Towards an Automatic Ice Navigation Support System in the Arctic Sea

International Journal of Geo-Information

Article Towards an Automatic Ice Navigation Support System in the Arctic Sea

Xintao Liu, Shahram Sattar and Songnian Li * Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada; [email protected] (X.L.); [email protected] (S.S.) * Correspondence: [email protected]; Tel.: +1-416-979-5000

Academic Editors: Suzana Dragicevic, Xiaohua Tong and Wolfgang Kainz Received: 18 January 2016; Accepted: 8 March 2016; Published: 14 March 2016

Abstract: Conventional ice navigation in the sea is manually operated by well-trained navigators, whose experiences are heavily relied upon to guarantee the ship’s safety. Despite the increasingly available ice data and information, little has been done to develop an automatic ice navigation support system to better guide ships in the sea. In this study, using the vector-formatted ice data and navigation codes in northern regions, we calculate ice numeral and divide sea area into two parts: continuous navigable area and the counterpart numerous separate unnavigable area. We generate Voronoi Diagrams for the obstacle areas and build a road network-like graph for connections in the sea. Based on such a network, we design and develop a geographic information system (GIS) package to automatically compute the safest-and-shortest routes for different types of ships between origin and destination (OD) pairs. A visibility tool, Isovist, is also implemented to help automatically identify safe navigable areas in emergency situations. The developed GIS package is shared online as an open source project called NavSpace, available for validation and extension, e.g., indoor navigation service. This work would promote the development of ice navigation support system and potentially enhance the safety of ice navigation in the Arctic sea.

Keywords: sea ice; Arctic; navigation; GIS; Isovist

1. Introduction Navigation in the sea is quite different from the way we navigate on land through road networks. A road network includes ﬁxed walkable or drivable connections to navigate with the help of navigation system and service. On the contrary, a navigable sea area is continuous and always changing due to variations of sea ice coverage over time. As a result, ice navigation requires great patience. Conventionally, navigation in the sea requires well-trained ice navigators to plan and handle ship routes between origin and destination (OD) in such areas where navigable conditions are constantly changing [1], whose experiences are heavily relied upon to guarantee the ship’s safety. According to Transport Canada (1998) [2], at least one experienced ice navigator must be on-board while a polar ship navigates in the sea. The navigators consider many factors (e.g., ice condition and ship movement) and sometimes needs to change ship route and speed under unexpected situations [3]. However, the required skills of ice navigators are not expected to fully guarantee the safety of ships in that area. According to the statistics from [4], shipping accidents have occurred in the last decades. Despite advances in information and communications technology (ICT) to facilitate navigation, conventional ice navigation is somehow manually operated, especially for ship route planning. To provide safer ice navigation and better guide ships in the sea, an automatic ice navigation support system is desirable. Clearly, sea ice information plays a key role in the design and development of such a system. Researchers and practitioners have long realized the direct connection between spatio-temporal ice database models and ice information systems and services. Related ice studies in the past decades

ISPRS Int. J. Geo-Inf. 2016, 5, 36; doi:10.3390/ijgi5030036 www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2016, 5, 36 2 of 12 have fallen into three groups: the first is to design spatio-temporal database models for storing and manipulating large-scale sea ice data for related studies [5–9]. The second focuses on using GIS and web tools to deliver ice information services such as online visualization and data access to end users (e.g., [10–13]). Meanwhile, a number of government agencies and research centers also provide online sea ice services and interoperable web services (e.g., [14–19]). The third is dedicated to sea ice related environmental issues such as identification of sea ice patterns (e.g., [20–22]). Despite numerous studies in ice database models and ice information system, policy-makers and ice navigators do not have tools to adequately visualize navigable sea areas and assist automatic ship navigation in real time, even if such ice data is available. The few studies that do consider sea ice navigation (e.g., [3,23,24]) are limited to the assessment and visualization of ice conditions and variation, not a practical and automatic ice navigation support system. Some existing marine navigation software tools (e.g., [25]) only focus on GIS visualization by overlaying multiple information layers such as seafloor, tide and ship location. This study fills that research gap by proposing a novel GIS methodology that aims to develop an automatic ice navigation support system. We propose a virtual road-like-graph in the sea that is built based on the delineation of navigable sea areas and unnavigable sea ice regimes or obstacles. We apply the Dijkstra shortest path algorithm and adopt the visual analytical Isovist method, and then we develop an integrated analytical GIS package, which we call NavSpace. NavSpace is an open source GIS project that is available for extension and validation in related ice studies. By applying the NavSpace to GIS vector-formatted ice data from the Canadian Ice Service, we demonstrate how the GIS-based ice navigation support system allows us to: (1) assist automatic ship route planning; (2) automatically identify navigable sea areas in case that emergency situations may begin to occur; and (3) interactively map for the purpose of higher visual and analytical navigation services. As a result, an automatic ice navigation support system may be achieved in real time at an operational level. The study is organized as follows. Section2 presents our proposed GIS-based methodology, including the delineation of navigable sea areas and generation of Voronoi Diagrams for unnavigable ice regimes, followed by the description of the ship routing algorithm and the visualization method Isovist. Section3 presents some implementation results using digital sea ice charts from the Canadian Ice Service. Section4 offers some concluding remarks.

