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N ansen Environmental and Remote Sensing Center

Edv. Griegsv. 3a

A non-profit environmental research center affiliated

N-5037 Bergen, Norway Tel: +47 55 29 7288

with the University of Bergen

Fax: +47 55 20 00 50

Technical Report No. 113

ICEW"ATCH Real-Time Sea Ice Monitoring of the Northern Sea Route Using Satellite Radar Technology A cooperative project between Russian Space Agency (RKA) and European Space Agency (ESA)

Final Report

by O.M.

Johannessen!, A.M. Volkov2, V.D. Grischenko3,

L.P.

Bobylev4,

V.

Asmus2,

T. Hamre!, V. Jensen1, K. Klosterl, V.V. Melentyev4, L. H. Petterssonl, S. Sandven1, V.G. Smirnov3, V. Alexandrov3,4, O. E. Milekhin2, V. A. Krovotyntsev2, J. Solhaug1 and 1Nansen

Environmental and Remote Sensing Center (NERSC)

2 NPO 3 Arctic

4 Nansen

Planeta

and Antarctic Research Institute (AARI)

International Environmental and Remote Sensing Center (NIERSC)

Agency representatives:

G.

Duchossois, ESA

May 16, 1997

V. Koslov, RKA

L.

Zaitsev4

Nansen

Environmental and Remote Sensing Center (NERSC) Edvar d Griegsvei 3a, N-5037 Solheimsvik, Norway Tel: + 47 55 29 72 88, fax: + 47 55 20 00 50

NPO Planeta B. Predtechenskii Street 7, 123242 Moscow, Russia Tel: + 7 095 255 97 49, fax: + 7 095 200 42 10 Nansen International Environmental and Remote Sensing Center (NIERSC) Korpusnaya Street 18, 197042 St. Petersburg, Russia Tel: + 7 812 235 74 93, fax: + 7 812 230 79 94 Arctic and Antarctic Research Institute (AARI) Bering Street 38, 199397 St. Peters burg, Russia Tel: + 7 812 352 15 20, fax: + 7 812 352 26 52

TITLE

REPORT IDENTIFICATION

ICEWATCH Real-Time Sea Ice Monitoring of the Northern Sea Route Using Satellite Radar Technology

NERSC Technical Report no. 113 Final Report

CLIENT

CONTRACT

European Space Agency

N° 11308/95/F /TB

CLIENT REREFERENCE

AVAILABILITY

G. Duchossois G. Kohlhammer G. Solaas

Open

INVESTIGATORS

AUTHORIZATION

O. M. Johannessen, S. Sandven, K. Kloster, T. Hamre, L. H. Pettersson, J. Solhaug, V. Jensen, A. M. Volkov, V. Asmus, O. E. Milekhin, V. A. Krovotyntsev, V. D. Grischenko, V. Smirnov, V. Alexandrov*, L. P. Bobylev, V. V. Melentyev, 1. Zaitsev, *

NERSC NERSC NERSC NERSC NERSC NERSC NERSC NPO Planet a NPO Planeta NPO Planet a NPO Planeta AARI AARI AARI/NIERSC NIERSC NIERSC NIERSC

affiliated with NIERSC from 01.12.1996.

Bergen, May 16, 1997

��

--Oi en Director �ERSC

-

ICEWATCH - Project Report Summary

i

Summary Background The Northern Sea Route is the sailing route along the coast north of Russia between the Barents Sea and the Bering Strait. The route is of vital importance for Russian transport in Arctic regions, but ice conditions restrict sea transportation which requires ice class vessels as well as icebreaker assistance throughout the year. In summer there is traffic in the whole sailing route, whereas in winter it is mainly the western part which is used serving the ports on the Yenisei River.

An extensive ice monitoring and forecasting service has been built up in Russia over the last 50 years to serve the sea transportation and icebreaker operations in the Northern Sea Route. The service "is based on data collection from satellites, aircraft, icebreakers, helicopters, coastal stations, automatic telemetry buoys, and drifting ice stations. But use of spaceborne SAR has not been a part of this service. The existing Russian ice monitoring and forecasting services are organised under the Hydro-Meteorological Committee and the Ministry of Transport of the Russian Federation. The key institutions operating the ice monitoring service are the Marine Operational Headquarters ( MOH ) located in respec tively Dikson for the western part (east to 1100E) and Pevek for the eastern part of the Northern Sea Route. The MOHs operate the ice services in co-operation with Murmansk Shipping Company and the Arctic and Antarctic Research Institute. Ice forecasting, both short-term and long-term, is also included in the service, using ice drift simulation models. The N ansen Environmental and Remote Sensing Center first demonstrated use of ERS-1 SAR data for near real-time ice mapping in the Northern Sea Route in August 1991, only a few weeks after the launch of the ERS-1 satellite. SAR derived sea ice maps were then sent by telefax to the French polar vessel L 'Astrolabe during her voyage through the Northern Sea Route from Norway to Japan. This demonstration was evaluated as very interesting by the captains and sea ice experts onboard the Russian icebreakers which es corted L 'Astrolabe through the ice-covered parts of the route. Since 1993 up to the start of the ICEWATCH project in 1995, SAR ice monitoring demonstrations had been carried out on several Murmansk Shipping Company icebreakers. In all these demonstration ex periments, a scientist from the Nansen International Environmental and Remote Sensing Center stayed onboard the icebreakers and analysed the SAR images in co-operation with the captain and ice experts. In addition to the ice navigation these experiments also had scientific objectives to study sea ice phenomena and their SAR signature along the North ern Sea Route at different times of the year. In August 1995 the project "ICEWATCH - Real-time Ice monitoring of the Northern Sea Route" was established as the first joint between Russian Space Agency and European Space Agency in earth observation. ICEWATCH objectives The overall objective of ICEWATCH is to implement satellite monitoring of sea ice by combined use of ERS SAR, Okean SLR and other remote sensing data to support ice navigation in the Northern Sea Route, offshore industry and environmental studies. The rationale for the project is practical as well as scientific: - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH - Project Report

ii

•

Ships traversing the Northern Sea Route along the Siberian coast need good knowl edge of ice conditions from day to day as well as in long term planning for safe and efficient navigation.

•

Oil exploration and production in areas such as the Eastern Barents and Kara Sea areas will require both reliable design statistics and timely monitoring and forecasts of sea ice behaviour.

•

Monitoring of Arctic sea ice conditions is essential for environmental impact inves tigations as well as to provide an early indicator of global climate change which is predicted to become most severe in polar regions.

ICEWATCH Tasks Several Russian organisations were involved in the project, with NPO Planeta, AARI ( Arc tic and Antarctic Research Institute ) , NIERSC ( Nansen International Environmental and Remote Sensing Center) and the Murmansk Shipping Company ( MSC ) , as the main Rus sian participants. NERSC ( Nansen Environmental and Remote Sensing Center) was the project coordinator.

The project started in 1995 with emphasis on demonstrating the capabilities of ERS SAR data to improve the ice monitoring services. In 1996 the project has focused on the trans fer and exchange of knowledge and technology ( tools ) preparing the ground for integrating ERS data in the operational ice forecasting services. During the demonstration project in winter 1996, the co-operation with MSC, one of the most important Russian end-users of an ice monitoring service, was continued. The results from this demonstration, as well as discussions with other end-users provided useful information for the design of a future operational system using satellite radar data. The project was divided into the following tasks, each of which are presented in separate sections in this report: 1. Study of ERS SAR backscatter characteristics of sea ice. Current techniques for ice classification form SAR images were reviewed, and a num ber of ERS-l SAR images from various ice conditions in the NSR were analysed with help from Russian ice experts. The feasibility of interpretation of important ice types and ice conditions from SAR images, is discussed and the usefulness of ERS SAR images as a tool ice navigation is assessed. 2.

Algorithms and methods for processing, classification and interpretation of SAR and SLR data. Procedures of exchange of ERS SAR and Okean SLR data between NERSC and NPO Planeta were established. A total of about 150 SAR scenes and about 50 SLR images have been exchanged in the project. The DESC software for ordering ERS SAR data have been used by NPO Planeta, and many of the SAR and SLR images have been obtained in coordination to be analysed jointly. Also technical information - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH - Project Report Summary

iii

about the instruments and satellites, as well as algorithms for ice classification and ice kinematics, were exchanged. 3.

Techniques and technologies for combined ERS SAR and Okean SLR analysis. Methods for inserting ERS SAR and Okean SLR data into a common data format ( data grid ) have been done both at NERSC and NPO Planeta. ERS SAR and Okean SLR pixels can thereby be directly compared. Such comparison was attempted but no good results could be obtained before some SLR calibration problems are solved. The most important combined use of SLR and SAR data is suggested to be as follows: Use Okean SLR data for large-scale, regional mapping of the main ice features such as extent of multiyear ice, first-year ice, open water and coastal polynyas; and use of ERS SAR data for detailed mapping of fioes, leads, ridged areas, and smooth ice in the most critical parts of the sailing route. Both ERS SAR and Okean SLR show roughly the same signatures for multiyear, first-year and thin ice types. However, more quantitative comparisons are needed to establish similarities and differences between ERS SAR and Okean SLR images of sea ice.

4. A scheme for polar ice radar monitoring, including user requirements, infrastruc ture and necessary equipment installation. First of all, a scheme for polar ice monitoring using satellite radar data need to be in tegrated with the existing operational monitoring system for the Northern Sea Route. Secondly, it is important to know what the user requirements are for ice information which can be obtained from satellite data. A user survey has been performed where more than 50 institutions, including both existing and potential users, have been contacted. The result of this survey shows that the users can be categorised into three main groups: 1. operational users ( shipping companies, oil companies, etc. ) 2. consultancy services ( environmental companies, etc. ) 3. scientific users ( research institutions, etc. ) The need for infrastructure and equipment, which include ground stations, data dis tribution systems, etc. has been been discussed. Finally, the specific requirements of a main user, Murmansk Shipping Company's Icebreaker Fleet, have been inves tigated in more detail, suggesting priorities for the development of an operational satellite ice monitoring system. S.

Application demonstration of ice monitoring for icebreakers in the Northern Sea Route including a cost-benefit assessment. A practical demonstration was carried out to transmit ERS SAR data in near real time to an icebreaker sailing in the NSR. The requirements to organisation, technical solutions, personnel, and other costs have been tested. The user, MSC, has seen how a near real-time system can function, and important comments were given regarding the cost-benefit of such service. The benefits of satellite data in ice monitoring can be divided in two main groups: one is related to cost-savings due to shorter travel time and less fuel consumption for icebreaker and cargo vessels transitting ice areas, the other is related to increased safety in operations, minimising the risk for accidents, damage to vessels and pollution of the environment. -

NANSEN

ENVIRONMENTAL

AND

REMOTE SENSING CENTER -

iv

ICEWATCH - Project Report 6.

Recommendation for a near-real time operational information system using satel lite radar data. The results of the ICEWATCH-project show that there is justification and needs for a satellite radar system for ice monitoring in the Northern Sea Route. This is due to the fact that not only traditional waterborne transports need ice information, such as freight of nickel from Dudinka on Yenisei, but also oil and gas exploration, production and transport, which it the most promising new industries i Siberia. The scope and functionalities of an operational system, the role of the partners and possible organi sation of the service are proposed. Also satellite data products, data processing and transmission to end users are recommended for the operational system. Financing of the system is presently not clear. It is foreseen that financial contribution must come from several sources, including the end users . . Conclusion ERS-1 SAR images have been used in ice monitoring of the Northern Sea Route in several demonstration campaigns since two weeks after its launch in August 1991. An important part of the ICEWATCH project is to develop methods for distribution and dissemination of SAR based image files and maps to icebreakers operating in the Northern Sea Route. Dig ital transfer of compressed images in near real-time have been successfully demonstrated using the INMARSAT - A telecommunication service. For example, on January 25-26 1996 the icebreaker Taymir was sailing from Dikson to Beliy Island between 70°-800E in 100% ice cover of first-year ice. With a PC and modem connected to the INMARSAT station on board ERS-1 SAR images were received on board only 5 hours after the satellite overpass. In the image areas of rough ice and hummocks ( brighter image signature) could be clearly distinguished from smooth undeformed ice (darker signatures ) . Based on this information the icebreaker changed its navigation course and selected a much quicker and safer sailing route in lighter sea ice conditions. Occasional use of SAR data like this is promising for demonstration of new technology, but there is a number of requirements which need to be satisfied before the SAR monitoring technology can become an operational tool.

The experience from use of SAR data and derived sea ice information onboard the Rus sian icebreakers to assist in ice navigation is very positive. The major caveat is although the limitations in the ERS SAR coverage, which implies that data only can provided in area limited parts of the Northern Sea Route at a given time. In the ICEWATCH project a concept for integration of ERS SAR data in the well developed Russian ice monitoring service is demonstrated. The experience on combined use of the Okean SLR and ERS SAR data in an operational concept is recommended. Such an integrated system has currently been tested in a pilot demonstration phase and will be further tested and implemented. In addition to data acquisition and interpretation the project will also address data integra tion and classification, data transmission techniques and assessment of users requirements in order to develop a service which meets the information needs of its users. In future the utilisation of spaceborne radar satellite data can be operationally implemented such as for the ESA's ENVISAT and RKA's RESURS ARCTICA satellites.

- NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH

Project Report for Task 1

Study of ERS SAR backscatter characteristics of sea ice

Task

ICEWATCH - Project Report for

1

i

Contents List of Figures

ii

List of Tables

ii

1

Study of ERS SAR backscatter characteristics of sea ice 1 . 1 State-of-the-art ice type classification by ERS SAR 1 . 1 . 1 The physical properties of sea ice . . . . 1. 1.2 Time evolution of the microwave signature 1.1.3 Backscatter value lookup-tables method . 1. 1.4 Second Order Methods . . 1.2 ERS SAR images of characteristic sea ice conditions . 1.3 Ice types and ice conditions with difficult backscatter characteristics 1.3.1 Ice edge region . . . . . 1.3.2 Pack ice region in coastal areas . 1.3.3 Pack ice region in the open sea areas 1.3.4 River ice region . . . . . . . . . . . . 1.3.5 Yamal and Amderma recurring polynya . 1.4 Assessment of ERS SAR as a tool in ice navigation 1.4.1 Ease of interpretation of important ice types 1.4.2 Availability of SAR data within area-time intervals 1.4.3 Speed and reliability of transfer methods for the SAR data 1.5 Summary of Task 1 .

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References


1-1

1-1 1-1 1-2 1-2 1-6

1-7

1-32 1-32 1-32 1-33 1- 33 1-33 1-34 1-34 1-35 1-35 1-36 1-36

ICEWATCH - Project Report for Task 1

ii

List of Figures 1.1 Schematic time evolution of (To from sea ice (from Onstott, 1992 ) . . . . . . 1 .2 ERS SAR backscatter values for different ice types obtained from several validation experiments ( Sandven et at., 1994) .. . .. . . . . . . . . . 1.3 An overview of WMO ice terms. . . . . . . . . . . . . . . . . . . . . . 1. 4 Case 1: SAR image from Baydaratskaya Bay on November 11, 1994. . 1. 5 Case 2: SAR image from the Vilkitsky Strait on October 24, 1995. . 1. 6 Case 3: SAR image from the Vilkitsky Strait on October 25, 1995. . 1.7 Case 4: SAR image from the Dikson area on November 15, 1995. . . 1. 8 Case 5: SAR image from Cape Kharasavey on November 20, 1995. . 1.9 Case 6: SAR image from the Yamal coast on November 26, 1995. . 1.10 Case 7: SAR image from Dikson and Yenisei Gulf on November 30, 1995. 1.11 Case 8: SAR image from the Yenisei Gulf on December 1, 1995. . . . . 1 . 12 Case 9: SAR image from east of the Kara Gate on December 17, 1995. 1.13 Case 10: SAR image from the Kara Gate on December 2 ,0 1995. . . 1.14 Case 1 1 : SAR image from Cape Kharasavey on December 21, 1995. 1.15 Case 12: SAR image from Western Kara Sea on December 30, 1995. 1 . 16 Case 1 3: SAR image from the Kara Gate on January 8, 1996. . . . 1.17 Case 14: SAR image from east of the Kara Gate on January 21, 1996. . 1.18 Case 15: SAR image at Belyy Island on January 28, 1996. . . .. . .. 1.19 Case 16: SAR image from Dikson and Yenisei estuary on February 8, 1996.

1-3 1-5 1-5 1-16 1-17 1-18 1-19 1-2 0 1-21 1-2 2 1-2 3 1-24 1-25 1- 62 1-27 1-28 1-29 1-30 1-31

List of Tables 1.1 Overview of selected SAR images in the Northern Sea Route . . 1. 2 Overview of ice terms observed in the selected SAR images.

- NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER

1-7 1-8

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1-1

Study of ERS SAR backscatter characteristics of

1

sea

Ice •

The objectives of Task 1: Study of backscatter characteristics of different ice types and icebergs including seasonal variability, are to: - provide a comprehensive summary of state-of-the-art ice type classification, - present examples of images showing typical sea ice conditions in the Northern Sea Route (NSR), - identify ice types and ice conditions with difficult backscatter characteristics, and - assess the capability of the ERS SAR sensor to detect ice types and ice phenomena which are important for ice navigation. 1.1

State-of-the-art ice type classification by ERS SAR

1.1.1

The physical properties of sea ice

The backscatter coefficient 0"0 observed in SAR images are related to the physical properties of the surface within the images. From the motivation of finding inverse propagation algorithms to classify the surface features of interest, various scattering mechanisms have to be considered. Most of the basic parameters important in microwave sensing of sea ice, are depending on the the temperature of the medium, and the basic SAR parameters frequency, angle of incidence, and polarization. The scattering mechanisms can be divided into three groups: 1.

the small-scale surface scattering properties, where the roughness, and the exis tence of melt ponds or frost flowers on the surface, are the most important,

2. the small-scale volume scattering properties, salinity, size and shape of brine inclusions in the ice, the wetness of snow cover, the ability to discriminate layers of different snowlice-types and the thickness of these, and, 3. the large-scale properties which includes the statistical distribution of different fea tures in one SAR pixel such as flow size, pancake ice and nilas, and waves, wind and current in open water regions.

Since many of the small-scale properties are correlated, the qualitatively analytical models of the backscatter coefficient 0"0 have so far only been successful in describing homogeneous conditions, which has shown hard to interpret in general microwave signatures (Winebren ner et ai., 1992). This is mostly due to the lack of accurate quantitatively descriptions of the small- and large-scale properties, both in vertical and horizontal directions, that are necessary to provide input for such backscatter models (Tucker et ai., 1992) . Most properties of the sea ice are related to seasonal changes, and it is also a fact that the relative distribution of ice types are large-scale regional dependent. Also measured - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-2

backscatter from satellite is frequently composed of signals from more than one ice type (mixed pixels) . This implies that classification algorithms constructed for classification of dominant ice types in the Beaufort Sea, might not be very effective in classification of dominant ice types in the Kara Sea. Ground based scatterometer measurements and SAR images combined with in situ data from the regions of interest, can provide fairly accu rate backscatter lookup tables to classify non-mixed ice-types in the SAR images. This has shown to be the most useful method in most applications in ice classification of SAR images.

1 .1.2

Time evolution of the microwave signature

A description of the evolution of the microwave signature of ice, has shown useful in describ ing some of the problems related to classification of different ice-types. Field measurements done by Onstott (1992), shows that the backscatter ao may vary more than 15dB as ice is formed from freezing of open water and continues until a thickness of 200+ cm is obtained, as shown in Figure 1. 1 . The open water may take a variety of backscatter levels due t o wind and currents. New ice may produce a signature that is greater or, the same, or less than that of open water, depending on the environmental conditions at the time. As the dielectric value of the ice decreases, the backscatter will be reduced. In calm conditions, frost flowers may then be produced on the surface of the thin ice. Brine from the thin ice might cause a higher dielec tric constant in the frost flowers than in the ice sheet, resulting in an increasing backscatter signal. Waves may cause the ice to brake up in the freezing process, and pancake ice with a thickness of 20 cm will give a high backscatter signal. The pancake ice and frost flower features, are produced in a few days depending on the temperature and wind conditions. Frost flowers are found in polynyas and refrozen leads. rv

As the ice gets thicker, the surface roughness may change and dry snow accumulate. The temporal changes in the surface roughness appears to be linked to age (thickness) and meteorological conditions. During the melt seasons spring and summer, wet snow is seen to reduce the backscatter signal. In the melt seasons spring and summer, multiple features like surface melt water and variable wetness of ice cover, might give locally increasing or decreasing backscatter signals.

1. 1.3

Backscatter value lookup-tables method

The variable backscatter seen in Figure 1 . 1 , might be divided into suitable time and surface domains. Combined with the experience of interpreting SAR images together with several validation experiments, the characteristic backscatter signals from different ice types mea sured in several validation experiments can be described in e.g. lookup tables. Such tables are used in manually analysis of SAR images. However, there are often many ambiguities in the backscatter signal from the different surface types, requiring expertise in the inter pretation. Figure 1 .2 depicts the typical SAR backscatter ranges for various open water - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


(Rough)

Open water

.......

Pancake Ice

New Ice Start

\/ ,

C> c:

�

b3 ca -g a:

,

(Calm)

Frost Flower Production

,

1-

�

-

1-3

!

SmaII FIoes

I snOWpaCk

Infiltration and Accumulation

Large Floes Firstv.ear Ice ,-

Thick Snow Layer and Saline

"New Ice Grease Ice

Snow - Ice Interface

2

5

20

- 1 hour

35

50

160

!

Multiyear Ice

T

15dB

1

Wet Snow/lce

Frozen Snow - Ice Interface 200 9 months

>200

Ice Thickness [cm] Time

Figure 1.1: Schematic time evolution of (To from sea ice (from Onstott, 1992). conditions and various ice types. In order to reduce the ambiguities in the backscatter, four different surface regimes are defined, as indicated in the bottom of the Figure 1.2. For correct determination of surface type based on backscatter, the general surface regime needs to be known, as: (a) open ocean, (b) ice-edge region, winter, (c) summer ice, or (d) interior of the pack ice, winter. Open water may be identified using texture, since it generally has a homogeneous appear ance. The correct ice regime is determined from knowledge of large-scale ice extent, and the winter versus summer is distinguished by date and if necessary or available, also using mean air temperature. Within each regime, some ambiguities still remain. The uncertainties must be solved using various methods, mainly based on a combination of image texture and knowledge of ice types generally expected in a region. The knowledge of the dominating ice types in the region of interest through validation experiments might also help in dividing the different ice types into groups such as in Figure 1.2. Open water backscatter is mainly determined by surface wind speed (Figure 1.2), and also to some degree by the wind direction relative to the SAR range direction. Wind speed and direction known by meteorological observations, can be used for computing the SAR signal. The algorithm can be used inversely to find the wind speed and wind direction over open water, given from the SAR signal. Using only backscatter value to classify ice type has shown severe limitations. From ice/water surface type, ice concentration can be computed. Steffens and Heinrichs (1994) found an error in ice concentration estimates of 5-8 % in the best scenarios. In the interior of the pack ice, they also observed that open water could not be discriminated from ice when the wind surface speed was 3-10 mis, as seen in Figure 1.2. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1-4

ICEWATCH Project Report for Task 1 -

Description of ice types and associated backscatter The main ice types in an image and their SAR backscatter depend to a great extent on the surface roughness characteristics as noted in the previous section. The ice types defined in Figure 1.2, are grouped into zones, one describing the evolution of the ice in agitated conditions at the ice edge, where wind and wave action brakes the ice into pieces, and one where the ice is formed in calm water conditions as observed in the interior of pack ice. An overview of WMO ice terms is also shown in Figure 1.3.

Ice edge zone (agitated conditions) •

Grease ice and frazil ice, which is formed in agitated open water regions. The ice dampens the waves and gives a low signal (s -25 to - 15 dB ) .

•

Shuga and pancake ice are small ice lumps that give a very high signal, due to edge effects (s - 10 to -4 dB)

•

Small floes ( 20-100m across) give a variable signal, high for smaller floes and decreas ing with increasing size (s -13 to -6 dB) .

•

Fields of small and medium sized floes formed in the melt season. The signal will increase with decreasing floe size, but to a lesser degree than under winter conditions (s = -15 to -12 dB) . ( Medium floes are 100-500m across. )

=

=

=

Interior of pack ice zone (calm conditions) •

New thin ice and dark nilas (e.g. smooth refrozen leads ) gives a low signal (8 to -15 dB ) .

•

New ice with frost flowers (e.g. rough refrozen leads) on top gives a high signal (s = -10 to -4 dB ) .

•

Grey-white ice (young ice) and first-year ice gives a middle signal ( 8 -10 dB) .

•

Multi-year ice (old ice ) gives a somewhat higher signal than first year ice ( s to -7 dB) .

