GLASNOWMAP
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GLA-RP-CGS-012
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GLASNOWMAP INFORMATION SERVICE – FINAL REPORT
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LISTA DI DISTRIBUZIONE
Name & Function
Signature
Date
DISTRIBUTION LIST
Preparato da:
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Prepared by:
R. Ratti (CGS)
N
A
I
Interna / Internal
T. Strozzi (GAMMA)
A. Rampini (IREA -MI)
P.A. Brivio (IREA -MI)
M. Pepe (IREA -MI)
P. Carrara (IREA -MI)
F. Rota Nodali (IREA -MI)
Approvato da:
S. Legramandi (PA)
Approved by:
C. Cinquepalmi (PC/CC)
Applicazione
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autorizzata da:
Manager)
Application
authorized by:
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F.M. Seifert (ESA)
1
A. Zmuda (ESA)
1
Customer / Higher Level Contractor
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REGISTRAZIONE DELLE MODIFICHE / CHANGE RECORD
EDIZIONE
DATA
AUTORIZZAZIONE
OGGETTO DELLA MODIFICA E SEZIONI AFFETTE
ISSUE
DATE
CHANGE AUTHORITY
REASON FOR CHANGE AND AFFECTED SECTIONS
Issue 1
28/11/2003
First issue
Issue 2
23/01/2004
Glacier mapping multisouce processing results , 2003 field
campaigns and recommendations
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LISTA DELLE PAGINE VALIDE / LIST OF VALID PAGES
PAGINA
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PAGINA
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PAGINA
EDIZIONE
PAGINA
EDIZIONE
PAGINA
EDIZIONE
PAGE
ISSUE
PAGE
ISSUE
PAGE
ISSUE
PAGE
ISSUE
PAGE
ISSUE
1 - 70
Issue 2
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TABLE OF CONTENTS
1.
INTRODUCTION................................................................................................................................7
1.1
APPLICABLE DOCUMENTS .....................................................................................................................7
1.2
REFERENCE DOCUMENTS AND RELATED LINKS ......................................................................................7
1.3
ACRONYMS .........................................................................................................................................9
2.
GLASNOWMAP-IS DESCRIPTION.................................................................................................10
2.1
PROJECT END-USERS .......................................................................................................................10
2.2
USER REQUIREMENTS ........................................................................................................................11
2.3
DEFINITION OF THE AREAS OF INTEREST................................................................................................16
2.3.1
SNOW cover ............................................................................................................................16
2.3.2
Glacier mapping.....................................................................................................................17
2.3.3
Snow Melting .........................................................................................................................18
2.4
SERVICE PORTFOLIO DESCRIPTION......................................................................................................19
2.4.1
Snow Cover Service .............................................................................................................19
2.4.2
Snow Melting Service...........................................................................................................21
2.4.3
Glacier Mapping Service......................................................................................................21
3.
SYSTEM SPECIFICATION..............................................................................................................23
3.1
HARDWARE SYSYTEM ARCHITECTURE .................................................................................................23
3.2
GLASNOWMAP–IS SOFTWARE LOGICAL MODEL ..............................................................................24
3.3
STRUCTURED ANALYSIS DIAGRAM ......................................................................................................25
3.4
SOFTWARE ARCHITECTURE LAYERS .................................................................................................27
3.5
DATA ORGANIZATION .........................................................................................................................28
3.6
GLASNOWMAP GRAPHICAL USER INTERFACE (GUI).......................................................................28
4.
DESCRIPITION OF PROCESSING DATA APPROACH ...............................................................32
4.1
SNOW COVER SERVICE .....................................................................................................................32
4.1.1
Snow Cover Mapping through the integrated use of MERIS and AATSR data ............32
4.1.2
Snow Cover Mapping with SAR data ..................................................................................34
4.1.3
Multisource Interpretation System for Mapping Snow Depth.........................................35
4.1.4
Final product generation......................................................................................................36
4.2
SNOW M ELTING SERVICE...................................................................................................................36
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4.2.1
Snow Melt Runoff model. .....................................................................................................36
4.2.2
Application to Dora Baltea River basin. .............................................................................37
4.2.3
Model Calibration ..................................................................................................................38
4.2.4
Calibration of the snowmelt runoff model using NOAA AVHRR data .............................38
4.2.5
Calibration of the snowmelt runoff model using SAR data..............................................41
4.3
GLACIER M APPING SERVICE...............................................................................................................44
4.3.1
Multisource interpretation system......................................................................................44
4.3.2
Final product generation......................................................................................................46
5.
INPUT DATA.....................................................................................................................................48
5.1
EO-DATA.........................................................................................................................................48
5.1.1
Snow Cover Service .............................................................................................................48
5.1.2
Snow Melting Service...........................................................................................................49
5.1.3
Glacier Mapping Service......................................................................................................50
5.2
GROUND DATA ..................................................................................................................................50
5.2.1
2003 ground campaigns........................................................................................................51
5.3
T EST SITE .........................................................................................................................................52
5.4
M EASURES .......................................................................................................................................53
5.5
T OPOGRAPHIC DATA ..........................................................................................................................55
5.6
HISTORICAL DATA..............................................................................................................................55
6.
DEMONSTARTION ACTIVITIES - RESULTS................................................................................56
6.1
SNOW COVER SERVICE .....................................................................................................................56
6.1.1
Snow Cover final products ..................................................................................................56
6.1.2
Snow depth final products ...................................................................................................57
6.2
SNOW MELTING SERVICE....................................................................................................................58
6.2.1
Runoff forecasting using MERIS + AATSR data................................................................59
6.2.2
Runoff forecasting using AATSR data................................................................................60
6.3
7.
Pagina
Data:
GLACIER M APPING SERVICE...............................................................................................................61
PROBLEMS......................................................................................................................................63
7.1.1
ENVISAT DATA.......................................................................................................................63
7.1.2
LANDSAT DATA.....................................................................................................................64
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8.
RECOMMENDATIONS....................................................................................................................65
9.
ASSESSMENT ACTIVITIES ............................................................................................................66
10.
SERVICE SUSTAINABILITY............................................................................................................67
11.
DISSEMINATION ACTIVITIES ........................................................................................................69
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1. INTRODUCTION
This document is the output of WP 1-AA of the GLASNOWMAP Project [AD2].
Its aim is to describe demonstration all the activities performed in the framework of GLASNOWMAP-IS
projects.
This document has been written by Carlo Gavazzi Space.
This Document is presented and discussed at the Final Meeting of the GLASNOWMAP Project.
1.1
APPLICABLE DOCUMENTS
[AD1]
ESA Statement of Work: DUP Phase II Call for Proposals. – Appendix 1: Statement of
Work and Technical Specification issue 5 revision 2 – 10-05-01 EEM-AEP/DUP/CFP2001
[AD2]
CGS proposal N°S1-031, “GLASNOWMAP Information Service”, June 2001
[AD3]
GLA-SB-CGS-001 GALSNOWMAP - IS Service Case Input Data Set
[AD3]
Space Engineering SW ECSS-E-40A, April 1999
[AD4]
GLA-RQ-CGS-001 GLASNOWMAP IS - User Requirements Document (URD)
[AD5]
GLA-RQ-CGS-002 GLASNOWMAP IS – Product & Service Specification Document
[AD6]
GLA-PL-CGS-003 GLASNOWMAP IS – Service Development Plan
[AD7]
GLA-PL-CGS-006 GLASNOWMAP IS – Service Quality Assessment Plan
[AD8]
GLA-SP-CGS-004 GLASNOWMAP-IS – System Specification Description
1.2
REFERENCE DOCUMENTS AND RELATED LINKS
[R1]
Baraldi A., Binaghi E., Blonda P., Brivio P.A., Rampini A., 2001, A Detailed Comparison of NeuroFuzzy Estimation of Sub-pixel Land-Cover Composition from Remotely Sensed Data, IEEE
Trans. on Geosc and Remote Sensing, Vol. 38 (5): pp. 994-1005, 2001.
[R2]
Bergstrom, S. 1979. Spring flood forecasting by conceptual models in Sweden. Proceedings,
Workshop on Modelling Snow Cover Runoff. US Army Cold Regions Research and Engineering
Laboratory, Hanover, New Hampshire. pp. 397-405.
[R3]
Binaghi E., P. A. Brivio, P. Ghezzi, A. Rampini and E. Zilioli, 1999. Investigating the behaviour of
neural and fuzzy-statistical classifiers in sub-pixel land cover estimations. Canadian Journal of
Remote Sensing, 25 (2), 171-188.
[R4]
Engman E. T. and R. J. Gurney, 1991. Remote Sensing in Hydrology. Chapman and Hall, London.
p. 225.
[R5]
V. Frost, J. Stiles, K. Shanmugan, J. Holtzman, A model for radar images and its application to
adaptive digital filtering of multiplicative noise, IEEE Pattern Analysis and Machine Intelligence, 4,
157-165, 1982.
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[R6]
D. K. Hall, K. J. Bayr, W. Schoener, R. A. Bindschadler and J. Y. L. Chien, 2003. Consideration of
the error inherent in mapping historical glacier position in Austria from the ground and space
(1893-2001), ELSEVIER, Remote Sensing of Environment 86, pp. 566-577.
[R7]
Martinec J., A. Rango and E. Maior, 1983. The Snowmelt Runoff Model (SRM) User's Manual.
NASA RP-1100. Greenbelt, Maryland, p. 110.
[R8]
T. Nagler and H. Rott, Retrieval of wet snow by means of multi-temporal SAR data, IEEE
Transactions on Geoscience and Remote Sensing, 38, 754-765, 2000.
[R9]
Nagler T. and H. Rott, 1999. SAR snow cover retrieval for snowmelt runoff modelling. Earth
Observation Quarterly, ESA n.62, 29-31.
[R10] S. Quegan and T. Le Toan, Analysing multitemporal SAR images, ESA SP-434, 17, 1998.
[R11] Robinson D., K. Dewey and R. Heim, 1993. Global snow cover monitoring: an update. Bull.
American Meteorological Society, 74: 1689-1696.
[R12] Romanov, P., G. Gutman, and I. Csiszar (2000), Automated monitoring of snow cover over North
America with multispectral satellite data, J.Appl. Meteorol., 39: 1866-1880
[R13] Secchieri F., "Problematiche relative alla raccolta e all'interpretazione dei dati per un nuovo
catasto dei ghiacciai", Geografia Fisica e Dinamica Quaternaria, Vol. 8, N.2, pp. 156-165, 1985.
[R14] Seidel K., U. Burkart, R. Baumann and J. Martinec, 1989. Snow cover monitoring by satellites and
real time runoff forecasts, IGARSS' 89, Vancouver, R. C., Canada. IEEE, pp.558-561.
[R15] J. Shi and J. Dozier, Interfering Snow Wetness Using C-Band Data from SIR-C Polarimetric
Synthetic Aperture Radar, IEEE Transactions on Geoscience and Remote Sensing, 33, 905-914,
1995.
[R16] T. Strozzi, A. Wiesmann, and C. Mätzler, Active microwave signatures of snowcovers at 5.3 and
35 GHz, Radio Science, 32, 479-495, 1997.
[R17] Swamy A. N. and P. A. Brivio, 1996. Hydrological modelling of snowmelt in the Italian Alps using
visible and infrared remote sensing. Int. Journal of Remote Sensing, 17 (16): 3169-3188.
[R18] C. Werner, U. Wegmüller, T. Strozzi and A. Wiesmann, Gamma SAR and interferometric
processing software, Proceedings of the ERS-ENVISAT Symposium, Gothenburg, Sweden, 1620 October 2000.
[R19] C. Werner, T. Strozzi, U. Wegmüller, and A. Wiesmann, SAR geocoding and multi-source image
registration, Proceedings of IGARSS 2002, Toronto, Canada, 24-28 June 2002.
[R20] Westertrom G., 1982. Estimating snow cover runoff by the degree-day approach. Vannet Norden,
3: 47-53.
[R21] Winther J. G. and D. K. Hall, 1999. Satellite-derived snow coverage related to hydropower
production in Norway: present and future. Int. Journal of Remote Sensing, 20 (15): 2991-3008.
[R22] Wang, F. 1990. "Fuzzy Supervised Classification of Remote Sensing Images", IEEE Trans. on
Geosci. Remote Sensing, Vol. 28, 194-200.
[R23] World Meteorological Organization, 1986. Intercomparison of models for snowmelt runoff
modelling. Operational Hydrology Report No. 23. WMO, Geneva, Switzerland.
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ACRONYMS
AATSR
APS
ARPA
ASAR
ATSR
CEOS
CGI
CGS
CTR
DEM
DUP
EO
ERS
ESA
ETH Zürich
ETM+
FR
GBT
GBTR
GLASNOWMAP
MERIS
PM
SAR
SGL
SLC
SMI
SRM
TM
WP
WSM
Advanced Along Track Scanning Radiometer
Alternating Polarization Mode Complex Image
Agenzia Regionale per la Protezione dell’Ambiente
Advanced Synthetic Aperture Radar
Along Track Scanning Radiometer
Committee for Earth Observation Satellites
Comitato Glaciologico Italiano
Carlo Gavazzi Space
Carta Tematica Regionale
Digital Elevation Model
Data User Programme
Earth Observation
European Remote sensing Satellite
European Space Agency
Eidgenössische Technische Hochschule Zürich
Enhanced Thematic Mapper Plus
Full Resolution
Gridded Brightness Temperature
Gridded Brightness Temperature/Reflectance
GLAciers and SNOW MAPping
MEdium Resolution Imaging Spectrometer
Progress Meeting
Synthetic Aperture Radar
Servizio Glaciologico Lombardo
Scan Line Corrector
Società Meteorologica Italiana
Snow Melt Runoff Model
Thematic Mapper
Work Package
Wide Swath Mode
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2. GLASNOWMAP-IS DESCRIPTION
The goal of GLASNOWMAP-IS is to define develop and implement an Information Service for monitoring
of glacier and snow cover changes in Alpine regions, carried out using Synthetic Aperture Radar (SAR)
data, optical satellite multiscale observations, ground collected data and geographical information.
