship of DN of chlorophyll meter vs. NDVI
- Land surface resolution
- Comparison of spatial resolution
- Seasonal vegetation changes
- Relationship between reflectance data and stand parameters
- Correlation coefficients between DN and stem volume
- Seasonal spectral changes
- Results of biomass estimation
- Problems in NPP estimation (conclusion/validation data)
A: (Dr. Awaya) We use
the data taken monthly as the basis of our calculation.
5. Agricultural Monitoring with Oblique Viewing Remote Sensing
(G Saito and T. Murakami, National Institute of Agro-Environmental
Q: (Dr. Ranga Myneni, Boston
University) What is the significance of the relationship between the
v and w indices?
- Objectives; using ADEOS/AVNIR and SPOT/HRV
- Data and Study area: Saga Plains (in Kyushu Island): the largest
granary in Kyushu Island
- Crop Calendar; 6 cropping systems: Rice+, Rice, Soybean+, Soybean,
Lotus and Rush+
- NDVI profiles for six cropping systems
- Separability Analysis
- Optimal Scene Selection; NDVI color composite image
- NDVI Color Composite Images; Rice and Soybean
- Area Estimation of Barley and Wheat planting fields; Area estimation
with VSW index
- Flow of Area Estimation
- VSW index; originally developed by Yamagata et al. in 1997
- VSW index image; comparing ADEOS/AVNIR image and VSW index image
- Relationships between V index and W index for barley and wheat
- Pixel sampling method for barley and wheat planting fields
- From VSW Image to sampled image
- Overall Accuracy of Area Estimation
- Conclusions (1) and (2)
A: (Dr. Murakami) As mentioned,
the shadow/plant canopy mixture is related to (affects) the v and
w index relationship.
6. Innovative land surface parameters from ADEOS-II (Marc
Q: (Dr. Simpson) I think that the POLDER pixel is large
compared to GLI pixel - could you comment on the issue of cloud
- Objectives: Biophysical parameter retrieval and snow water
- Inversion protocol
- Algorithm applied to the data (leaf area index)
- Temporal analysis of LAI (leaf area index)
- Surface BRDF
- Methodology for POLDER, GLI, SeaWinds and AMSR (snow water
- Conclusion - biophysical parameter retrieval (methodology)
A: (Dr. Leroy) Yes, cloud contamination is a
source of error in POLDER data, but it is possible to make a selection of
data by identifying areas and time periods where cloud contamination is
Q: (Dr. Simpson) But this requires careful selection
A: (Dr. Leroy) Yes, that is true. It requires close
7. Precise Geographical Position (Dr. T.
Hashimoto)Dr. Hashimoto presented his studies on the topic
above starting off talking about Geometric Accuracy of OCTS.
- Geometric Accuracy of OCTS
- Precise Geometric Correction
- Flow for collecting GCP
- Flow for exterior orientation
- Flow for mapping; mapping projection types
Dr. Tateishi of Chiba University took over the chair at
this stage and apologized for arriving late.
He announced a 'change' in
the program, to please note that the presentation from Dr. Trotter is only
20 minutes, not the scheduled 40 minutes which is a mistake in the
8. Global Land Surface Radiation Budget Products from the ADEOS-II
GLI (F. Prata)Dr. Prata explained that this presentation
was just an update from the last meeting to cover the following:
Q: (Dr. Mervyn T. Lynch, Curtin
University of Technology, Perth, Australia) What are the major changes
which brought about the albedo sequence?
- Land surface temperature (global GLI/LST algorithm)
- LST Regression Coefficients
- Global land cover glasses and fractional vegetation cover
- GLI land surface temperature algorithm coefficients
- terrain height - digital elevation model and DEM variance flag
- precipitable water - water vapor path, radiosonde precipitable water
- Emissivity - under investigation, using interferometer, measurements
- CSIRO scanning radiometer, measurements and results
- Field measurements, location, data, and topography
- Lake Tahoe (USA) radiometer, ATSR data
- Validation, algorithm against GMS data
- composite land surface temperature
- Surface albedo, validation of against surface albedo measurements
- POLDER BRDF parameters (work still in progress)
- Albedo sampling
- Composite of short wave data from ATSR
- Summary/future plans
A: (Dr. Prata) Mostly
related to rainfall.
Q: (Dr. Rachel T. Pinker, University of
Maryland) With your bi-directional models, and satellite radiance, do you
carry out atmospheric correction first?
A: (Dr. Prata) We have
mostly clear skies and few aerosols, so simple parametric models seem to
work. This may not be the case in more polluted areas, we hope that others
will be able to make atmospheric corrections.
9. Development of Topographic Correction Algorithm for ADEOS-II/GLI
Data, for Use in Forest Carbon Cycling and Primary Production Studies
(C. Trotter) Dr. Trotter explained that he is mainly
concerned with experimental measurements to be presented here:
Q: (Dr. Verstraete) I didn't understand
the dual flight procedure.
- Objectives of the research
- Topographic correction - current status
- Conifer Canopy, LANDSAT/TM
- Topographic Effect studies on model canopies
- Conifer and Broad leaved canopy - nadir view
- Observed and calculated radiance (off-nadir view also)
- Topographic correction, LANDSAT/TM
- Radiance Variation, off-nadir view, and with topography
- Validation of topographic correction using imagery
A: (Dr. Trotter) You don't need to
have two planes. If there is a short flight (around 5 km), two flights
could be done at midday within 15 minutes of each other with the same
transect on the ground, taking data from the right and left sides. Can't
think of another possible definitive test for this algorithm.