•

Large floes in the melt season. The signal is uniformly low. It is only weakly de pendent on the ice age, since the the backscatter is mainly determined by the wet snow/ice layer on top (8 = -16 to -12 dB ) .

=

=

-25

- 13 to =

-11

Some ice features can be observed in both zones. Ridged and rafted ice is formed when layers of ice are piled up one upon another under pressure. This may take place under both cold and warm conditions. Ridged and rafted ice will give a significantly higher signal than undeformed ice, both in winter and summer ( s -8 to -4 dB). =


ICEWATCH

- Project Report for Task 1

1-5

0' 0

4. 5

-

5 .8

Wind s peed: 8m1s

-

-7.4

4m1s

-9.5 -12

2m1s

-15

1.5mls

-21

5cm)

S mOOt h i Ce Rough ice Rafted ice

Ice forms

Ridged i ce

Grey ice Grey-white i ce Thin (or White i ce)

Ice fractures

-c

Thi ck

Ice breccia Lead S Polynyas

Medium

Edge features

� Ice edge �

Ice eddy

Second-year

-1 [

�

Grease i ce

.

Old i ce

---i

--

Fast sea ice

Ice classes-----if--- Pack i ce (sea)

Multi -yea r Co mpact pa ck ice (l0/10) Close pack ice (7-8/1 0 ) Open pack ice (4-6/1 0 ) Very open pack ice (1-3110 ) Open wate r «

1 /10 )

Ri ver i ce

Figure 1 .3: An overview of WMO ice terms. - NANSEN ENVIRONMENTAL

AND


1-6


Landfa8t ice. This is first-year ice attached to the coast, and will generally give the same low signal as ice formed in calm waters far from the coast. It is often quite smooth and reaches out to the so-called " flaw line" , the boundary of the moving ice (8 - 13 to -11 dB) . =

River ice. Ice formed during the winter which resembles landfast ice ( 8 = - 13 to -11 dB) . In the spring (generally during May in Siberia) the ice will be carried out to the sea in a strong current and the many ice blocks can give a higher signaL

1.1.4

Second Order Methods

Use of backscatter table-lookup does not take advantage of all the information that can be extracted from the SAR images. Second-order approaches using statistical, structural and frequency based texture methods might also be a part of a classification algorithm. In these methods the spatial information which arises from the statistical relationship be tween each SAR resolution cell and its neighbors, are utilized in order to better classify spatial variation in the backscatter. Adding texture analysis done by Barber et al. (1993), by using various second-order methods, show that smooth first-year ice, rough first-year ice, and multi-year ice can be discriminated with a higher confidence level (76-84 %), than by using first-order approaches only (33-51 %). They suggest that operational ice classification of SAR images should in volve a hybrid system, based on manual backscatter interpretation of the SAR images (lookup-table) aided by second-order methods.



1-7

Table 1 . 1: Overview of selected SAR images in the Northern Sea Route. NZPIM Novozemelsky pack ice massif, SZFIM Severozemelsky fast ice massif. -

I

Case

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1.2

I

Date

l l-NOV-1994 24-0CT-1995 25-0CT-1995 15-NOV-1995 20-NOV-1995 26-NOV-1995 30-NOV-1995 01-DEC-1995 17-DEC-1995 20-DEC-1995 21-DEC-1995 30-DEC-1995 08-JAN-1996 21-JAN-1996 28-JAN-1996 08-FEB-1996

-

I

Time

17:08z 14:05z 14:05z 06:30z 07:14z 16:48z 06:27z 15:50z 07:34z 17:33z 07:08z 07:24z 17:36z 07:34z 07:12z 06:27z

I

Location

Baydaratskaya Bay Vilkitsky Strait Vilkitsky Strait Dikson & Yenisei Gulf C. Kharasavey Yamal coast Dikson & Yenisei Gulf Yenisei Gulf Yugor Strait Kara Gate C. Kharasavey South-Western Kara Sea Kara Gate Yugor Strait Belyy Island Dikson & Yenisei Gulf

I

Region - ice process

NZPIM - ice cover formation SZFIM -ice shearing SZFIM -ice drift Coastal zone - fast ice formation NZPIM - ice cover formation NZPIM - ice cover formation Coastal zone - fast ice formation River ice region NZPIM - warm waters charge NZPIM - warm waters charge NZPIM - Baydaratskaya Bay spur formation NZPIM - pack ice region Ice edge region NZPIM NZPIM - ice compacting Fast ice zone - polynya opening

ERS SAR images of characteristic sea ice conditions

This subsection contains 16 SAR images which demonstrate the features of ice cover pa rameters in the different regions of the western part of the Northern Sea Route. Most of the images are from the first part of the ICEWATCH project; i.e. from November 1995 to February 1996. Several images demonstrates the ice conditions at the straits areas ( Kara Gate, Yugor Strait, entrance to the Vilkitskogo Strait) and river estuaries (Yenisei Gulf, Ob B ay) where ice navigation is especially difficult, and SAR data are very useful for mon itoring the ice conditions and for optimal way selection. During this period, a substantial number of SAR images have been transferred in near-real time by fax or by file transfer to Murmansk Shipping Company, to Dikson MOH (Marine Operation Headquarter) and to all 4 Russian nuclear icebreakers (NIB), which operated at that time in the western part of the Northern Sea Route: NIB Sovetskyi Soyuz, NIB Vaygach, NIB Taimyr, and NIB YamaL All 4 icebreakers used SAR data for improving the ice navigation, and official documents " Statements on accomplishment" were signed by captains of 3 of these ships (in the period 19.01-15.02.96 NERSC/NIERSC responsible representatives and ice expert were on board the icebreakers) . Up to now, about 150 SAR scenes have also been transferred to NPO Planeta. The date, time, location, region and ice process in the Novozemelsky (NZPIM) and the Severozemelsky (SZFIM) ice massifs of the images selected for presentation in this report, are summarized in Table 1 . 1. A summary of WMO ice terms that can be seen in the selected SAR imagery is given in Table 1.2. A brief description of each image is then presented in the following sections. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2: > 2:

Table 1.2: Overview of ice terms observed in the selected SAR images. New ice: F=frazil ice, G=grease ice. Nilas: D=dark nilas, L=light nilas. Young ice: G=grey ice, W=grey-white ice. First year ice: T=thin, M=medium. Fast sea ice: T=thin, M=medium, G=grey. Ice fractures: L=leads, P=polynyas. Rafted ice: F=finger rafted, R=rafted. "Residual ice" is a term used in Russia for second-year and multi-year ice ( old ice). WMO ice

r:n

tr:l 2:

terms observed

�

lbl. Nilas (D,L)

1. Ice development

la. New ice (F,G)

Ib2. Pancake ice lc. Young ice (G,W) ld. First-year ice (T,M) Ie. Old/Residual ice

2:

�

� � t""i

�

� �

r:n

�

r:n

Z

Q Q tr:l 2: t-3 tr:l �

2

3

4

5

6

G D,L

X

X

X X

G D

F,G D,L

G,W

W

W

W

G,W

G,W

X

X

2. Ice classes

t:i

o t-3 tr:l

1

I I

I

4. Ice forms

4a. 4b. 4c. 4d. 4e.

Smooth ice Rough ice Rafted ice (F,R) Ridged ice Ice breccia

-I I F

5. Ice fractures -----

6. Edge features

6a. Ice edge 6b. Ic� eddy

X X

I X

X

X X

-I I

X

9

X

W T

X

=r=I

G,W

M

--I I

X

X

X

----

I X

L,P X

11

12

G,W T

G,W

14

G,W T

G,W

X

X X

X

G,W

T,M

T,M

�

X

X

X

I 1_

X X

X

R

[p I

X X

T,M X

I I

I

16

G,W

M X

T

R

15

I

X

-I I

X

13

L

G

X

I

10

X X X

F

I

Case

X

T,M,G

X

--I I

8

X

X T,M

2a. Fast sea ice (T,M,G) 2b. Pack ice (sea ice) 2bl. Compact pack ice 2b2. Close pack ice 2b3. Open pack ice 2b4. Very open pack ice 2b5. Open water 2c. River ice

3. Water (ice free)

X

7

I-'

00

X

X

X

I X

X

L

I

X

Q tr:l

�

H Q ::q I-cJ

8 ...... . co (") c:-t-

l §.

0'

I-j

�

00

p;-'

�

ICEWATCH

-

Project Report for Task

1

1-9

The following description of ice massifs are taken from an article in preparation (Melentyev et al. , 1997) : According to M.M.Somov, the Kara Sea contains the Novozemelsky local massif of pack ice (NZPIM) , with a maximum area of 335000 km2 ; the Severozemelsky local massif of fast ice (SZFIM) , with a maximum area of 278000 km2 ; and the Severokarsky massif of pack ice (SKPIM) , which is an oceanic massif spur with a maximum area of 217000 km2 . The last two massifs divide the north-eastern sector of the Kara Sea along the line Cape Zhelaniya Pioner Island (the Severnaya Zemlya Archipelago) . Note separately that ice conditions at the western part of of the NSR at several stages of our activity at frames of this pilot-study were hardly defined by the behaviour of the Taymyr massif of pack ice (TPIM) . TPIM is adjoining to the Kara Sea, displaced at the north-west part of the Laptev Sea and is also an oceanic massif spur which joins the east shores of Severnaya Zemlya Archipelago and the Taymyr Peninsula. TPIM creates many problems to convoy steering from Murmansk to the sea ports in the eastern part of the NSR.

Case 1: SAR image in the Baydaratskaya

Bay

region on November 1 1 , 1994

Main features: ice edge, new ice: grease ice, nilas (dark and light) , young ice: grey ice (including pancake ice) , grey-white ice.

The image in Figure 1 .4 is not from the project period, but is included since it is an ex cellent example of the ice edge structures that can be found at the first stage of ice cover formation in the south-western part of the Kara Sea. The image is located in the Bay daratskaya Bay region (about 70.5°N,65.00E) , and shows an arrangement of pack ice in the ice edge region. Various types of ice can be seen, including grease ice, nilas, grey ice, pancake and grey-white ice. Close to the coast there are two belts of grey and grey-white ice with some fractures with dark nilas. Different areas of grey and grey-white ice which were located on the shallow waters region have different SAR signatures. The brightest signatures may be caused by many small floes of grey ice (pancake) . In the upper half of the images there are patches of grease and pancake ice, being shaped by ocean currents and wind. The grease ice has a very dark signature, while the pancake ice has a very bright signature. This descripti0n is made based on the results obtained from previous 1993/95 NERSC/NIERSC validation experiment in the project "Real-time sea ice monitoring of the Northern Sea Route" in the winter ice edge region. This assumption is made based on results obtained from previous SAR validation experiments in the winter ice edge region (Figure 1 .2).

Case 2: SAR image from the Vilkitsky Strait on October 24, 1995 Main features: nilas, young ice, rafted young ice, grey-white ice, residual ice, fractures.

The image in Figure 1 .5 shows the ice conditions at the entrance to the Vilkitsky Strait. Main part of this area compose from young ice (thickness 10-30 cm) . At the middle part of this image and close to the mainland on the southern side of the strait there is a wide band of rafted young ice. It is likely that this band has been formed by strong wind and currents - NANSEN ENVIRONMENTAL

AND

REMOTE SENSING CENTER

-

1-10

ICEWATCH Project Report -

for

Task 1

moving the drifting ice towards the strait and pressing it towards the islands and coast. In the middle of the strait there are mainly vast and big floes. Near 1000E,76.8°N are areas of smaller floes, with grey and homogeneous backscatter signatures. This is grey-white ice. Along the mainland coast between 99°E and 101°E there is a dark area interpreted as nilas (finger rafted ice) . In the bay, around 76.6°N,101°E there is probably fast ice, with a bright signature (rough ice). Westward from Bolshevik Island disposes the residual ice floes with very bright sigmlture.

Case 3: SAR image from the Vilkitsky Strait on October 25, 1995 Main features: nilas, young ice, rafted young ice, grey-white ice, residual ice, fractures.

Figure 1.6 contains an ERS-2 SAR image covering the same area as the image in the pre vious case. The concentration of young ice has increased in the north-eastern part of the observed area, as the pack ice has been flowing eastwards between the Bolshevik Island and the Taymir Peninsula. West of the rafted young ice (marked with a dotted line near 100° E) the ice arrangement has changed strongly (concentration 10) from the day before. Near the left image edge, two large areas of nilas (dark and light) ate now found. A big piece of the bright ice has broken off and moved considerably.

Case 4: SAR image from the Dikson area on November 15, 1995 Main features: new ice, nilas, young ice, thin and medium fast ice, rough and smooth fast ice, leads.

The area around Dikson and the Yenisei Gulf and river are very important areas for the ship traffic in the Northern Sea Route, since Yenisei has several large harbors, like Dudinka, which are open throughout the year. Much ship traffic is taking place on the Yenisei river, also when the river is covered by ice, then with the assistance of icebreakers. Figure 1 . 7 shows a SAR image which fixed the stage of fast ice forming (November 15, 1995). Between Sibiriakova Island and the mainland there is a mixture of sea ice, brackish water and river ice. The young, thin and medium fast ice, smooth and rough fast ice zones with dark signatures are seen along the coasts of islands and mainland. A ship track can also be seen as a thin, white line near 800E (indicated by arrows) . This is caused by an icebreaker passing shortly before the image was taken. Icebreakers always use the Dikson Marine Operation Headquarter (MOH) information on their way to and from the harbors along the Yenisei river, and MOH are obliged to supply them with updated information on the ice situation in the area. West of Dikson there are several ridged and hummocked areas, seen as very bright features, and further north (near the upper, left image corner) there are nilas zones, having a dark signature. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-11

Case 5: SAR image from Cape Kharasavey on November 20, 1995 Main features: ice edge, frazil ice, grease ice, nilas, pancake ice, grey ice, grey-white ice, fast ice.

The ERS SAR image in Figure 1.8 covers an area including the coast near Cape Kha rasavey. In the Kruzenshtern Bay there is a fast ice area (grey-white ice) , and a narrow belt of nilas northward of the Cape. Often, a lead opens between the fast ice and the pack ice outside, making it easier for ships to navigate in the area. This is called a flaw lead. Outside of the coastal areas is found the pack ice with grey and grey-white ice floes. The fractures can be seen as dark areas between the ice floes, and some of them covered by nilas are indicated by arrows in Figure 1.8. The ice floes have varying size. Generally, the floes south of 70.5°N are medium and small, while the majority of floes north of this latitude are big (more than five hundred meters across) . From about 71.5°N and north-eastwards there is an area dominated by grey ice and nilas, which have bright and dark signatures, respectively. The ice edge is located at about 66°E, and has numerous belts of ice stretching out into the open water. The signature of open water is grey, indicating that there is a moderate wind, about lO m/s, in the area, blowing south-west (away from the ice boundary) .

Case 6: SAR image from the Yamal coast on November 26, 1995 Main features: ice edge, nilas, pancake ice, young ice, grey ice, grey-white ice, ridges, leads, polynyas.

Figure 1.9 shows an ERS-1 SAR image located near the Yamal coast in late November, when south-western part of the Kara Sea is partly covered by young ice. Near the coast in the lower, left corner of the image, there is an area with light nilas having fairly bright signatures. North of this area, around 72°N and 66 - 67°E, there is some open water, also with very high backscatter indicating that the wind was strong (about 15 m/s) when the image was taken. The ice edge is outlined by a dotted line. Along the coast there is an area with leads. The signatures of these leads covered by dark nilas are generally darker than that of the area further off from the coast. In the upper part of the image, there is a area of grey-white ice, and an area of grey ice closer to the Yamal coast. However, there are some areas with pancake and grey ice in the middle part of image, and a few of them are indicated by arrows in Figure 1.9. Close to the top of the image there are also some leads, which can be seen as dark features.

Case 7: SAR image from Dikson and Yenisei Gulf on November 30, 1995 Main features: pack ice: grey-white and thin first-year ice, fast ice: grey-white fast ice, thin fast ice, medium fast ice. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1-12


The SAR image in Figure 1 .10 covers the area around Dikson and the northern part of the Yenisei Gulf. The most significant difference from the image in Case 4 taken 15 days earlier ( Figure 1 . 7) , is that pack ice in the area west of Dikson contains were developed with compare with the previous image. No clear nilas area can be observed in Figure 1 . 10. North of the Sibiriakova Island and between the Oleniy Island and the mainland, there are areas with fast ice which have not changed much in two weeks.

Case 8: SAR image from the Yenisei Gulf on December 1 , 1995 Main features: new ice, medium fast ice, smooth ice, leads, river ice.

Figure 1 . 1 1 shows a SAR image further down the Yenisei river estuary. In the northern part of the Gulf, there are many large river ice floes, with variable signatures. Few leads are visible north of 72.25°N. The river ice is partly broken into small and medium floes. The ice surface may be smooth or rough (rafted or ridged ice) . This can be seen as dark or bright signatures, respectively. A large, smooth floe is clearly visible at 72.5°N,80oE. This floe and all surrounding ice floes that can also be recognized in Case 7. They have all moved very little in the 34 hour period between the images. Case

9: SAR image from the Yugor Strait on December 17, 1995

Main features: ice edge, nilas, young ice, rafted grey ice, grey-white ice, fast ice: grey white ice, open water.

The ERS-1 SAR image in Figure 1 . 12 covers an area just east of the Kara Gate and include the Yugor Strait, which both are an important straits for NSR ship traffic. South of Vay gach Island, there is an area with grey ice, seen as a bright area along the left image edge. Just south of the Vaygach Island, there is also some young ice having bright signatures ( but not as bright as the area with grey ice ) . Close to the mainland of the Yugor Peninsula there is an area with grey-white ice which has a darker and more homogeneous signature. The Yugor Strait is covered by smooth fast ice, the area just north of the strait is open water ( ice concentration less than 1). Further north of the strait, the grey-white ice floes are generally larger. From about 70.5°N and northwards there are several areas of rafted grey ice, with very bright signatures. Around 71 ON there are areas of nilas, which are significantly darker than the surrounding rafted ice.

Case 10: SAR image from the Kara Gate on December 20, 1995 Main features: ice edge, young ice, grey ice, grey-white ice, thin first-year ice, open water, open pack ice, close pack ice, compact pack ice, leads. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-13

Figure 1 . 13 shows an ERS-l SAR image from the Kara Gate area, taken in late December. In the strait there is close pack ice (concentration 7-8), that is young ice. The signatures of the floes vary, but the majority of grey ice floes have a bright signature. South-west of the the strait ( near the left image corner) there is an area of open pack ice (concentra tion 4-6) , and some patches of open water (ice concentration less than 1) seen as darker features. South of the strait, there are generally floes of young ice, but a more detailed interpretation of ice development of individual floes cannot be given. Along the southern coast of Vaygach Island, there are some open water areas, with a darker signature. Just north of the strait, there are several very distinct leads. Two large leads are marked with arrows in Figure 1. 13. Near the east coast of Novaya Zemlya, a belt of thin first-year ice with dark signatures can be found. Further east, the floes are grey-white ice, and north of approx. 71 ON compact ice ( ice concentration 10) can be seen in the SAR image. Case 1 1 : SAR image from Cape Kharasavey on December 21, 1995 Main features: nilas ( light ) , young ice, grey ice, rafted ice, fast ice: thin first-year fast ice, polynyas.

The SAR image in Figure 1 . 14 covers the Cape Kharasavey area. The main feature in this image is the so-called Yamal recurring polynya which are found along the coast. It is formed by opening and closing of coastal polynya by tidal and wind actions, and has often been observed in ERS SAR imagery from previous winter seasons ( see e.g. Johannessen et al. ( 1995)). Outside of the Yamal recurring polynya, there is a bright area with grey ice and rafted grey ice. Further out, there is a belt of young ice, followed by another belt of grey-white and grey ice, but with more high concentration. Then, close to the coast of the Yugor Peninsula, the Amderma recurring poynya is found, covered by grey ice and light nilas with bright signatures.

Case 12: SAR image from Western Kara Sea on December 30, 1995 Main features: young ice, grey ice, grey-white ice, thin first-year ice, fractures.

The image in Figure 1.15 shows ice in the middle of the Western Kara Sea, far from coastal areas. There is mainly large, thin first-year ice floes (with dark signatures) in the upper part of the image, and in the lower, right corner there is an area of grey-white ice ( also having dark signatures ) . Between these dark ice signatures, there is a bright belt contain ing young ice and grey ice small floes, and fractures with open water roughened by the wind. Strong wind causes the high backscatter seen.


1-14


Case 13: SAR image from the Kara Gate on January 8, 1996 Main features: ice edge, young ice, grey ice, grey-white ice, medium fast ice, open pack ice, very open pack ice, leads.

This case shows the ice situation in the Kara Gate area. North of the strait there is mainly young ice, but northward in the Kara Sea there are also some large areas of open water. Throughout the strait there is also grey ice and grey-white ice with different concentration. Small area of fast ice (medium thickness) near the coast of Novaya Zemlya and also along the northern coast of Vaygach Island (Guba Dolgaya). The area south-west of the strait is dominated by grey-white ice. West of approx. 57°E, we find areas of grey ice (open pack ice with concentration 4-6) . The ice edge is diffuse. Along the south coast of Novaya Zemlya there are small areas of very open pack ice (ice concentration 1-3) . Case 14: SAR image from the Yugor Strait on January 21, 1996 Main features: nilas, young ice, grey ice, grey-white ice, thin first-year ice, ice breccia (thin/medium first-year ice) , open water, ice eddy.

The image in Figure 1.17 covers the same area as Case 9. Most of this sea is now covered with thin first-year ice and grey-white ice, with some areas of nilas, grey ice and open water. At about 71°N,60oE there is an ice eddy rotating counter-clockwise. On the eastern side of the eddy, there are an areas of nilas and open water which may have been generated by the wind/currents that formed the eddy. The ice north of 71 ON contains giant and vast ice breccia floes (large areas with thin and medium first-year ice) . The largest floes are some 20 km across.

Case 15: SAR image from north of Belyy Island on January 28, 1996 Main features: medium first-year ice, thin first-year ice, ridged ice area, fast ice, rough ice, leads.

Figure 1 . 18 shows a SAR image northward from Belyy Island. The northern part of this area is covered with medium first-year ice. In the lower part of the image, openings in the ice (large leads with open water) are seen. The leads in the lower third of the image have very bright signatures, which indicate that they contain open water roughened by strong winds. The ridged ice area surrounding Belyy Island created many problem for convoy operations in late January 1996 when NIERCS staff participated in ice navigation activities in the western part of the Northern Sea Route. Close to the shores of the island and the Yamal Peninsula, areas of fast ice (thin/medium) are located.

Case 16: SAR image from Dikson and Yenisei Gulf on February 8, 1996 Main features: river ice, grey ice, grey-white ice, fast ice, smooth ice, rough ice, fractures.

The SAR image in Figure 1 . 19 covers the same area as in Case 7, taken more than two -

NANSEN

ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH

- Project Report for Task 1

1-15

months earlier. The river ice seem to have remained quite stationary. The large ice floe located at 72.5°N ,80oE has remained at very much the same position, although its shape has changed a little. The areas of fast ice close to the Sibiriakova Island and the Oleniy Island have grown somewhat in extent, and there is now a large area of fast ice also between these two islands. In Case 7 this strait was dominated by rough ice and ice floes of varying signatures. Now there is only a small belt of rough ice left, just south of the Sibiriakova Island. There are also some areas with dark smooth pack ice. West of Dikson there are zones of grey and grey-white ice, and also some fractures. A ship track is visible as a thin, white line in the lower, right part of the image ( indicated by an arrow) .


ICEWATCH Project Report for Task 1

1-16

-

ERS-1 SAR Image. Date: 1 1 .1 1 .94 Time: 1 7:08 GMT

ERS-1 SAR Coverage November 1 1 , 1 994 55

ice

Dark nilas

60

65

70

76

76

75

75

74

74

73

73

72

72

71

71

70

70

69

69

68

60

65

70

68

Figure 1.4: Case 1 : SAR image from Baydaratskaya Bay on November 11, 1994. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1- 17

-

ER S - l

S AR

2 4 - 0CT - 1 9 9 5

96 " E

97" E

7 8 " 19 8 " E

1 4 : 0 5 GMT

99" E

/ 100" E

96 " E

hevik Is land 78" N

7 7 . 5" N

97U E

7 7 . 5° N

d you ng ice

7T N 98 " E

102" E

7T N

7 6 .99"w,: N ilas (Fi nger rafting)

o

Original Data

103" E

Signal value

255

© ESA/TSS 1995. Image Analysis NERSe.

Figure 1.5: Case 2: SAR image from the Vilkitsky Strait on October 24, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1-18


ER S - 2

S AR

96 U E

2 5 - 0CT - 1 9 9 5 9 7 " E 7 8 " N9 8 " E

1 4 : 0 5 GMT

99" E

/ 100" E

shevik Island 78" N

ilkitsky Strait

101U E

N

102" E

77" N 7 6 .�U�

Nila (Finger rafting)

103" E

o

Signal value

255

Original Data © ESA/TSS 1995. Image Analysis NERSe.