Moreover in the framework of the project a dynamic modeling of snow melt runoff for water resource
management was developed.
The main purpose of this project is to provide the following services managed by a unique IS:
• Glacier Mapping Service: provides glacier monitoring through the use of SAR and Landsat
observations.
• Snow Cover Service: provides snow-cover maps using ENVISAT both radar and optical data.
• Snow Melting Service: quantify the water availability setting up a snow melting runoff model.
2.1
PROJECT END-USERS
The Information Service has been set up on the basis of the characteristics of the participants users and
their requirements. The role of end-users was fundamentals during the project. In fact they defined user
requirements of the information service and supported assessment activities concerning both products
and Information Service.
Società Meteorologica
Italiana
Servizio
Glaciologico
Lombardo
ARPA e Regione
Lombardia
ARPA e Regione
Piemonte
ARPA e Regione Val
d'Aosta
ETH - Zurich
Comitato Glaciologico
Italiano
The previous figure indicates for each service of GLASNOWMAP-IS project end-users interested in that
service.
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USER REQUIREMENTS
The specification of the requirements of the users is necessary to define the characteristics of the whole
service. A great interest in the use of remote sensing techniques for environment monitoring has been
demonstrated by the project users both for the evaluation of the present environmental conditions and for
global changes trends.
The following tables summarize user requirements for each service.
End-User
ARPA Valle d’Aosta
ARPA and Regione Lombardia
ARPA and Regione Piemonte
Snow Cover Functional Requirements
Requirements
Interest
Periodical monitoring of the snow cover
distribution and extention in order to
obtain statistical evaluations of snow.
Used for the evaluation of snow water
equivalent and glacial mass balance for
hydrological purposes.
Evaluation of the relations between the
presence of permafrost and the snow
cover extent and evolution. These
Hydrogeological hazard purposes
phenomena are related to climatic
Presence of snow
changes and are linked, in complex
Persistence of snow cover
mountain landscape, to land surface
Status of snow cover (albedo,
processes as slope instability.
temperature)
A quantitative estimation of the snow
Depth of snow (desirable)
cover extent and persistence during an
entire season may improve the
knowledge about permafrost
Glaciological
and
hydrological
purposes
Extent of snow cover
Snow cover classes
Depth of snow (desirable)
Regione Valle d’Aosta
Touristic purposes
Extent of snow cover
Snow cover classes
Depth of snow (desirable)
Società Meteorologia Italiana
Periodical monitoring of the snow cover
distribution and extention independent
from the analysis performed by punctual
measurements of the meteorological
Snow cover in Western Italian Alps stations. This interest is related to
(Piemonte and Val d'Aosta) for variations of snow cover extent both for
glaciological
and
hydrological the evaluation of snow water equivalent
purposes
and
glacial
mass
balance
for
Extent of snow cover
hydrological purposes and for the
Snow cover classes
analysis of climate change phenomena.
Depth of snow (desirable)
The implementation of a procedure to
compute the snow depth by inteprolating
data of snow depth measured at single
points distributed in the region is also
desirable.
Periodical knowledge of the extension
and conditions of snow for tourist
purposes
Table 2-1 Snow Cover Service Functional Requirements
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SNOW COVER PRODUCT REQUIREMENTS
End-User
Requirements
Resol.
Georeferred snow cover maps in 300 m
which different types of snow are
150 m
identified in GIS format..
ARPA and
d’Aosta
Regione
Deliver
frequency
10 days
20 days
Period
From
November to
July
Valle Geometric
parameters
and
statistics on classes identified
Information on depth of snow,
obtained
from
ground
measurements.
ARPA and Regione Lombardia
Georeferred snow cover maps in
which different types of snow are
identified in GIS format.
300 m
10 days
150 m
20 days
Geometric
parameters
and 300 m
statistics on classes identified
150 m
Information on depth of snow,
obtained
from
ground
measurements
10 days
Albedo and temperature maps.
Information on depth of snow,
obtained
from
ground
measurements.
From
November to
July
Georeferred snow cover maps in
which different types of snow are
identified in GIS format.
ARPA and Regione Piemonte
20 days
From
November to
July
Georeferred snow cover maps in
which different types of snow are
identified in GIS format.
Regione Valle d’Aosta
Società Meteorologia Italiana
Geometric
parameters
and
statistics on classes identified: 300 m
percentage of snow cover in user 150 m
defined areas
10 days
20 days
From
November to
July
Information on depth of snow,
obtained
from
ground
measurements, is also desirable
Table 2-2 Snow Cover Service Product Requirements
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Snow Cover Area Of interest
End-User
Area
ARPA and Regione Valle d’Aosta
Valle d’Aosta Region
ARPA and Regione Lombardia
Lombardia Region
ARPA and Regione Piemonte
Piemonte Region
Società Meteorologia Italiana
Piemonte and Valle d’Aosta Region
Table 2-3 Snow Cover Service Area of interest
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End-User
Pagina
Water availability from snowmelt
Snow cover depletion in
Basins for hydrological
purposes
Snow cover extent
ARPA and Regione Valle d’Aosta
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Snow Melting Functional Requirements
Requirements
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Interest
Information about effects of climate
change on the environment, in particular
snow-water equivalent, is useful for
environment management related to
different territorial aspects (e.g.
agriculture operators, mountain
communities, National Forrest Body,
Electric Energy Producers, Water
quality management)
Table 2-4 Snow Melting Service functional Requirements
Snow Melting Product Requirements
Deliver
Requirements
Forecasting
frequency
End-User
ARPA and Regione Valle
d’Aosta
River
discharge
values
derived from snow cover
Daily
maps through the use of a
snow-melt runoff model
3 days forecasting
Period
April - July
Table 2-5 Snow Melting Service Product Requirements
Snow Melting Area Of interest
End-User
ARPA and Regione Valle d’Aosta
Area
River: Dora Baltea
Basins:
Aymavilles
Morgex
Brissogne
Table 2-6 Snow Melting Service Area of interest
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ARPA and Regione Lombardia
ARPA and Regione Valle d’Aosta
Servizio Glaciologico Lombardo
Comitato Glaciologico Italiano
ETH Zurigo
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Glacier Mapping Functional Requirements
Requirements
Morphological
and
mass
variations of glaciers
Annual maps of the glaciers
body
ELA: Altitude of equilibrium
line
Altitude of terminus
Accumulation and Ablation
basin extension
Data:
Interest
Evaluation of the use of remote sensing
techniques in identifing morphological
and mass variations of glaciers and in
the comparison of the results obtained
with ground truth data collected during
the traditional glaciological campaigns.
Table 2-7 Glacier Mapping Service functional requirements
Glacier Mapping Product Requirements
End-User
Requirements
-
ARPA and Regione Lombardia
ARPA and Regione Valle d’Aosta
Servizio Glaciologico Lombardo
Comitato Glaciologico Italiano
ETH Zurigo
-
Resol.
Georeferred glacier maps
in which accumulation
basin, ablation basin and
snowline are identified in
30 m
GIS format.
Geometric parameters and
statistics
on
classes
identified in GIS format.
Deliver
frequency
Period
Every 1 year
AugustSeptember
Table 2-8 Glacier Mapping Service Product Requirements
Glacier Mapping Area Of interest
End-User
Area
ARPA and Regione Valle d’Aosta
Lys Glacier
Comitato Glaciologico Italiano
Lys Glacier
Adamello Glacier
ETH Zurigo
Gorner Glacier
Allalin Glacier
Servizio Glaciologico Lombardo
ARPA and Regione Lombardia
Adamello Glacier
Table 2-9 Glacier Mapping Service area of interest
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As concern Interface Requirements,. main goals of the user interfaces, outline in the previous figure, are:
-
Access to the services and specify the user requests
-
Search for suitable data
-
Download results
It must be noticed that all users are interested in obtaining the results of data processing in formats
compatible with the de-facto standards of wide-spread commercial Geographic Information Systems.
2.3
DEFINITION OF THE AREAS OF INTEREST
On the base of the user requirements, for each service of the GLASNOWMAP-IS, different areas of
interest were selected. In the following the areas covered by the three different services are illustrated
2.3.1
SNOW cover
The area of n
i terest for Snow Cover Service includes three regions of northern Italy: Valle d’Aosta,
Piemonte e Lombardia, as depicted in Table 2-10. The following table identifies this area.
Area Of Interest:
Centre
Radius
Val D’Aosta, Lombardia and Piemonte
regions, Northern Italy
45° 40' lat
8° 40' lon
250
km
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Table 2-10 now cover service areas of interest
2.3.2
Glacier mapping
In the following tables the two different areas, selected for Glacier Mapping Service demonstration, are
identified. The former includes Lys (Valle d’Aosta – Northern Italy) Gorner and Allalin glaciers
(Switzerland), the latter includes Adamello Glacier (Lombardia – Northern Italy).
Table 2-11 shows the glaciers studied during the project and gives information about their localisation.
Area Of Interest:
Centre
Radius
Area Of Interest :
Centre
Radius
Lys Glacier , Val D’Aosta Italy
Gorner and Allalin Glaciers, Switzerland
46° 10' lat
8° 00' lon
50
km
Adamello Glacier , Lombardia Northern Italy
46° 00' lat
10° 33' lon
75
km
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Table 2-11. Area of interest for Glacier Mapping Service.
2.3.3
Snow Melting
Due to unavailability of discharge ground measures of Morgex and Brissogne basins, the area of interest
for Snow Melting Service has been restricted to Aymavilles Basin of Dora Baltea river (Valle d’Aosta). The
extension of this basin is about 12000 Km 2.
Aymavilles Basin, considered for the service demonstration, is depicted in Table 2-12 and outlined by a
red line. This basin is localised in the West part of Valle d’Aosta.
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Table 2-12. Area of interest for Snow Melting Service.
2.4
SERVICE PORTFOLIO DESCRIPTION
Each service allows to generate different products. These products were design on the base of user
requirements. In particular requirements about: coverage, ground resolution, time availability and format
of files were considered. Final products generated from GLASNOWMAP-IS for each service are briefly
described in tables reported hereafter.
2.4.1
Snow Cover Service
Products
Description
Snow Cover Thematic Map
Snow Cover Thematic Map is an hard classification map which identifies
snow covered surface (pixel value is equal to 255), snow free surface (pixel
value is equal to 0) and area covered by clouds (pixel value is equal to 1).
Snow cover thematic maps are provided for each region and they are
delimited using region boundaries.
Geometrical Aspects Ground Resolution: 300 m
Coverage: Regional Scale
Time Aspects
Weekly
Output
Provided in GIS-SW compatible format
Products
Description
Snow Boundaries
Snow Boundaries final product is vector file format and can be visualised in
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GIS environment. This product is provided for each region. As Snow
Boundaries can be visualised in GIS environment they can be considered
as a Layer GIS. Therefore they can be used together with other GIS Layer,
for example CTR or DEM, in order to obtain detailed information about snow
covered area.
Geometrical Aspects Ground Resolution: 300 m
Coverage: Regional Scale
Time Aspects
Weekly
Output
Provided in GIS-SW compatible format
Products
Description
Snow Cover Statistic Data
Snow Cover Statistic Data is a textual file which gives information about the
total extension of snow covered area and accuracy of provided map
Geometrical Aspects Regional scale
Time Aspects
Weekly
Output
Textual file
Products
Description
Snow Depth Thematic Map
Snow Depth Thematic Map can be obtained only if sample data (i.e. snow
depth values measured at ground station) are available. Snow depth
thematic maps gives information of snow depth value estimated for each
pixel. The –1 value is assumed in case of DEM missing information.
Geometrical Aspects Ground Resolution: 300 m
Coverage: Regional Scale
Time Aspects
Generated only if. snow depth values measured at ground station are
available
Output
Provided in GIS-SW compatible format
Products
Description
Snow Depth Statistic Data
Snow Depth Statistic Data is a textual file which gives information about
extension of snow covered area, snow depth vs. expo sure classes, snow
depth vs. elevation and minimum elevation of snow presence
Geometrical Aspects Regional scale
Time Aspects
Generated only if. snow depth values measured at ground station are
available
Output
Textual file
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Snow Melting Service
Products
Description
River discharge forecasting values (3 days)
River discharge forecasting (3 days) final product is the river discharge
values forecasted for 1, 2 and 3 days and daily upgraded. This file gives the
following information:
- Name of River
- Name of Basin
- River discharge measurement date
- Last measured discharge value: date and measured value
- Start value
- Forecasting discharge values for three days
- Date and measured value of forecasted rain and temperature data
used
Geometrical Aspects Local scale (Catchments scale) – Aymavilles Basin, Dora Baltea River
Time Aspects
Provided during the melting season
Output
Textual file
2.4.3
Glacier Mapping Service
Products
Description
Glacier thematic map
Glacier Thematic Map is an hard classification map which identifies snow
(pixel value is equal to 255) and ice (pixel value is equal to 125). Glacier
thematic maps are provided for each glacier and they are delimitated using
glacier boundaries.
Geometrical Aspects Ground Resolution: 30 m
Coverage: Local scale (Glacier scale)
Time Aspects
This map is provided yearly, at the end of ablation period
Output
Provided in GIS-SW compatible format
Products
Description
Glacier Statistic Data
Glacier Statistic Data is is a textual file which gives information about the
total extension of Glacier, Snow accuracy and Ice accuracy.
Geometrical Aspects Local scale (Glacier scale)
Time Aspects
Statistic data are provided yearly, at the end of ablation period
Output
Textual file
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Products
Description
Ice Boundaries
Ice Boundaries final product is vector file format and can be visualised in
GIS environment. This product is provided for each glacier. This product
delimits area of glacier not covered by snow. Therefore in this area only ice
can be identified.
Geometrical Aspects Ground Resolution: 30 m
Coverage: Local scale (Glacier scale)
Time Aspects
This map is provided yearly, at the end of ablation period
Output
Provided in GIS-SW compatible format
Products
Description
Snow Boundaries
Snow Boundaries final product is vector file format and can be visualised in
GIS environment. This product is provided for each glacier. This product
delimits area of glacier covered by snow.