2. Methodology The main objective of this study is to develop integrated analytical GIS-based tools to support ship route planning, visualization and navigation services, so that the potential for improving sea ice navigation safety may be achieved. This section illustrates in detail our proposed GIS-based methodology by using artiﬁcial data for the sake of convenience.

2.1. A GIS-Based Two-Stage Solution A GIS-based two-stage solution is proposed to this problem (Figure1). First, it is straightforward to divide navigable sea areas and unnavigable sea areas based on the calculation of ice numeral (IN) of vector-formatted digital sea ice data. The IN serves as an indicator of whether or not it is safe for a type of ship to enter a sea ice area, which means we are able to divide sea areas into two parts: navigable sea areas and unnavigable sea areas. The calculation of IN will be discussed in detail in Section 2.2. Second, the generation of skeleton for navigable sea areas is a process that discretizes a continuous open space into many individual lines or segments. The skeleton lines can be used to create a road-network-like graph, based on which we can apply any route planning algorithm to assist navigation in the sea. The generation of Voronoi Diagrams for ship routing will be discussed in Section 2.3. We implement this GIS-based two-stage solution as a GIS package and prove that the generated ship routes are not only in consistent with the recommended routes, but also the shortest-and-safest routes. The main advantage of this method is that such a simple and elegant ship routing method can provide the safest option. ISPRSISPRS Int. Int. J. Geo-Inf. J. Geo-Inf.2016 201,65,, 5 36, 36 3 of3 of12 12 ISPRS Int. J. Geo-Inf. 2016, 5, 36 3 of 12

Figure 1. A two-stage solution to automatic ice navigation. FigureFigure 1. 1.A A two-stage two-stage solutionsolution to automatic ice ice navigation. navigation. 2.2. Delineating Navigable Sea Area 2.2.2.2. Delineating Delineating Navigable Navigable Sea Sea Area Area Sea areas can be divided into two parts: navigable sea areas and unnavigable sea area, from SeaSeanavigation areas areas can canperspective. be be divided divided While into into both two two types parts:parts: of areas navigablenavigable are constantly sea sea areas areas changing and and unnavigable unnavigable due to variation sea sea area,in area,ice from from navigationnavigationcoverage perspective. perspective. over time, While While the first bothboth part typestypes is cont ofinuous areas andare theconstantly constantly second partchanging changing is consisted due due to of to variation numerous variation in inice ice coveragecoverageseparated over over time, pieces. time, the To firstthe determine first part partis continuous whether is cont orinuous not and itthe is and safe second the to enter second part an is ice part consisted regime, is consisted [ of2] numerousintroduced of numerous Ice separated Numeral (IN) method as the indicator and [23] proposed a GIS method to calculate IN. We follow pieces.separated To determine pieces. To whether determine or notwhether it is safe or not to enterit is safe an ice to regime,enter an[ 2ice] introduced regime, [2] Iceintroduced Numeral Ice (IN) Numeralthe same (IN) methodmethod to as delineate the indicator navigable and sea [ 23areas] proposed from ice regimes.a GIS method For the saketo calculate of convenience, IN. We we follow methodsimply as the introduce indicator the and method [23] proposed[2,23] using a artificial GIS method data. For to calculatemore details, IN. please We follow refer to the the same above method the same method to delineate navigable sea areas from ice regimes. For the sake of convenience, we to delineatetwo papers. navigable sea areas from ice regimes. For the sake of convenience, we simply introduce simply introduce the method [2,23] using artificial data. For more details, please refer to the above the methodSea [2,23 ice] usingcan be artificialclassified data.by its Forstage more of developme details, pleasent and concentration. refer to the above Concentration two papers. is the two papers. Sea“ratio icecan expressed be classified in tenths bydescribing its stage the ofarea development of the water surface and concentration.covered by ice as Concentrationa fraction of the is the Seawhole ice area” can be[26 ].classified An ice regime by its (colored stage of polygons developme in Figurent and 2a) concentration. could consist of Concentration a mixture of ice is the “ratio expressed in tenths describing the area of the water surface covered by ice as a fraction of the “ratiotypes expressed with varying in tenths concentrations describing and the thicknesses area of the [ 23water] and surfacecan be digitally covered represented by ice as aas fraction egg code of the wholewhole area”(Figure area” [26 2b) [26]. with]. An An “concentrations, ice ice regime regime (colored(colored stages of polygonspolygons development in in Figure Figure(age) and 22a)a) form could (floe consist consistsize) of ofice” of a a inmixturemixture a simple of of ice ice typestypes withoval with varying form varying [26]. concentrations concentrations and and thicknessesthicknesses [ 23] and and can can be be digitally digitally represented represented as as egg egg code code (Figure(Figure2b) 2b) with with “concentrations, “concentrations, stages stages of of development development (age)(age) and form (floe (floe size) size) of of ice” ice” in in a asimple simple ovaloval form form [26 [26]. ].