Figure 1 .6: Case 3: SAR image from the Vilkitsky Strait on October 25, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


ER S - 2

S AR

79" E

1-19

1 5 - NOV - 1 9 9 5 SO" E

SlU E

0 6 : 3 0 GMT 7 3 . 5 '� N

New i

7S" E

7 3 . 5"

N

Sl" E

G rey-white ic

h water ice 7 3' " N

ce (g rey-wh ite) hip track

SO" E

7 2 . 5V N

72" N

77" E o

•

7 2 " N7 S " E Signal value

79" E

I

255


Figure 1 .7: Case 4: SAR image from the Dikson area on November 15, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1-20


ER S - 2

S AR

2 0 - N OV - 1 9 9 5

66U E

0 7 : 1 4 GMT 68" E

72" N

68" E 7 1 . 5° N 65" E

7 1. 5 ° N

71"N

7 1u N

67" E

64° E

66" E

64" E o

Signal value

Original Data © ESA/TSS 1995. Image Analysis

255

NERSC.

Figure 1 .8: Case 5: SAR image from Cape Kharasavey on November 20, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-21

-

S AR

ERS - l

64" E

2 6 - NOV - 1 9 9 5 6S" E

1 6 : 4 8 GMT 74" N

67' E

rey ice 7 3 . S" N

68" E 73" N

69' E

72 " N

7 l . S' N

o

Signal value

255

Original Data © ESA/TSS 1995. Image Analysis NERSC.

Figure 1.9: Case 6: SAR image from the Yamal coast o n November 26, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-22

-

ERS - l

S AR

3 0 - N OV - 1 9 9 5 80" E

8l0 E

0 6 : 2 7 GMT

82" E

79" E

\ Thi n first-year/ grey-white ice (pack ice) 7 3 . 5" N

h ite)

7 8 " 19

7 2 . 5" N

Fast i ce (med i um) 77" E o

78" B2 " N Signal value

79" E 255

Original Data © ESAjTSS 1995. Image Analysis NERSe.

Figure 1. 10: Case 7: SAR image from Dikson and Yenisei Gulf on November 30, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-23

-

ER S - l

S AR

7 9" E

O l - D EC - 1 9 9 5 80" E

8l0 E

1 5 : 5 0 GMT 82" E

Variable signatu re, many large ice floes

73" N

7 2 . 5° N

72" N

8 2 " E 7 1 . 5" N o

Signal value

83" E 255

Original Data © ESAjTSS 1995. Image Analysis NERSC.

Figure 1 . 1 1 : Case

8:

SAR image from the Yenisei Gulf on December 1, 1995.



1-24

-

ER S - l

S AR 60 · E

1 7 - D EC - 1 9 9 5 61" E

0 7 : 3 4 GMT 62" E

71" N

G rey ice ( rafted) 59" E

7 0 . 5U N

Fast i ce (grey-white)

70" N

rey-white ice water lO E

You ng ice

Yugor Strait

70" N

5S " E

5S " E o

Signal value

255


Figure 1 . 12: Case 9: SAR image from east of the Kara Gate on December 17, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH

-


1-25

S AR 2 0 - D E C - 1 9 9 5

ER S - l

56 " E

1 7 : 3 3 GMT

71" N

56 " E

70" N

5T E

Open wate

58" E o

59" E Signal value

255


Figure 1 . 13: Case 10: SAR image from the Kara Gate on December 20, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1-26


ERS - l

S AR

2 1 - DEC - 1 9 9 5 67° E

0 7 : 0 8 GMT

68" E 71" N

68" E

70 " N

70" N

Grey-wh it� and grey Ice 6 9 . SO N

6T E

69" N

o

Jt

Signal value

255


Figure 1 . 14: Case 1 1 : SAR image from Cape Kharasavey on December 21, 1995.

- NANSEN ENVIRONMENTAL

AND


ICEWATCH - Project Report for Task

S AR

ERS - l

65" E

1

1-27

3 0 - DEC - 1 9 9 5 66" E

6T E

0 7 : 2 4 GMT

7 3 . 5" N

64" E

73" N

Young ic 72° N

ice

62' E 7 1 . 5" N

o

Signal value

255


Figure 1 . 15: Case

12: SAR

image from Western Kara Sea on December 30, 1995.


1-28


ER S - l

S AR 55" E

0 8 - JAN - 1 9 9 6 56" E

1 7 : 3 6 GMT

7 J5.7S "EN

55" E 71"

N

Very open pack ice

70. 5° N

56 " E Ice edge

70" N

o

Signal value

Original Data © ESAjTSS 1996. Image Analysis NERSC.

Figure 1 . 16: Case 13: SAR image from the Kara Gate on January 8, 1996. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


ERS - l

S AR

61" E

1-29

2 1 - JAN - 1 9 9 6 62" E

72° N

0 7 : 3 4 GMT

63" E

in first-year ice g rey-white ice

breccia (thi n/medium fi rst-year ice)

Ice

62" E

pen water

70" N

Open

o

Signal value

255


Figure 1 . 17: Case 14: SAR image from east of the Kara Gate on January 21, 1996. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


1-30

S AR

ERS - l

2 8 - J AN - 1 9 9 6

7 17 E. S " N

72 ' E

73' E

0 7 : 1 2 GMT

7 S . S' N 70' E

7S' N

6 9" E

7 4 . S' N

68 " E 74" N

Ridged i water

7 3 . S' N 67'E

o

Signal value

255


Figure 1. 18: Case 15: SAR image at Belyy Island on January 28, 1996. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

1

ICEWATCH

-


ER S - l

S AR

1-31

0 8 - FEB - 1 9 9 6 SlU E

SOU E

0 6 : 2 7 GMT S2a E

7 9" E S2U E

7 3 . 5" N

73" N

Sl" E

72" N SO" E

. Fast ice 77" E

\

77" E o

7 8 " E2 " N

Ii

7 9" E

Signal value

255


Figure 1.19: Case 16: SAR image from Dikson and Yenisei estuary on February 8, 1996. - NANSEN ENVIRONMENTAL

AND


ICEWATCH Project Report for Task

1-32 1.3

-

1

Ice types and ice conditions with difficult backscatter char acteristics

1 .3 .1

Ice edge region

The separation of ice and water is a major task in interpreting SAR imagery to ice maps, especially in this region. Case 1 and Case 5 show situations with generally easy separation of the main ice /water surface types. Here, ice is either very dark (grease ice) or very bright (pancake ice and small floes ) , while most of the water surface has wind with backscatter value about -5 to 8dB (4-8 m/s ) , causing a medium grey tone. Thus, the contrast between ice and water is good, although the ice covered areas can be both bright and dark. -

Along the coast in the lower part of Case 1 , medium and larger size ice floes give some difficulty, since the backscatter from such ice floes are quite similar to the adjacent water surface. Also, in the lower part of Case 5, a high surface wind is seen to give a higher backscatter that is similar to the pancake ice signature. This problem is accentuated by the fact that the outer band of grease ice often disappears in higher wind. Therefore, high wind situations at the ice edge may easily cause a diffuse appearance of the edge. Cases 9 and 10 shows these more difficult situations with fairly high backscatter from both ice and water in the ice edge region of the image. It is very difficult to estimate the amount of water in this case, leading to a diffuse ice edge. The texture is used as an aid, but typical open water texture is not seen in these cases. Also, the recognition of larger, resolved ice floes is used for estimating the position of the edge. Cases 6 and 13 are intermediate situations. Even if the wind is high, the ice close to the edge is darker, either as grease ice or as medium floes ( not small floes or pancake floes ) . The separation line, or edge, can here be fairly well estimated.

1.3.2

Pack ice region in coastal areas

Most of the selected cases are from within the pack ice with little or no open water present. Case 2 and Case 3 show relatively easy situations with well defined large floes (with medium to bright signatures) separated by frozen leads with thinner ice ( dark signatures ) . More homogeneous areas may be assumed to be unresolved, packed floes. The backscatter from the large floes surfaces and the packed smaller floe surfaces is seen to vary. Ice properties that can cause this variation are: 1 . In the ice interior: the degree of rafting/ridging of the ice. Heavier rafting/ridging will cause higher backscatter. 2. At the ice edge: the floe size in fields of unresolved packed floes. Smaller floes will give higher backscatter. The degree of ice compression is an important ice parameter. It may be assumed that the brighter ice is under more compression, as heavy rafting/ ridging will generally give a higher backscatter. However, the estimate of ice compression is very uncertain, since both - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER

-


1-33

old ridges and small floes in non-compressed ice can also give rise to a high backscatter. Case 4, Case 7 and Case 16 west of Dikson all show complex ice situations. Here both newer and older pack ice as well as river ice from Yenisei is mixed together in various states of compression. The easiest surface type to map is the smooth, landfast ice seen as dark areas close to the coast with generally a sharp, brighter outer boundary ( the flaw-lead) . The main problem is to separate the effects of the various ice properties on backscatter: 1. the effect of surface roughness ( ridging, rafting and small floes ) , and 2. the effect of the small scale ice type ( new, young, first-year, mUlti-year, river ice) . The recognition of larger, resolved ice floes and judgement of the image texture is also used to aid the interpretation in these cases. Problems related to the effect of surface roughness on the backscatter values are also dis cussed in Section 1 . 1 .

1.3.3

Pack ice region in the open sea areas

Case 13 and the upper part of Case 1 5 show two different situations in the central Kara Sea. In both cases, the features of large floes and leads are most easily recognized. The large leads may be either dark ( smooth new ice, or water in low wind) , or bright ( rough ice, or water in high wind) . The broad very bright band of ice in the lower part of Case 12 is difficult to classify without in situ data or without knowledge of ice regime of each concrete region. It may be small floes, heavily ridged / rafted ice and also unresolved areas of water in high wind.

1.3.4

River ice region

Case 8 shows the river ice at the mouth of the Yenisei River. Most easily mapped are the large floes and areas of dark, new ice, and also the ship track visible as a bright line. The landfast river ice is very bright. It is not certain if this is caused by heavy ridging or some other surface structure, such as e.g. ice breccia or the presence of brackish water ice floes.

1.3.5

Yamal and Amderma recurring polynya

Recurring polynyas are geographical areas where the ice has opened up and closed a num ber of times ( cycles) . Recurring polynyas may be either open or covered by new ice, nilas or young ice. The large ice structures in Case 1 1 is easily interpreted. The dark and bright bands are caused by repeated ice motion against the coast of Yamal ( to the left ) by wind and / or tidal currents. White bands are rafted ice, while dark bands are smooth ice (nilas ) .


AND



1-34 1.4

Assessment of ERS SAR a s a tool i n ice navigation

Factors that affect the use of ERS SAR as an ice navigation tool are: 1. ease of interpretation of important ice types, 2. availability of SAR data within the area-time intervals, and 3.

speed and reliability of transfer methods for the SAR data (interpretations or image data) to the end users.

These three factors will be discussed in the sections below.

1.4.1

Ease of interpretation of important ice types

The examples and discussion of ERS SAR imagery in the previous sections have shown that the SAR sensor is capable of detecting important ice features for navigation, such as the ice edge and leads. However, it has also been demonstrated that in some situations it is difficult to interpret SAR images, i.e. to distinguish between important types of ice and between ice and open water. Several validation experiments have resulted in look-up tables for the various ice types, but with significant overlap between many ice types (Figure 1.2). Thus, a pure thresholding algorithm is not sufficient, and the classification methods should be augmented with additional information such as image texture and feature analysis. The ERS SAR is independent of daylight and cloud conditions, but its signatures are to some degree dependent on meteorological conditions. In particular, the backscatter from open water will vary with the wind speed. ( More details on the physical properties of the ERS SAR sensor have been reported in Section 1. 1.) Thus, it is sometimes difficult to accurately map the ice edge, since the backscatter signatures of open water and ice may overlap, and no clear distinction is visible in the SAR image. The same ambiguity may occur in distinguishing between certain ice types. Overlap in backscatter signatures and similar textures make it difficult to classify ice types correctly. Other types of data, like SSM / I ( Special Sensor Microwave/ Imager) from US DMSP satel lites, can be used as an aid in large-scale separation between ice and open water. Ice concentration derived from SSM / I data will give a rough outline of the ice edge location, defined as the 20 or 50% ice concentration contour. If visible and near-infrared satellite data are available, these can also be used to detect the ice edge. However, clouds will often exclude this possibility. Distinguishing between different ice types with overlapping backscatter signatures can sometimes be improved by including texture parameters in the analysis. This and other techniques for improved ice type classification in ERS SAR imagery will be addressed in Task 2 of this project. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH - Project Report for Task 1 1 .4.2

1-35

Availability of SAR data within area-time intervals

Due to power limitations of the ERS SAR instrument, it is only turned on for about 10% of each orbit, and usually only descending orbits will have the SAR turned on. The use of SAR in ice navigation is affected by data availability in the areas of interest. The schedul ing of the instrument on are determined by ESA, and acquisition must be requested about one month in advance. During this project it has been good coverage over the Northern Sea Route, which is a high-priority area for sea ice mapping, but a capability of requesting scenes closer to the acquisition date would be very useful. The swath of the ERS SAR sensor is 100 km wide, and the swath is displaced 25° westwards for each orbit. This gives a near repeated coverage of an area after 3 days, but there are large areas between the swaths which are not imaged in this 3-day period. The coverage is latitude dependent (Jo hannessen et al. , 1995) and increases with increasing latitUde. A large area in the Laptev Sea, from approx. 107°E to approx. 135°E, is not in range of any currently operational ground station, and no ERS SAR images from this area can be obtained. During the first part of the ICEWATCR project, both ERS-1 and ERS-2 SAR have been operational in the so-called tandem phase, allowing the same area to be imaged with 1-day intervals. With two ERS SAR sensors there were also more data scenes to choose from when ordering scenes.

1.4.3

Speed and reliability of transfer methods for the SAR data

Third, methods for fast and reliable transfer are needed to distribute (1) SAR data to NERSC, and (2) SAR images and/or their interpretation to the end users. With maxi mum priority on all links of the processing chain, the interpreted images can be finished at NERSC with a delay of minimum 1.5 hours (Johannessen et al. , 1995). The stan dard product from NERSC is a gridded and annotated hardcopy which is suitable for fax transmission. A digital version of the gridded SAR image can also be made available in compresses JPEG format, for retrieval by ftp using telephone line and a modem. The size of these compressed images are about 50 K. During the field stage of the ICEWATCR project such images have been received on board all 4 Russian icebreakers belonging to Murmansk Shipping Company and which operated in the Northern Sea Route area at this time. (More details on this in the project report for Task 5.) Both fax and modem transfer of data use the INMARSAT system, which is working fairly well up to about 75°N. North of these latitudes, connection is frequently very difficult. The advantage of file transfer is that the ship personnel can chose the time of transfer and position the vessel to get op timal conditions for INMARSAT transmissions. The ships need to have proper hardware and software (modem, PC, transmission program, etc.) to be able to use the compressed SAR images from NERSC. Also data have been sent to Dikson MOR, where it can be included in their operational ice mapping system. Fax transfer to Dikson (at about 73°N) has worked well, since the antenna there is stationary.


AND



1-36 1.5

1

S ummary of Task 1

A summary of the current techniques for ice type classification in SAR imagery was pre sented in Section 1 . 1 . At NERSC, the thresholding technique is currently being used for ice type classification in ERS SAR imagery, using the results from several SAR ice validation experiments between 1991 and 1996 ( Figure 1.2). Improvements fo the classification using texture analysis techniques are currently tested in other research projects, but are not yet ready for operational use. Selected ERS SAR images from the Northern Sea Route are has presented and ice con ditions with difficult SAR backscatter characteristics have been discussed, based on these images. Finally, factors that affect the use of ERS SAR as an ice navigation tool were identified and assessed. These factors include the ease of interpretation of important ice types in SAR images, the availability of SAR data in given area-time intervals, and the speed and reliability of the data transfer methods. The most important exchange of technology in this task has been: • transfer of state-of-the-art methods for SAR ice type classification from NERSC to the other project partners . •

Russian ice expertise in the Northern Sea Route have been transferred to NERSC for more reliable interpretation of SAR ice images.

References Barber, D. G., Shokr, M. E., Fernandes, R. A . , Soulis, E. D., Flett, D. G., and LeDrew, E. F. (1993), A Comparison of Second-Order Classifiers for SAR Sea Ice Discrimination, Photogram metric Engineering & Remote Sensing, 59(9) :1397-1408. Johannessen, O. M., Sandven, S., Kloster, K., Miles, M., Melentyev, V. V., and Bobylev, L. (1995) , A Sea Ice Monitoring System for the Northern Sea Route Using ERS-1 SAR Data, Technical report 103, Nansen Environmental and Remote Sensing Center. Melentyev, V., Johannessen, O. M., Sandven, S . , Pettersson, L. H., and Kloster, K (1997) , ERS1 SAR monitoring of dangerous ice phenomena along the Northern Sea Route, Submitted to the International Journal of Remote Sensing. Onstott, R. G. (1992) , SAR and Scatterometer Signatures of Sea Ice, in Microwave Remote Sensing of Saa Ice, edited by Carsey, F., pp. 73-104, American Geophysical Union, AGU Geophysical Monograph 68. Sandven, S . , Johannessen, O. M., Kloster, K , and Miles, M. (1994) , SIZEX'92 ERS-1 SAR ice validation experiment, EARSeL Advances in Remote Sensing, 3(2-XII) :50-56. Steffens, K and Heinrichs, J . (1994) , Feasibility of sea ice typing with synthetic aperture radar (SAR) : Merging of Landsat thematic mapper and ERS 1 SAR satellite imagery, Journal of Geophysical Research, 99( C11 ) :22,413-22,424. Tucker, W. B., Perovich, D. K, Gow, A. J., Weeks, W. F., and Drinkwater, M. R. (1992 ) , Physical Properties of Sea Ice Relevant t o Remote Sensing, in Microwave Remote Sensing of Sea Ice, edited by Carsey, F., pp. 9-28, American Geophysical Union, AGU Geophysical Monograph 68. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH Project Report for -

Winebrenner, D. P.

Task

1

1-37

(1992) , Microwave Sea Ice Signature Modeling, in Microwave Remote Sensing of Sea Ice, edited by Carsey, F., pp. 137- 175, American Geophysical Union, AGU Geophysical Monograph 68. et ai.

- NAN SEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH

Project Rep ort for Task 2

Algorithms and methods for processing, classification and interp retation of SAR and SLR data

ICEWATCH

-


i


ii

List of Tables

ii

2

Algorithms and methods for processing, classification and interpretation of SAR and SLR data 2-1 2 . 1 Exchange of tools and algorithms . . . . . . . . . . . . . . . . 2-1 2 . 1 . 1 Tools for ordering SAR and SLR data . . . . . . . . . 2-1 2-2 2 . 1 .2 Tools for processing and display of SAR and SLR data 2.1.3 Ice algorithms/methods for SAR data at NERSC . . . 2-3 2 . 1 .4 Processing methods and ice algorithms fot SLR and RM08 data at NPO Planeta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 2.1.5 Ice algorithms/methods for interpretation of satellite data at AARI 2-21 2.2 Exchange of ERS and Okean data . . . . . . . . . . . . . . . 2-31 2-34 2.3 Improving algorithms for SAR ice type classification . . . . . 2.4 Comparison of ice classification using SAR and Okean data . 2-35 2.5 Summary of Task 2 . . . . . . . . . . . . . . . . . . . . . . . 2-39

References


2-40


ii

L i st of F i gures 2.1

2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2 . 10 2.11 2.12 2 . 13 2.14 2.15 2.16 2.17 2. 18

Ice motion in the O b river estuary computed from ERS-l SAR images on February 24 and 27, 1994. Mean displacement is 14.7km over a 72 hours period, providing a mean ice speed of 5.7cm/sec. . . . . . . . . . . . . . . 2-6 Ice motion computed from ERS SAR imagery in the Bolshevik Island area, on October 24-25, 1995. Mean displacement is 14.2km over a 24 hour period, giving a mean displacement of 16.4cm/sec. . . . . . . . . . . . . . . . . . 2-7 A distinct ice edge in ERS SAR imagery from November 20, 1995 near Cape Kharasavey. Linear enhanced image. . . . . . . . . . . . . . . . . . . . . . 2-10 A diffuse ice edge in a ERS SAR image from January 8, 1996 near the Kara Gate. Linear enhanced image. . . . . . . . . . . . . . . . . . . . . . . . . 2-10 Example of computer-classification of an ERS-l SAR image in the pack ice south-east of Svalbard during winter conditions ( 05.03.92 ) . FY means first-year and MY means multiyear. . . . . . . . . . . . . . . . . . . . . . 2-12 Example of computer-classification and ice concentration from an ERS-l SAR image ( 12.09.94) in the Vilkitsky Strait. From left to right are the original image, the classified image and the derived ice concentration. . . . 2-13 Calculation of ice concentration from classified ERS SAR data. . . . . . . . 2-15 Example of a raw and radiometric corrected SLR image from NPO Planeta. 2-20 Example of SLR-RM08 ice type classification by NPO Planeta. . . 2-22 SLR mosaic from November 1994, used for classifying ice types. . . . . . 2-23 RM08 mosaic from November 1994, used for classifying ice types. . . . . 2-24 2-25 SLR-RM08 ice type classification from May 1996 made by NPO Planeta. SLR mosaic from May 1996, used for classifying ice types. . . . . . . . . 2-26 RM08 mosaic from May 1996, used for classifying ice types. . . . . . . . 2-27 Ice charts from ERS SAR imagery on November 15, 1995, prepared by AAR1.2-28 Ice charts from ERS SAR imagery on February 25, 1996, prepared by AARI. 2-29 ERS-l SAR image from the Vilkitsky Strait area on May 18, 1995. . 2-37 ERS-l SAR image from the Yamal coast on May 19, 1995. . . . . . . . . . 2-38

List of Tables 2.1 2.2 2.3

Backscatter for winter sea ice types measured by various groups in different 2-14 areas . . . . . . . . . . . . . . . . . . . . . . . 2-32 Okean N7 data received from NPO Planeta. 2-33 Okean N8 data received from NPO Planeta.



2

2-1

Algorithms and methods for processing, classifi cation and int erpretation of SAR and SLR data

The objectives of Task 2: Implement and improve state of art algorithms and methods for processing, classification and interpretation of radar data, are to: - exchange SAR processing tools and ice algorithms for analysis of SAR and SLR data (technology transfer) between NPO Planeta and NERSC, - exchange ERS and Okean data between ESA/NERSC and NPO Planet a , - improve SAR algorithms for ice type classification and interpretation, and - compare SAR ice classification results with Okean data. 2.1

Exchange of tools and algorithms

This section describes the tools and algorithms used for ordering, processing and analyz ing ERS SAR and Okean SLR data. In this report we only outline the main processing steps and present a rough outline of the algorithms. For more detailed descriptions of the tools and algorithms used, we refer to other written material which can be obtained from ESA/ESRIN, NERSC/NIERSC NPO Planeta or AARI.

2.1.1

Tools for ordering SAR and SLR data

ESA's DESC software (ESA, 1996) has been installed at NPO Planeta to facilitate order ing of ERS SAR data. In addition to the software, necessary update files for DESe have also been transferred. These update files contains a list of scheduled and planned SAR scenes, and are updated every Monday morning. Both the DESC software and update files are available through the World Wide Web. They were transferred to NPO Planeta on Internet, via NERSC and a ftp server at the Space Research Institute (SRI) in Moscow. During the project new versions of the DESC software and associated update files have been transferred on request. Using DESC, the personnel at NPO Planet a has ordered specified scenes of low-resolution SAR data (via NERSC) from Troms¢ Satellite Station (TSS) in Norway. The DESC soft ware may also be used by NPO Planeta to order high-resolution SAR scenes directly from ESRIN. We have not been informed of such orders. Selection and distribution of Okean SLR data have been carried out by NPO Planeta. Some of these selections have been based on rather general requests from NERSC, e.g. for data from the summer of 1995 in the East Siberian Sea. It is assumed that NPO Planeta has coordinated orders of SAR with their SLR data so that the data sets overlap geograph ically, and are close in time (preferably less than about 1 day). - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2-2 2.1.2


Tools for processing and display of SAR and SLR data

SAR tools TSS has delivered the low-resolution SAR scenes to NERSC in near-real time. The data were delivered in CEOS format (TSS, 1991), with two headers containing information about acquisition date and time, orbit and frame number, center and corner coordinates, etc, as well as the low-resolution image (LRI) itself with the backscatter for each pixel stored as a 16 bit unsigned integer.