Geometrical Aspects Ground Resolution: 30 m
Coverage: Local scale (Glacier scale)
Time Aspects
This map is provided yearly, at the end of ablation period
Output
Provided in GIS-SW compatible format
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3. SYSTEM SPECIFICATION
In this section hardware architecture and logical model of GLASNOWMAP-IS system are describe.
3.1
HARDWARE SYSYTEM ARCHITECTURE
The hardware system architecture of GLASNOWMA-IS, shown in the following figure, is based on three
different hardware platforms connected by an intranet and allows to minimise hardware resource
utilisation and. Each workstation of the system has a proper task.
Prod
uct d
ry th
elive
l
E-Mai
rough
AT
VIS
EN
uest
a req
nd dat
Grou
Image
Workstation for
Image Elaboration
(Windows OS)
browse
r and re
GROUND DATA
PROVIDERS
quest
Prod
uct d
eli
physi very thro
cal m
ugh
edia
ESA Ground Station
PDS
Workstation for
Image Elaboration
(LinuxOS)
Modem
Work PC
INTERNET
Modem
Home PC
GLASNOWMAP
WEB & FTP Server
Modem
Personal Laptop
MMI – OP workstation (Man Machine Interface and Optical System). Optical processing system, multisource process, MMI software are installed on this workstation. Moreover most of the data involved in the
product generation (i.e. topographic data, ground data, intermediate data and final products) are stored on
this workstation.
RP workstation (Radar Processing). Radar software modules are installed on this workstation. Those
modules have to be installed on a Linux operating system workstation.
WS (Web Server) workstation. GLASNOWMAP WEB home page is installed on this workstation. Here
the end users can access to view or download final products. A copy of final products shall be done on
this platform.
Both WS (Web Server) and MMI – O P platforms are connected to Internet in order to exchange data with
the end users or data providers. The MMI – OP platform sends ground data or satellite data requests to
the data providers by e-mail. The ground data providers send by e-mail the ground data to this platform.
The satellite data shall be provided to system on CD-ROM supports. In the case of data provided on
exabyte cassette, they have to be stored on CD-ROM support to be read by the system. The system has
not an exabyte cassette driver.
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GLASNOWMAP–IS SOFTWARE LOGICAL MODEL
The Structured Analysis (SA) method is used to describe the logical model of the GLASNOWMAP IS
software. Data and Control flow diagrams show the user the logical structure of the software by different
levels (Here will be called “SA Diagrams”). Such diagrams decompose the system into functions (called
processes, represented with bubbles), which exchange data (called data flows, represented with straight
lines).
The top diagram in the hierarchy is the context diagram, shown in the following figure, which forms the
interface between the system and the outside world. This diagram specifies the parts of the outside world
with which the GLASNOWMAP-IS system should communicate
As shown in the context diagrams, GLASNOWMAP Information Service is able to interface with:
-
Satellite Data Providers. To retrieve satellite data (images, ancillary data) that are used to generate
the products.
Ground Measurement Stations. to retrieve the ground measured data (e.g.: meteorological data) that
are used to generate the final products.
Service Manager Interface. GLASNOWMAP-IS interfaces the Service Manager through this
dedicated interface. The Service Manager is the operator in charge of the maintenance and the
operations of the Service.
User. The End User sends to the service its Requests related to the desired products, and the
service makes available to the User the elaborated “Final Products”.
The Level-1 structured analysis diagram, depicted in the following figure, shows all main processes of
GASNOWMAP-IS and their input and output data. It describes both data flows between elements (store
and process) and control flows between processes.
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3.3
Data:
STRUCTURED ANALYSIS DIAGRAM
Figure 3-1 GLASNOWMAP–IS Level 1 Structured Analysis Diagram
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As shown in Level-1 structured analysis diagram main processes of GLASNOWMAP-IS are:
-
MMI (MAN MACHINE INTERFACE): is dedicate interface used by the Service Operator. This process
allows the operator to interface with GLASNOWMAP Information System and manages all the
operation concerning data processing, data selection, data collection and data visualisation and
system status. This process shall be implemented on MMI – OP hardware resource.
-
DATA SELECTION AND ORDER: is the process dedicated to the data selection and order, both for
satellite data and ground data. This process has been divided into two main activities: SATELLITE
DATA SELECTION AND ORDER, and GROUND DATA ORDER.
-
GROUND DATA COLLECTION: is the process that receives ground observations as input data from
ground data providers, and collects them in the Ground Data Store. This process has to store the
meteorological data, used by the snow melting process, and snow depth data into an internal
archive.
-
SATELLITE DATA PROCESSING SYSTEM: is the core of the GLASNOWMAP-IS. This process
elaborates the radar and/or optical satellite data and produces all snow classification maps for snow
cover and glacier mapping services.
-
FINAL PRODUCTS: this process produces all final products for the three services on the base of the
classification maps produced in the SATELLITE DATA PROCESSING SYSTEM.
-
WEB PUBLISHING: is the process that publishes final products on the Web server. This process
shall store these products compressed on a Web server local store. Moreover it shall update all the
user home page to make new products visible to the end user. The operator shall choose the snow
cover or glacier equilibrium line layers features in order to visualise them on the Web GIS.
-
INFORMATION SERVICE SERVER: its main function is to provide the users with final products for
the different service using web facilities. This process shall be implemented on RP hardware
resource. For each user a personal web page shall be created, where the user can login in a
password protected mode. Here the user shall perform two different actions: product visualization
and product download.
All intermediate and final products are stored in a dedicated data store. The data stores are:
-
GROUND DATA STORE: contains all meteorological and river discharge data for River discharge
forecasting. The discharge data can be given in input as discharge value or hydrological level values.
-
TOPOGRAPHIC DATA STORE: contains topographic data involved in the optical and final product
generation processing.
-
TOPOGRAPHIC DATA STORE ON RP: contains topographic data involved in the processes on RP
(Radar Processing).
-
INTERMEDIATE DATA STORE : contains all intermediate products output of optical and radar
processing system.
-
CLASSIFICATION DATA STORE: contains definitive classification maps, output of the Multi-source
Interpretation System process.
-
RUNOFF MODEL DATA STORE: contains all information given in input to the Snow Melting software
module.
-
FINAL PRODUCTS STORE: contains all final products, output of the Final Products process.
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WEB PRODUCTS STORE ON WS :contains all final products visible and downloadable by the
WEB, on WS (WEB Server)
-
3.4
SOFTWARE ARCHITECTURE LAYERS
The GLASNOWMAP-IS implementation architecture is layered as depicted in Figure 3-2
Layer 4
GLASNOWMAP INFORMATION SERVICE MANAGER
Layer 3
Shell
Scripts
Web
request
Shell
command
Layer 2
Radar
Image
Proc. SW
IIS (Internet
Information
Server)
PCI (optical
processing)
Layer 1
LINUX
REDHAT
WINNT
Hardware
PC PIV
1.8 GHz,
RAM :
256 Mb
1 Hd:
40 Gb
PC PIV
1.7 GHz
RAM:
512 Mb
2 Hd:
40 Gb
ENVI
module
Shell
Shell
Shell
s
command command command
(optical
proc.)
Outlook
IExplorer
DescW
IDL
Optical
Proc.
Modules
IDL
LIBRARY
IDL v. 5.5 Run Time Env.
WINDOWS 2000
PC Pentium IV 1.8 GHz RAM 256 Mbytes 1 Hd 40 Gbytes
Figure 3-2: GLASNOWMAP-IS System Layers
The Layer 1 represents the operating systems installed on the GLASNOWMAP-IS system.
The Layer 2 includes the all external applications called by the system in order to process radar images
and to orthorectificate optical ones. Moreover, this layer includes the IDL run time environment, the web
server IIS, the Outlook mail tool to send ground and satellite request, the DescW and IExplorer software
to select satellite images.
The Layer 3 describes the interface between the GLASNOWMAP-IS, the external applications and the
software module used for image processing. The optical processing software modules are developed in
IDL language, and they are directly called by the system.
The Layer 4 represent the GLASNOWMAP-IS, whose execution is possible though the IDL runtime
environment, and that allows the usage of processing software in a controlled way. Moreover, this layer
implements all the graphic capabilities needed for the interaction with the users and for display both
textual and graphical data.
All software modules are developed in IDL language version 5.6 on win32 platform. IDL (Interactive Data
Language) is a software package for interactive analysis and visualization of scientific and engineering
data, developed by Research Systems, Inc. The full package consists of the language itself, a real-time
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compilation-execution environment, and extensive subroutine libraries. The software needs a runtime IDL
license to be executed.
3.5
DATA ORGANIZATION
A great amount of data is involved in this project, and its organization is fundamental. Images are the
input and output of the great part of the processes and they require a large amount of storage space. So
a directory tree shall be created in order to organise them and moreover to permit an easy back-up and
restore of the data involved in a single product generation.
The data involved shall be divided into two classes: data produced and data used. In order to group the
data produced, that need a large storage space, the work session concept has been introduced. A work
session is the process starting from the processing of satellite data and ending with the final product. The
data stores, containing data produced during a single work session, are stored in a separate directory
tree, where location and name is decided by the operator. A separate directory tree shall be created for
each work session. The data stores, containing data used during a work session, are stored in a
directory tree located in the same directory of the application.
3.6
GLASNOWMAP GRAPHICAL USER INTERFACE (GUI)
The service manager interface is realized creating in five main graphical windows: application main
window, snow cover window, snow melting window, glacier mapping window and update data window.
Using the main window the operator shall manage the application and shall choose to visualize the data
window or the windows dedicated to the services production.
In Figure 3-3 the main window of the Glasnowmap IS is shown. The operator can choose to open a new
session, to create a new session or to manage data involved in the processing. Once the session has
been opened the operator can choose to elaborate data in one of the three available services.
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Figure 3-3. Main window of the Glasnowmap IS
In Figure 3-4 the Glacier Mapping Service window of the Glasnowmap IS is shown. The operator can
choose one of the six actions that are involved in the products generation. The first two buttons (Create
Maps and Visualize map) are dedicated to the satellite data processing both for radat and optical images.
The third and the fourth buttons are dedicated to the final product generation on the selected glacier. Then
the operator can publish data on the Web or view the processing status visualized selecting report
button.
Figure 3-4. Glacier Mapping Service window of the Glasnowmap IS
In Figure 3-5 the Snow Melting Service window of the Glasnowmap IS is shown. The operator can
choose one of the six actions that are involved in the products generation. The first two buttons (Create
Maps and Visualize map) are dedicated to the satellite data processing both for radar and optical images
in order to create snow cover maps or to update depletion curves. The third and the fourth buttons are
dedicated to the discharge forecasting on the selected basin. Then the operator can publish data on the
Web or view the processing status visualized selecting report button.
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Figure 3-5. Snow Melting Service window of the Glasnowmap IS
In Figure 3-6 the Glacier Mapping Service window of the Glasnowmap IS is shown. The operator can
choose one of the six actions that are involved in the products generation. The first two buttons (Create
Maps and Visualize map) are dedicated to the satellite data processing both for radat and optical images.
The third and the fourth buttons are dedicated to the final product generation on the selected region. Then
the operator can publish data on the Web or view the processing status visualized selecting report
button.
Figure 3-6. Snow Cover Service window of the Glasnowmap IS
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In Figure 3-7 the Data update window of the Glasnowmap IS is shown. Here the operator can select and
order EO data, view satellite images, order ground data and update Glasnowmap ground data database
Figure 3-7. Data update window of the Glasnowmap IS
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4. DESCRIPITION OF PROCESSING DATA APPROACH
In this section data processing procedure are described for each service. Processing of input data allows
to generate final products from EO-data, topographic data and ground data.
In next paragraphs the procedures followed to produce Glasnowmap final products are reported. It has be
pointed out that all algorithms used for the data processing are integrated in the Glasnowmap IS and all
final products have been generated using the Information Service developed.
4.1
SNOW COVER SERVICE
The Snow Cover Service, developed in the framework of the Glasnowmap project, allows the operator to
generate snow cover products at regional scale using Envisat on board instruments. ASAR Wide Swath
(WSM) data are processed to produce snow map from radar and MERIS combined to AATSR
(Advanced Along Track Scanning Radiometer) observations allows to generate snow cover from optical
data. The combined use radar and optical images may ensure that almost one thematic map could be
produced every two weeks, independently from meteorological conditions.
Snow Cover Mapping through the integrated use of MERIS and AATSR data
4.1.1
The MEdium Resolution Imaging Spectrometer (MERIS) gives across the range 400-1050 nm, not
enough to distinguish snow from clouds. For this reason the Advanced ATSR is used to integrate MERIS
data. The AATSR data, acquired in the same moment of the Meris, includes spectral short-wave infrared
and thermal infrared bands that allows to discriminate snow form clouds.
A multisource classification scheme has been developed and implemented to combine the results
obtained by the classification of MERIS data with the information on cloud distribution as derived from
AATSR data; the integration is performed with the aid of the Digital Elevation Model. In the following
paragraphs the procedure implemented in the Glasnowmap IS is described.
4.1.1.1
PROCESSING OF MERIS IMAGES FOR SNOW COVER CLASSIFICATION .
The first step of the snow cover map production is the classification of MERIS images. At first the image
is geocoded using a set of Ground Control Points (GCP) with 2° order polynomial warping algorithm, with
a root mean square error less than 1 pixel; nearest neighbour radiometric resampling technique is used.
The image is then classified by means a soft classifier based on fuzzy set theory.
The supervised fuzzy statistical classifier designed by Wang [R22] has been chosen being particularly
suited for the representation of cover class mixture [R1,R3]. The classifier bases estimates of the
distribution of pixels in multispectral space on the concept of the probability measure of fuzzy events to
produce an output of the proportions of individual components.
The result of the classification is an image for each class in which pixel value represents the degree of
membership to that class. The degree of membership provided by the fuzzy-statistical classifier may be
interpreted as fractional cover [R22].