(a) (b)

Figure 2. The color-coded daily ice chart example (a) and Egg Code (b) of ice regime. The colored areas are ice regimes; Ca, Cb, and Cc and Fa, Fb, and Fc correspond to Sa, Sb, and Sc, respectively. (Source: [26], where two figures are directly cited without copyright issue). (a) (b) Ice numeral (IN) was designed by [2] to measure the ability of a type of ship to navigate Figure 2. The color-coded daily ice chart example (a) and Egg Code (b) of ice regime. The colored Figurethrough 2. The this color-coded ice regime. IN daily can icebe calculated chart example by using (a) Equation and Egg (1): Code (b) of ice regime. The colored areas are ice regimes; Ca, Cb, and Cc and Fa, Fb, and Fc correspond to Sa, Sb, and Sc, respectively. areas are ice regimes; Ca,Cb, and Cc and Fa,Fb, and Fc correspond to Sa,Sb, and Sc, respectively. (Source : [26], where two figures are directly cited without copyright issue). (Source: [26], where two ﬁgures are directly cited without copyright issue). Ice numeral (IN) was designed by [2] to measure the ability of a type of ship to navigate throughIce numeral this ice (IN) regime. was IN designed can be calculated by [2] to measure by using the Equation ability (1): of a type of ship to navigate through this ice regime. IN can be calculated by using Equation (1):

ISPRS Int. J. Geo-Inf. 2016, 5, 36 4 of 12

IN “ pCa ˚ IMaq ` pCb ˚ IMbq ` ... ` pCn ˚ IMnq (1) where Ca is the concentration in tenths of ice type a, and IMa is the ice multiplier (IM) for ice type a, and the rest can be done in the same manner. IMs are used to indicate how much weight can be given to an ice type for a type of ship (see Table1).

Table 1. Ice Multiplier (IM) based on ship types (Source: [27]).

Ice Multiplier for each Ship Category No. Ice Type E D C B A CAC4 CAC3 1 Old/Multi-Year Ice –4 –4 –4 –4 –4 –3 –1 2 Second-Year Ice –4 –4 –4 –4 –3 –2 1 3 Thick First-Year Ice (>120 cm) –3 –3 –3 –2 –1 1 2 4 Medium First-Year Ice (70–120 cm) –2 –2 –2 –1 1 2 2 5 Thin First-Year Ice (30–70 cm) –1 –1 –1 –1 2 2 2 6 Thin First-Year Ice (2nd Stage 50–70 cm) –1 –1 –1 1 2 2 2 7 Thin First-Year Ice (1st Stage 30–50 cm) –1 –1 1 1 2 2 2 8 Grey-White Ice (15–30 cm) –1 1 1 1 2 2 2 9 Grey Ice (10–15 cm) 1 2 2 2 2 2 2 10 Nilas, Ice Rind (<10 cm) 2 2 2 2 2 2 2 11 New Ice (<10 cm) 2 2 2 2 2 2 2 12 Brash (ice fragments < 2 m) 2 2 2 2 2 2 2 13 Bergy Water 2 2 2 2 2 2 2 14 Open Water 2 2 2 2 2 2 2

If the ice numeral calculated for a type of ship is zero or positive, the ship can navigate through that ice regime. Conversely, if the ice numeral is negative, this type of ship cannot navigate through that ice regime unless an alternative plan such as using ice breaker to assist the ship is to be adopted. For example, suppose that an ice regime consists of 8/10ths total ice concentration, of which 1/10th is old ice and 7/10ths is thick ﬁrst-year ice. While doing the calculation, remember to incorporate the 2/10ths open water [27]:

Type A ship : p1 ˆ ´4q ` p7 ˆ ´1q ` p2 ˆ 2q “ ´7 pNegative regimeq (2)

CAC 4 ship : p1 ˆ ´3q ` p7 ˆ ` 1q ` p2 ˆ 2q “ `8 pPositive regimeq (3)

The calculated INs reﬂect how they ﬂuctuate for the same ice with structurally different ships. In the above example, Type A ship cannot navigate through the ice regime, while Type CAC4 ship can navigate it through safely. In this way, we are able to delineate navigable sea areas for different types of ships by using GIS vector-formatted digital ice charts.

2.3. Generating Voronoi Diagrams for Ship Route Planning When navigating in the sea, ice navigators must be aware of where, what type and how much of ice regime is around them. Theoretically, ships can navigate at any location in positive regimes. However, to select the safest ship route, the best strategy is to keep them as far as possible from the negative regimes with high concentration [27]. In Figure3, there are 14 artiﬁcial obstacles in black that are unnavigable sea areas, and the minimum bounding rectangle is used to describe their boundaries. We generate Voronoi Diagrams for these 14 obstacles, which are colored areas in Figure3a. Based on the mathematical deﬁnition of a Voronoi Diagram, the distance between each point within a Voronoi Diagram and the obstacles is shorter than the distance between the point and any other obstacles. As a result, the boundary (blue lines in Figure3b), which we call skeleton, of a Voronoi Diagram is the bisector of the space between any two obstacles, and any point on the boundary has the farthest distances to all the closest obstacles at the same time. Because of this, skeletons fully comply with the safest navigate principle in the sea. ISPRS Int. J. Geo-Inf. 2016, 5, 36 5 of 12 ISPRS Int. J. Geo-Inf. 2016, 5, 36 5 of 12 ISPRS Int. J. Geo-Inf. 2016, 5, 36 5 of 12

(a) (b) (a) (b) FigureFigure 3. Voronoi 3. Voronoi Diagrams Diagrams (colored (colored regions) regions) of 14 obstacles obstacles (black (black rectangles) rectangles) on the on left the ( lefta) and (a) and Figureskelet 3.on Voronoi (boundaries Diagrams of Voronoi (colored Diagram regions)s) in blue of lines14 obstacles on the right (black (b). rectangles) on the left (a) and b skeletonskeleton (boundaries (boundaries of of Voronoi Voronoi Diagrams) Diagrams in) in blue blue lines lines on on the the right right ( ().b). To this point, we have explained why it is safe to navigate along the skeletons of negative ToregimesTo this this point, in point, the wesea. we have There have explained are explained 39 skeletons why why it and is safeit 26 is tojunctions safe navigate to (blue navigate along lines the alongand skeletons red the points skeletons of in negative Figure of 4a) regimes negative inregimes thethat sea. arein There the generated sea. are There 39 for skeletons the are 14 39 unnavigable skeletons and 26 junctions and obstacles 26 junctions (blue shown lines as (blue black and lines red rectangles points and red in in Figurepoints Figure 3. 4in a) These Figure that are 4a) generatedthatskeletons are generated for theand 14junctions unnavigablefor the are 14 a unnavigablekind obstacles of road shown- network obstacles as- blacklike shown graph, rectangles asand black Figure in Figure rectangles 4b is3 the. These dualin Figure skeletonsgraph, 3.of These and junctionsskeletonswhich are blueand a kind pointsjunctions of mean road-network-like are skeleton a kind lines of roadwhile graph,- networkred and lines Figure -mlikeean graph,4connectionsb is the and dual amongFigure graph, them. 4b of is which Basedthe dual on blue such graph, points of a graph, we can apply any route planning algorithm (e.g., [27]) to automatically calculate ship routes meanwhich skeleton blue points lines mean while skeleton red lines lines mean while connections red lines amongmean connections them. Based among on such them. a graph, Based weon cansuch between any pair of origin and destination in the sea. applya graph, any we route can planning apply any algorithm route planning (e.g., [27 algorithm]) to automatically (e.g., [27]) calculate to automatically ship routes calculate between ship any routes pair ofbetween origin and any destination pair of origin in theand sea. destination in the sea.