Processing at NERSC has included: 1 . Gain normalization in range, which is done to correct for 3 effects, including antenna gain pattern and slant range variation. 2 . Conversion from 16 to 8 bit pixels, which is done by division by a constant factor. TSS digital 16 bits values over ice and ocean are normally all in the range 0-512. Dividing by a factor of 2.0 is found appropriate for ERS-1 data. The same factor is used for all images from the same satellite. 3. Resampling to square 100m pixels, which is done using nearest-neighbor interpola tion. 4. Averaging to 200m pixels, which is done to reduce the amount of data. It also results in reduced speckle noise. 5. Resampling to a given stereographic projection of scale 200m/pixel. Tools or algorithms may easily be transferred to NPO Planeta. Transferring tools implies porting them from a Unix platform (at NERSC) to a PC environment. Algorithms can be transferred as pseudo-code, accompanied by available documentation with format descrip tions and other auxiliary information. For practical reasons, the low-resolution ERS SAR scenes have been transferred via NERSC, to an ftp server at SRI where NPO Planeta has collected the SAR image files. The SAR data processed for Dikson MOH and the Russian icebreakers were processed in the same way excluding step 5 above, and faxed or file-transferred by modem in near-real time. More details on this are given in Section 5 (the demonstration project in winter 1996) . SLR tools The SLR data were transferred to NERSC via the ftp server at SRI, without any ground processing. Thus, no corrections have been applied to the SLR imagery transferred to NERSC in this project.

The SLR processing at NPO Planeta includes: (1) radiometric correction, including ac curate gain normalization in range, and (2) geometric resampling to chosen projections. These processing steps are further described in Section 2.1.4. Unless all radiometric cor rection steps are carried out, it is not possible to compute absolute sigma-zero values from the image pixel values. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-3

At the project meeting in St. Petersburg in April 1996, NPO Planeta officials gave the fol lowing information about the SLR sensor on the Okean satellites. The radiometric range of the SLR sensor is -20 to 0 dB, and the reason for the two different types of images received at NERSC (600 vs. 300 columns wide) is that different ground stations have dif ferent equipment. Thus, the Okean satellites sends data at the best sampling rate (600 columns per swath line) to all stations, but stations with the so-called "MET" system can only handle data with lower sampling rate (300 columns per swath line) . The SLR sensor is " on" for 7 minutes per orbit, and the scheduling is determined by requests from the end-users. In the winter time, monitoring of ice covered areas has the highest priority. The Okean satellites (N7,NS) is capable of storing data on board the satellite, until they come within the range of a ground station. The Arctic areas can be covered in 3-4 days, and the wanted data must be requested at least two weeks before the acquisition. The RMOS sensor, a passive microwave instrument, which is also mounted on the Okean satellites, has a brightness temperature accuracy of 1-3 K at the antenna, and 3-5 K at the sea/earth surface. Display tools NERSC and NPO Planet a have used their own systems for displaying the SAR and SLR data (after having converted the data to their respective in-house format ) . In this way, the new data for each organisation could be handled similarly to the other types of satellite images that were already in use.

2.1.3

Ice algorithms/methods for SAR data at NERSC

Algorithms for derivation of the following ice parameters from single or pairs of ERS SAR images, exist at NERSC: 1. Ice motion: is derived using a maximum-cross correlation (MCC) method ( Kloster et al., 1992; Hamre, 1995) . 2. Ice edge: can be found by manual analysis of the image or by using an edge enhance ment operator ( Sandven et al. , 1993). 3. Ice type classification: is done by a simple thresholding algorithm which uses results from several SAR validation experiments (Sandven et al. , 1993; Onstott, 1992) . As reported in Task 1 , some cases are ambiguous as the backscatter signatures of differ ent ice types overlap.

4. Ice concentration: is computed from a classified ice/water image (Sandven et al. , 1993; Sandven et al., 1991). However, this classification is difficult in some cases, especially for areas close to the ice edge. Transfer of these algorithms to NPO Planeta can be made when requested by NPO Plan eta. So far we do not know if NPO Planeta has such algorithms already. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-4

1 . Estimating ice motion from pairs of ERS SAR images

Background The algorithm for estimation of ice motion from pairs of satellite images are based on work by Fily and Rothrock (1987) and Vesecky et al. (1988). The algorithm was further developed and customized for SAR data at NERSC (Flesche, 1988; Kloster et al., 1992), and now runs in an Unix environment. Implementation was done in the C programming language.

There are also other methods for estimating ice motion from sequences of satellite images, e.g. methods using image segmentation (Korsnes, 1993) and neural networks (Rau et al. , 1994) . More information on these can be found in the the given references. We will only describe the method used at NERSC in this report. Principles for the maximum cross-correlation method The MCC method builds an image pyramid and computes ice kinematics on each level, starting at the coarsest. Results on one level are used as initial values on the next (finer) level. On each level, the method uses a 2D binary search in a window of size d, and a · displacement of up to d/2 pixels up/down/left/right, can be found. The pair of blocks with the highest correlation is always kept. The correlation between blocks of pixels from image 1 and blocks from image 2, are given by:

c

=

2::� 1 2::j= 1 ( Block1 [i] [j] - ml ) . ( B lock2 [i] [j] - m2 ) . Vl ' V2

where w is the width and height of the pixel blocks. The value of the correlation factor, will be between -1.0 and 1.0, and the higher positive value, the better correlation.

c,

Input Two MxN images covering the same area taken at a time interval of a few days. The images must be 8 bits per pixel, and their dimension must be divisible by 4 for successful generation of an image pyramid. Optionally a "vector file" with a mean displacement (de fault: zero) , or the initial displacement can be given as arguments to the program. The size of the search window and of the block size can also be set by the user. Output The MCC method produces a "vector file" containing the computed ice motion vectors, one for each block. Each vector has a correlation factor c E [-1.0 . . 1.0] indicating how reliable the vector is. The vectors can later be plotted on top of the images by an auxiliary program. Examples Figure 2.1 shows an example of ice motion computed by the MCC method in the Ob river estuary using ERS-1 SAR data from February 24 and 27, 1994. Only vectors with a cor relation factor c 2: 0.75 are plotted. In the upper part of the images the ice motion have been uniform, and thus the MCC method has made a good estimate. In the lower part of - NANSEN ENVIRONMENTAL

AND


2-5


the image, the ice motion is either too large to be found with the given search window or there is fast ice which does not move. ( There is also land and an island in the lower part of the image. ) Figure 2.2 shows an example of ice motion computed by the MCC method in the Bolshevik Island area near the Vilkitsky Strait. Only vectors with a correlation factor c ;:::: 0.6 are plotted. The images are from the ERS tandem phase, from October 24 and 25, 1995. In this case, the ice motion is not uniform because the ice is moving towards the strait where some of it will be pressed north of the Bolshevik Island ( in the upper, right corner of the images ) . Evaluation of the MCC method MCC is a well documented and much used method for ice motion estimation. The al gorithm is sensor independent, and has been tested successfully on both AVHRR and SAR data ( Sandven et al., 1991, Kloster et al. , 1992 and Moctezuma Flores et al., 1994). ( Note: both images from the same sensor. ) The methods uses simple computations ( ad ditions&multiplications ) , and each computed displacement vector has a correlation factor telling how reliable it is. This allows vectors with low correlation to be marked or filtered out during plotting. The user may give an initial displacement to start the search, and the block size and search window size can be chosen.

However, the MCC methods also has its drawbacks. It expects uniform displacement in the entire image, and thus does not handle divergence, convergence or rotation very well. Since the correlation builds on statistical measures, it does not recognize land, which should be masked out with a "no-data" value. The correlation measure assumes little variation in pixel values between the two images, which makes it hard to combine data from different sensors. Further, the algorithm is time consuming. The time complexity is T ( n) O( n ) , where n = M · N, i.e. the total number of pixels in the images. This means that doubling the number of lines and columns increases the run-time by a factor of 4. There also seems to be some dependency on the given initial displacement. Although, both the block and search window size can be set by the user, these parameters should not be set randomly. Due to the binary search performed the search window size should not be larger than two times the block size. Certainly the block size can be increased, but this can increase the chances that two blocks are higher correlated than two smaller blocks would be. =

Suggestions for improvements The MCC method has some drawbacks, and is may be improved in the following manners: 1.

2.

Do an automatic estimate of the initial displacement. This can possibly be solved by using (1) image segmentation techniques, like out lined in Fily and Rothrock (1987) , (2) wind data, or (3) an extra level in the image pyramid, which is searched more exhaustively to get a better initial estimate for the displacement. Try other block sizes. A smaller block size will lead to increased run-time, but more vectors with high correlation may be found. A large block size will reduce the run time, but fewer - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2-6


Original Data © ESA/TSS 1994. Image Analysis NERSC .

Figure 2.1: Ice motion in the Ob river estuary computed from ERS-1 SAR images on February 24 and 27, 1994. Mean displacement is 14.7km over a 72 hours period, providing a mean ice speed of 5.7cm/sec. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-7


Figure 2.2: Ice motion computed from ERS SAR imagery in the Bolshevik Island area, on October 24-25, 1995. Mean displacement is 14.2km over a 24 hour period, giving a mean displacement of 16.4cm/sec.


2-8


high-correlation vectors will probably be found. 3. Try a larger search window. The search wind size can be increased by the user, but the binary search method moves the block half this size the first time. Thus, with a large search window much of it will never be investigated if the block size is much smaller.

4. Modify the binary search algorithm. It should be ensured that d :::; bs in every step, so the algorithm will not skip areas larger than a whole block. Otherwise there is no point in specifying a larger search window. However, modifying the binary search algorithm in this way will increase run-time, but it may give vectors with higher correlation. 5. Modify the median filtering routine. The correlation factor should be taken into consideration in the filtering procedure. Only high confidence vectors should be used (Vesecky et al. , 1988) . 2. Determination of the ice edge

The ice edge is defined as the the demarcation between the open sea and sea ice of any kind. It is therefore a special case of the lines that may be drawn to show ice concentrations on an ice map. In some cases the edge must be drawn as an area. Two main types of edges are defined: A. A compacted ice edge (or a sharp edge) is formed if wind and wave actions press the ice toward the main pack at the edge. The ice concentration then goes from 0/10 (open water) to a value close to 10/10 in a short distance, often only in a few hundred meters. B . A diffuse ice edge is formed if wind and wave action moves the ice away from the main pack at the edge. The region between the open water and the close ice can be many kilometers and filled with various shapes of ice bands and free-floating ice floes. A. Determination of a compacted ice edge position In the SAR images, a compacted ice edge will nearly always be present as a easily detected spatial gradient in the image. The magnitude (dB/km) and the sign (increasing or de creasing backscatter toward the water) will vary with the specific conditions: - with the dominant ice type(s) present at the edge, and - with the wind speed present over the water surfaces.

In winter, pancake ice (with very high backscatter) and/or grease ice (with very low backscatter) are frequently the main ice types found at the edge. In both summer and winter, small to medium size ice floes can also be the main edge ice type, backscatter values from the ice are then moderate and dependent on the ice floe size (smaller floes give a higher backscatter). The wind velocity will also be of critical importance for the magnitude and the sign of the gradient at the ice edge, since ocean backscatter is very - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2- 9

dependent on both wind speed and wind direction. For manual determination of the ice edge, image contrast enhancement has been found to be a very useful tool. Contrast is varied, either by choosing the two "handle-points" pixel values for black and white image intensity interactively, or by using a contrast stretch routine to determine these, e.g. histogram equalization. In a specially designed program (imstretch) the lower 5% of pixels are set to black, the upper 5% are set to white, and a linear mapping of pixel value to greyscale is used in-between. Histogram-equalization tool is also available, but often found to give images that are more difficult to interpret than a linear relation between pixel value and greytone. For semi-automatic determination of the ice edge, a gradient filter can be applied as a first step. The image is convoluted with a NxN size mask, giving as output an image showing the magnitude of the spatial gradients in the image. A much-used mask with N=3 is the so-called Sobel operator. In most cases, the output image must be further filtered, before being used manually as basis for determination of the ice edge position. Many other gradi ents not connected with the ice edge are frequently present, and no fully automatic routine is presently available. B. Determination of a diffuse ice edge A diffuse ice edge is generally more difficult both to determine and to map than a com pacted edge. Instead of a line, the edge is now defined as an edge region between an inner edge (close to 10/10 concentration) , and an outer edge (c lose to 0/10 concentration) . The determination of these lines is especially difficult if the difference in backscatter is small between the main ice type(s) at the edge on one hand, and the water on the other. Even with a large difference in dB, the variation is now spread over a region many kilometers in width, resulting in a low value of the spatial gradient measured in dB/km.

Manual determination is for most cases the only way of mapping diffuse ice edges. Here also, contrast enhancement is an important tool, and can be used as described above. Examples An ERS SAR image where the ice edge can be easily distinguished is shown in Figure 2.3. After image enhancement by linear stretch, the open water signatures are clearly different from the signatures of grease ice (dark) and pancake ice (bright). Figure 2.4 shows a dif fuse ice edge, and in this case it is far more difficult to distinguish water from ice. Open water has a light grey signature in these images (medium wind) , but in Figure 2.4 there are belts of ice stretching out into the open water making it necessary to mark both the outer and inner ice edge. These images are also discussed in Section 1.2, Case 5 and Case 13.

3. Ice type classification in ERS SAR images in the cold season

The method has been developed using SAR data from the SIZEX'92 experiment in the Barents Sea, for classification of ice in the cold season. - NAN SEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2-10



Figure 2.3: A distinct ice edge in ERS SAR imagery from November 20, 1995 near Cape Kharasavey. Linear enhanced image.


Figure 2.4: A diffuse ice edge in a ERS SAR image from January 8 , 1996 near the Kara Gate. Linear enhanced image.


ICEWATCH

-


2-1 1

The method is based on the assumption that a unique set of backscatter values exists within defined zones of the image. These zones are: A. The ice interior. This zone is dominated by large floes of first-year and multiyear ice, smooth frozen leads, and rough ice surfaces (e.g. ridges, rafted ice) . B. A transition zone between ice interior and ice edge. C. The neighborhood of the ice edge. This zone is dominated by medium and small floes, pancake floes and smooth thin ice (e.g. grease ice) . Open water is also included. Open water outside the ice edge in zone 3 must be given a special treatment due to its large range of backscatter value with different wind conditions. Given the ice edge from method 2, all areas to one side of this line is classified as water. Input to the routine is a SAR image with calibrated backscatter values, normalized in range to a fixed incidence angle (e.g. near-range at 20°). For each part of the image, the zone type (A, B or C) is also given, from a manual inspection the input image. Dominant ice types and their given range of backscatter values are used in the routine (as lookup table). As the values are mostly non-overlapping within each zone, a simple threshold between ice types can be used. Manual separation into zones and thresholding The main steps of this thresholding algorithm are as follows:

1. Identify open water, classify as water outside of the ice edge. 2 . Classify the rest of the ice edge zone as grease ice, pancake ice and small ice floes.

3. Identify the "mid" zone and classify as medium ice floes of FY ice and refrozen leads.

4. Tre at the rest of the image as the interior pack ice zone. Classify as large ice floes of MY, FY, rough ice, and refrozen leads. Brief evaluation of the thresholding algorithm It is easy to threshold within each zone, provided that there is no overlap between the backscatter signatures of the ice types therein. Problems arise from the fact that different ice types have overlapping signatures, and open water may overlap with all the ice types for certain meteorological conditions (cfr. Section 1 . 1 ) . Backscatter of sea ice may also vary with geographical area (Table 2 .1). Suggestions for improvements of the ice classification algorithm This will be addressed in Section 2.3. Examples Figure 2.5 shows an ERS-1 SAR image and the classification of ice types in the cold season south east of Svalbard. The method is somewhat modified to classify ice during the melting season. Figure 2.6 (middle) shows a classified SAR image in the melting season in the Vilkitsky Strait. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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2-14


Table 2.1: Backscatter for winter sea ice types measured by various groups in different areas. Ice type

MY FY Rough ice New ice Open water

Greenland/Barents Sea

East Siberian Sea

Beaufort Sea

Beaufort Sea

(8-93)

(GF-94) mean ± std.dev (dB) -10.0 ± 1.46 -16.4 ± 1.10 -14.1 ± 1.24 -18.8 ± 1.41 (not given)

(GF-94) mean ± std.dev (dB) -9.9 ± 0.88 -17.2 ± 1.07 -14.3 ± 1.37 -20.2 ± 1.34 (not given)

(K-92) mean ± std.dev (dB) -8.6 ± 2.2 (not given) -14.0 ± 2.1 < -18.0 < -18.0

min - max (dB) -11.1 - -7. 8 -11.4 - -10.5 -7.8 - -3.4 -23.9 - -14.3 -23.9 - +0.7

8-93 is 8andven et at. , 1993; GF-94 is Gineris and Fetterer, 1994; K-92 is Kwok et at. , 1992. MY is multi-year ice, FY is first-year ice, new ice includes grease ice. All measurements are for winter/early spring conditions.

4. Estimating ice concentration from

ERS

SAR images in the melting season

In the warm season, most ice types have low and near-uniform backscatter different from open water, enabling a simpler thresholding to be used for separating ice and water pixels, and thus to compute the ice concentration. Input data An ERS SAR image with backscatter signatures in C-band ( 5.3Ghz, vertical polarization ) . The image is usually averaged to 100 x 100m, but other pixel sizes may also be used. Output data A new "image" with ice concentration in %. Principles Computation of ice concentration from ERS SAR is based on thresholding. First, each pixel are classified as ice or open water, and then M x N pixels are combined to get a ice concentration in % (Figure 2.7) . We assume that open water either give low backscatter values ( pixel values close to 0) or high backscatter values (pixel values close to 255) for no/moderate and high speed wind, respectively ( Sandven et al. , 1993). If this is not the case, computing ice concentration from SAR data alone will be difficult. Thresholding algorithm The main steps are:

1 . Compute the histogram for the image, and find the two peaks LOW and HIGH 2. Classify each pixel with value Pi ,j as ice if LOW :::; Pi ,j :::; HIGH, or open water if Pi ,j < LOW or Pi ,j

>

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3. The user gives the desired resolution of ice concentration map. Compute the size of the area (M x N) in the original image which must be transformed into one pixel in the ice concentration image. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

ICEWATCH

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Figure 2.7: Calculation of ice concentration from classified ERS SAR data. 4. Combine M x N pixels in the ice/open water image to match the specified pixel resolution, so that the new pixel value equals the percentage of ice in the area covered by the pixel. Store the map as a new image. Brief evaluation of the algorithm The selection of LOW and HIGH determines the ice concentration, but these values will vary with wind condition and may also vary with season and geographic area ( Kwok et al., 1992; Sandven et al. , 1993; Drinkwater et al. , 1994). Some images may not have two peaks in their histogram. A possible solution to this is to use the first peak as LOW and set HIGH = 255, i.e. no water pixels with high backscatter values. Since the ERS-l SAR has only 1 channel, there is no additional data to aid in the analysis, as is the case for multi-channel sensors. In some cases texture may help distinguish open water from ice, but ambiguities may still remain. SAR images also contain speckle noise that may result in single pixels being classified as ice in the open water. Filtering can be used to reduce noise, but should be used with care as single ice pixels surrounded by open water may occur. For instance, icebergs or patches of ice can be found a long way from the ice edge. Examples An example of ice concentration computed from ERS-l SAR data is shown in Figure 2.6. It has been generated by first classifying the SAR image into a open water/ice binary im age, and then the ice concentration within a lxlkm grid has been computed by combining lOxl0 water/ice pixels from the classified image.

2 . 1 .4

Processing methods and ice algorithms for SLR and RM08 data at Planeta

NPO

At NPO Planeta, SLR data are processed and classified in conjunction with simultaneous

RM08 data from the same satellite. The main steps are: 1.

Radiometric correction of SLR data.

2. Geometric processing of SLR data. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2-16


3. Mosaicing of multi-orbit SLR and RM08 data to cover a larger area. 4. Cluster analysis (unsupervised classification) of the two band image (SLR + RM08) . 5. Generation of ice distribution map (supervised classification) . The main steps of the combined SLR-RM08 algorithm for ice type classification can be further detailed as follows: 1. Radiometric processing of SLR data: •

Radiometric correction of SLR data is not used in operational mode as a rou tine procedure. This processing is performed by NPO Planet a (and not by the ground station) as a first step of thematic processing (before SLR image classi fication and before combined processing of SLR and RM08 data) . An example of a raw and a radiometric corrected image is shown in Figure 2.8. The main steps of SLR data radiometric correction are: (a) Values vary along-track due to gain variations in the data transmission. Assuming the " radiometric scale" that follows each image to be constant, the variations along-track are minimized. (b) Values vary from image to image due to gain variations in the data transmis sion. Using values on the radiometric scale as tie points, a linear transfor mation of the image values is performed. The tie points are known constant values, different for each satellite instrument. (c) Cross-track (range) direction variations are normalized. On board the satel lite, a linear gain varying from 1 at near-range to 6.9 at far-range is applied to the backscatter signal. This makes an approximate normalization for range-varying effects and yields a near-constant cross-track grey tone for standard surface (ice?) . The resulting image is suitable for visual analysis. (d) At NPO Planeta, the original signal is reconstructed (reverse of the above procedure) . Then an accurate normalization for the range-varying effects is applied, using as the normalization reference a slant range value of 710 km and an incidence angle value of 25°. The resulting image is suitable for automatic classification. (e) If the absolute backscatter coefficient for the image is needed, it is necessary to use surface areas with known backscatter values seen in the image. These are used as tie points for processing of other image values to backscatter coefficient values. Appendix B contains a paper from NPO Planeta on the calculation of backscat ter coefficients from Okean SLR data. Other literature describing this processing includes Burtsev et al. (1985) , Asmus et al. (1985a) , GID90 ( 1990) , Spiridonov et al. ( 1989) , Milekhin et al. (1989) , Nikitin et al. (1989), Krovotyntsev et al. ( 1991 ) , Asmus et al. (1986) , Asmus et al. (1985b) , Vajen et al. (1992) , Vajen et al. (1993) and Milekhin et al. (1991).



2-1 7

2. Geometric processing of SLR data. •

The ability of transforming imagery to given map projections is required to solve problems such as: (1) locating common points in different scenes of the same area, (2) performing multitemporal analysis by combining images of the same area taken at different times, (3) bringing adjacent images into register so that they can be mosaiced together, (4) combining images of the same area produced by different sensors. A two step correction procedure is often used: Step 1 : a coarse transformation of initial image (Image A) into the map projec tion (Image B) is carried out using the orbital model for the satellite location and geometry of the sensor imaging process. Step 2: a finer transformation of Image B into the map projection (Image C) is carried out using ground control points ( GCP). In both steps the simplest resampling procedure, a nearest-neighbor interpola tion method, is used. step 2 increases geometric accuracy of the step 1 product, but is only possible if a sufficiently high number of GCPs can be found. With a sufficient number of GCPs, it may sometime be possible to eliminate step l. This is usually not the case for our SAR and SLR images. Input data for the step 1 are: - scanning geometry of SLR - orbital data file - cartographic data coordinate system - actual image data - initial image Input data for the step 2 are: - line and pixel numbers of GCPs on Image B and Image C The scanning geometry of SLR is defined by the time delays corresponding to the first and the last pixels, and the satellite altitude above the Earth. These time delays are programmed in a ground satellite station. The scanning is car ried out at the left side off nadir from the right to the left relative to satellite flight direction. The number of pixels in each SLR image line is constant. The orbital data file contains Keplerian elements, satellite position and velocity vectors for measured orbits. The time interval between measured orbits is 7 days. The map projection system is defined by: - the reference ellipsoid - the map projection, defined by given mathematical cartography equations - reference scale in km/pixel (cartographic resolution) - geographic area, delimited by the latitude and longitude range. The Krasovsky V.V. reference ellipsoid is used. The stereographic polar projec tion is chosen with central meridian 90E. The scale is 2 km/pixel at north pole. Geographic area is delimited by 35N-90N and 180W-180E.