Two types of map are produced by the classification process: soft classification maps and hard landcover map. The first type is a set of raster images, one for each class: each soft map represents the
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degree of membership provided by the fuzzy-statistical classifier, interpreted as fractional cover in
percentage, hence ranging from 0 to 100. The second type is a single raster image, the hard land cover
map, in which each pixel is labeled as belonging to only one class through a harderization process in
which we assign to the pixel the label of the class whose degree of membership is maximum in the soft
maps.
The hard land-cover map is used to evaluate classification accuracy, where test set pixels are
considered and compared to classification results. The percentage of agreement between manually and
automatically classified pixels – i.e. expected and obtained classification values - is used as a measure of
the overall accuracy of classification.
If the overall accuracy is less then 80%, the classification procedure is repeated after an appropriate
editing of the training set.
4.1.1.2
PROCESSING OF AATSR IMAGES FOR CLOUD MAPPING.
The objective of the processing of AATSR data is to extract cloud cover information to be merged to the
information extracted from MERIS data. The first step is to resample and register the AATSR image to
the MERIS simultaneous overpass image. The co-registration the image provides also the geocoding
since the reference MERIS image is taken already geocoded.
The brightness temperatures of middle wave infrared (band 5, centred at 3.7 µm) and thermal infrared
(band 6, centred at 11 µm?
) are used to calculate a normalized difference index (Cloud Index - CI) for
cloud recognition. This index is an integral part of the optical processing algorithm for the identification of
clouds:
CI =
AATSR band 6 − AATSR band 5
AATSR band 6 + AATSR band 5
Having its heritage with the Normalized Difference Snow Index [R6] and band-rationing techniques for
automatic snow/cloud classification [R12], the CI is used to identify clouds in an automated-algorithm
environment.
The utility of the CI is based on the fact that the reflectance of clouds remains high in the middle infrared
(while reflectance of snow drops to near-zero values). However this is not true for cirrus clouds, since
their absorption in the mid-infrared is similar to that of snow, and then the thermal infrared is helps in the
discrimination.
In testing cloud-cover areas on AATSR scenes over the Alps, CI values in the range 0.012-0.026 and 1
were found to well represent cloud pixels, and to separate clouds from snow. Results reveal that there is
not a fixed CI threshold for clouds, but a credible threshold for cloud mapping can be established, mainly
depending on the season, increasing with the mean temperatures: in fact the same value (0.012-1) was
found suitable for both November and December imagery, an higher one (0.020-1) was found suitable for
both April and May acquisition, and the highest one (0.026-1) for June.
Most likely a constant relationship between the threshold value and some statistical descriptors of the
thermal infrared channel can be found; unfortunately the yearly set of images is not large enough to
perform such kind of analysis.
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The main drawback of the use of a different source for detecting clouds from the one used for snow, lies
in the fact that different spatial resolutions together with registering accuracies could negatively affect the
exact superimposition of the two different kind of information.
The problem is mainly connected with border conditions between clouds and underlying covers, as well
as to cloud shadowed areas. The adopted solution is to be very conservative in the CI threshold
determination, using the lower CI value corresponding to clouds, even if representing very few portions of
the image.
4.1.1.3
M ULTISOURCE PROCEDURE FOR SNOW COVER MAPPING.
The input to the procedure are the Snow Cover derived from the classification of MERIS images, the
Cloud Cover Map obtained by AATSR data and the digital elevation model of the area. The output snow
cover product is a byte map where 0 correspond to snow free surfaces, 255 to snow surfaces and 1 to
cloud cover.
The procedure implemented in the Glasnowmap IS assigns the final snow cover thematic map pixel
value to 1 for each pixel mapped as cloud in the Cloud Cover Map produced analysing AATSR satellite
data. Moreover all snow pixel found under the elevation threshold of 300 m is assigned to snow free pixel.
Snow Cover Mapping with SAR data
4.1.2
Snow Cover Mapping with SAR data is base on the difference between measurements of wet snow
covers and dry snow covers or snow-free covers, which is, generally, evident at C-Band (5.3 GHz, 5.6
cm wavelength). Low backscattering, in fact, is observed in the case of wet snow covers because of
vanishing ground contribution as a result of increasing absorption and reflection of the incidence radiation
in the wet snow pack. The capability of SAR to mapping wet snow is well proven both using SIR-C
polarimetric SAR [R15] and ERS-SAR data [R8].
Nevertheless, it is important to take into account situations where the presence of wet snow cannot be
discriminated by means of the backscattering intensity at C-Band, small incidence angles, VV
polarization (ERS configuration). In-situ measurements [R16] showed that discrimination of wet snow is
critical with the ERS SAR configuration when the integrated column height of liquid water in the snow
pack is smaller than 1 mm (begin of melting season) or when the wet snow surface is very rough (end of
melting season). At large incidence angles, for example ENVISAT ASAR acquisitions, this problem does
no more exist.
4.1.2.1
FUNCTIONAL DESCRIPTION OF THE SAR IMAGE PROCESSING.
The function of the SAR image processing is to derive a map of wet snow cover from SAR images. The
general software architecture design of the SAR image processing is shown in Figure 2. The input to the
processing is a SAR image in RAW, SLC or PRI format with standard leader file. If SAR RAW data are
used they are first focused to Single-Look Complex (SLC) images [R18]. SAR focusing is performed with
absolute radiometric calibration including range spreading loss correction, antenna gain correction,
normalization reference area correction, and calibration factor. Doppler ambiguity resolution and
estimation is very important for ERS-2 SAR acquisitions in zero-gyromode. If SLC images are used
focusing step is avoided.
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In order to account for the topographic (i.e. incidence angle) dependence of the backscattering
coefficient, a ratio between a reference image during the snow free period and the image to classify is
performed. The reference image is also used as master image in SAR geometry and to support multitemporal speckle filtering [R10]. Orthorectification is performed only once with the master image and all
other images are co-registered to the master one in SAR geometry [R19]. Elements of the reference
image (single-look complex image, multi-look intensity image, geocoding lookup table) are therefore
computed once and retained in the system for elaboration of further images.
The standard procedure for any new acquisition includes co-registration with the reference image,
filtering, rationing, classification and orthorectification. Co-registration is based on automatic intensity
cross-correlation [R19]. Filtering is required to reduce the speckle before classification. Here we used a
combined approach based on the multi-channel intensity filter [R10] and the Frost filter [R5] after multilooking of the intensity images. Rationing of the input SAR image with the reference one is required to
account for the radiometric distortions due to the rugged topography in alpine regions. Other methods to
correct for the strong dependence of the backscattering coefficient of wet snow with the local incidence
angle were not considered effective [R8]. Classification of the SAR ratio image is performed to retrieve a
map with wet snow extent. Different classification methods, from simple thresholding to more trained
methods, were tested. During orthorectification the wet snow images are resampled from SAR to map
geometries with advanced interpolation algorithms. Information on layover and shadow regions are
retained and these areas masked out. At this stage, images from crossing orbits may be combined, if
available, to reduce the information loss due to layover and shadow.
4.1.2.2
POST-PROCESSING OF THE WET SNOW COVER MAP.
Post-processing of the wet snow map is performed to retrieve the complete snow cover map or a glacier
map. For snow cover mapping post-processing is required to extrapolate the snow cover information for
zones above that classified as wet snow, to interpolate in the regions of layover/shadow, and to remove
pixels of changing backscatter intensities in flat areas erroneously classified as wet snow. The postprocessing is based on a statistical analysis of the classified wet snow cover with the elevation.
The procedure implemented in the Glasnowmap IS is so descripted:
o
Snow extension in areas covered by dry snow which lying in zones more elevated than those
classified as wet snow
o
Snow extension over the regions of radar layover/shadow in zones more elevated than those
classified as wet snow
o
Snow correction in areas erroneously classified as wet snow due to changing backscatter
intensities in flat areas (e.g. lake surfaces).
Meteorological station data are employed for accuracy assessment and training of classification.
4.1.3
Multisource Interpretation System for Mapping Snow Depth.
This module of the Glasnowmap IS allows to integrate snow cover maps derived by the multisource
classification of satellite data and ground data in order to obtain snow depth maps.
The snow depth mapping procedure starts from these main inputs: the fractional snow cover map
produced by the Snow Cover classification procedure, and the snow depth as measured at some ground
stations, and provided by the end-users and DEM. The snow depth mapping strategy is based on a local
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multiple regression between measured snow depth at ground stations, elevation and aspect, triggered
and weighted on the basis of the fractional snow cover values - as mapped from MERIS and AATSR
data. In case of ASAR analysis the hard classification shall be used. The spatial distribution of snow
characteristics is taken into account considering local relationships between the unit to be mapped and
the 8 nearest ground measurement stations.
Ground snow depth measurements at the date of the satellite pass are acquired in the system and preprocessed for removing negative values (errors, or error flags); snow depth values and stations
coordinates in UTM system are retained for the following contextual analysis. It has to be pointed out that
the number of ground station measurements have to be enough large and representative of the ground
situation. Moreover the measurements have to be distributed on the base of elevation and exposure. In
case of measurements sample non representative, the snow depth map can not be produced.
For each pixel of the snow cover map, Euclidean distances from all ground stations are computed and
ranked to find out the 8 nearest stations. Values of snow depth measured at these 8 stations are
correlated by means of multiple linear regression to elevation and aspect values, derived from the digital
elevation model for the same locations. The regression equation and the correlation coefficients are
returned.
The equation is used to extrapolate the snow depth at the pixel position (center), while the correlation
coefficient value is interpreted as a confidence measure and stored in the quality image metafile at the
corresponding position (sample/line). Final snow depth value for each pixel is given by weighting the
extrapolated snow depth value with the corresponding snow cover fraction. Weight is assumed as 1 for
pixels with snow cover fraction higher than 80%.
4.1.4
Final product generation
The snow cover maps generated from SAR or optical image are masked on the base of regional
boundaries. For each region the statistical data, the accuracy of the classification are reported. The
classification raster image is masked with regional boundaries and converted in ArcView BIL format. The
snow cover regional contour is extracted and stored in a ArcView shape file. The snow depth raster
image is given in ArcView BIL format.
4.2
SNOW MELTING SERVICE
The degree-day method is a temperature index approach that computes the daily snowmelt depth by
multiplying the number of degree-days, i.e. a temperature difference between the daily average air
temperature and a base temperature (usually 0°C), by the degree-day factor [R20]. An advantage of the
degree-day method is that it is easy to use operationally because a limited amount of data is required for
the forecast, usually precipitation and temperature.
4.2.1
Snow Melt Runoff model.
In the GLASNOWMAP IS the Snow Melt Runoff Model (SRM) is used for calculating daily stream flow. In
the SRM [R7] the snowmelt contribution to runoff is calculated with the degree-day method using time
series of snow cover maps, which are derived from satellite data.
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The Snow Melt Runoff model has been used in more than 20 countries of different continents. As
example we recall here the experiments conducted in the European alpine areas: in Switzerland [R14], in
Austria [R9] and in Italy [R17].
Satellite remote sensing offers the advantage of providing low-cost, repetitive, multispectral, synoptic and
uniform observations over large areas: these spatially distributed observations are in principle more
directly linked to snowmelt because this process is extremely variable, temporally and spatially, mainly in
areas where the topography is complex, as in mountain range.
The basin is subdivided into elevation zones, and the remote observations are used to determine the
portion of each elevation zone where the snow cover remains and degree-day approach should be
applied to melt the existing snow pack.
Mathematical formulation of the SRM model, for a watershed subdivided in i elevation zones, each having
an area Ai , is given by the following expression:
Q n +1
 I
A i ⋅ 10 − 2
=  ∑ [α in (T in + ∆ Ti )Cs in S in + Cr in Pin ]
86400
 i =1

 × (1 − k n +1 ) + Q n ⋅ k n +1

(2)
where Q is the average daily river discharge [m 3/s]. The SRM model needs in input a set of variables,
derived from ground measurements and satellite observations, together with a set of parameters,
describing geomorphologic and hydrological characteristics of the watershed, that have to be calibrated
and tuned during the simulation phase.
Model variables are given in input with daily frequency for each of the elevation zones: they are T mean
daily air temperature [°C], P rain precipitation [cm day-1], and S snow cover as percentage of each
elevation zone area [%].
Model parameters include the degree-day factor α [cm °C-1day–1], the runoff coefficients for snow (Cs)
and rain (Cr) which account for the basin losses, and the recession coefficient K that has to be obtained
by the analysis of historical discharges. Factor (10-2/86400) converts from [cm m-2 day-1] to discharge
units [m 3 s -1].
4.2.2
Application to Dora Baltea River basin.
The selected area concerns with the upper part of the Dora Baltea River basin, located in North-Western
Italian Alps (Valle d’Aosta). The area of the basin is 1290 km 2 and the elevation range is between 619 m
and 4729 m a.s.l., with an average elevation of 2300 m.
Six meteorological stations measuring data on air temperature and precipitation were available:
Villeneuve, Morgex, Rhemes Notre Dame, La Thuile, Cogne and Valsavarenche. The highest location of
meteorological stations is Valsavarenche (1951 m) that is lower than the average elevation of the basin.
The watershed was subdivided into three elevation zones whose characteristics are summarised in
Table 4-1.
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Elev. Range [m]
Zone 1 619-1810
Zone 2 1810-2500
Zone 3 2500–4729
Average Elev. [m]
1405
2191
2891
Area [Km2]
263.94
478.75
545.13
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Area [%]
20.5
37.1
42.4
Table 4-1. Elevation zones defined for the application of Snowmelt Runoff Model in the Dora Baltea watershed.
Hydrological measurements, i.e. water level, is collected at Aymavilles gauging station (619 m), and were
provided by Regione Valle d’Aosta (Dipartimento Territorio e Ambiente) together with meteorological data.
Those data are stored into the Glasnowmap IS.
Mean value of daily temperature derived from meteorological station measurements are corrected for the
temperature lapse rate ∆T in order to represent the temperature of the average elevation for each zone.