(Dr. Tateishi) What GLI data will be suitable?
Trotter) We are not yet sure. We have 2 models, so need to think about
scaling before requirements for ancillary data.
Tateishi) What amplification is required for 1km resolution data?
(Dr. Trotter) Can't give definite answer. Standard is 20m contour
data, but not sufficient, maybe 10m would be OK. GLI is 250m, so at least
100m contour data (or data from other sensors)
10. Development of Spectral Indices Optimized for the GLI
Instrument (Dr. M. M. Verstraete) Dr. Verstraete
presented his studies on the topic above especially focusing on the
evaluation of performance of the indices
Q: (Dr. Prata)Are you assuming
sky is clear?
- Construction of the LUT
- Optimization of the GLI VI (1)
- Implementation issues
A: Yes, in principle, it is clear, but it is a
Q: (Dr. Simpson) How is cloud cover
A: (Dr. Verstraete) Scheduled to be done
I'd like to go into discussion and talk
about details of my studies which I presented just now.
- Performance and evaluation of the GLI VI
- Comparisons between the FAPAR and the GLI VI (TOA), NDVI(TOA) and
- Intercomparisons between the FAPAR derived from GLI VI (GLI), OVNI
(VEGETATION) and MGVI (MERIS)
- Sensitivity of vegetation indices with respect to extreme
atmosphere/aerosol types and amounts
- Visualization of heterogeneous 3-D images
- FAPAR derived from SeaWiFS data acquired under two extreme viewing
- NDVI distribution etc.
- Land Cover map from FMERS-I Projects
- Spectral Analysis
Prata) Has the algorithm been tested in the presence of noise (white noise
and structured noise)?
A: (Dr. Verstraete) The last example that
I gave is actual data - the simulation uses mode as-is and this leads to
the data shown, and this is just one example.
Q: (Dr. Rachel T.
Pinker) What were the vegetation indices used in the comparisons for
FAPAR? (How was FAPAR estimated?)
A: (Dr. Verstraete) We use the
same models for both FAPAR and top atmosphere reflectance. The diagram
shows FAPAR as estimated by direct model (horizontal axis) against the
index value (vertical axis), so showing the FAPAR retrieved after applying
index on simulated data.
Q: (Dr. Prata) Why have you not tested
on POLDER data?
A: (Dr. Verstraete) We are working with 9
different instruments, and each instrument has its own characteristics, so
cannot apply across board. Your question implies that index should be
optimised for each instrument, and we are working on that, but not for
Also, the scale of pixel is so large, there may be
11. Development of New Vegetation Indices and Algorithms for
Detecting Vegetation Change (Dr. Fujiwara , Nara Women's
Dr. Furumi took over the presentation
- Pattern Decomposition Method for GLI Data Analysis
- Normalized standard patterns
- Data used in this analysis
- Results of approximately 600 samples shown on the graph
- Radiation balance on the terrestrial area; mainly developed by Dr.
Q: (Dr. Craig Trotter) Was
your NPP field data above ground data only?
- Motivation and purpose
- Vegetation Index (VIPD); characteristics of VIPD
- Model of NPP estimation using VIPD; GPP Estimation, Respiratory
losses and Net Primary Production
- Simulation results; photosynthetic rate at strong and weak PAR
- Validation of NPP values for paddy fields
- Annual NPP map around tropical rain forest
- Conclusion and Next Plan
A: (Dr. Furumi) In
this relationship (indicating graph), NPP values mean whole - above and
Q: (Dr. Trotter) But you seem to be comparing with
above ground data only
A: (Dr. Furumi) For the 2 forest areas,
there was whole NPP. For the paddy fields, above ground biomass data
Q: (Dr. Trotter) How are you planning to capture various
changes in your model (e.g. as the forest ages, hydraulic conductivity
reductions, temperature deviations etc)
A: It was agreed to
discuss this issue later.
Q: (Dr. Duong) What about universal
spectral pattern of water/saturated matter?
A: (Dr. Furumi) 3
universal patterns were taken from LANDSAT data - water, vegetation and
soil patterns. They are fixed, so don’t move for satellite data.
(Dr. Tateishi) Universal pattern is not needed - using 3 basic
patterns, other patterns can be applied.
Q: (Dr. Duong) Did you
try an urban area?
A:(Dr. Furumi) Urban area is similar to soil
Q: (Dr. Duong) I suggest that you don't use soil
component, but include bare surface (asphalt, concrete etc)
12. Land Cover Classification by GLI 250m-Data Dr. Tateishi
explained how he made land cover classification by using every HR data.
Q: (Dr. Prata) I could be a user of your product, but I
can perceive 3 possible problems. Firstly, how soon can this be produced
after launch? Secondly, how can we manage the large dataset produced from
- AARS Asia 30-second land cover data set
- GLI channels for LAND; more channels than AVHRR
- Ground truth data
- Background for GLCGT-DB
- Proposal of GLCGT-DB
- original and generalized ground truth data
- Conclusion, wishes to discuss GLI-PI in group meetings tomorrow.
A: (Dr. Tateishi) My method uses 1 year of data,
so production will be at least 1 year after launch, plus around 6 months
Q: (Dr. Prata) The third issue is that if it is
based on the satellite data that I use, then there is a risk that I could
use the same information twice.
A: (Dr. Tateishi) This needs to