-

NANSEN


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Important image data are: (1) orbit number, (2) time of the first image line, (3) date of the first image line, (4) time delays corresponding the first and the last pixels, and (5) image size (lines x pixels ) . The initial image to be corrected is in a digital format with digitization 1 pixel = 8 bits, without header. The transformation is made as follows: Step 1: Rectification of image by use of orbital model: ( la) Generation of Map base for image B: This procedure consists in calculating a lat /long grid and outlines of coast lines in the chosen map projection. ( lb ) Adjusting of orbital data for the actual orbit for image A: The procedure calculates orbital Keplerian parameters, corresponding to the actual or bit by using the orbital data file. Depending on the last orbit number in the orbital file and the actual orbit number, interpolation or extrapolation methods are used. ( lc ) Data referencing: For a given pixel in image A defined by a pair of line and pixel number ( l,p ) the procedure calculates its corresponding geographic coordinates ( Lat,Long) . They are coordinates of a point at which scanning ray intersects the Earth surface. This procedure uses calculated orbital data, time of the first image line, and scanning geometry. Then the corre sponding Map base coordinates in image B (X,Y) are calculated by using the map projection equations. This calculation from ( l,p) to (X,Y) is made at 5x5 tie points equally spaced in line and pixel directions. ( ld) Coordinate transformation: The transformation of image A to image B in volves finding a relationship between image A coordinates ( l,p ) and image B coordinates (X, Y) by means of polynomial coefficients and is based on a modified Gauss-Newton least-squares minimization method for calculat ing these coefficients. A quadratic relationship is found to be adequate for SLR images transformation into stereographic polar projection. Nearest neighbor interpolation is used ( ? ) . (l e ) Drawing coast lines on the transformed image B: Drawing coast lines is carried out by overlaying the transformed image with corresponding geo graphical coast lines image of the Map base.

Step 2: Post-rectification of image by using ground control points: ( 2a) Here we correct the offset between screen coordinates (x,y) of drawn coast lines and coordinates of recognized coast lines in the transformed SLR image B. The selected points should, as far as possible, be evenly distributed over the image. The corrected image C is then generated. Several factors are important for the geometrical accuracy of image C . The main factors are: (1) accuracy of the orientation angles of the satellite, (2) accuracy of the first line image time, (3) GCP identification errors, and (4) insufficient number of GCPs and a poor distribution of these in the image, in the sense that they are not evenly distributed in the image. The mean square error ( m.s.e. ) - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-19

in ground location for the image B product is ±12 pixels or not more than 1820 km. The m.s.e. for the image C product is less than 1 pixel or not more than 1.5 km. Literature on geometric corrections can be found in e.g. Puccinelli (1976) , Ho and Asem ( 1986) and Forrest (1981). 3. Making a mosaic of multi-orbit SLR or multi-orbit RM08 data to cover a larger area. •

Once the images have been geometrically corrected, they can be transformed to a common map projection. The polar stereographic projection is used. By using several orbits close in time a large area of the Northern Sea Route can be covered by radar and passive microwave data.

•

A detailed description of the morphological matching technique used in the mosaicing process is given in Appendix C.

4. Step 4 and 5 is the classification of image pixels into surface types. There are two types of classification methods: unsupervised and supervised. Details can be found in Appendix D. •

In the unsupervised type, no classes are predefined. The algorithm determine the clustering of pixels in n-dimensional space, where n is the number of inde pendent characteristics (typically the number of channels in multispectral data) . This is followed by cluster identification and separation into classes, each corre sponding (hopefully) to some unique physical surface. A well-known type of iterative clustering methods has the name " K-means al gorithm" , and within these methods the " Isodata" algorithm is much used. For range-searching in 2-dimensional space, two-dimensional (2D) trees are the data structures used, utilising a binary search trees in the search algorithm. At NPO Planeta, the two channels (bands) SLR and RM08 are used with the k-d trees search approach (Friedman et al., 1977) to generate the unsupervised clusters (to be clarified further with Planeta) . Unsupervised classification can be done as the first step to select suitable training fields for supervised classifi cation.

•

In the supervised type, the operator initially selects the number of desired classes and also the main characteristics of each class. So-called training-field with known surfaces are often used for this purpose. The algorithm thereafter com pare each pixel to the class characteristics to determine its class and thus its surface type. The algorithm is based on a rather new and general maximum likelihood model using spatial correlation and context between pixels. This clas sification method generally has a higher accuracy than using single pixel-by-pixel classification.

Compared to approaches based on separate classification of SLR and RM08 data, this clas sification method makes it possible to discriminate more classes of ice and increases the accuracy of interpretation. An example of the result of this classification method for an East Siberian Sea images from November 1994 is shown in Figure 2.9. The SLR and RM08 -

NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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RPA Planeta Department of thematic processing

Sat. Okean N7, SlR Orbit: 8622 Date: 96 May - 1 8 -

a)

b)

a) row SlR image b) SlR image after radiometric correction

Image Courtesy NPO Planeta

Figure 2.8: Example of a raw and radiometric corrected SLR image from NPO Planeta. -

NANSEN ENVIRONMENTAL AND

REMOTE

SENSING

CENTER -


2-21

data from which the ice map was generated, are shown in Figure 2.10 and Figure 2 . 1 1 , respectively. Another classification example for a Kara Sea image from May 1996 is shown in Figure 2. 12, and the associated SLR and RM08 data are shown in Figure 2. 13 and Figure 2.14, respectively. It is not known whether NPO Planeta has an algorithm for ice type classification using only SLR data. If such an algorithm exists, it may be a simple thresholding algorithm like the one applied to SAR imagery at NERSC, or a more elaborate algorithm using e.g. texture parameters to distinguish better between different ice classes.

2.1.5

Ice algorithms/methods for interpretation of satellite data at AARI

Algorithms and software are available at AARI for automated ice chart composition from satellite images. This software is used in operative processing of satellite data. The algo rithm includes the following main procedures: 1. geolocation of satellite images using orbital data and coordinates of ground control points, 2. image transformation, 3. image brightness enhancement, 4. interactive composition of ice chart from satellite image, which is displayed on a computer monitor. In the process of ice chart composition zones with different sea ice parameters are delineated on the image and sea ice parameters in these zones are estimated visually. 5. coding of composed ice chart into the " Contour" format. This procedure is carried out automatically in the process of interactive ice chart composition. The software for visualization of the coded ice charts is also available. An algorithm of automated determination of sea ice concentration from visual satellite images are available at AARI. This algorithm consists of interactive delineation of zones with different ice concentration and calculation of ice concentration in delineated zones. Ice concentration is determined by means of an interpolation technique; specifically the ice concentration for every image pixel is determined by linear or non-linear interpolation between tie points for compact sea ice and open water. The algorithm of automated classification of sea ice type on ERS-1 SAR images. The automated classification of sea ice type on the basis of learning areas in the image ( using a maximum likelyhood algorithm) was used for some ERS-1 SAR images, for which we had ground truth icebreaker data. Examples of classification of ERS SAR imagery are shown in Figure 2.15 and Figure 2.16. Determination of sea ice parameters from ERS-1 SAR images Conducted analysis of ERS-1 SAR images of sea ice have shown a possibility to determine - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-22

Satellite OKEAN N7, SLR. RM08

RPA Planeta

Date: 1 994 N ov 7-1 5, Orbits: 391 ,449. 478,508

Department of thematic processi ng 155" E

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178° W

Thematic Map of the Sea Ice Distribution i n the East Arctic , Obtained as Result of Su pervised Classification

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smooth first year ice

168° W

Polar Stereographic Projection

_

8

I

I

188

288

I

388

I

km

mul1y year ice

rough ice

new ice. nilas

rough ice and ridges

shore line


Figure 2.9: Example of SLR-RM08 ice type classification by NPO Planeta.


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2-23

Satellite OKEAN "Nl. 7 . SLR Date: 7 - 15 November. 1994

RPA PLANETA Department of Thematic Processing

iSS0 E

1611 ° E

16S 0 E

Orbits: 1711°E

1811°

1?5 ° E

175° U

391 . 449. 478. 508 1711° U

165° U

1611° U

:::---f-Ll 74° 1'1

72° H

711° H

68° H

1?SO E

Digital

18110

1750 W

17110 W

Radar Map of Chukotka and Alaska Area

16S0 U

1611° U

Polar Stereograph ic Project ion II 1811 21111 3811 111'1 I I I I


Figure 2.10: SLR mosaic from November 1994, used for classifying ice types.



2-24

Satellite OKEAN :rw 7, RM 08 Date: 7 - 15 November, 1994 Orbits: 391 , 449, 478, 508

RPA PLANETA Department of Thematic Processing

1SS0 E

17S 0 E

1600 E

180°

16S 0 E

1700E

175° II

180°

17S 0 E

175° II

170° II

Digital Radiometric Map of Chukotka and Alaska Area

170° II

1650 II

16S0 II

160° II

168° II

Po lar Stereograph ic Project ion 8 188 288 388 1m I I I I


Figure 2.11: RM08 mosaic from November 1994, used for classifying ice types.


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Sat. Okean N7, SLR , R M 08 Orbits: 859Z, 8606, 8622, 8652 Date: 1 996, May, 16 2:1


-

600E

Thematic Map of the Sea Ice Distribution i n the Kara Sea, Obtained as Result of Su pervised Classification _ _

open sea land

_

_

smooth first year ice rough first year ice

_

_

1 200E

Polar Stereographic Projection 0 1 00 200 300 400 km

multy year ice rough multyyear ice

-

shore line


Figure 2.12: SLR-RM08 ice type classification from May 1996 made by NPO Planeta.


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RPA Planeta Department of thematic processing 400E

Sat. Okean N7, SLR Orbits : 8 5 92, 8606, 8622, 8652 Date: 1 996, May, 16 - 21 600E

��==��

SLR images for the Kara Sea is covered by SAR (ERS-1).

Digital mosaics of Marked region

900E

1 200E

Polar Stereographic Projection o I

1 00 200 300 400 km J

,

,

,


Figure

2 . 13:

SLR mosaic from May 1996, used for classifying ice types.


ICEWATCH

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2-27

Sat Okean N7, SlR, RM08 Orbits: 8592, 8606, 8622, 8652 Date: 1 996, MaY, 16 21


-

400E

600E

65°E

700E

75°E

800E

600E

85°E

900E

900E

95°E

1 000E

Polar Stereographic Projection Digital mosaics of RM08 images for the Kara Sea Marked region is covered by SAR (ERS-1 ).

o

'

1 00 200 300 400 km ,

,

,

,


Figure 2. 14: RM08 mosaic from May 1996, used for classifying ice types.


2-28


2 5 1

� _ - lcec:cncentration!fl teflIIlS(Fcfast lce)

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I§I lEI El El IZl � � � o

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-- Grey lce(10·15cmj -- Grey-whi\elce (15-30cm) -- Thlnlirsl-yearlce (30-7Ocm) -- Me
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__ Vasllloe (2_10l«n),code 6 -- �ll(le (O.5·2km), code5

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Figure 2. 15: Ice charts from ERS SAR imagery on November 15, 1995, prepared by AARI.



Sate l l i t e Ice Map Co l lect ion pe r i od : 13 55

2-29

250296-250296 1'1

15

"

ss

1D


Figure 2. 16: Ice charts from ERS SAR imagery on February 25, 1996, prepared by AARI.

- NAN SEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

2-30


different sea ice parameters from these images. ERS-1 SAR sea ice images significantly differ from SLR " Okean" images or airborne SLAR images (wavelength 2-3cm) . A very lim ited set of validational subsatellite observations was used in our analysis. Our experience in interpretation of satellite and airborne radar images and knowledge of sea ice conditions in Pechora and Kara seas helped us in analysis of ERS-1 SAR images. Nevertheless, the proposed description should be considered as preliminary and deals with ERS-1 images of 100-200 m resolution. It is necessary to carry out subsatellite validational observations in different seasons for making it more concrete and detailed. Areas with different sea ice backscatter can be delineated from ERS-1 SAR images. The following parameters can be determined in delineated areas: •

Delineation of open water. Brightness of open water on ERS-1 images strongly varies due to water roughness. Calm water are characterized with dark brightness. Wind roughened water repre sent areas of high brightness, in almost all cases exceeding sea ice brightness. Areas of wind roughened water have a specific texture and can be distinguished from sea ice.

•

Ice type determination When systematic monitoring of studied areas is carried out, sea ice in delineated areas sea ice can be classified to the following ice types: (1) grease ice, (2) nilas , (3) young ice (grey and grey-white) , (4) level first-year ice, (5) ridged first-year ice, and (6) multi-year ice. In delineated areas different ice types can present. In such case a partial concentration of these ice types is determined for each area. Detection of different ice types is carried out with the use of image brightness and texture.

•

Fast ice forms Fast ice boundary can be distinguished from drifting ice, but not in all cases. Ridged ice areas in fast ice can be detected.

•

Ice concentration, ice forms and ice edge position Ice concentration is determined in areas, delineated in ERS-1 SAR images. Giant, vast, big and biggest among medium ice floes can be identified. Ice edge position is determined due to sharp change of brightness.

•

Channels and fractures in compact sea ice Elongated fractures in ice cover are evident, if their width exceeds 20-30 meters. Fractures, covered with new ice, nilas and young ice are evident among first-year and multi-year ice.

•

Ice surface features Areas of level ice, ridged ice areas and ridges among level ice can be identified from ERS-1 SAR images. - NANSEN ENVIRONMENTAL AND REMOTE

SENSING

CENTER -

ICEWATCH - Project Report for Task 2 2.2

2-31

Exchange of ERS and Okean data

During the ICEWATCH project, about 150 ERS SAR scenes have been transferred from NERSC to NPO Planeta. In the same period, about 50 SLR images have been sent from NPO Planeta to NERSC. For some of the SLR data, simultaneous RM08 (passive microwave data) were also supplied. An overview of the received SLR data is given in Ta ble 2.2 and Table 2.3. All data have been transferred via a ftp server at the SRI in Moscow, which had a Unix machine connected to the Internet. NPO Planeta then used diskettes to copy the data to and from their PC systems. For each SAR image, an accompanying header file contained information about acquisition GMT date/time, location, pixel size, etc. For each SLR image there has also been a header file with name of satellite, image dimensions and Moscow date of acquisition. Most of the time, data could be transferred by ftp between NERSC and NPO Planeta without problems. On a few occasions the lines were down or very slow, but as none of the data to and from NPO Planeta were to be used in near-real time this had little influence on the project. The amount of ERS SAR data were reduced at NERSC, by conversion to 8 bit values and averaging to 200 meter pixel size. Files were also compressed using a standard Unix file compression command. In this way, a SAR image could be transferred in 5-10 minutes, provided that the lines were not too busy. A SAR image was usually composed of several BAR scenes; typically 2-4 scenes on the same orbit was concatenated. On request from NPO Planet a, all SAR data were also transformed to a polar stereo map projection and coast lines were overlaid. This increased the size of each file since the ERS satellite have polar orbits with an inclination of approx. 98.5°, resulting in a diagonal position in a north-south oriented map. The file size varied from about 1 to 3MB (depending on the number of SAR scenes concatenated) , but after compression each (compressed) SAR image file typically was about 0.5-1MB. The files had to be uncompressed at SRI's ftp server before being copied to NPO Planeta's P C system. The size of the SLR images also varied, depending on how many lines were received along track. Typically the size of each file (containing RM08 data) was about 1 .4MB, with ap prox. 600 pixels in range direction. Some of the SLR images were also delivered with only 300 pixels in range direction, due to readdown at different ground stations.


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Table 2.2: Okean N7 data received from NPO Planeta.

Date (n) 28.DEC.94 29.DEC.94 29.DEC.94 07.AUG.95 10.AUG.95 14.AUG.95 15.AUG.95 16.AUG.95 18.AUG.95 20.AUG.95 23.AUG.95 24.AUG.95 25.AUG.95 22.DEC.95 30.DEC.95 08.JAN.96 16.JAN.96 26.JAN.96 01.FEB.96 03.FEB.96 10.FEB.96 l1.FEB.96 15.FEB.96 17.FEB.96 l1.MAR.96 15.MAR.96 17.MAR.96 l1.MAY.96 12.MAy'96 16.MAY.96 17.MAY.96 18.MAY.96 20.MAY.96 23.MAY.96 24.MAY.96 29.MAY.96 31. MAY. 96

Orbit 1143 1157 1157 4413 4456 4516 4532 4554 4584 4612 4658 4672 4686 6438 6555 6680 6804 6959 7038 7068 7170 7185 7243 7273 7610 7670 7698 8518 8534 8592 8606 8622 8652 8696 8710 8782 8811

Sat. dir. Ase. Ase. Ase. Dese. Dese. Dese. Dese. Ase. Ase. Ase. Ase. Ase. Ase. Dese. Dese. Ase. Dese. Ase. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Dese.

n Ion

(OE) 51.1 67.2 67.2 351.0 14.6 340.4 307.4 126.9 109.8 141.9 91.7 107.8 123.8 280.4 286.0 95.0 288.6 80.6 299 282 296 288 303 286 287 252 285 300 267 282 298 265 248 247 263 294 302

n time Area ( GMT) 03:01 Nov. Zemlya 01:50 Ob bay 01:50 Ob bay 02:50 N.Sib.Isl. 00:53 Wrangel lsI. 02:38 N.Sib.Isl. 04:43 Sev.Zemlya 16:33 Lena delta 17:26 Laptev Sea 15:03 N.Sib.Isl. 18:00 Sev.Zemlya 16:49 Laptev Sea 15:37 Lena delta 14:04 Dikson 12:41 Kara Sea ( 90E) 00:21 Sev.Zemlya 10:22 Nordensk.Areh. 22:54 N .Areh/Dikson 07:37 Sev.Zemlya 08:29 Kara Sea ( 80E) 06:40 Sev.Zemlya 07:06 Kara Sea ( 90E) 05:36 Sev.Zemlya 06:28 Kara Sea ( 90E) 03:31 Kara Sea (90E) 05:17 Nov.Zemlya 02:54 Kara Sea ( 85E) 18:51 Sev.Zemlya 20:55 Nov.Zemlya 19:25 Dikson 18:13 Sev.Zemlya 20:17 Nov. Zemlya 21:09 Nov. Zemlya 20:51 Nov. Zemlya 19:39 Nov. Zemlya 16:57 Sev.Zemlya 16:12 Sev.Zemlya

No. of lines (y) 1435 1350 1149 1020 1020 1020 1020 1020 1020 676 1020 1020 1020 1468 1484 1460 1400 1460 1460 1460 1460 1460 1460 1450 1415 1460 1260 1340 1460 1460 1460 1460 1460 1460 1460 1460 1460

No. of eoI. (x) 600 600 749 1385 1385 1385 1385 1385 1385 1385 1385 1385 1385 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700

SLR (yin) y y y y y y y

y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

RM08 ( Yin) n n n y y y

y y y y y y y y y y y y y y

y y y y y y y y y y y y y y

y

y y

( 1 ) 4658 had wrong file name ( 4656) and has been renamed. ( 2 ) Image on polar stereographie grid.


(2 )

(1)

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Table 2.3: Okean N8 data received from NPO Planeta.

Date (0) 24.DEC.95 27.DEC.9 5 31.DEC.95 05.JAN.96 0 7.JAN. 96 10.JAN.96 12.JAN.96 18.JAN.96 22.JAN.96 24.JAN.96 26. JAN.96 01.FEB.96 02.FEB.96 13.FEB.96 15.FEB.96 19.FEB.96

Orbit 01705 01735 01801 01881 01911 01954 01984 02071 02131 02160 02182 02277 02291 02452 02482 02540

Sat. dir. Dese. Dese. Ase. Dese. Dese. Dese. Dese. Dese. Dese. Dese. Ase. Dese. Dese. Dese. Dese. Dese.

o Ion

(OE) 270.7 253.4 71.3 265.2 247.9 271.1 253.7 275.6 240.9 248.2 67.5 252 268 271 254 269

o time (GMT) 23:51 00:45 12:19 22:42 23:36 21:41 22:34 20:22 22:10 21:25 09:17 20:07 18:56 17:20 18:14 16:45

Area North Nov.Zemlya Nov. Zemlya Ob bay Yamal pen. W.of Nov.Zemlya Ob bay Nov.Zemlya W.of Nov.Zemlya Barents Sea Nov. Zemlya Ob bay Nov.Zemlya Nov. Zemlya Nov. Zemlya Nov.Zemlya Nov. Zemlya

No. of lines (y) 1 0 76 1458 1450 1140 1460 1030 875 1460 1120 1460 1440 1450 1460 1460 1460 1460

No. of col. (x) 700 700 315 315 315 315 315 315 315 315 315 315 315 315 315 315

SLR (yin) y y y y y

y y y y y y y y

y y y

( 1 ) RM image contains only noise.


RM08 (yin) y y n n n n n n

n n n

n

n

n

n

n

(1)


2-34 2.3

Improving algorithms for S AR ice type classification

The thresholding algorithm for ice type classification that was described in Section 2.1.3 is a very simple algorithm that will produce good results only in areas where there are few classes with little overlap in backscatter signatures. Thus, an improved algorithm is needed to increase the classification accuracy, and that can be used in areas with many different ice classes, such as in the ice edge zone. We propose such an algorithm using the following input data: •

the SAR image to be analyzed,

•

the ice history in the area (derived from a series of previous SAR images) ,

•

knowledge of ice experts (including results from validation experiments) , and

•

any other auxiliary data, such as wind fields and bathymetry.

This algorithm can use the simple thresholding algorithm as a first step, to make a first classification. The intermediate result will then be analyzed with respect to the ice history and other available data, and corrected where needed to get a better correspondence with these additional data and the knowledge from ice experts. By taking more information into consideration during the classification process, the final ice type classification result will be more accurate than that from a pure thresholding algorithm. The case studies in Section 1.2 have been analyzed by means of the proposed algorithm, using previous SAR data, auxiliary data and input from an ice expert at NIERSC, Dr. Vladimir Melentyev. For instance, when analyzing a SAR image from the Dikson area acquired on November 30, 1995 (Case 7) , the thickness of fast ice have been estimated based on the analysis of earlier images (November 15, 1995, Case 4) and knowledge about freezing processes. Specifically, an area of fast ice near the mainland at about 81°E, was classified as fast ice (30-70 cm thick), while this area in the previous image taken 15 days before was classified as grey-white ice (15-30 cm thick) . More work is needed to further develop this algorithm into a tool that can be used in operational ice monitoring. In particular, the knowledge of the ice experts needs to be for malized for efficient use in ice type classification, and routines for obtaining other valuable data, e.g. meteorological data, must be established. The potential benefits of incorporating texture parameters is also an issue that need further investigation. Open water, for in stance, may overlap with many different ice classes, but often be discriminated by its more homogeneous texture. In such cases, inclusion of texture parameters in the classification algorithm will be useful. The knowledge of ice experts may include: • what ice types can be found in a given area/region, and what are the " typical thick ness" and " typical cover" during the winter (and in some cases the summer) season. •

knowledge of freezing and melting processes, e.g. how long does it take to form a particular type of ice with a specific thickness, with given meteorological conditions. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-35

•

results from validation experiments, including measurements of ice thickness at spe cific locations, and measurements of backscatter from different ice types under differ ent meteorological conditions. Records over several years are important to capture annual variations.

•

identification of ice types which are especially difficult to discriminate, and possibly link these difficulties to weather conditions.

Other texture parameters than homogeneity may be useful for ice type classification, for instance those of the Grey-Level Co-Occurrence Matrix (GLCM), or the Neighboring Grey Level Dependence Matrix (NGLDM) which are both described in Barber et ai. ( 1993) . These may improve the initial estimate of ice classes, but careful analysis where expert knowledge and auxiliary data are taken into account, is still needed.