Precipitation recorded at meteorological stations includes both snow and rain, whose contributions are
separated by means of a critical temperature Tcrit (T> Tcrit is rain contribution). Critical temperature has
been fixed at Tcrit = 0 °C.
EO-data was utilized to map the snow cover extent in the basin. Snow cover maps were obtained from
satellite images by means of a classification process, previously described. Seasonal variation of snow
cover extension for each elevation zone is described by Snow Depletion Curves (SDC) obtained trough
the interpolation of values of snow cover percentage in time. Depletion curves represent the day-by-day
decreasing of snow cover extent during the melting season.
4.2.3
Model Calibration
Objective of the project was the provision of a snowmelt runoff model based on integrated use of the
optical and radar images for the identification of snow cover and the assessment of snow cover area to
build the depletion curves in the calibration phase of the snow melt runoff model, and to use the modified
depletion curves in the forecasting simulation phase.
Due to the unavailability of ENVISAT observations for the melting season 2002, two different calibration
has been carried out: one using NOAA AVHRR images and one using ERS data. As reported in the
following paragraphs the NOAA – AVHRR data gives the best results and the model has been calibrated
on the base of this input.
4.2.4
Calibration of the snowmelt runoff model using NOAA AVHRR data
Seven snow cover maps were produced using NOAA-AVHRR data. Those images were acquired in the
2002 snowmelting season and are : 5 April 2002, 27 April 2002, 14 May 2002, 14 June 2002, 7 July 2002,
11 July 2002 and 22 July 2002. The processing applied to produce snow cover maps was based on fuzzy
classification. The percentage of the snow cover the for the three zones, in which the basin has been
subdivided, and the graph of the derived depletion curves are here shown.
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Acquistion
Z1
Z2
Z3
05/04/02
17,50%
76,40%
96,50%
27/04/02
23,10%
68,20%
83,40%
14/05/02
20,60%
73,80%
94,90%
14/06/02
12,40%
27,00%
54,80%
07/07/02
0,00%
4,90%
28,90%
11/07/02
5,50%
10,90%
29,40%
22/07/02
0,00%
0,00%
14,10%
Table 4-2. Percentage of snow-cover area derived from NOAA AVHRR images (2002)
1,0
Z1
0,9
Z2
0,8
Z3
0,7
S [%]
0,6
0,5
0,4
0,3
0,2
0,1
0,0
0
20
40
60
80
100
120
giorni
Figure 4-1. Depletion curves derived from NOAA AVHRR for the 2002 year
On the basis of the depletion curves derived by NOAA AVHRR images and meteorological data of the
snowmelt season 2002 the model has been calibrated.
Figure 4-2 shows that daily runoff simulation during spring and early summer 2002 agree well with
measured runoff. Evaluation of the accuracy of the daily river discharges simulated by the model was
based on the comparison with measured discharges.
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Q [m3/s]
350
Q sim
Q mis
300
250
200
150
100
50
0
9-apr
29-apr
19-mag
8-giu
28-giu
18-lug
Figure 4-2. Distribution of measured and simulated discharges with NOAA AVHRR data for Dora Baltea river basin
during the 2002 snowmelt season (9 April – 31 July 2002)
Evaluation of the accuracy of the daily river discharges simulated by the model during the 2002 melting
season was based on the comparison with measured discharges.
Comparison was based on the analysis of a set of parameters according to the suggestion of WMO
(World Meteorological Organisation) during an inter-comparison study of different model [R23].
Accuracy evaluation is based on three criteria:
Ø regression analysis between measured and simulated discharges (correlation coefficient)
Ø Nash-Sutcliffe coefficient, defined as
N
∑ (Q
sn
R2 = 1 −
− Qm n) 2
n =1
N
∑ (Q
mn
− Qm ) 2
n =1
where
Qsn is daily simulated discharge, Qmn is daily measured discharge and Qm is seasonal
average value of measured discharges for the melting season
Ø Volume deviation
where VS is total seasonal discharge simulated and Vm is total seasonal discharge measured
Quantitative evaluation was realized computing a set of parameters according to the suggestion of World
Meteorological Organisation during an inter-comparison study of different hydrological models for
snowmelt runoff [R23].
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Statistical analysis through linear regression between measured and simulated discharges gave a
correlation coefficient r=0.89. The standard Nash-Sutcliff coefficient, that quantifies the success of the
prediction capabilities of the model, resulted to be R2=0.71 and volumetric difference of –6.0%. Volume
underestimation occurs mainly in two short time intervals, at the beginning of April and at the end of July.
If we consider the main snowmelt period, May and June, the performance of the model improves: R2
increases from 0.71 to 0.81 and volumetric error reduces to +4.2%.
4.2.5
Calibration of the snowmelt runoff model using SAR data
Snow cover map generated using ERS SAR data are available for melting season 2002. ERS-2 SAR
images were acquired on April 10, April 29, May 15, June 3 and June 19 over the Aosta Valley. For April
29 and June 3 images were available for both ascending and descending orbits.
As concern processing of these data, images acquired on August 8 and 27, 2001 were considered as
reference. It turned out that during August 2001 wet snow was present over part of the glaciers. In order
to avoid classification errors at the end of the melting season, wet snow on the glacier was classified as
a decrease of the backscattering coefficient from April 2002 to August 2001 and retained in the system as
a further class.
Results of the wet snow classification (including layover and shadow mask) and of the post-processing
(illustrated in the Post-processing of the wet snow cover map. Paragraph) are shown in Figure 4-3.
Orthorectification was performed in the Italian UTM 33 N projection with a pixel spacing of 250 m.
(a)
(b)
Figure 4-3. Wet snow cover map of the Aosta Valley from the ERS-2 SAR acquisition of May 15, 2002, in
descending mode. The gray scale coding is the following: 0 no data (outside the ERS frame and/or the
Swiss and Italian territory covered by the DEM), 100 layover/shadow mask, 150 no snow, 200 wet snow on
the glaciers during the summer reference image, 255 wet snow on the date of the acquisition). (b) Snow
cover map after post-processing superimposed to a shaded relief of the DEM (snow cover is in violet,
respectively dark gray on a b&w reproduction).
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The percentages of the snow cover the for the three zones, in which the basin has been subdivided, have
been derived from SAR snow cover map.
Table 4-3 shows the percentage of snow cover for Z1, Z2 and Z3 obtained.
Multi-source SAR
Z1
Z2
Z3
10 April 2002
0.05 %
63.37 %
99.95 %
29 April 2002
0.00 %
17.11 %
99.81 %
15 May 2002
0.05 %
21.66 %
94.11 %
03 June 2002
0.00 %
1.55 %
74.09 %
19 June 2002
0.05 %
14.91 %
66.09 %
Table 4-3. Percentage of snow cover for Z1, Z2 and Z3 derived by processing SAR data and multi-source processing
of SAR (year 2002).
The depletion curve graph derived is shown in Figure 4-4. This curves was computed using second order
polynomial interpolation as for the NOAA-AVHRR data. It is clearly visible that while the depletion for the
zone 3 is meaningful, the curve of zone 2 can not be considered representative of the snow cover
depletion phenomenon; for the zone 1 there is not any contribution of snow cover.
1
R2 = 0,9622
0,9
Z1
2
R = 0,8889
0,8
Z2
2
R = 0,0009
0,7
Z3
0,6
0,5
0,4
0,3
0,2
0,1
0
01/04/2002
21/04/2002
11/05/2002
31/05/2002
20/06/2002
10/07/2002
30/07/2002
Figure 4-4. Depletion curves derived from SAR data by second order polynomial interpolation
As the depletion curves obtained by a second order polynomial interpolation are not usable for calibrating
model a first order polynomial interpolation has been performed (Figure 4-5).
It can be noticed that there is a valuable difference between the percentage of snow cover extensions
derived by NOAA and by SAR data (see Table 4-1 and Table 4-3: in zone 1 and 2 there is a strong
underestimation of snow for the results derived by SAR with respect to NOAA data, while for the zone 3
the estimations of snow cover are comparable.
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1
0,9
R2 = 0,8842
0,8
Z1
2
R = 0,6033
0,7
Z2
2
R = 0,0009
0,6
Z3
0,5
0,4
0,3
0,2
0,1
0
01/04/2002
21/04/2002
11/05/2002
31/05/2002
20/06/2002
10/07/2002
30/07/2002
Figure 4-5. Depletion curves derived from SAR data by first order polynomial interpolation
Measured vs Simulated (SAR 2002)
Q (m3/s)
Q mis
Q Sim
350
300
250
200
150
100
50
0
9-apr
24-apr
9-mag
24-mag
8-giu
23-giu
8-lug
23-lug
Figure 4-6. Distribution of measured and simulated discharges with SAR data for Dora Baltea river basin during the
2002 snowmelt season (9 April – 31 July 2002)
The clear effect of underestimation could be explained by the underestimation of snow covered areas
obtained fro the season from the multi-source of SAR data.
Statistical analysis through linear regression between measured and simulated discharges gave a
correlation coefficient r=0.86. The standard Nash-Sutcliff coefficient, that quantifies the success of the
prediction capabilities of the model, resulted to be R2 = 0.308 and volumetric difference DV= -35.72%.
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GLACIER MAPPING SERVICE
The integration of the optical and radar remote sensing derived information is currently investigated. The
discrimination between accumulation and ablation basins in the demonstration phase has been
performed using multispectral optical images if the image is acquired at the end of the ablation period,
that is when the ablation basin is totally snow free, showing the characteristics of ice, while the surface of
the accumulation basin is snow covered.
The availability of images in this short time is often severely limited by the cloud cover. The use of SAR
images integrated with optical images could help in such discrimination. The objective of the multisource
interpretation system is to exploit the capability of radar images to identify different types of snow and ice
and the ability of multispectral optical images to identify glaciers with the aim of accumulation and ablation
basin mapping.
Due to the complex topography of the Alpine regions and the high resolution of images a preliminary
orthorectification has been performed using a Digital Elevation Model with 30 meters of resolution. Control
points are selected on a 1:10,000 scale map
The orthorectified optical image is then classified by using the fuzzy statistical classifier (used for the
snow cover classification) which provides the partial membership of pixels to land cover classes,
including the “snow cover” and “snow free/ice covered” classes which allow to identify the glacier body.
The radar imagery processing provides information on the quality of snow. To this aim two ASAR images
are used: a reference image acquired in winter when a wide distribution of dry snow prevails and further
image acquired at the end of the ablation season, as near as possible close to the optical image time
acquisition. The procedure described for ASAR image processing for snow cover mapping is applied
providing the wet snow cover distribution.
4.3.1
Multisource interpretation system
In the multisource interpretation system of Glacier Mapping , the first step is to use snow and ice soft
maps obtained by the fuzzy statistical classification applied on optical images. This step produces a
mask of the glaciers surface. Particularly pixels for which the sum of fractional snow and ice covers is
not null are considered as belonging to the glacier area. This mask is then applied to snow soft map, ice
soft map and SAR ratio map to extract only glaciated areas.
Different classification algorithms, both supervised and unsupervised, have been tested using as input
different data sets. From the comparison of results, the Neural Network Multi Layer Perceptron algorithm
provided the best fitting with the ground truth information. The data used as input to the Neural Network
are the six TM bands, the fuzzy maps of snow, ice, and shadowed snow, and the HH and HV derived
from radar process based on rationing technique.
In particulare two ASAR images have been preprocessed according to the procedure described in the
System Specification Description document: an acquisition on January, 5th 2003 used as reference for
rationing, and the acquisition under study on September 7th 2003.
The result of the radar processing system is related to the wetness of snow covers. It has been
computed for the two polarizations , HH and HV.
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HV polarization
Figure 4-7. ASAR APS HH and HV ratio between January and September 2003
About the optical contribution, the September 14t 2003 Lansat TM image has been orthorectified and
classified with 8 classes: snow, ice, moraine, pastures, woodland, water, shadow and shadowed snow.
From the set of 8 soft maps, one for each land cover class, only the glacier covers - snow, shadowed
snow, and ice- were used for the subsequent multisource process.
snow
shadowed snow
ice
Figure 4-8. 14 September 2003 Landsat 5 TM (195/28) fuzzy Classification
In order to train and validate the neural network classifier six training sets (from image interpretation and
in situ measurements) have been selected (Figure 4-9). Ground truth data collected during the on field
campaign of September 7th , 2003, validated by the Centro Nivometeo of Bormio (ARPA Lombardia),
were used to train the Neural Network and test the final resulting map.
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Figure 4-9. Neural network classification training sets
4.3.2
Final product generation
The glacier classification generated is masked on the base of glaciers boundaries. For each glacier the
statistical data, the accuracy of the classification are reported. The classification raster image is masked
with glacier boundaries and converted in ArcView BIL format. The glacier total body, the snow cover and
ice boundaries are extracted and stored in a ArcView shape file.
(a)
(b)
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(d)
Figure 4-10. Adamello (a), Allalin (b), Gorner ( c ) and Lys (d) basins definition.
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5. INPUT DATA
Input data used in order to generate final products includes:
-
EO-data, included both optical sensor and microwave active sensor acquisitions.
-
Ground data
-
Topographic data
-
Glacier historical data
Input data used during GLASNOWMAP-IS project are summarised in this section.
5.1
EO-DATA
5.1.1
Snow Cover Service
Input data used in order to generate snow cover final products include MERIS, AATSR and ASAR Wide
Swath Mode (WSM) data, acquired by Envisat satellite on board instruments.
As concern optical data (i.e. MERIS and AATSR) they were combined used in order to estimate snow
coverage (MERIS data) and Cloud coverage (AATSR data). Input data used during demonstration
activities to generate final products are listed in the following table.
Sensor
Acqusition date
Production date
Track Frame
Orbit
MERIS
12 Nov 2002
22 Jan 2003
3669
108
2655
AATSR
12 Nov 2002
16 May 2003
3669
108
2655
MERIS
1 Dec 2002
22 Jan 2003
3941
380
2655
AATSR
1 Dec 2002
16 Apr 2003
3941
380
2655
MERIS
19 Feb 2003
26 June 2003
5086
22
2679
MERIS
17 Apr 2003
30 Apr 2003
5902
337
2669
AATSR
17 Apr 2003
2 June 2003
5902
337
2669
Table 5-1 Meris and AATSR data used for snow cover maps production
With regards to ASAR acquisitions, the processing of these images allows to extract a wet snow cover
map. In order to produce this map two ASAR images are needed, the former, used as reference,
acquired during snow free period, and the latter acquired during snow period.
The reference images for radar snow cover map production and the products that will be produced are
listed in the tables below.
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Date
Orbit
Track
31 Oct. 2002
3497
437
14 Jun 2003
6732
165
2700
23 Jun 2003
6861
294
2700
23 Jun 2003
6868
301
900
13 Aug 2003
7598
29
900
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Frame
Table 5-2. ASAR WSM new reference images
Due to processing problems the images listed below are not jet available for the project. The radar
product listed in the following table will been generated when those images are available.
Date
Orbit
Track
Frame
15 Jan 2003
4592
29
900
1 Mar. 2003
5229
165
2700
10 Mar. 2003
5365
301
900
10 Mar. 2003
5358
294
2700
5 Apr 2003
5730
165
2700
10 May 2003
6231
165
2700
Table 5-3. ASAR WSM products to be realized
5.1.2
Snow Melting Service
To forecast river discharge value snow cover maps estimated over the area of interest are needed. In
order to generate these maps EO-data, acquired during melting season, were used and methodologies
developed for the snow cover service final products generation are applied.
Input data used in order to generate snow cover final products during melting season 2003 include
MERIS, AATSR and ASAR Wide Swath Mode (WSM) data, acquired by Envisat satellite on board
instruments.
Input data used during demonstration activities to estimate water discharge forecasting values were
acquired for the following dates:
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Sensor
Acqusition date
Production date
Track Frame
Orbit
MERIS
17 Apr 2003
30 Apr 2003
5902
337
2669
AATSR
17 Apr 2003
2 June 2003
5902
337
2669
MERIS
3 May 2003
12 May 2003
6131
65
2673
AATSR
3 May 2003
01 July 2003
6131
65
2673
MERIS
10 Jun 2003
23 June 2003
6675
108
2667
AATSR
10 Jun 2003
2 July 2003
6675
108
2667
Table 5-4. Meris and AATSR data used for snow melting service
5.1.3
Glacier Mapping Service
Glacier mapping final products were generated at the end of the ablations period. Source EO-data for
glacier mapping include ASAR APS, LANDSAT TM/ETM+, and TERRA ASTER scenes.
Input data used in order to generated final products during demonstration activities are listed hereafter:
Acqusition date
Sensor
Glaciers Mapped
24 Aug 2001
ASTER
Allalin
18 Aug 2002
Landsat 7 ETM+
Allalin, Lys and Gorner
14 Sep 2003
Landsat 5 TM
Allalin, Lys and Gorner
5 Jan 2003
ASAR APS
Allalin, Lys and Gorner
7 Sep 2003
ASAR APS
Gorner
30 July 2003
Landsat 5 TM
Adamello
Table 5-5. EO data used for glacier mapping service
It has to be pointed out that 7th September 2003 ASAR APS image has missing lines due to a wrong
processing. The Lys and Allalin glaciers are not covered. Only Gorner glacier is covered by this image.
5.2
GROUND DATA
The ground data used in this project are:
Ø Meteorological data: rain and temperature measured in the Aymavilles basin for Snow
Melting Service. The ground station are: Villeneuve, Morgex, Rhemes N.D., La Thuile,
Cogne and Valsavarenche;
Ø Discharge data: hydrological levels collected at Aymaville ganging station, then converted
to discharge data. Those data are used in the Snow Melting Service;
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Ø Snow depth data: measures collected in Piemonte (66 stations) and Valle d’Aosta (24
stations);
Ø Glacier ground campaign validation: those campaign were organised in 2002 and 2003
summer season on Lys and Adamello glaciers.
5.2.1
2003 ground campaigns
In order to perform and validate the glacier mapping analysis, field campaigns were organized as shown
in Table 5-6.
Glacier
Date
Satellite overpass
Problems
Lys and Gorner
1 August 2003
ASAR APS
Not acquired
Lys and Gorner
6 September 2003
ASTER
Cloudy
Lys and Gorner
7 September 2003
ASAR APS
Table 5-6. Field campaigns
Considering the 7th September campaign some constraints affect the analysis of the data set as regards
the remotely sensed imagery and the ground and weather conditions. ASTER image acquired on the 7th
September was too cloudy to be useful, so that a Landsat 5 TM image of the area acquired on 14th
September was used for the multisource processing of data. Therefore there is a significant temporal
gap between SAR and optical data acquisitions. Moreover the Lys glacier is only partially covered by the
ASAR image acquired on the 7th September.
A snowfall occurred on the night of 6th September, so that the two glaciers, particularly the Lys, were
covered by a fresh snow layer, whose depth, according to the ground measurements, ranges from 20 to
50 cm. Such an in-homogeneous distribution was due to the strong wind during the snowfall. Fresh snow
is mostly dry so that the wet snow facies was likely masked, also because snow depths recorded in situ
seems to exceed the likelihood of microwaves penetration.
The last constraint concerns with the complexity of the morphology and steepness of Lys glacier so that
it is not trouble-free for this kind of studies, which are still experimental.
The snow testing is a traditional glaciological measurement conducted to characterized the snow
condition. Contemporary to the satellite overpass the following measurements were acquired:
Ø snow density
Ø kind and diameter (size) of crystals (snow flakes)
Ø liquid water content (snow/firn superficial layers)
Ø roughness
Ø temperature of snow/firn and ice
Ø determination tongue position (+/- 5 meters)
Ø determination of glacier boundarys debris covered
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The last two measurements can be carried out some days before or after the satellite overpass except in
the case of snowfalls. The glaciers evolution in fact is slower than snow and firn surfaces therefore for
the purpose of Remote Sensing characterization; measurements on glacier tongue are not necessarily
carry out contemporary to the satellite overpass.
The following table describes the typical instrumentation used and the relative measurements for the
snow testing protocol.
Instrument
Measurements
1 shovel
Trench to evaluate the snow layers
5/6 bayonet thermometer (for snow)
Temperature sup. and layers
Probe (5 meters decomposable)
Depth
Dynamometer
Density
Crystallographic tablet (platelet)
Crystallographic analysis
1 lens (10x)
Crystallographic analysis
1 penetrometer (hammer penetrometric probe)
Hardness, compression strength
Further analysis request also the measurements computed as follows:
Measurement
Method
Crystallographic analysis
Stratigraphy observation
Hardness
Test “della mano”
Humidity
Test “della palla di neve”
5.3
TEST SITE
1. Colle del Lys Occidentale 4140 m a.s.l. (06-09-03)
2. Colle Gnifetti 4450 m a.s.l. (07-09-03)
3. Colle Sesia 4295 m a.s.l. (07-09-03)
4. Colle del Lys Orientale 4260 m a.s.l. (07-09-03)
5. Colle Vincent 4045 m a.s.l. (07-09-03)
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3
5
1
4
2
figure 5-1. Location of the test sites an the Lys and Gorner Glaciers.
5.4
MEASURES
Measure
Elevation
Stauts
Snow depth:
3600 - 4600
snow cover due to snowfalls happened after the first half of
august. The cover is made up of a single layer
3600 - 4000
the cover is not uniform and the depth is very thin due to the
slight snowfalls occurred on the 4 and the 5 of September.
Single layer snow cover ranging from 1 cm at 3600 and 5 cm
at 4000 m.
4000 - 4100
the snow cover is continuous and uniform with an average
depth of 30 cm. From the single-layer the cover becomes a
multiple layer cover related to at least 3 different snowfalls.
4000 - 4100
the snow depth and the number of layers depend upon the
wind in terms of erosion and accumulation
3600 - 4000
It is characterised by small dips parallel to the slope that are
deeper than 20cm in the impluvium zones.
4000 - 4600
the snow surface is almost flat but hardly windblown zones in
which small irregular dips, few centimetres deep, are present
3600 - 4000
a thin recently felt snow layer overlay ice or firn cover. This
layer during the day is wet due to daily thermal raise.
4000 - 4600
the surface layer is dry also during the day
4000
boundary elevation between wet snow and dry snow of the
surface layer
Roughness
Wetness
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4045 - 4450
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snow pack characteristics are homogeneous:
−
the surface is characterised by a snow crust
(crystals 3b,6a) produced by both the wind and the
solar radiation; the snow density increases as the
elevation decreases;
−
the middle layer is composed by hail grains,
which can reach a 8 mm diameter;
the bottom layer is composed by grains of different types
depending on the elevation and aspect: 6a-6b, 3b-3c.
4000 – 4500
type 3 is dominant
4000
type 6 is dominant as related to the higher warming and solar
irradiation and to the consecution of snow and rainwater falls
during the layer accumulation
In the image, blue colour shows homogeneous snow pack of dry snow and a surface with low
roughness. Red colour indicates single layer snow pack; the snow is wet and there are a lot of
dips.
Figure 5-2. Glaciers snow pack (blue: dry snow, Red: wet snow and dips)
Stratigraphic analysis and penetrometric test have investigated the characteristic of the residual snow
deposited on the 2002-2003 season.
Snow testing measurements carried out on Lys glaciers allowed to detect the different kind of snow found
on the test site during the satellite overpasses. It has been possible to characterise the type of snow in
hardness and humidity. The penetrometric test allows visualising different snow condition in a schematic
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way. This test added to traditional measurements of snow quality is specially recommended to monitor
upper zone of the glaciers where the fallen snow is accumulated.
These measurements had been strongly influenced by the recent snow events on the glacier. Ice areas
of the upper part of the two glaciers were reduced due to the strong snowfalls. This methodology allows
also monitoring wind activity on glacier slopes eroding snow pack. It’s necessary to consider the
temperature and the trend condition on the current Summer season to have a global view of the snow
condition on the site. Observations are also related to the general characteristics of the glacier. A big or
very sloped glacier has a different response and behaviour than a smaller one or flat one.
At the Colle del Lys test site we found a 52 cm thick snowpack with a mean density of 381Kg/mq and a
total weight of 342 Kg/mq. The upper part, is composed by recent snow, that is characterized by several
fusion and refreezing cycles. The lowest part is more packed and strongly accumulated, being the
residual snow of the last seasons. This is also clear from the analysis of snow flakes and crystals with a
lens.
5.5
TOPOGRAPHIC DATA
The ground data used in this project are listed in the following table.
Modello di Elevazione
del Terreno
Carta Tecnica Regionale
Ghiacciaio Allalin
DEM 30 m (DLR)
Carta nazionale della Svizzera
Ghiacciaio Gorner
DEM 30 m(DLR)
Carta nazionale della Svizzera
Ghiacciaio Lys
DEM 30 m(DLR)
Copertura del suolo CORINE
1:100.000 livello 3
Regione Valle d'Aosta DEM 250 m (SGI)
Regione Lombardia
Ghiacciaio
dell'Adamello
Regione Piemonte
DEM 250 m (SGI)
Dati Tematici
CTR 10000
Copertura del suolo C ORINE
1:100.000 livello 3
DEM 40 m R.Lombardia
DEM 10 m R. Trentino
CTR 10000 + Trentino
DEM 250 m (SGI)
CTR 10000
Copertura del suolo CORINE
1:100.000 livello 3
Table 5-7. Topographic data used in Glasnowmap project
5.6
HISTORICAL DATA
In order to allow the user to monitor the trend of glaciers over last years, historical classification using EO
data of Lys and Adamello are available on Glasnowmap WebSite. Those data have not been produced by
Glasnowmap IS, but only published on the web. The historical classifications were processed in “Italian
Glacier Monitoring from Space” project funded ASI. Those data are:
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Acqusition date
Glacier Mapped
16 Aug 1986
Adamello
20 Sep 1993
Adamello
13 Sep 1999
Adamello
12 Sep 1985
Lys
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Table 5-8. Historical data available on website, coming from ASI projects
6. DEMONSTARTION ACTIVITIES - RESULTS
6.1
SNOW COVER SERVICE
In this paragraph example of Snow Cover final product derived from EO-data available at consortium
premises are described.
6.1.1
Snow Cover final products
The products are available for the users in two different formats:
Ø BIL: 8 bit Raster ArcView format where pixel values set to 0 represent snow free, 255 snow and 1
cloud cover;
Ø SHAPE: vector ArcView format where the snow contour is reported;
Moreover a statistical file is given, where the total coverage of snow is reported in km2 and the
classification accuracy is given.
In the following figure snow cover thematic maps obtain over Valle d’Aosta are reported. Here the snow
free region is represented in black, the snow region in white and the clouds in gray.
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(b)
(a)
Accuracy: 0.94
Pagina
Data:
Accuracy: 0.87
(c)
Accuracy: 0.99
(d)
Accuracy: 0.91
Figure 6-1. Valle d’Aosta Region in 12 Nov 2002 (a), 1 Dec 2002 (b), 19 Feb 2003 (c), 17 Apr 2003(d).
6.1.2
Snow depth final products
The products are available for the users in BIL: 16 bit Raster ArcView format where pixel values
represents the snow depth expressed in cm. The value –1 is placed where the DEM data are unavailable.
Moreover a statistical file is given, where the snow depth is analyzed on the base of elevation and
exposure.
The snow depth final products was produced only for Piemonte Region. It has to be pointed out that this
product is available only if snow depth ground measures are present. During the demonstration phase
both Valle d’Aosta Region and Piemonte Region gave us the snow depth measurements. Those data
were collected into the Glasnowmap IS. Due to the low number of snow depth measures available on
Valle d’Aosta Region and to their distribution, it was not possible to give a significant map of snow cover
depth extended to all Valle d’Aosta Region. The snow depth final product is available only on Piemonte
region and the an example is reported below. The maps values are related to the snow depth. White color
correspond to high snow depth values while black one correspond to no snow.