2.4

Comparison of ice classification using SAR and Okean data

In general, it cannot be expected to see exactly the same features in SAR and SLR imagery due to the large differences in sensor resolution. The SAR imagery will reveal more details in a small area, while SLR images will give the overall picture of the ice situation. However, comparing the results of ice classification from these two sensors can still be useful to find ice types/conditions where the results coincide and cases where they differ. This will be useful for improving the classification algorithms for both types of data. NPO Planeta has prepared a classification of SLR-RM08 data from May 1996 in a large area in the Northern Sea Route (Figure 2.12). In the corresponding data sets (Figure 2.13 and Figure 2.14) the location of two ERS SAR stripes are marked with white rectangles. These stripes are shown in Figure 2. 17 and Figure 2. 18, and were taken on May 18 and 19, 1996, respectively about 20 hours after the corresponding SLR image. Ice classification of these two SAR stripes have not been made, but from looking at the images a preliminary comparison can be made. In the Vilkitsky Strait the SLR-RM08 classification states that there are mainly smooth first-year ice and some rough first-year ice. The SAR image (Figure 2.17) shows some dark, linear features (leads?) which does not seem to be present in the SLR-RM08 classification. This may be because they are too small to be seen in SLR-RM08 data (�1.5km resolution) or because they have opened before/closed after the Okean satellite passed over the area. Close to the coast there is an area of fast ice and rough ice in the SAR image, and the SLR-RM08 c lassification shows an area of rough first-year ice close to the mainland. All together, the different classification results seem to correspond well, taking into account the different sensor characteristics and the time interval between the images. Note that classification also may be difficult due to changing temperatures near 0° Celsius. Near the Yamal coast, the SAR image from May 19, 1996 (Figure 2.18) shows a band of fast ice, followed by a small area of rough ice (bright area), some areas of new ice or open smooth water (dark areas) , a belt of ice floes (first-year ice) and the pack ice furthest away - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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from the coast. In the SLR-RM08 classification (Figure 2.12) , a thin band close to the coast is classified as rough multi-year ice, followed by an area of open water, and further away from the coast there is mainly rough first-year ice and some small areas of smooth first-year ice and of multi-year ice. Thus the preliminary classification of the SAR image corresponds well to the SLR-RM08 classification near the Yamal coast. (The SLR-RM08 classification algorithms does not seem to distinguish between fast ice and rough multi-year ice.) When comparing the classification done by AARI (Figure 2. 15) and the interpretation made in Case 4 (Figure 1. 7), is must be kept in mind that AARI had access to frequent ice observations prior to the image acquisition, while the analysis for Case 4 was made mainly on basis of the ERS SAR image itself and only limited auxiliary information about the current ice situation in the area. Also the analysis in Case 4 identified only some of the ice phenomena in the SAR scene, while AARI divided the area into regions and estimated the percentage of each of the three most important ice types. We thus consider AARI's classification to be "ground-truth" , and look for correspondence between some of the ice features identified in Case 4 and the regions given in AARI's classification. Between Sibirakova Island and the Dikson mainland, the AARI classification says that there is 40% of thin first-year ice, 40% of grey-white ice and 20% of grey ice. In Case 4, this area has been classified as grey-white ice. Both classification states that the ice is land-fast. AARI's refinement of the classification is assumed to be due to their detailed knowledge of the ice situation in this area, which is very important for ship traffic. As the SAR signatures of grey-white ice and thin first-year ice may overlap, these ice types may be difficult to distinguish in a SAR image. South of Sibirakova Island, AARI has classified a region of 100% grey ice, while in Case 4 this area is indicated to be rough ice. As grey ice may well raft under pressure, the two classifications are not contradictory. But Case 4 does not state the ice development, only that the ice surface is rough. In Case 4 the area is classified as fast ice, but not in AARI's classification. All in all, it is difficult to compare the the two classifications, as AARI has done a quan titative analysis of ice type percentages, while NERSC /NIERSC has done an analysis for the main ice type. The only thing that be compared is if the main ice type indicated at a point P in Case 4 is present in the region enclosing P as classified by AARI.


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Figure 2. 17: ERS-1 SAR image from the Vilkitsky Strait area on May 18, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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Figure 2.18: ERS-1 SAR image from the Yamal coast on May 19, 1995. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2.5

2-39

Summary of Task 2

NERSC and NPO Planeta have established procedures for exchange of SAR and SLR data, and for ordering of ERS SAR data from ESA (via TSS and NERSC) . Up to now, about 150 ERS SAR scenes and 50 SLR images have been exchanged, and NPO Planeta and NERSC have successfully converted the data to fit their respective analysis and presen tation systems. The DESC software to enable NPO Planeta to order SAR data, has also been transferred from ESA/NERSC. Project communication has been done by means of fax and email, and report contributions from NPO Planeta have also been sent digitally ( by ftp or email) . The main exchange of data and technology in this task has been: •

ERS SAR data provided to NPO Planeta and AARI.

•

Okean SLR data provided to NESC from NPO Planeta.

•

Transfer of the DESC software and update files from ESA (via NERSC) to NPO Planet a, which enabled NPO Planeta to order ERS SAR data.

•

Exchange of file formats to enable NERSC and NPO Planeta to process SLR and SAR data, respectively.

•

Exchange of information about sensor/satellite parameters for the ERS-l,2 SAR and the Okean N7,8 SLR, to enable transformation of data to a common map projection ( grid) .

•

Exchange of algorithms for derivation of ice parameters from SAR and SLR data.



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References Asmus, V. V., Elansky, L. V., Efimov, V. B., Nikitin, P. A., Pichugin, A. P., Popov, A. E., Popov, V. L , and Spiridonov, Y . G. (1986) , Digital processing of KOSMOS-1500 radar images, Proceedings of GOSNICIPR, 1986, N25, p.37-57. Asmus, V. V., Nikitin, P. A., Popov, V. L, and Spiridonov, Y. G. (1985a), KOSMOS-1500 satellite radar images digital processing, Studying the Earth from the Space,1985, N3,p. 107-114. Asmus, V. V., Popov, V. L , and Spiridonov, Y. G. (1985b) , Cluster analysis of multidimensional microwave radiometer data, Remote Sensing of the Earth from METEOR-PRIRODA satellite. GIDROMETEOIZDAT, Leningrad, 1985, p.127-134. Barber, D. G . , Shokr, M. E., Fernandes, R. A., Soulis, E. D., Flett, D. G., and LeDrew, E. F. (1993) , A Comparison of Second-Order Classifiers for SAR Sea Ice Discrimination, Photogram metric Engineering fj Remote Sensing, 59(9) :1397-1408. Burtsev, A. L, Krovotyntsev, V. A., Nazirov, M., Nikitin, P. A., and Spiridonov, Y. G. (1985), Arctic and Antarctic radar maps on the base of KOSMOS-1500 data and the previous results of their analysis, Studying the Earth from the Space, 1985, N3, p.54-63. Drinkwater, M. R., Early, D. S . , and Long, D. G. (1994) , ERS-1 Investigations of Southern Ocean Sea Ice Geophysics Using Combined Scatterometer and SAR Images, in Proc. of IGARSS'94, pp. 165-167. ESA (1996) , DESC User Guide, Available from ESA/ESRIN. Fily, M. and Rothrock, D . A. (1987) , Sea ice tracking by nested correlations, IEEE Transactions on Geoscience and Remote Science, GE-25(5) :570-580. Flesche, H. (1988) , Estimation of ice motion from satellite images, Master's thesis, Division of Electronics and Computer Technique, Norwegian Institute of Technology (NIT), (In Norwe gian) . Forrest, R. (1981) , Simulation of orbital image-sensor geometry, Photogrammetric Engineering fj Remote Sensing, 47(?) : 1 187-? Friedman, J. H., Bentley, J. L., and Finkel, R. A. (1977) , An algorithm for finding best matches in logarithmic expected time, Ass. Comput. Mach. Trans. Math. Software, 3:209-226. GID90 (1990) , Earth surface radar investigations from the Space, GIDROMETEOIZDAT, Leningrad, 1990. Gineris, D. J. and Fetterer, F. M. (1994) , An Examination of the Radar Backscatter of Sea Ice in the East Siberian and Chukchi Seas, in Proc. of IGARSS'94, pp. 499-502. Hamre, T. (1995), Estimation of ice motion, classification of ice types and detection of ice features from SAR imagery, in Proceedings NOBIM'95, NOBIM (Norwegian organisation for image processing and pattern recognition) . Ho, D. and Asem, A. (1986), NOAA AVHRR image referencing, International Journal of Remote Sensing, 7(7) :895-904. Kloster, K., Flesche, H., and Johannessen, O. M. (1992), Ice motion from airborne SAR and satellite imagery, Adv. Space Res. , 12(7) :49-53. Korsnes, R. ( 1993), Estimation of rigid areas of polar pack ice based on time series of ERS-1 SAR images, in Proc. 8th Scandinavian Scandinavian Conference on Image Analysis. Krovotyntsev, V. A., Milekhin, O. E., Popov, V. L, and Spiridonov, Y. G. (1991), Space-borne radar observation of ice shore-line dynamic and iceberg drift in Antarctic, Studying the Earth from the Space, 1991, N4, p.87-96. Kwok, R. , Rignot, E., and Holt, B. (1992), Identification of Sea Ice Types in Spaceborne Synthetic Aperture Radar, Journal of Geophysical Research, 97(C2) :2391-2402. Milekhin, O. E., Obukhov, Y. V., Popov, V. L, Spiridonov, Y. G., and Terentjev, E. (1991), - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


2-41

Antarctic Radar Maps Synthesis with the help of a personal computer, Journal of Advanced Science, 3(3):131-134. Milekhin, O. E., Popov, V. 1., Spiridonov, Y. G., and Volpyan, G. V. (1989) , Surface radar backscattering coefficient definition under the KOSMOS-1500 SLR data, Proceedings of GOS NICIPR, 1989, N33, p. 104-113. Moctezuma Flores, M. , Maitre, H., and Parmiggiani, F. (1994) , Sea-Ice Velocity Fields Estima tion on Ross Sea AVHRR Images, in Proc. of IGARSS '94, pp. 1300- 1302. Nikitin, P. A., Gaft, V. G . , Milekhin, O. E., Popov, V. 1., Sizenova, E. A., and Shapovalov, S. V. (1989) , Experience of satellite radar images utilization for safety sea-ice navigation, Proceedings of GOSNICIPR, 1989, N33, p. 135-141. Onstott, R. G. (1992) , SAR and Scatterometer Signatures of Sea Ice, in Microwave Remote Sensing of Saa Ice, edited by Carsey, F . , pp. 73-104, American Geophysical Union, AGU Geophysical Monograph 68. Puccinelli, E. F. (1976) , Ground location of satellite scanner data, Photogrammetric Engineering (3 Remote Sensing, 42(4) :537-543. Rau, y'-C., Comiso, J. C . , and Lure, F. Y. M. (1994) , Application of Neural Networks for Iden tification of Sea Ice Coverage and Movements from Satellite Imagery, in Proc. of IGARSS '94, pp. 1407-1409. Sandven, S., Johannessen, O. M., and Kloster, K. (1993) , Real Time Use of Satellite Data in Ice Monitoring for Arctic Operations, in Proc. POA C '93, 12th International Conference on Port and Ocean Engineering under Arctic Conditions.

Sandven, S., Kloster, K., and Johannessen, O. M. (1991), SAR Ice Algorithms for Ice Edge, Ice Concentration and Ice Kinematics, Technical report 38, Nansen Environmental and Remote Sensing Center. Spiridonov, Y. G., Milekhin, O. E., Popov, V. 1., and Sizenova, E. (1989) , Antarctic radar map compiling, Proceedings of GOSNICIPR, 1989, N33, p.126-134. TSS (1991) , ERS-1 Product Specification, Available from TromSj2Se Satellite Station. Vajen, H.-H., Spiridonov, Y. G., Reimer, R. , and Milekhin, O. E. ( 1992) , Empfang, Verar beitung und Nutzung der Fernerkundungsdaten des operativen russischen Systems OKEAN, Proceedings of the 9.DFD Nutzerseminars, DLR, OberpfafIenhofen 1992. Vajen, H.-H., ViehofI, T., and Spiridonov, Y. G. (1993) , Usage of OKEAN SLR Data for Sea Ice Studies, Proceedings of the Euro-Russian Space Conference, Munich, 28-30.10. 1993. Vesecky, J. F., Samadani, R., Smith, M. P., Daica, J. M., and Bracewell, R. N. (1988) , Obser vations of Sea-Ice Dynamics Using Synthetic Aperture Radar Images: Automated Analysis, IEEE Transactions on Geoscience and Remote Science, GE-26(1):38-48.


ICEWATCH

Proj ect Report for Task 3

Techni ques and technologies for combined ERS SAR and Okean SLR analysis

ICEWATCH

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i


ii

List of Tables

ii

3

Techniques and technologies for combined ERS SAR and Okean SLR 3-1 analysis 3-1 3.1 Methods for converting data into the same data format 3-1 3. 1 . 1 Inserting SAR into MIS . . . . . . . . 3-2 3. 1 . 2 Inserting SLR into MIS . . . . . . . . . . . . 3-2 3.2 Techniques for SAR-SLR pixel-by-pixel correlation . 3-3 3.3 Examples of combined SAR-SLR products . 3-3 3.3.1 SAR-SLR pixel-by-pixel comparison . . 3-7 3.3.2 Overlay of SAR in SLR imagery . . . . . 3-8 3.3.3 Combination of SAR and SLR at AARI 3-15 3.4 Exchange of tools and algorithms for SAR-SLR combinations . 3-15 3.5 Summary of Task 3 . . . . . . . . . . . . . . . . . . . . . . . . .

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.

References


3-16

ii


List of Figures 3.1

Scatter plot of the overlapping area of SAR and SLR data o n August 10, 1995. ERS SAR and Okean SLR image from the Wrangel Island area on August 10, 1995, at 23:34GMT and 00:53GMT (n time) , respectively. ERS Data © ESA/TSS 1995. . . . . . . . . . ERS SAR and Okean SLR image from the Bayadaskhara Bay area on De cember 27 and 29, 1994, at 07:01GMT and 01 :50GMT (n time). The SAR image is located in the lower, left corner of the SLR image. The location is marked with a rectangle in the SLR image. ERS Data © ESA/TSS 1994. . Scatter plot of the overlapping area of SAR and SLR data on December 27 and 29, 1994. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overlay of SAR and SLR data on January 18, 1996, at 16: 14GMT and 20:22GMT (n time) , respectively. Upper left: coverage map. Upper right: SAR-SLR overlay. Lower left/right: only SAR/SLR in common grid. ERS Data © ESA/TSS 1996. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overlay of SAR and SLR data on January 26, 1996, at 06:36GMT and 09: 1 7GMT (n time) , respectively. Upper left: coverage map. Upper right: SAR-SLR overlay. Lower left/right: only SAR/SLR in common grid. ERS Data © ESA/TSS 1996. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ice chart of Yamal area, composed from SAR image on January 28, 1996. Prepared by AARI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fragment of composite ice chart, prepared with the use of Okean SLR imagery (25-31 January 1996) . ERS SAR coverage is shown in frame. Prepared by AARI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ice chart of Novaya Zemlya area, composed from SAR image on February 25, 1996. Prepared by AARI. . . . . . . . . . . . . . . . . . . . . . . . . . Fragment of composite ice chart, prepared with the use of Okean SLR imagery (23-28 February 1996) . ERS SAR coverage is shown in frame. Prepared by AARI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2 3.3

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3-10 3-1 1 3-12 3-13 3-14


3

3- 1

Techniques and technologies for combined ERS SAR and Okean SLR analysis

The objectives of Task 3: Develop techniques and technologies for combined anal ysis of ERS SAR and Okean SLR data, are to: - develop methods for inserting SAR and SLR data into the same data format, - propose techniques to correlate SAR and SLR data pixel by pixel, - develop combined ice products from SAR and SLR data, and - exchange tools and algorithms for combination of SAR and SLR between NERSC and NPO Planeta. 3.1

Methods for converting data into the same data format

It is required to insert SAR and SLR into the Marine Information System (MIS) at NERSC. Also, inserting SAR and SLR into the PC-based GIS-system at NPO Planet a is required.

3.1.1

Inserting SAR into MIS

The MIS at NERSC work with image files in the gop-format (described in Appendix A). In this file format the first 20 bytes defines the number of header bytes, the dimension and the code-type of the image. Standard code-type is 8 bits per pixel and the layout is line-by-line. The remaining header bytes contain auxiliary data for the image, called " gopinfo" , such as acquisition date and time, geometry, location, radiometric factor, etc. Files in this format have an " .imf" suffix. Data description. Received SAR data from ESA-PAFs and from TSS are swath-oriented, using range-azimuth as line-columns. These are converted to gop-files. Header information included with the raw image data is used to fill in the gopinfo data fields. For some of the important fields, data has to be computed and/or manually inserted (for data from ESA-PAFs) .

For work with ice analysis, a SAR image has the following conversions and characteristics: .. Gop-file pixels are first made square by resampling with a selected size, e.g. 12.5m (PRI) and 100m (LRI) . The most common pixel size for ice analysis is 200m, made by averaging blocks of N x N square pixels with a finer resolution . .. Averaging to 100m pixels reduces the speckle noise (Le. increases the radiometric resolution) to approx. 8dB below the backscatter mean value. .. A fixed multiplication factor is used for converting raw image pixel values to gop-file pixel values. For SAR, the knowledge of a stable calibration factor for all raw images enables conversion from gop-file values to absolute sigma-zero values. For TSS LRI - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

3-2


data, the relation between pixel value p and sigma-zero value 0"0 is given by: 0"0 2 0 x lOglO(P) 47.4 =

-

•

In range direction, normalizing to mid-swath angle for the three geometric range variations is done using the method described in ( Laur, 1992 ) . For LRI images from TSS, the calibration accuracy is ±0.5dB (Vachon, 1995 ) .

•

Ascending passes are turned 180 degrees to get " north up" .

3.1.2

Inserting

SLR

into MIS

Data description Image files including SLR data are received from NPO Planeta in swath oriented range azimuth format, 8 bits per pixel, without header bytes. A small additional text file gives the date and image dimensions. Several file types have been received, characterized by the number of pixels in range (x) : •

Type A. Containing both SLR and RM08 data including 2 scale-bars. Range pixel size is 0.75km, and x=600.

•

Type B. Both SLR and RM08 data including scale-bars. Range pixel size is 1 .5km, and x=300.

•

Type C. Only SLR data including scale-bar with range-pixels=1.5km, and x=300.

Original SLR pixels are rectangular with an aspect ratio of 1.8. For work with ice analysis, a GOP format SLR image is made with the following conversions and characteristics: •

Resampling to square pixels of size 0.9km.

•

Ascending passes are turned 180 degrees to get " north up" .

•

Using recognized shorelines in display, gridding parameters are determined.

As no quantitative data on SLR radiometry have been available, the pixel values are not corrected or transformed. It has been observed that pixel values vary from image to image in a way that suggest varying conversion factors to sigma-zero. Cfr. Section 2 . 1 .4. 3 .2

Techniques for S AR-SLR pixel-by-pixel correlation

ERS SAR and Okean SLR data that overlap geographically and are close in time have been transformed to a common map projection for comparison. Some examples of this will be included in Section 3.3. However, there are problems, especially regarding the calibration of SLR data, that have to be solved. The ERS SAR sensor and the Okean SLR sensor have very different resolutions, basically �30m vs. �1.5km. The images we have received in this project have a pixel size of 100m for SAR LRI and �800m and 1.5km for SLR. To be able to do a pixel-by-pixel comparison of these data sets, they must be resampled to a common grid. At NERSC, a common grid - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


3-3

of 500m pixel size have been chosen for this task, and existing software was used to carry out the transformations ( Kloster, 1990) . Analysis of scatter diagrams made by resampled SAR and SLR data pixel by pixel has been difficult due to the lack of proper calibration parameters for the SLR sensor. The gain (and possibly the offset ) is varied by the Russian ground stations from orbit to orbit, and the gain values have not been made available to NERSC. Nor is it known how the SLR data are calibrated, or details of the gain normalization in range that has been performed. These issues makes it impossible to compute accurate backscatter (sigma-zero) values from SLR data, and the correlation between SAR and SLR will vary from one image pair to another. Thus, the analysis performed at NERSC will focus on the trends and backscatter variations in the two data sets, rather than on absolute sigma-zero values. It

is not known whether NPO Planeta or AARI have performed pixel-by-pixel comparison of SAR and SLR data.

3.3

3.3.1

Examples o f combined SAR-SLR products

SAR-SLR pixel-by-pixel comparison

To combine SAR and SLR data, images from both sensors are resampled to a common projection. The grid chosen at NERSC has a pixel size of 500m. The resampling has used the nearest-neighbor method. Pixels from the two images are displayed in a scatter plot diagram. A scatter plot for the first pixel-by-pixel comparison is shown in Figure 3. 1 . The images from which the comparison was made, are from the Wrangel Island area on August 10, 1995 (Figure 3.2) . In this case pixel values from the two sensors show little correlation. The water is generally bright in the SAR image due to moderate wind, while in the SLR image the water is generally dark. This may be due to: 1. the difference in time between the two images (22 hours ) , which makes it likely that the wind speed is not the same in the two images, and 2. the difference in incidence angle between the two images ( about 23° for ERS SAR

and about 40° for Okean SLR) .

Ocean backscatter is very sensitive to both wind velocity and to incidence angle, and vari ations can explain the poor correlation found. Figure 3.3 shows an ERS-1 SAR image and an Okean SLR image from the Bayadaskhara Bay area. The SAR image was taken on December 27 and the SLR image on December 29, 1994. In the SAR image, there is a clear variation in backscatter signature within the bay. This may reflect that ice in different areas has different thickness. A number of leads can also be seen as dark linear features in the SAR image. In the SLR image, taken two days later, there are also differences in backscatter signatures in the bay, but these variations - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


3-4

200 1 80

Long Strait, 1 0 August 1 995

��--��-��

1 60

1 40 1 20

1 00 80

60

40

20

o

��--��-

o

20 40 60 80 1 00 1 20 1 40 1 60 1 80 200 SLR data value

Figure 3.1: Scatter plot of the overlapping area of SAR and SLR data on August 10, 1995.

does not coincide spatially with those of the SAR image. The reason for this may be both the fact that the SLR image is not radiometrically corrected, and that within the time interval of 2 days the surface layer of the ice may have changed due to changes in the meteorological conditions. Figure 3.4 shows a scatter plot of SAR vs. SLR digital values for December 27 and 29, 1994. Again the correlation is poor, which may be due to the lack of calibration of the SLR image and because of the long time interval which makes it likely that the meteorological conditions have changed. In addition, the freezing processes may contribute to the changes in backscatter values in the two images. Brief discussion of pixel-by-pixel correlation A better correlation can be expected if accurate correction procedures are applied to the SLR data ( as described in Section 2.1.4) , but until such procedures are implemented at NERSe, comparing SLR and SAR with respect to trends and backscatter variations may be more appropriate.

Another factor which affects the correlation is the time interval between the two images. In some of the examples presented here, the interval has been 1-2 days, which may be too long to get a good correlation. Ice movement is also a factor that needs to be considered, as in some areas the ice can move considerably in just a few hours. Near the ice edge, the ice situation can change quickly, and in this zone a pixel-by-pixel comparison is unlikely to give a good correlation unless the SAR and SLR images are very close in time (not more than a few hours ) . - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


3-5

O K EAN

ER S - l

SAR

S LR

l O - AUG - 1 9 9 5

l O - AUG - 1 9 9 5

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179'£

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Figure 3.2: ERS SAR and Okean SLR image from the Wrangel Island area on August 10, 1995, at 23:34GMT and 00:53GMT (0 time) , respectively. ERS Data © ESAjTSS 1995.

- N ANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

3-6


O K E AN

S LR

2 9 - D EC - 1 9 9 4

75 E

60 E

76 N

76 N

65 E

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6 7 E69. ':"'

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2 7 - DEC - 1 9 9 4 ('E

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Signal value

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Figure 3.3: ERS SAR and Okean SLR image from the Bayadaskhara Bay area on December 27 and 29, 1994, at 07:01GMT and 01:50GMT (0 time) . The SAR image is located in the lower, left corner of the SLR image. The location is marked with a rectangle in the SLR image. ERS Data © ESA/TSS 1994.



Kara Sea, 27-29 December 1 994

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- NANSEN ENVIRONMENTAL AND

REMOTE

SENSING CENTER -


5-14

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5-16


ble 5.1). The location of these stripes are shown in Figure 5.6. All images after January 15 (19 stripes ) were put on the NERSC computer as JPEG-files ( Pennebaker and Mitchell, 1993) ready for transmission by call from the ships ( Table 5.2) . Almost all available SAR images along the coast between approx. 70E and Yenisei were processed in the period the ships were positioned in this area. Figure 5.7 shows some of the SAR stripes that were processed during the demonstration expedition. ( A few of the other stripes are shown in the case studies in Task 1 . ) Hardcopies o f images were faxed t o the icebreakers, and some were also sent t o Dikson MOH. The possibility of fax connection to the ships varied, mainly due to the ship's vary ing antenna position, while fax to Dikson's fixed antenna was stable. Fax to C I S Tkachyov was never possible, while fax to NIB Yamal worked well. Fax to NIB Vaygach was diffi cult with a weak tone and error message, but most faxes got through. The transmitted hardcopies were dithered images (to avoid image degradation in the fax machine ) with grid and time annotations. Most faxes also included a simple drawing of the main ice features. An example of a fax sent to NIB Vaygach during the demonstration expedition is shown in Figure 5.5. For convenience, the fax contains a dithered version of 2 scenes, and their interpretation on the right hand side. If the ship's location is known more accurately, it may be sufficient to send only one, which can then transferred with a better resolution. Both NIB Vaygach and NIB Taimyr had the necessary computer and modem equipment to call up the NERSC computer and to receive the image stripe files over their INMARSAT telephone line. On board NIB Vaygach, this task was taken over by Mr. A. Schekotikhin after the departure of the ice experts from NIERSC, and the experiment continued for a couple of months. During the demonstration on board the ships, about 80 connect attempts were made and 16 of the files were reported to have been successfully received and analyzed. This is a satisfactory result, considering the difficulties of the INMARSAT connection on board ships at sea in high latitudes. The computers used were Mac-Classic and PC-AT, and the modem types were MuTZDXI, Bouch and Unique. Real baud rate varied between 2400 and 9600, and connect time for transfer of a file varied from about 2 to 13 minutes.