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Figure 6-2. Snow depth on Piemonte Region.
6.2
SNOW MELTING SERVICE
On the base of meteorological forecasted data, the Glasnowmap IS produces a three days discharge
forecasting. The final product of Snow Melting Service Glasnowmap IS module is a table were discharge
forecast data are reported. An example of this table is given in Table 6-1
River DoraBaltea
Basin Aymavilles
Date 24/4/2003
Last measured discharge value on 24/4/2003
Start Value
79.5130 [m3/s]
Forecasting
Discharge value on 25/4/2003
Discharge value on 26/4/2003
Discharge value on 27/4/2003
Forecasted data used:
Zone 1
Temperature
25/4/2003
Rain
25/4/2003
Temperature
26/4/2003
Rain
26/4/2003
Temperature
27/4/2003
79.5130 [m 3/s]
75.737728 [m3/s]
75.683364 [m3/s]
69.643506 [m3/s]
Zone 2
9.83
0.00
6.78
1.01
8.07
Zone 3
4.79
0.00
2.65
0.58
3.66
0.59
0.00
0.00
0.00
0.00
Table 6-1. Example of snow melting result published on the web
During the demontration phase two different simulation has been carried out: one based on the combined
use of MERIS + AATSR data and one base on the sole use of AATSR data. This decision was based on
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the consideration that AVHRR data and AATRS data are more consistent due to similar spatial and
spectral resolution.
6.2.1
Runoff forecasting using MERIS + AATSR data
The snow cover maps were extracted using the Snow Cover Service module of Glasnowmap IS. The
percentage of snow covered surface was directly updated using Glasnowmap IS. The values computed
in the three elevation zones of Dora Baltea are reported in Table 6-2.
Acquisition date
Z1 (619 m-1810 m)
Z2 (1810 m-2500 m)
Z3(2500 m–4729 m)
17 April 2003
0.18 %
34.17 %
75.78 %
03 May 2003
1.42 %
62.78 %
95.75 %
10 June 2003
0.14 %
8.27 %
35.56 %
Table 6-2. Percentage of snow-cover area derived from the combined use of AATSR and MERIS images.
The percentage computed combined with temperature values was used to update Modified Depletion
Curves used in the simulation of the river discharge forecast.
The plot of the three days forecasted values is reported in Figure 6-2.
Measured vs Simulated using MERIS + AATRS 2003
data
350
Q(m3/s)
300
250
Measured
200
1 day forecast
150
2 day forecast
100
3 day forecast
50
16/06/03
11/06/03
06/06/03
01/06/03
27/05/03
22/05/03
15/05/03
10/05/03
05/05/03
30/04/03
25/04/03
20/04/03
15/04/03
0
Figure 6-2. Distribution of measured and forecasted discharges with ENVISAT MERIS data for Dora Baltea river basin
during the 2003 snowmelt season (15 April – 20 June 2003).
Quantitative evaluation was realized computing the set of parameters as the correlation coefficient r,
Nash-Sutcliff coefficient R2 and volume deviation. The quality of the runoff forecast decreases slightly
from the one to the three day forecast, in particular when considering the volume deviation that pass from
–6 % to –17 %.
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Day 1 forecast
Day 2 forecast
Day 3 forecast
Correlation coefficient r
0.986
0.964
0.937
Nash-Sutcliffe coefficient R2
0.945
0.857
0.751
Volume deviation DV %
-6.74
-12.06
-17.36
Table 6-3. Qualitative evaluation of discharge forecasting for the period 15 April – 20 June 2003.
6.2.2
Runoff forecasting using AATSR data
Several differences can be observed between snow areas derived from images with different spatial
resolution (1 km vs. 300m), specially in the areas at lower elevations in the basin. As expected results
from AVHRR and AATSR are much more consistent due the similar spatial and spectral resolution of
these sensors. The snow cover maps were extracted from the fuzzy classification of AATSR images.
The percentage of snow covered surface was directly updated using Glasnowmap IS. The values
computed in the three elevation zones of Dora Baltea are reported in Table 6-4.
Acquisition date
Z1 (619 m-1810 m)
Z2 (1810 m-2500 m)
Z3(2500 m–4729 m)
17 April 2003
18.50 %
56.60 %
82.90 %
03 May 2003
9.80 %
45.30 %
87.70 %
10 June 2003
0.80 %
13.40 %
43.30 %
Table 6-4. Percentage of snow-cover area derived from AATSR images.
The percentage computed combined with temperature values was used to update Modified Depletion
Curves used in the simulation of the river discharge forecast. The plot of the three days forecasted values
is reported in Figure 6-3
Measured vs Simulated using AATSR 2003m data
350
Q(m3/s)
300
Measured
250
1 day forecast
200
150
2 day forecast
100
3 day forecast
50
17
/6/
03
3/6
/03
10
/6/
03
27
/5/
03
20
/5/
03
6/5
/03
13
/5/
03
29
/4/
03
22
/4/
03
15
/4/
03
0
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Figure 6-3. Distribution of measured and forecasted discharges with ENVIST AATSR data for Dora Baltea river basin
during the 2003 snowmelt season (15 April – 20 June 2003).
Quantitative evaluation was realized computing the set of parameters as the correlation coefficient r,
Nash-Sutcliff coefficient R2 and volume deviation (Table 6-5). The quality of the runoff forecast decreases
slightly from the one to the three day forecast, in particular when considering the volume deviation that
pass from –7 % to –18 %.
Parameters
Day 1 forecast
Day 2 forecast
Day 3 forecast
Correlation coefficient r
0.989
0.979
0.972
Nash-Sutcliffe coefficient R2
0.945
0.867
0.777
Volume deviation DV %
-7.16
-12.80
-18.28
Table 6-5. Qualitative evaluation of discharge forecasting for the period 15 April – 20 June 2003.
Analysis of this results indicates that river discharges forecasted with SMR model are very similar using
snow cover areas derived from AATSR or MERIS data.
6.3
GLACIER MAPPING SERVICE
Landsat (both TM and ETM+), Envisat ASAR and ASTER acquisitions have been used for glacier
monitoring. ASAR APS processing results are currently under investigation.
In the following table the product generated are illustrated, where the dates and the sensors used for the
processing are reported.
Acqusition date
Sensor
Glaciers Mapped
24 Aug 2001
ASTER
Allalin
18 Aug 2002
Landsat 7 ETM+
Allalin, Lys and Gorner
14 Sep 2003
Landsat 5 TM
Allalin, Lys and Gorner
5 Jan 2003
ASAR APS
Allalin, Lys and Gorner
7 Sep 2003
ASAR APS
Gorner
Table 6-6. Data acquired for glacier mapping and glaciers analysed
The products are available for the users in two different formats:
Ø BIL: 8 bit Raster ArcView format where pixel values set to 0 represents no glacier, 255 snow area
and 125 ice area;
Ø SHAPE: vector ArcView format where the glacier body and the snow area contour is reported ;
Moreover a statistical file is given, where the total coverage of the glacier is reported in km2 and the
classification accuracy is given.
In the following figures shown glacier thematic maps obtain over Allalin, Lys and Gorner glaciers are
reported. Here the snow area is represented in white and the ice area in gray.
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(b)
Table 6-7. (a) August 18, 2002 Landsat ETM+ false color composition (RGB 5,4,3) and (b) classification results on
Gorner Glacier. In blue is represented the “ice covered” class and in light blue the “snow covered” class.
In order to allow the user to monitor the trend of glaciers over last years, historical data of Lys and
Adamello are available on Glasnowmap WebSite. Those data have not been produced by Glasnowmap
IS, but only published on the web. The historical data were processed in previous research projects done
in the framework of ASI programs.
The equilibrium line definition is usually performed by the glacier expert. It is visible on the image only at
the end of the ablation period. The analysis of the elevation distribution in the border zones between
accumulation and ablation basin allowed to approximately compute the elevation of the snow line.
The combined use of ASAR and optical images, integrating the results obtained by the classification of
optical images with ASAR wet snow analysis, could help in defining the equilibrium line. The aim of the
multisource interpretation system is to identify different glacier facies of glaciological interest, particularly
ice, dry and wet snow. During the demonstration phase the multisource interpretation system was
applied in order to generate the Gorner, Allalin and Lys ( only partially due to ENVISAT image missing
lines). The images used for this process are: 5 Jan 2003 and 7 Sep 2003 ASAR APS, 14 Sep 2003
Landsat 5 TM. The classification result is shown Figure 6-4
Error matrix of the multisource classification
Ground Truth (%)
exposed
Class
ice ice fall
snow
Unclassified 0
1.3 0
dry snow 100
0
0
ice
0
93.51 0
ice fall
0
0
100
wet snow 0
5.19 0
Total
100
100 100
shadowed
snow
19.05
0
0
0
80.95
100
Total
3.37
43.82
26.97
11.61
14.23
100
Overall Accuracy = 95.13%
Kappa Coefficient = 0.9299
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Figure 6-4. Results of the multisource classification process
7. PROBLEMS
The main problem met in the demonstration phase regards the satellite data availability, both for ENVISAT
and for LANDSAT data. During the demonstration two blockages were met: the former regards the data
availability and the latter regards the delay in products delivery due to satellite data production and
reprocessing time.
About Snow Melting Service, the forecasted rain and temperature need for river discharge forecasting
were not available during the demonstration phase. The simulation has not been performed in real-time
and it has used measured data.
Hereafter a detailed description of the ENVISAT and LANDSAT data procurement problems is reported.
7.1.1
ENVISAT DATA
As concerns ENVISAT data availability was low for ASAR products and a medium for MERIS/AATRS
images. About WSM ASAR no product had been delivered in the demonstration phase due to image
unavailability. In order to produce a snow cover map using radar data, a reference image is needed with
same track and frame value of the image used to produce the map. The reference image has to be
acquired during the snow free period. In the demonstration, only one reference image was acquired
period. It was the track 437 frame 2700 image acquired on 31st October 2002.
This need has limited the radar data snow cover map production. Only track 437 frame 2700 ASAR WSM
images could be used to produce snow cover maps and no other image with these track and frame
values was acquired during the demonstration.
At the end of the snow cover and snow melting demonstration phase, it was decided to order new
images in the 2003 snow free season to use as reference for the images planned and acquired in the
demonstration phase, but no image was acquired. So due to the anomalous 2003 season with a great
increase of the temperature and decrease of precipitation, it was decided to use as reference the images
acquired in June and July 2003. This choice was made to provide the users with snow cover products
with a higher frequency, although with a great delay. The list of the products to be realized is reported in
paragraph 5.1 .
About ASAR APS data images, currently the 7th September 2003 image user for glacier mapping service,
has missing lines due to a wrong processing. The Lys and Allalin glaciers are not covered. Only Gorner
glacier is covered by this image.
About the MERIS and AATSR snow cover map production, only six images could be used in the
demonstration phase, four for the snow cover service and three for the snow melting service. This
means that the optical data availability was less than one image a month, where the users expected
about one image a week. The low number of available images is mainly due to cloud coverage, as shown
in the following table, that is a great limitation in optical image usage.
Planned
Not Planned
Acquired
Cloudy
Not used
Used
34
3
21
13
2
6
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Table 7-1. Meris data images
A great problem during the demonstration phase was the delay of the data production and reprocessing.
Most of the data received during the demonstration had to be reprocessed due to a wrong coverage of
the area of interest or to image calibration or to header files missing. This caused a great delay in product
delivery and moreover the satellite data management became a very expansive and a time consuming
task.
One of the user requirements of the service was the near real-time maps production. As shown in the
following table this requirement had been never satisfied due to reprocessing and production delay. The
AATSR products have a greater delay due to the unavailability of the product for the first period of the
demonstration. The snow cover maps in this period have been produced and given to the user in draft
version, not cloud cover corrected.
Sensor
Acqusition date
Production date
Track Frame
Orbit
MERIS
12 Nov 2002
22 Jan 2003
3669
108
2655
AATSR
12 Nov 2002
16 May 2003
3669
108
2655
MERIS
1 Dec 2002
22 Jan 2003
3941
380
2655
AATSR
1 Dec 2002
16 Apr 2003
3941
380
2655
MERIS
19 Feb 2003
26 June 2003
5086
22
2679
MERIS
17 Apr 2003
30 Apr 2003
5902
337
2669
AATSR
17 Apr 2003
2 June 2003
5902
337
2669
MERIS
3 May 2003
12 May 2003
6131
65
2673
AATSR
3 May 2003
01 July 2003
6131
65
2673
MERIS
10 Jun 2003
23 June 2003
6675
108
2667
AATSR
10 Jun 2003
2 July 2003
6675
108
2667
Table 7-2. Meris and AATSR data production dates
The minimum delay is 22 days and the maximum is 4 month and a week.
7.1.2
LANDSAT DATA
A failure of the Landsat 7 Scan Line Corrector (SLC – i.e. an electro-mechanical device that
compensates for the forward motion of the spacecraft) was detected on 31st May 2003. For this reason
Landsat 7 ETM+ images have been not available from May 2003 Glacier Mapping Service. Starting from
September 2003 it was possible to order Landsat 5 TM images over Europe. This resolve the Landsat 7
failure for glacier mapping.
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8. RECOMMENDATIONS
On the base of the experience done during the project some recommendations have been pointed out.
For each one of the three services available during the demonstration phase, some recommendations
have been collected and reported here below.
About the snow cover service a known limitation in using optical data is the cloud cover. In order to
overcome this limitation two parallel strategies have to be carried out. The first is the combined use of
radar and optical data, that is a good solution. Radar data can be used in presence of cloud cover and
can integrate the snow cover information independently to weather conditions. This instrument can only
detect wet snow, so it can not be used in presence of dry snow. The ENVISAT acquisition time over the
area of interest is in the early morning (about 9 am) or in the evening (about 9 pm), when the temperature
is not enough to change the snow status from dry to wet in the winter season. In order to obtain better
results is convenient to apply the radar analysis in the melting season, to be sure to detect wet snow.