5.1.3

Successful real-time demonstration January 26-27

Most images were of interest for the ships even if it was difficult to get the optimal area-time correspondence between the SAR and the ship. The SAR-ship route diagram in Figure 5.8 shows this correspondence. The optimal configuration is to acquire a SAR image shortly before the ship enter the area covered by the radar sensor. On one occasion there was an excellent correspondence between the SAR coverage and the sailing schedule. This was the SAR image of January 26, 06:35 GMT of the pack ice area west of Dikson shown in Figure 5.9. This image was received on file on board NIB - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


5- 17

-r�� : ___I� �� /�B � � �__ I _________________________

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Figure 5.5: Example of fax sent to NIB Vaygach during the winter 1996 expedition.


5-18


Table 5 . 1 : Processed SAR stripes at NERSe from January 8 to February 17, 1996. Date

Time

( 1996) OB.Jan. 09.Jan. 11.Jan. 15.Jan. 21.Jan. 25.Jan. 26.Jan. 2B.Jan. 2B.Jan. 01.Feb. 02.Feb. 02.Feb. 03.Feb. 05.Feb. OB.Feb. 09. Feb. 11.Feb. 13.Feb. 14.Feb. 15.Feb. 16.Feb. 17.Feb. 17.Feb.

(GMT) 17:36 07:1 1 07:4B 13:56 07:34 07:07 06:35 07:1 2 07:13 06:47 06:16 06:47 07:25 06:22 06:27 15:50 06:33 OB:53 06:35 06:35 IB;49 06:44 OB:27

Area

Fax to

Image file generated

Kara Gate Belyy Island, Kharasavey Pechora Sea Vilkitsky Strait Vaygach Island Belyy Island West of Dikson West of Belyy Island Kharasavey Gydan Peninsula Yenisei Gydan Peninsula Amderma Yenisei Dikson MOH Dikson MOH Dikson MOH White Sea, Onega Peninsula Gydansk Peninsula Gydansk Peninsula White Sea Neck Gydansk Peninsula White Sea, Kanin Peninsula

Dikson MOH Dikson MOH Dikson MOH PINRO-VM NIB Yamal NIB Yamal NIB Yamal + Vaygach NIB Vaygach NIB Yamal NIB Yamal NIB Vaygach, Dikson MOH NIB Vaygach NIB Yamal + Vaygach NIB Vaygach, Dikson MOH NIB Taimyr + Vaygach NIB Vaygach + Taimyr · NIB Vaygach, Dikson MOH NIB Taimyr + Vaygach NIB Vaygach NIB Taimyr NIB Vaygach NIB Taimyr, Dikson MOH

x

x

x

x

x

x x

x x x

x

x

x x

x

x x

x

x

Table 5.2: List of successful file transmissions during the winter 1996 expedition.

I

Icebreaker

NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Vaygach NIB Taimyr NIB Taimyr NIB Taimyr

I

Trans. date

25.Jan.1996 26.Jan.1996 26.Jan.1996 26.Jan.1996 30.Jan.1996 30.Jan.1996 05.Feb.1996 05.Feb.1996 05.Feb.1996 05.Feb.1996 -.Feb.1996 -.Feb.1996 -.Feb.1996 -.Feb.1996 07.Feb.1996 12.Feb.1996 13.Feb.1996

I

File name

siOl2107.jpg si012507.jpg si012606.jpg si121107.jpg si012B07.jpg si012BOB.jpg si020205.jpg si020206.jpg si020307.jpg si020506.jpg si020106.jpg si020B06.jpg si020915.jpg si021106.jpg si020B06.jpg si020915.jpg si02130B.jpg

I

Area of image

Vaygach Island, 61E Belyy Island, 70E West of Dikson, 7BE Kara Sea, 65E North of Belyy Island, 70E Kharasavey, 67E Yenisei, B2E North of Gydansk, 76E North of Amderma, 63E Yenisei Gulf, 72N, BOE Ob Bay, 74E ( 1 ) Yenisei-Dikson, BOE ( 1 ) Yenisei-Dikson, BOE ( 1 ) West of Dikson, 79E (1) Yenisei-Dikson, BOE Yenisei-Dikson, BOE White Sea, 36E

( 1 ) After Dr. Melentyev's departure, Mr. A. Schekotikhin received the image files.

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5-20


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Figure 5.7: (a) SAR stripe processed at NERSC from Belyy Island, January 25, 1996.


ICEWATCH

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5-22

ICEWATCH

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Figure 5.7: (c) SAR stripe processed at NERSC from Dikson area, February 9, 1996.


ICEWATCH

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5-23

Vaygach less than 6 hours after acquisition in Troms¢ and was used in near-real-time for the westward navigation of the icebreaker. The ship could then avoid the rough thicker ice, shown as bright areas on the SAR image. It navigated through the smoother and thinner ice areas, thereby making important fuel saving. On the SAR image in Figure 5.9 the chosen ship route is shown. Note that the ice moves relative to the ship route so that the route relative to the ice is somewhat displaced from the shown route.

5 . 1 .4

Reports from the expedition ships

The following are the main points from statements made by the responsible staff on board the three ships used and signed by the Captain, the Radiocenter-master and the two ice ex perts from NIERSC: Chief scientist Dr. V.V. Melentyev and Leading engineer L.V. Zaitsev. 1.

Test results on board CIS V. Tkachyov January 19-25 The tests showed that the orientation of the ship's INMARSAT antenna was crucial to the reception of image faxes and files. For ships in convoy, achieving optimal orientation is often impossible. The receiving station "Volna-C" on board gave a low signal, even if the modem data rate was reduced to 2400 baud and the ship was turned to the optimal direction. Reception of files was therefore not possible on board this ship.

It is recommended that an updated INMARSAT communication system of type " Saturn" should be used in the future, together with more powerful computer equipment on board. 2. Test results on board NIB Vaygach January 25 - February 15

The communication system INMARSAT-A JRC j JUE-358 ( Japan, 1990 ) was used to re ceive the image files and faxes. Out of 104 calls, 25 connections with the NERSC computer were made, 17 of these were stable and 13 image files were received. This means that most of the prepared files were successfully transferred to the Russian icebreakers ( cfr. Table 5.1 and Table 5.2 ) . The data rate varied from 9600 baud (set as a maximum) and down to 2400 baud. Thus, the test was generally successful with 13 out of 19 files transferred while NIERSC personnel was on board, and 4 more file after they left the ship. However, with better equipment the numbers of calls that are required to transfer a file may be reduced some, but other factors such as the orientation of the ship when trying to transfer data and the ice situation around the ship must also be considered. For instance, if the ship moves through rough ice, it may be difficult to get a stable connection via INMARSAT. The image files were received with the time delay after satellite data acquisition in Troms¢ varying from about 5 hours up to 2 days. Image information were used by the NIB Vaygach navigators, the hardcopies were also delivered to Dikson MOH and to several other ships in the vicinity. The image of January 26 was particularly useful, being used in choosing the optimal route for the ship when it was inside the SAR swath between January 26.Jan 10:20GMT and January 27 07:30GMT ( Figure 5.9 ) . In Figure 5 . 10 a more detailed ship - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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ICEWATCH

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5-27

route in this area is shown, prepared by the ice expert on board, Dr. V. Melentyev. Fax transmitted image hardcopies were also received on board. These were showing only about 40-50% of the details that could be seen using the image files. The fax hardcopies did not fill a full A4-format since the stripes, especially if composed of 3 scenes, are quite narrow. It is suggested in the future to use as much as possible of the full A4 page for the image, to make its details clearer. Most faxes also had a simple drawing of the main features in the available space beside the image, this was generally of little use to the navigators and can be omitted. Geometric scaling and rotation of the image to fit with the ship's maps was done on board without problems. The operation of the ZDXI modem was not trivial, and it required frequent and time consuming adjustment when connected to the INMARSAT line. As the use of this line is expensive, it is recommended in the future to make as much as possible of these adjust ments using a usual telephone line prior to the voyage. Here also, it is recommended that an updated INMARSAT communication system of the " Saturn" type should be used in the future, together with more powerful computer equip ment on board for image files processing, printing and storage. 3. Test results on board NIB Taimyr February 5-15 The communication system, the computer system and the achieved baud rate were much the same as for the test on NIB Vaygach. 3 image files were received and also here the test was generally successful with excellent image quality from the files. An image of the White Sea of February 13 was ordered on short notice from the icebreaker for the White Sea emergency operation, it was received about 7 hours after SAR data acquisition in Troms(2J. The image is shown in Figure 5.11.

Recommendations made are generally the same as for the NIB Vaygach test. Summary of recommendations from the ship personnel The personnel agreed that an INMARSAT communication system of type " Saturn" should be installed on board all ships, for better reception of SAR imagery. In addition, more powerful computer equipment should be made available to the personnel, for image pro cessing, printing and storage. A good modem is also important, and all equipment should be tested before going to sea, using an land telephone line to lower the cost.

5.2

5.2.1

D iscussion of the user requirements for future service

SAR coverage requirements

As the ice situation in the pack ice along the NSR may change greatly in periods as short as one day, it should ideally be available one SAR image every day at any place along the - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

5-28


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5-29

ice covered Kara Sea coast. With 100km wide swaths spaced 950km in one direction ( as cending or descending) at 70oN, the coverage situation is far from ideal, even if all swaths in this direction is scheduled. The main scheduled direction has been descending in the western part of Kara Sea, and ascending in the eastern part. It is assumed that passes in only one direction is available due to restrictions in the use of the AMI instrument package on board ERS. The coverage requirement is then to have all passes in one direction scheduled every time the swath passes over areas of the Siberian coast where a thick ice cover is expected.

5.2.2

Processing requirements

Images with a spatial resolution of 100-200m that also have a high radiometric resolution (the number of looks exceeds 20) , will satisfy the requirements for general ice mapping along the NSR. These images are presently delivered by TSS in the LRI-format. The raw data should be available in image form 2-3 hours after acquisition. This require ment is presently met by TSS. All processed SAR images are to be put in a dedicated directory in the Nansen Center's computer immediately after processing to image file, to be ready for transfer by call from the ship. A message that the file is ready is immediately to be sent to the ship by fax. This may be combined with a hardcopy of the image and a message giving the ordered scenes for the immediate future.

5 . 2 .3

Transmission requirements

The transmission of SAR image stripes as files over the INMARSAT-A connection is rec ommended also for future operations. The equipment on board should be of high quality, using e.g. a " Saturn" receiver, a Pentium computer, a high-quality printer, etc. The fax connection is useful for transfer of messages, transfer of SAR coverage maps, and also as a backup for transfer of images. Fax should also be used frequently for reporting back from the ships to the Center at NERSC, informing about the reception quality, possible problems and the ships position and immediate sailing plans. Thereby, the optimal selection of SAR scenes and the most efficient processing will be assured. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


5-30 5 .2.4

Other suggestions

Suggestion has been made of the possibility of sending all finished SAR products to the MSC headquarter in Murmansk. Connection to the icebreakers will then be only from MSC. This will have to be further discussed.

5.3

Discussion of the benefits of using SAR in ice navigation

The

following discussion describe the benefits that were observed during the application demonstration.

5.3.1

Minimizing fuel consumption by using SAR to avoid heavy ice

During the 7th stage expedition, the most obvious benefit of the SAR was made in con nection with the image west of Dikson on January 26, 1996. The commanding officers on NIB Vaygach used this image, available on board 5-6 hours after its acquisition in Tromso, for selecting the easiest route through a region of heavy ice with varying thickness. By avoiding the thicker ice (brighter on the SAR image) , it used less fuel to reach its desti nation. The achieved reduction of fuel is difficult to quantify in this particular case, but is nevertheless a direct benefit of the use of SAR. A general example of total savings ob tained when selecting the optimal route compared to following a straight line, is shown in Figure 5.12. In this example, about 24 hours can be saved in sailing time by following the optimal route. With a daily cost of $50,000 for icebreaker assistance from NIB Vaygach to foreign vessels (Wergeland, 1991), this demonstrates that significant savings, including saving of fuels, can be made by applying SAR images to select the optimal sailing route. Ice concentration: 9/ 1 0 Ice thickness:

2m

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Figure 5. 12: Possible time savings when using an optimal ship route.


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ICEWATCH - Project Report for Task 5 5.3.2

5-31

Use of SAR acquisition schedule in ship steering

The maximum possible SAR coverage by the ERS satellites is quite sparse in time and space. The steering of ships using SAR should ideally be guided by the SAR schedule maps, available 1-2 weeks in advance, and be on their way into the SAR swaths at the time the images are supposed to be ready. A benefit of the type realized on January 26, would then be more frequently achieved. However, such stering of the ships has not been common practice yet, but such use of SAR in the near future is a possibility to be explored. As the Russian officers get more acquainted with the SAR products, they may use them more actively in their operations.

5.3.3

SAR ice information in areas other than in the ships route

In numerous areas SAR ice information is available, but not used directly for route selec tion due to large differences in time and space between image coverage and ship passage. Still, the general ice information is often of great interest to ship officers. In these cases, direct benefits are less obvious and therefore very difficult to quantify. For instance, dur ing the emergency operations near Arkhangelsk, SAR was not immediately available in the operational area due to the short notice of the need (2 days before the ships arrived in the area) and unfavorable position of possible SAR swaths. However, some scenes showing the ice situation at the White Sea entrance and also in the western part of the White Sea were obtained. These were studied on board the ships in near-real time with great interest.

5 .3.4

Summary of user benefit aspects in sea ice monitoring

User benefit aspects in sea ice monitoring can be summarised as: •

The main benefit elements are: 1 . economic savings, and 2. increased safety in operations.

•

NOAA AVHRR data are the most used remote sensing data set due to its favorable cost-benefit ratio

•

SAR data is the most powerful tool for ice monitoring, but operational use has been limited due to high cost of data and insufficient coverage. With RADARSAT and ENVISAT the coverage will be significantly improved.

•

Use of satellite data is necessary in operational ice monitoring and can potentially replace aircraft surveys if the data quality is good enoiugh and the cost-benefit ratio is favourable,

•

The overall ice navigation safety is normally a service provided by the ice centers free-of-charge, but it is unclear how expensive SAR data should be included in this service. -


5-32


•

The price level for SAR data must be adapted to operational use in order to be used by ice centers on regular basis. This can be achieved if the data providers offer lower prices or data purchase is subsidized by governments.

•

The ice centers have limited budgets, and if there is a need for improved quality of the ice monitoring services by use of SAR data, it is expected that the users must pay for this.

•

With improved satellite data and communication systems in ice monitoring, there is a potential of large savings because of reduced sailing times in ice-covered seas compared to a situation with "perfect" ice information.

5.4

Summary o f t he demonstration:

exchange o f knowledge

and technology

The knowledge and technology for using near-real time SAR as a tool for ice monitoring is presently available at NERSC. This can be transferred to the Russians as follows: •

Planning and implementation of practical demonstrations: The following elements must be included: - ordering of SAR data in coordination with ship schedule. - routines for data processing, analysis and distribution. - coordination between ice centre, vessel, and SAR data provider.

•

Instructions to ship personnel: All MSC officers on board icebreakers can learn how to use SAR efficiently for ship steering avoiding difficult ice. SAR images and maps have been shown to give them important ice information in addition to the other ice information sources that are available, such as Russian ice maps, near-real time SLR readdown on board ships, etc) . They will also learn how to request the specific SAR scenes that they will need.

•

Instructions to on-shore personnel: MSC on-shore personnel are given the necessary resources and will learn to order near-real time SAR data directly. The techniques and knowledge now available at NERSC can be transmitted to MS C , thus enabling this organization to process the SAR data and transmit the products directly to the icebreakers. An important part of the system will be the optimal scheduling of the SAR that must be done by ESA. The need for last-minute request (less than 4 weeks in advance) should be kept to a minimum.

The ice data and service providers, such as NERSC, AARI and NPO Planeta have learned about the requirements from one of the most important users, MSC. With detailed knowl edge about the daily work routines, and the major problems encountered by the users, it will be possible for the data and service providers to improve the ice monitoring products. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


5-33

References Pennebaker, W. B. and Mitchell, J. L. (1993) , JPEG still image data compression standard, Van Nostrand Reinhold, New York. Wergeland, T. (1991) , Pilot Project Report: Commercial Shipping and the NSR, in The Northern Sea Route Project: Pilot Studies Report, edited by 0streng, W. and J(Zlrgensen, A., pp. 180--2 32, Fridjof Nansen Institute, Lysaker, Norway.


ICEWATCH

Proj ect Report for Task 6

Recommendation for a near real-time op erational information system using satellite radar data


i


ii

List of Tables

ii

6 Recommendation for a near real-time operational information system 6- 1 using satellite radar data 6-1 6.1 Background and justification . . . . . . . . . . . . . . . . . . . . . . 6-4 6.2 The current Russian sea ice monitoring system . . . . . . . . . . . . Recommended operational real-time system using ERS SAR and Okean SLR 6.3 6-5 data . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5 6.3.1 Scope of the recommended operational system . . 6-9 6.3.2 Functionality of the operational system . . . . . . 6-9 6.3.3 The role of the partners in the operational system 6-1 1 6.3.4 Organisation of the operational system 6-1 1 6.3.5 End users . . . . . . . . . . . . . . 6-12 6.3.6 Future operational system . . . . . 6-13 6.3.7 Financing of an operational system 6-14 6.4 Satellite programming of priority areas 6-14 6.5 Recommended products . . . 6-14 6.5.1 ERS SAR products: 6-17 6.5.2 Okean SLR products: . 6-1 7 6.6 Data processing and transmission to end users 6-18 6.7 Transmission to end users . . . 6-18 6.8 Further activities in ICEWATCH . . . . . . . .

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ii


List of Figures 6.1 6.2 6.3 6.4 6.5 6.6

Oil and gas reserves in the eastern Barents, Pechora and the southern Kara Seas including the Yamal Peninsula. . . . . . . . . . . . . . . . . . . . . . 6-2 General scheme of the Russian Sea Ice and Hydrometeorological service for the Northern Sea Route. The shaded boxes indicate the components which will be included in the near real-time operational system. . . . . . . . . . 6-6 Sample weekly coverage of ERS SAR (upper) and Okean SLR (lower) . 6-7 Flow of information and data in the operational system. Dotted line: infor mation about SAR scheduling. Bold line: Main sea ice information flow. . 6-10 Overall management of the operational system (first phase) .' . . . . . . . . 6-12 The NSR with indication of the prioritised coverage of study areas: 1 White Sea-Arkhangelsk, 2 Kara/Jugor Strait, 3 Ob river, 4 Yenisei Gulf, 5 Vilkitskogo Strait, 6 New Siberian Island and 7 Long Strait. The lines in the upper map show the normal sailing routes between Murmansk and Dikson. 6-15

List of Tables 6.1 6.2 6.3 6.4 6.5

Number of convoys in different parts of the NSR in different periods of the year. Ships passing without icebreaker support in summer (July - October) are not included. . . . . . . . . . . . . . . . . . . . . . 6-2 Brief summary of user survey. (See also Chapter 4) . . . . . . . . . . . . . 6-3 Requirements for an operational ice monitoring service. . . . . . . . . . . . 6-8 Synthetic Aperture Radar on the planned RESURS - ARCTICA satellite. 6-13 Priority areas for ERS SAR overage. . . . . . . . . . . . . . . . . . . . . . 6-16


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ICEWATCH

6

-


6- 1

Recommendation for a near real-time operational information system using satellite radar data

The objectives of Task 6 : Recommendation for a near real-time operational in formation system using satellite radar data, are to: - gather information on which the design can be based, including a study of data re quirements in operational ice monitoring and of the existing data transmission and communication system, and - propose a design for an operational system incorporating satellite SAR data, includ ing a recommendation of needed data products. 6.1

Background and justification

The user investigations performed in Task 4 indicates that there is a large potential of Rus sian and non-Russian users of SAR sea ice information in the Northern Sea Route (NSR) . The users include shipping companies, oil-gas and offshore companies, Russian govern mental institutions, engineering companies, various consulting and service companies and environment institutions and agencies. More than 50 individual institutions have expressed interest in SAR derived ice information which can be provided by ERS SAR-data and EN VISAT data from 1999. In ICEWATCH focus has been on three user categories - shipping companies, offshore industry and environmental monitoring - which all are considered to be priority users of sea ice information. In 1997 it will be important to provide a more regular SAR ice service to these users, and demonstrate the SAR ice mapping capability to potential new users. Shipping companies are responsible for a major part of all transport of goods in Siberia, and this transport is of vital importance for the entire country. Although the volume of goods, transported by Russian shipping companies in the NSR has decreased in the last few years, it is expected that the ship traffic will grow in the future, due to increased offshore activities in Siberia as well as to increased traffic from Europe to the far east. Table 6 . 1 shows the number of convoys in different parts of the NSR for a typical year. The main motivation for an operational near real-time SAR ice monitoring system is that it will, by integration into the existing Russian ice service, contribute to increased safety and cost savings for ship traffic and oil/gas industry, and improve environmental monitoring. The huge oil and gas reserves in the eastern Barents, Pechora and the southern Kara Seas including the Yamal Peninsula are only in the beginning of its exploration and only few sites have been opened for operational drilling (Figure 6.1). With increased offshore ac tivities in these areas, which are ice-covered during winter and spring time, the demand for ice monitoring and forecasting services will grow. Several other organisations are also interested in SAR ice monitoring, and a summary of users are listed in Table 6.2. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -


6-2

Table 6.1: Number of convoys in different parts of the NSR in different periods of the year. Ships passing without icebreaker support in summer ( July - October) are not included. Number of convoys March-June July-August Sep.-Oct. 17 17 15 10 12 17 27 24 13 41 2 27

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Figure 6.1: Oil and gas reserves in the eastern Barents, Pechora and the southern Kara Seas including the Yamal Peninsula. - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

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Examples of some users HydroMet Service (AARI, NPO Planeta, etc.) Russian Academy of Science (Water Problem Institute) ; Ministry of Geology (VNIIKAM, Arkhangelsk GEOLOGIYA) Murmansk Shipping Co. , Vladivostok Shipping companies Experienced users in ice navigation Far Eastern Shipping Co. , Arkhangelsk and new users cover both icebreakers and, merchant vessels White Sea/Onega Shipping Co. , Petrozavolsk New users both in .marine Norilsk Nikel Engineering and terrestrial applications Arctic Marine Engineering Geological companies Expedition Important potential users GAZPROM Oil, gas and with capability to pay for PeterGAZ offshore industry high quality service (some AMOCO Norsk Hydro A/S companies have already Heerema B.V used SAR ice information Nordeco Inc. commercially) Statoil A/S Shell International Institute of Water Transport Engineers Institutions using sea ice Service companies Ecosystema Ltd information for characterisation, design and implementation of operations of vessels/platforms to be used in ice covered areas. Several experienced users Murmansk Marine Biological Research Institute (PINRO) Environmental Institute of Geography (Siberian Dep. RAS) , research: water/ice and many potential users AARI biosphere, climate User category Russian national institutions

User characterisation Experienced users in ice monitoring including intercontinental waters and permafrost

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The current Russian sea ice monitoring system

The current operational sea ice monitoring system in Russia uses satellite data, aircraft data, in situ observations and data from automatic buoys, but no spaceborne SAR-data which could be an important component. The most important satellite data for real-time ice monitoring are NOAA AVHRR data, METEOR-3 data and Okean data, and aircraft surveys were performed regularly until 1994. The main products are ice charts using the WMO terminology. The ice charts for the western part of the Northern Sea Route (from 55°E till 1200E) are produced by the Special Operational Ice Group (SOG) in Dikson, which is organised under the Federal Hydrometeorological Service of Russia and Marine Operations Headquarters of Murmansk Shipping Company (MOH MSC) . The SOG produces sea ice maps 3 times per week (Monday, Wednesday, Saturday) , synoptic meteorological maps (2 times per day) and water level forecasts for the Yenisei mouth. A similar activity is done for the eastern part of the Northern Sea Route by the SOG at Pevek, which is also organised under the Federal Hydrometeorological Service and MOH of the Far East Shipping Company (FESCO) . The Dikson Center operates around the year, but the Pevek Center is only operational during the summer navigation period from June to November. In addition to the ice maps produced by the SOGs, the Ice Center of AARI produces: •

Charts of sea ice forecasts for different prediction periods.

•

Charts of regular weather forecasts in the Arctic.

•

Charts of forecasts of wind speed and direction.

•

Sea level forecasts for river estuaries.

•

Sea waves forecasts.

•

Recommendations for mariners in local areas (on request) .

The communication channels for distribution of products are; •

Radiorelay facsimile communication network (to Murmansk, Dikson, Pevek)

•

Facsimile communication through INMARSAT (to Murmansk, Dikson, Icebreakers) . The INMARSAT system is currently not used regularly, due to financial limitations.