The SAR wet snow map can be extended in order to obtain snow cover maps using topographic data
contained in the DEM. This extension is not recommended in the layover areas; in the alpine area snow
presence is strictly related to the ground exposure, mainly in the melting season.
The second strategy is to acquire a large number of optical images. It is sure that many images may be
covered with clouds and thus may not be useful, but the total number of useful images could be
significantly more.
About the snow melting services, during the demonstration phase two different simulation have been
carried out: one based on the combined use of MERIS + AATSR data and one base on the sole use of
AATSR data. As shown in the results paragraph (6.2 Snow melting Service) the two simulation gave very
similar results. The image and processing cost is higher in the case of combined use of MERIS and
AATSR ENVISAT data, so it convenient to use only AATSR in this case.
As regard glacier mapping service a multisource classification strategy has been defined to integrate data
from different sources. An experiment has been performed to identify different snow and ice covers of
glaciers using optical and radar data.
Although this type of observations is particularly suitable for glacier monitoring objectives, as remote
sensed data allow to provide a simultaneous synoptic view of a large number of glaciers, some
limitations may be underlined in order to provide suggestions for an improvement of glacier studies from
space data. Interesting information on the status of glacier surface may be derived by the integrated use
of radar and optical data, but further analysis is necessary in order to confirm the procedure defined, as
data used were not acquired in optimal way.
Contemporaneous acquisitions of optical, radar and ground data are required in order to provide efficient
and reliable results. In any case the main constrain of glacier monitoring lies in the too low resolution of
available data both optical and radar.
As regard optical data the 30 m resolution of Landsat TM is not sufficient for glaciologists which require a
resolution of almost 5 m. The use of high resolution multispectral data from IKONOS or QuickBird is
suggested. A backup solution could be provided by the integrated use of multispectral 10 m images and
panchromatic 2.5 m resolution of SPOT satellite.
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9. ASSESSMENT ACTIVITIES
The quality of the information service was assessed involving the end-users. In particular, the service
cases results [AD10, AD11] were critically analyzed and compared to the end-users operational needs
compiled in [AD4]. The performance of the services was tested for operationality of the processing
chains, near real-time availability of products (if relevant), spatial and temporal resolutions of the
products, usefulness of the output data format and of the delivery, integration into user practices (i.e.
other surveying techniques).
The user support was fundamental during the service assessment activities. End-users participated to
the 6th Progress Meeting of the project, which can be also considered as the Service Demonstration. In
addition, selected end-users contributed to the assessment of the performance of the GLASNOWMAP
Information Service chain with their answers to a questionnaire. The questionnaire was prepared for the
users to critically assess the performance of the services for operationality, near real-time availability of
products (if relevant), spatial and temporal resolutions of the products, usefulness of the output data
format and of the delivery, integration into user practices (i.e. other surveying techniques), and service
sustainability.
The questionnaire was distributed to selected persons, each of them representing one user organization:
Ø ARPA Valle D'Aosta, Umberto Morra, [email protected]
Ø Regione Piemonte, Marco Cordola, [email protected]
Ø ARPA Lombardia, Enrico Zini, [email protected]
Ø Comitato Glaciologico Italiano, Valter Maggi, [email protected]
Ø Swiss Glaciological Commission, Martin Funk, [email protected]
The users were requested to answer regarding their specific service element. For Aosta Valley, the user
was requested to prepare different questionnaires for the different services.
The analysis of the replies to the questionnaire led to the overall quality assessment of GLASNOWMAP
information system in the form of a brief, concise, and straightforward presentation of user requirements
versus achieved information.
Spatial resolution
Snow cover : delivered 300 m – expected 30 m
Glaciers: delivered 30 m – expected 5 m for glacier outlines, 30 m feasible for snow line
Temporal resolution
Snow cover: delivered 1 month – expected 1 week
Snow melting: delivered 1/2/3 days – expected ...
Glaciers: almost every year – expected ...
Spatial coverage
Snow cover : delivered Valle d’Aosta, Lombardia, Piemonte – expected ...
Snow melting : delivered Aymavilles Basin of Dora Baltea river (Valle d’Aosta) – expected ...
Glaciers : Adamello, Lys, Gorner, Allalin – expected ...
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Output data format
Meeting requirements or not.
Dedicated GIS-WEB server
Meeting requirements or not.
Near-real time availability of the product
Meeting requirements or not.
Operationality of GLASNOWMAP service
Meeting requirements or not.
Sustainability of GLASNOWMAP service
Meeting requirements or not.
10. SERVICE SUSTAINABILITY
The sustainability of the services was investigated in terms of availability, reliability and affordability. Easy
access to suitable remote sensing data, consistency with the user needs and costs are carefully
investigated. The possibility to continue the service after the lifetime of the project was considered with
the end-users.
Availability
Satellite data available for the snow cover mapping service at a spatial resolution of 300 m and with a
revisiting time of one week include ENVISAT ASAR and MERIS only. Reliability of data acquisition was
therefore a serious problem (see below). Other optical and radar systems at higher resolutions
(LANDSAT, ASTER, SPOT, VHR systems, RADARSAT) are more expensive, do not cover in one single
image the entire service area and in most of the cases data programming is required. MODIS was not
investigated.
Availability of appropriate sensors and ground data for the snow melting service is less critical, if
acquisitions with 1 km resolution are considered. NOAA AVHHR, ERS ATSR and ENVISAT AATSR are
sensors, which make for sure at least four good images available during the time period April to June also
in future. Most serious limitations to the service are found for the availability of meteorological ground
stations and hydrometric gauging stations and for having these data available.
For the glacier mapping service at 30 m resolution LANDSAT and ENVISAT ASAR narrow swath are
available. Reliability of data acquisition is an issue to carefully consider (see below).
Reliability
Reliability includes affidability and periodicity of the satellite data acquisitions. Processing of the satellite
data to GLASNOWMAP products is consistent with the requirements, when satellite data of sufficient
quality are available.
ENVISAT data need programming and do not permit the development of consistent historical series.
Various problems regarding satellite data availability were found during the demonstration activities for the
snow cover mapping service, in spite of having programmed the ENVISAT acquisitions.
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As concern radar data, few ASAR images were acquired for a number of problems, see [AD11]. Further
problems were missing lines, images not correctly centred on the region of interest, and images
presenting calibration or production errors.
In conclusion, reliability of satellite data for the snow cover mapping service could not be assured. Indeed,
only one image per month could be generated and not one image per week as required.
Reliability of the snow melting service was, on the other hand, established.
For the glacier mapping service, a failure of the Scan Line Corrector of the Landsat 7 satellite was
detected on May 31, 2003. Landsat 7 ETM+ images were therefore not available during August 2003 for
Glacier Mapping Service. A backup solution for monitoring glacier was found with Landsat 5 TM
acquisitions. For other seasons, appropriate satellite acquisitions could not be found, because of cloud or
snow cover over the glaciers. ENVISAT ASAR acquisitions could be assured for the hydrological year
2003.
Affordability
The cost of the services was estimated based on satellite data prices and processing effort. An hourly
rate of 75 Euro was applied to the processing effort.
For the satellite data considered in this project, we applied the following prices:
• ENVISAT (ERS) SAR RAW, SLC or PRI: 800 Euro
• MERIS Full Resolution (FR) product: 600 Euro
• LANDSAT: 1500 Euro.
For the snow cover mapping service we estimate, for future service provision, a snow map every week
and 3 MERIS data every one ASAR, i.e. 650 Euro for week. The processing effort is about 12 hours for
map, i.e. 900 Euro for week. The cost a weekly image is therefore 1550 Euro.
A co-operation between user organizations may reduce costs. By dividing the total cost of one map by
the total area of the service of 52’523 km 2, we have a cost of 0.03 Euro per km 2, with the following
separation:
• Lombardia
• Piemonte
23’861 km 2
705 Euro
2
745 Euro
25’399 km
• Valle d’Aosta 3’263 km
2
100 Euro.
The monthly cost for a map every week is estimated as the cost of one map times 365/7/12, i.e. 3065
Euro for Lombardia, 3235 Euro for Piemonte and 435 Euro for Valle d’Aosta, if cooperation between these
three regions may be assured.
For the snow melting service we need four snow maps, a processing effort of half an hour every day
during the period April to June (90 days, i.e. 3375 Euro), and a processing effort for the calibration on the
order 3 days every year (1800 Euro). Processing costs are therefore 5175 Euro. The cost of the snow
maps is 1550 Euro for an area of more than 400 km * 400 km, and may be significantly reduced by
cooperation between users organizations to cover large areas.
For glacier mapping, the processing effort is on the order of 15 days for every image, i.e. 9000 Euro. The
price of a LANDSAT image, which may well cover various glaciers, is 1500 Euro. Service costs may be
significantly reduced for consideration of various glaciers on the same image.
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Business opportunities
The previous considerations on availability, reliability and affordability of the three GLASNOWMAP
information services led to the following conclusions regarding business opportunities.
For the snow cover mapping service reliability of satellite data was the most important limiting factor.
Only one map every month could be produced instead of the four promised. Also spatial resolution was a
key issue, but we believe that if a consistent data set with a spatial resolution of 300 m and a temporal
resolution of one week could be certainly made available, the possibilities to continue the service would
have been different.
For the snow melting service, we faced serious difficulties in having access to data of meteorological
ground stations and hydrometric gauging stations. This also indicates a certain lack of interest of the
users to the offered service.
For the glacier mapping service, the most limiting factor was the spatial resolution. A pixel size of 30 m is
not considered sufficient by the users to track chances in alpine glaciers; on the other hand, as for most
glaciers on the Alpine regions no data are available, thematic maps provided by the service are anyway
of great interest for the user. Moreover the maps of wet snow cover over glaciers cannot be obtained by
other means. As follow own, the users proposes to elaborate an Atlas book of all Alpine glaciers by
using the the Glasnowmap Service. This proposal will be further evaluated by the project team.
The same methodology implemented in Glasnowmap can be applied to higher resolution satellite data; it
is intention of the project team to test the service using Quickbird /Ikonos data, to offer to the user higher
resolution products, comparable to the scale of data already available by the user.
A general remark has to be done with reference to the capabilities, in term of both finanacial and human
resources , of the Glasnowmap Users; indeed the regional institutions involved in the project have
nowadays no enough resources to be able to exploit the service.
In conclusion, there are not obvious possibilities to continue these services with the users of the current
project on a commercial base, at least in the short time. As an exception, Arpa Lombardia has recently
started an activity for interest on Glaciers and snow mapping by remote sensing data , and discussions
have already been started in order to find agreements for a collaboration between the ARPA and the
Glasnowmap consortium.
These considerations were made for the Piemonte, Lombardia and Valle d’Aosta regions, the Dora
Baltea - Aymavilles basin in Valle d’Aosta, and the Lys, Adamello, Gorner and Allalin glaciers in Italy and
Switzerland, but may be valid for other areas. The extension of the service to other regions should
therefore be possible with similar products at a similar accuracy and a comparable cost. Service
promotion was therefore not limited to the area targeted with the current study but also to other regions.
In order to continue the work in this topic, the consortium was therefore forced to find alternative financial
strategies to that of a direct contribution by the users and of the DUP. At the moment, possibilities are
envisaged in other programmes supported by the Agency or other international bodies.
11. DISSEMINATION ACTIVITIES
In order to promote the services worked out within the project, a Service Promotion Plan was developed.
The Service Promotion Plan included activities to prepare and disseminate promotion material in form of
web pages, brochures, posters, papers and other. User contribution to the definition of the Service
Promotion Plan was essential. During various meetings with users, the GLASNOWMAP Information
Service was presented, the available services described, the results available at the web site
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GLASNOWMAP
GLA-RP-CGS-012
Doc N°:
Ediz.:
2
Issue:
GLASNOWMAP INFORMATION SERVICE FINAL REPORT
Pagina
Data:
23-01-2004
Date:
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(www.cgspace.it/glasowmap) illustrated, the interest of the users in the EO-products evaluated, and the
possible use of the final products identified. An important objective of this activity was to involve final
users in the overall evaluation of the Information Service.
Service promotion was focused on concrete examples where efficiency, additional information and
potential saving were demonstrated. Promotion of the three GLASNOWMAP services was therefore
different depending on the quality of the products and their commercial perspectives. Emphasis was also
put to the publication on dedicated journals and workshop instead of remote sensing ones.
An important dissemination activity was the PM6 progress meeting held in Couramyer on 4th November
2003. The Valle d’Aosta Region made available the Congress Centre “Villa Comeron – Fondazione
Montagna Sicura” as meeting site. Partecipants of the meeting were the Alpine region ARPA (Agenzia
Regionale Per l’Ambiente), the Glasnowmap users and other potential users. Here the Glasnowmap
project team presented the main activities developed during the project and the obtained results. Those
items has been discussed with all the participants.
In order to disseminate Glasnowmap results, several press agency were informed about the project. A
brief description of the project contents to the main alpine area press agencies.
In particular those activities were carried out and material prepared:
Ø PROJECT BROCHURE: a project brochure was prepared in Italian and English to give a concise
description of the service and the products.
Ø INTERNET PRESENTATIONS: Web pages to illustrate the project were prepared at CGS premises
(www.cgspace.it/glasowmap) and at the ESA DUP dedicated site (dup.esrin.esa.it/projects).
Ø PRESENTATIONS
24/04/2002 DATA USER SYMPOSIUM , ESRIN
7° Conferenza Nazionale ASITA - 28, 31 ottobre 2003, Verona
MERIS Workshop http://envisat.esa.int/cgi-bin/confmeris.pl?abstract=219
7° Conferenza Nazionale ARPE , Milano, 24 - 25 - 26 Novembre 2003
Ø PRESS REVIEW
AIR-Press 1-9-2003
La Stampa, 5 November 2003
www.montagna.org 7 November 03
ANSA PRESS AGENCY 14 November 2003 (www.ansa.it)
Ø RAI-TV SERVICES VAL D ’AOSTA
3 / November / 2003 USER Workshop Courmayeur
7 / September /2003 Lys Ground Campaign
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