ICEWATCH - Project Report for Task 6 6.3

6.3.1

6-5

Recommended operational real-time system using ERS SAR and Okean SLR data

Scope of the recommended operational system

It is suggested to implement an operational radar ice monitoring system which will be included in the general Russian operational system as outlined in Figure 6.2. It will use Okean SLR data for large scale surveying and ERS SAR data for detailed observations in specific key areas, which are identified as difficult for the navigation (for seasonal variation of their location, see Section 6.4) . It is recommended to obtain weekly coverage of SLR for the whole Northern Sea Route, and desirable to obtain 6-10 ERS SAR stripes of 2-5 scenes per week, covering the identified key areas in prioritised order. However, with one Okean satellite and one ERS operational satellite, it is only possible to implement a limited operational system, with respect to weekly coverage of the high priority areas. In the first phase of the system, 1997-1999, the data sources will be ERS-2 SAR and Okean SLR data. About 200 stripes per year is a more realistic number of ERS-2 SAR stripes than the desirable number given above. This will give on average 5 stripes a week for 40 weeks of the year. Each stripe will contain 2-5 scenes, depending on whether it covers a smaller area, like a strait, or a larger areas, such as the Yenisei estuary. The normal SLR data volume will be 4-6 stripes, which will be used to produce weekly mosaics of sea ice information. These SLR mosaics will cover large areas of the NSR, but there will still be some gaps between the Okean satellite's swaths. To illustrate the coverage of ERS-2 SAR and Okean SLR data, we have produced a map of available data in an arbitrary week ( Figure 6.3) . For simplicity, the most eastern part of the NSR has been left out. The rectangles indicate all available ERS-2 SAR data, while the bold line rectangles indicate those scenes likely to have been chosen for ice monitoring. Due to the satellites' polar orbits, the coverage will be shifted from one week to another, but the maps indicate the size of the areas that can be covered by one ERS and one Okean satellite. The number of ERS-2 SAR scenes available per week will also vary, depending on schedule, and more scenes can be expected to be available during the winter season. From 1999, with wide-swath ENVISAT SAR data available, and possibly also the new Russian SAR satellite - RESURS-ARCTICA - the system can be fully operational using SAR data every day. Then the desired weekly, or more frequent, coverage of the prioritised areas can be realised. The planned system will provide SAR and SLR products which will be distributed to a selection of users in near real-time: icebreakers, the headquarters of Murmansk Shipping Company, and ice centres in Dikson and AARI. Offline products will be made available for offshore industry and environmental agencies. The system will be open to include also other users who need radar ice information. The basic user requirements for an operational service are summarised in Table 6.3.


6-6


DATA SOURCE I DATA TYPE SPECIALLY

SHORE

EQlITPPED

ICEBREAKERS

AND SHIPS

AIRCRAFT AND HELICOPTER

-METEOROLO

DRIFT BUOYS

GICAL STATIONS

Ice conditions,

hydrometeorological

ice types, ice

data, ice data

Meteorological data, ice drift data

DIKSON,

MOSCOW

NOAA

PEVEK

Hydrometeorolo

ARGOS

RC

gical Centre of Russia

•

• •

•

•

•

recommendations to mariners development of special services weather forecasts for Arctic regions level sea forecasts for estuary surface wind speed and direction forecasts sea waves forecasts

SLR data SAR data

Figure 6.2: General scheme of the Russian Sea Ice and Hydrometeorological service for the Northern Sea Route. The shaded boxes indicate the components which will be included in the near real-time operational system.


6- 7


70

70

65

65

50

40

70

60

80

90

The rectangles mark all available ERS-2 SAR scenes.

Bold line rectangles mark scenes likely to be selected for ice monitoring. 10

20

30 40 50 60 70 80 90 1 00 1 1 0

1 20

70

70

65

65

40

50

60

70

80

90

Figure 6.3: Sample weekly coverage of ERS SAR (upper) and Okean SLR (lower) .


AND


Table 6.3: Requirements for an operational ice monitoring service.

0':1 I

00

z > z C/l

trj Z

� Z

�

trj z

� r > Z tJ

Real-Time Support to Ship IN avigation monthly schedule Satellite Programming: seasonally priority areas

weekly coverage Satellite Programming: Okean Satellite data geolocated images interpreted ice maps products compressed image files long term continuity Processing

�

C/l

� C/l Z

o Q

�f-3 trj i':!

Environmental Monitoring Risk Management -long-term coverage in key areas -real-time in specific cases

-full resolution images -interpreted ice maps - statistical data -interpretation of optical/IR data

-full resolution images -interpreted ice maps -statistical data -integration of -optical/IR data GIS-integration

ERS

�

o f-3 trj

Support to Offshore Industry -long-term coverage in key areas -real-time in specific cases/operations

Data Transmission to end-user

Prioritised areas Archive Key Users

24 hour service land-contours and geographical grid streamlined procedures for near real time services shortest possible delivery time modern satellite tel. streamline procedure file and fax transfer along the NSR with emphasis on straits yes shipping companies icebreaker fleet services ice stations AARI

GIS-integration

....

Q trj

modern sat. tel. internet

�

internet express mail

f-3 Q iJ:1 'i:l "'I o CD

;'" 0':>

ICEWATCH - Project Report for Task 6 6.3.2

6-9

Functionality of the operational system

The core activities of the operational system will be: •

acquisition of ERS SAR and Okean SLR data for key areas in the Northern Sea Route.

•

near real-time preparation of SAR and SLR data products.

•

production of ice maps using WMO ice nomenclature.

•

near real-time transmission of SAR and SLR data products from ground stations to icebreakers, Murmansk Shipping Company headquarter and the ice center in Dikson.

The focus will be on streamlining the procedure for acquisition, image product preparation, ice map production and distribution of ice images and maps. Supporting activities for the operational system will be marketing, user feedback and user requirement investigations, improvement of the EO based ice products, and use of offline satellite data. These activities are important for involvement of new users and the future development of a fully operational service for parts of the Arctic Oceans. The structure of the recommended real time system is outlined in Figure 6.4. Based on suggestions from NPO Planeta, AARI and the end-users, NERSC will coordinate the SAR scheduling and data ordering for optimal use of the ERS SAR capacity. NPO Planeta will schedule acquisition, order data and generate Okean SLR data products. The ERS SAR data products will be transferred to NPO Planeta and AARI by ftp for use in the joint SLR-SAR analysis and ice map production. AARI will receive both ERS SAR and Okean SLR data and use these data for production of ice maps and ice forecasts. AARI will use also other ice and hydrometeorological data available in near real-time. These ice maps and forecasts will be transferred to current users who need ice information.

6.3.3

The role of the partners in the operational system

European Space Agency (ESA) : ESA will be responsible for provision of ERS SAR data, and later ENVISAT ASAR data, for operational ice monitoring. Real time SAR data for the western part of the Northern Sea Route will be delivered from Troms¢ Satellite Station via NERSC to the end-users. Offline SAR data will be delivered from ESRIN. Russian Space Agency (RKA): RKA will be responsible for provision of Okean data and coordination of the Russian ac tivities related to processing, analysis and distribution of satellite data. NERSC: Project leader of the development of an operational SAR monitoring system. NERSC will - NANSEN ENVIRONMENTAL

AND


6-10


I

ERS data TSS (real-time data) ESRlN (offline data) A .

.

.

.

. . .. ..

.

>

NERSC

Project coordination

Project coord. RKA

ESA _

Okean SLR + products - stripes, mosaics

- Geolocation

.

.

>

J

NPO Planeta

ERS-I SAR data products - SAR stripes , ....

Okean data

-weekly coverage

- Calibration

- classification

- Land contours - Annotated images -Marketing and user investigations

AARI

Ice interpretation

I----m). The classical procedure is a per-pixel decision rule: x E cq, if p (x I cq) max p (x I OOj), 1 � j � m (1) . where p ( I cq) - class conditional density function. This decision rule classify pixels alone using only spectral characteristics. The procedure for the object classifier is the following: (2) x E cq, if p (X I cq) = max p (X I OOj), 1 � j � m where x is the central pixel of object X, p (X I cq) = {2n)-1 /2( l KI )- 1 /2exp ( - 1 /2 (X - Mj)TK-l (X - MD), K - covariance matrix of an object X of size Nq x Nq, M = (�, ... ,�)T mean vector of X of size Nq x 1 . The direct usage of (2) in general case is very restricted: for large N and q it is difficult to estimate K because of the limited size of the learning sample. To overcome these problems we are to make assumption about the structure of K . .Ei.rn, it is assumed that the correlation between pixels of an object does not depend on q, i.e. =

=

-

R

K = R ® L,

- spatial correlation matrix of size N x N, ® - Kronecker product.

Secondly, the assumption about the structure of matrix R are made. Often it is assumed, that the pixels inside the object are independent, in this case R=I and spatial information is employed indirectly. More natural to assume that an object is a Markov random field, which is represented by causal autoregressive model. In this case matrix R is characterized by few parameters, the number of which depend on the order of Markov model: p (X I roj) = P (X l I x 2 , ··· ,x N , roj) P (x 2 I X3 , ··· , X N , roj) P ( x N - l I x N , roj) P (x N I roj)

For instance, for the 3-rd order causal Markov model we have p (x I roj) = p (Xj I Xj1 , Xj2, xj3, roj), where Xj 1 . Xj 2 and Xj3 right horizontal, right vertical and right low diagonal neighbours of pixel X j . All object classifiers are based on the popular separable correlation function: corr ( x ij , x kl) = Ph l i-kl Pv iH I , where P h and Pv are the spatial correlation coefficients between adjacent pixels in horizontal and vertical directions. -

III. COMPUTATION OF OBJECT DECISION RULES To estimate the complexity of an object classifier as an example we show the horizontal and vertical dependences of central pixel

3

x 3 object decision rule with the

I X1 I X2 1 X3 1 1 1 1 1 I X4 1 XS I X6 1 _ _ _ _

1 1 1 1 I X7 I Xa l X9 1 _ _ _ _

1

1

1

_ _ _ __

1

xs from adjacent horizontal

(X4,X6)

and vertical

(X2,XS)

pixels,

p(x IWi)=TIp(xjlwi )TIp(xjlxs,wi) TIp(xjlxs,(Oj)

jE N/(N 1 UN2) jE N1

In

this case

jE N2

And the decision rule can be written in the form

XS

E

Wi , if g(X I

(OJ) = min

g(X I

where g(X I wi) = 9 1nl Li i +

�), 1

::; I ::; m,

LQijj + L[q In(1 - Pih2 ) + ( 1 /( 1 - Pih2»Qijj j E N/(N 1 UN2) j E N 1

- (2Pih/( 1 - Pih2»QijS + (Pih2/( 1 - Pih2»QiSS] + L,[q In(1 - piV2) + ( 1 /( 1 j E N2 piV2»Qijj - (2PiV/(1 - PiV2»QijS + (piv2/( 1 - PiV2»QiSS] , where N = { 1 , 2 , 9}, N 1 = {4, 6}, N 2 = {2 , a}, Qilj = (XI - l-1i)TLr 1 (Xj "

"

l-1i )

The computational complexity of the object classifier depends on the efficiency of the calculation of the quadratic form

Theorem

[ a ] , Let X=( X 1 ,X2) T, where dim X=N,

I L l l L 12 I 1:= I I L I L2 1 22 I

L- 1

dim X1 =n, dim X2=N - n , then

A ll A 12 =

I I

I

A21 A22

I

/-1= (1-11 , 1-12)T,

I

In order to minimize the mean error of D2(X) calculation on the base of using information only about n components of X we must take the system of eigen vectors of a matrix wisp (LllAll+L12A21)-CXLll. A mean error in this case would be M � = MD2(X)-L Ai ,

n

i=1

where Ai - eigen values of matrix wisp (LllAll +L12A21)-CXLll. I n the case of n = N and L=I one can get from this result well known Karhunen-Loeve transform. This theorem is a base for fast calculation of object classifiers ( where we work with vectors of high dimensionality).

IV. SOFTWARE SYSTEM FOR IMAGE CLASSIFICATION [ 1 0] System offers you the choice between 9 classifiers: one classical per-pixel maximum likelihood classifier and 6 object maximum likelihood classifiers and 2 object minimum distance classifiers which incorporate spatial characteristics of images during classification. Also there is a possibility to work with per-pixel minimum distance classifier. System allows to visualize the results of classification and to perform post-classification and cluster analysis. You can use system to analyse multispectral images of remote sensing or other 20 images to extract thematic information.

1 . TRAINING FIELDS When the image is viewed on the screen we can select the fields of training and control for selected classes. Allows to select fields simultaneously on several images of different bands, as well as on the results of cluster analusis and maps.

2. CLUSTER ANALYSIS ,[9] The use of unsupervised classification help to select training fields more accurate. The package for cluster analysis realize four main steps: a)correction of mixed pixels b) data compression ( plotting of multidimensional histogrames with a help of k-d trees, identification of homogeneous subsets) c)clustering itself ( iteration techniques, hierarhic grouping, modal analysis) d)cluster map compilation and matching the spectral and thematic classes. The package realize different approaches to clustering and presents the processing procedure as a sequence of several modern algorithms. 3.

POST-CLASSIFICATION

The post-classification can be performed interactively with the help of two filters for a n x n window. First assigns the label of the class which is most frequent in that window, second changes the class of the central pixel if the class of all surrounding pixels is the same and different from the class of central pixel. 4.

SELECTION OF CLASSES AND FIELDS

Up to 1 6 classes can be analysed with system. The last class is used for the class of rejects. For each class we can create up to 6 training or control fields. The coordinates of left uper corner and right bottom corner of the field can be entered. The class is selected when at least one field in the class is selected. Other fields of the class are used for control. 5.

STATISTICAL CHARACTERISTICS

For each class which consist of several training fields the following statistical characteristics are calculated: mean vector, covariance matrix, coefficiens of spatial correlation between neighboring values of pixels in horisontal and vertical directions. They are maximum likelihood estimates. All these characteristics and additionally correlation matrix can be written in user defined file. Otherwise, statistical characteristics can be read from user's file and used in the system for training of classifiers. 6. CLASSIFICATION

There are 9 classifiers: one per-pixel classifier PIX and 8 object classifiers. Object classifiers classify the central pixel of a n object or block o f an image. Then t h e window i s moved b y o n e pixel . Four classifiers a r e for cross-shaped block and fou r are for square - shaped block. Two classifiers incorporate the spatial characteristics of an image directly on the base of causal Markov random field model of the third and first order respectively. Two classifiers a re based on the assumption that the pixels inside the the block are independent and with covariance matrix equal identity matrix. When the object size is equal 1 xl then all object classifiers become per-pixel classifier, except two classifiers, which become perpixel minimum classifier. Classifiers are used in two regimes: TEST and RUN. In TEST regime all 9 classifiers are evaluated on training and control fields. The probability of corre ct classification is calculated on training PR and on control PK fields separately. Also the pure time of classification used per one pixel is calculated.

( )

( )

In RUN regime we can choose one of 9 classifiers and run it on the whole training image or any other image. The result of classification is the file with the same name and with extension corresponding to classifiers name. There is a possibility to select bands. Thresholds for creating reject class can be used for a l l classifiers. For maximum likelihood classifiers the chi-quadrat distribution function is used. 7 . FILE OF CONFIGURATION The following information is saved: names of training and classification files; their sizes, number of bands; classes and fields with their coordinates; selected classes, fields and bands; parameters of classification: prior probabilities of classes, threshold method, threshold values for pixel classifier, for cross object and square object classifiers, object size; the list of file names to be viewed.

References 1.

Fukunaga K., 1 972 I ntroduction to Statistical Pattern Recognition,

Academic Press, New-York, p . 50-67. 2 . Palubinskas G., 1 988 A comparative study of decision making a lgorythms in images modeled by Gaussian Markov Random field . Int.Journal o f Pattern recognition and Artificial Intelligence, v.2, N4, p.62 1 -639. 3 . Landgrebe D.A., 1 980 The d evelopment of spectral-spatial classifier for earth observational data . - Pattern Recognition, v. 1 2, N3, p . 1 65- 1 75. 4. Guyon

X.

and Yao I.F., 1 987 Analyse discriminante contextuelle.

Proc. of the 5th International symp. on Data analyses and Informatics, v. l , Paris, p.43-52.

5. Kalaych H . M . and Landgrebe O.A., 1 987 Stochastic model utilizing spectral and spatial characteristics. -IEEE Trans. on Pattern Ana lysis and Machine I ntelligence, v.9, N3, p.457-46 1 .

6. Mardia K.V., 1 984 Spatial discrimination and classification maps.

Communications in Statistics. Theory a nd methods, v. 1 3, N 1 8, p.2 1 8 1 -2 1 97 . 7. Sclove S.L., 1 98 1 Pattern Recognition in image processing using interpixel correlation. - IEEE Trans.Pattern Anal. and Machine Intelligence , v.3,

N2,

p.206- 208.

8. Asmus V. , 1 99 1 Hybrid decisionrules for the classification of the multi spectral data in Methods of processing of aero- and satellite data . ed. Asmus V. Gidromet, 1 99 1 , 1 96 p.

/

9 . Asmus V. , Vadas V., 1 988 Methods and software for the cluster analysis of multyspectral data. Issled. Zemli iz Kosmosa, N 3 p.86-94. 1 0. Palubinskas G. Asmus V. Image cluster and supervised analysis on the base of spatial characteristics, priv. commun., 1 995, 1 1 2 p.


E

E-l

S ummary of SAR and SLR processing

Below is a summary of the present known main characteristics of the two sensor- and processing systems available.

E.l

Characteristics of SAR dat a and processing

A short description of processing of SAR LRI data at TSS and NERSC is given. 1 . Platform data. Satellites are ERS-1 and ERS-2. Orbit inclination is 98.54°. Nodal period is 100.6min. Height (over Equator) is 790km. Drift relative mean sun is 0° /day (sun synchronous) , ase.node at 22:30 local time. Exact repeat period is 35days and 501 orbits. 2. Instrument data. The look direction is right. The incidence angle is 20-26° . Mid-swath is 23° . Swath-width is 100km. 3-100k resolution is 30m. The signal is C-band, 5.3 GHz, 5.7cm, VV-polarized. The stability is better than 0.5dB (assumed). 3. Processing steps for geometry. Image is available in a near-conformal range-azimuth (oblique) projection with relative lo cation accuracy of 1-1.5km and a pixel aspect ratio of 1.26. Mid-point and corner locations are available. An absolute location offset-error of 5km may be present in range (and also in azimuth) . At NERSC , the aspect ratio is converted to 1.0 using nearest-neighbor resampling. The location offset-error is corrected. A geographical grid and shorelines are superposed. 4. Processing steps for radiometry. The data are in amplitude (voltage) , 16bits/pixel, A conversion factor to sigma-zero is available for the processing station (TSS). No corrections for range-varying effects are applied in the satellite or at TSS. At NERSC, values are downscaled using a fixed factor, to fit within 8bits (for normal polar surfaces) . At NERSC, corrections for 3 range-varying effects are applied, to normalize the signal to mid-swath. At NERSC, an optimal linear contrast enhancement is done before display. The linear function is shown as a separate " scale-bar" below the image. E.2

Characteristics of SLR data and processing

A short description of SLR, processed at Planeta as received at NERSC is given. 1. Platform data. Satellites are Okean-7 and Okean-8. Orbit inclination is 82.5° .. Nodal period is 97.8min. Height (over Equator) is 650km ± 20km. Drift relative mean sun is 2° / day. There is no exact repeat period. 2. Instrument data. The look direction is left. The incidence angle variation 24-52°. Mid-swath angle is 40° . - NANSEN ENVIRONMENTAL AND REMOTE SENSING CENTER -

E-2

ICEWATCH - Project Report Swath width is 450km. Resolution is 1.5-2km (range-varying) . The signal is X-band, 9.4GHz, 3.2cm, VV-polarized. The signal stability is unknown (vary . mg7. ) .

3. Processing steps for geometry. Image is available in a near-conformal range-azimuth (oblique) projection with unknown relative location accuracy and a pixel aspect ratio of 1 .8. No reference point locations are available. At NERSC, the aspect ratio is converted to 1.0 using nearest-neighbor resampling. A geo graphical grid and shorelines are superposed. 4. Processing steps for radiometry. The data are in amplitude (voltage) , 8bitsjpixel. Conversion factor to sigma-zero is not available. The data are supplied with "radiometric scale values" . A range-varying gain, linear from value=l at near-range to =6.9 at far-range is applied in the satellite, for an approximate range-correction. At NPO Planeta, a precise range-correction of the 3 range-varying effects is made. to normalize the signal to 25° incidence angle. At NPO Planeta, a correction for azimuth-varying gain is done by using the supplied radiometric scale values.



F

F- l

Estimation of ocean backscatter from

an

Okean

SLR image 1. Images received. A SLR image over the North Atlantic on 17.Sep.96 at 2 1 GMT has been received from V. Kudryavtsev at the Marine Hydrophysical Institute of the Ukraine Academy of Science in Sev astopol. It has the name " Corrected" . It contains a calibration wedge in the azimuth direction with 8 levels of specified signal value, enabling the transform of image pixels to radar signal strength. Brightest level corresponds to -119.7dB and darkest level to -141 .0dB ( information given by V. Kudryatsev) . The wedge is repeated approx every 300 km with pixelvalues varying about 15% over the full azimuth range of the image. Assuming that this represents uncorrected radar variations in azimuth, they can thus be corrected using the wedge values. The same image was also received with the name "Normalised" without the wedge, but with a linear conversion formula from image pixel value to normalised sigma-zero given. 2. Image value corrections. In general, the nessessary corrections to a radar image designed to show the sensor-independent backscatter value sigma-zero will be the following: 1. Removal of the 3 known signal variation in range: antenna gain, range spread loss and the sine-dependence on incidence angle. 2. Removal of possible signal variations in azimuth, (e.g. by using a calibration wedge signals ) . Application of corrections 1 and 2 will give an image that has its binary pixel values as a cho sen function of sigma-zero values. For some radars, e.g. for ERS SAR, the function chosen is the square root of sigma-zero, called the "voltage signal" . The ERS radar signal is stable in azimuth, and with the 3 range corrections applied an absolute sigma-zero can easily be calculated. 3. Image value normalisation. For most wind situations over ocean, sigma-zero is strongly varying with the incidence angle. The main term in the standard Bragg wind waves scattering theory for vertical polarized C and X band radar frequencies specifies a sigma-zero variation near to · the inverse 4-th power of the sine of the incidence angle. For ERS SAR, the voltage-signal at far range ( 26°) will then be 61% of the signal at near range (20° ) . For Okean SLR, it can be assumed that the pixelvalue is a " power signal" , that is: proportional to sigma-zero. This signal will at far range ( 52° ) be only 7% of the signal at near range ( 24° ) . Therefore, a Bragg-normalisation is essential for SLR in the range-direction of radar imagery over ocean. Comparison of SLR with SAR. It is assumed that the "Normalised" SLR image has the following properties: 1 . it is a power signal image, 2. all corrections in range and in azimuth has been applied, 3. pixelvalues has been normalised in range (to near-range incidence angle) using standard Bragg theory with the inverse 4-th power sine-function. - NANSEN ENVIRONMENTAL

AND


F-2

ICEWATCH Project Report -

This SLR image has then been compared with a near-simultaneous SAR image of 3 scenes on 17.Sep.95 at 21:45 GMT covering the central part of the SLR area. A comparison of the signal variations within the images shows a very good agreement, the variation over the SAR frame is about 2dB in both cases, and can easily be attributed to a known spatial shift in the wind velocity. As no reliable absolute calibration is available for the SLR, the absolute calibrated SAR image can in this case be used to find the SLR image's absolute calibration constant. This in turn can be used for wind estimation over the larger SLR area. It will be valuable to have a confirmation if the properties 1-3 as specified above also are present for ice images received from Planeta. It will also be valuable to have the SLR radar stability quantified, so that images over different areas and dates can be compared. It could be possible to use available SAR as a mean of absolute calibration of SLR, as we know that ERS SAR is stable to within ±10.5dB.

- NANSEN ENVIRONMENTAL AND REMO TE SENSING CENTER

-


G

G-l

Colour plates

This appendix contains colour plates for some of the figures in this report, namely: •

Figure 1.4 Case 1: SAR image from Baydaratskaya Bay on November 11, 1994.

•

Figure 2.5 Example of computer-classification of an ERS-1 SAR image in the pack ice south-east of Svalbard during winter conditions (05.03.92) . FY means first-year and MY means multiyear.

•

Figure 2.6 Example of computer-classification and ice concentration from an ERS-1 SAR image ( 12.09.94) in the Vilkitsky Strait. From left to right are the original image, the classified image and the derived ice concentration.

•

Figure 2.9 Example of SLR-RM08 ice type classification by NPO Planeta.

•

Figure 2.12 SLR-RM08 ice type classification from May 1996 made by NPO Planeta.

•

Figure 5.3 Photos from the winter expedition 1996. ( a) - (c)

•

Figure 5.3 Photos from the winter expedition 1996 ( continued) . (d)- ( f)

-

NANSEN