(a) (b)

Figure 4. Skeleton (39 blue lines) and junctions (26 red points) on the left (a) and its dual graph on the right (b): blue points means 39 skeleton lines and 78 red lines means the connections. (a) (b) 2.4. Isovist for Identifying Safe Navigable Areas Figure 4. Skeleton (39 blue lines) and junctions (26 red points) on the left (a) and its dual graph on the FigureAn 4. IsovistSkeleton is a (39visibility blue lines) area andfrom junctions a single (26point red of points) view in on an the open left space. (a) and The its dualconcept graph of Isovist on the right (b): blue points means 39 skeleton lines and 78 red lines means the connections. righthas been (b): blue introduced points means into and 39 skeleton useful for lines spatial and 78 analysis red lines [28 means]. In the navigable connections. sea areas, an Isovist represents the safe navigable areas from current viewpoint. The integration of Isovist into ice 2.4. Isovist for Identifying Safe Navigable Areas 2.4. Isovistnavigation for Identifying service would Safe potentially Navigable Areasbe able to help navigator to identify such safe navigable areas An Isovist is a visibility area from a single point of view in an open space. The concept of Isovist An Isovist is a visibility area from a single point of view in an open space. The concept of Isovist has has been introduced into and useful for spatial analysis [28]. In navigable sea areas, an Isovist been introduced into and useful for spatial analysis [28]. In navigable sea areas, an Isovist represents represents the safe navigable areas from current viewpoint. The integration of Isovist into ice the safe navigable areas from current viewpoint. The integration of Isovist into ice navigation service navigation service would potentially be able to help navigator to identify such safe navigable areas

ISPRS Int. J. Geo-Inf. 2016, 5, 36 6 of 12 ISPRS Int. J. Geo-Inf. 2016, 5, 36 6 of 12 wouldin real potentially time, given be data able availab to helpility navigator. Figureto 5 identify is an example such safe of navigableIsovist based areas on in the real artificial time, given data datadescribed availability. in Section Figure 2.2.5 is an example of Isovist based on the artiﬁcial data described in Section 2.2.

Figure 5. 5.Isovist Isovist example example in the in artiﬁcial the artifici environment,al environment, where the where black the rectangles black are rectangles the unnavigable are the areas,unnavigable the yellow areas, point the yellow is current point position, is current the position, red area the is thered visibility area is the area, visibility and the area, blue and line the is blue the ridge,line is whichthe ridge, is the which longest is the line longest inside line the visibilityinside the area. visibility area.

3. Results Results and and discussion discussion InIn thisthis section,section, wewe formalizeformalize and and implement implement the the methods methods and and algorithms algorithms described described in in Section Section2 as 2 aas GIS a GIS package, package, namely namelyNavSpace NavSpace, which, which is shared is shared online online as an as open an open source source project project for validation for validation and extension,and extension, and whichand which we hope we hope to further to further attract attract contributions contributi fromons relatedfrom related communities. communities. A case A study case isstudy further is further carried out carried by applying out by applyingNavSpace toNavSpace the digital to vector-formattedthe digital vector sea-formatted ice charts sea produced ice charts by theproduced Canadian by the Ice Service.Canadian For Ice more Service. details For of more the seadetails ice data,of the please sea ice refer data, to please its ofﬁcial refer website to its official [6]. website [6]. 3.1. Implementation and Experiments in Arctic Sea 3.1. ImplementationWe design and and develop Experiments an openin Arctic source Sea Environmental Systems Research Institute (ESRI) ArcGISWe extension, design and namely developNavSpace an open, to help source ship Environmental route planning Systems in Arctic Research Sea. The Institute development (ESRI) environmentArcGIS extension, is Microsoft namely Visual NavSpace Studio, to 2010 help (VS2010) ship route ASP.net planning usingC# in programming Arctic Sea. The language, development which isenvironment object-oriented is Microsoft and user-friendly Visual Studio developing 2010 tool(VS2010) for the ASP.net Windows using platform. C# programming We choose ArcObjects language, providedwhich is object by ESRI-oriented ArcGIS and to implement user-friendly visualization developing and tool spatial for the analysis Windows functions. platform. The We advantage choose ofArcObjects ESRI ArcObjects provided lies by in ESRI its capabilityArcGIS to toimplement provide visualization visualization andand spatialspatial analysisanalysis interfacesfunctions. thatThe canadvantage be integrated of ESRI into ArcObjects the NavSpace lies, which in its greatly capability reduces to provide the time visualization commitment and in the spatial development. analysis Moreover,interfaces that both can Microsoft be integrated and ESRI into ArcGIS the NavSpace are mainstream, which greatly software reduces packages the time that commitment provide good in continuitythe development. in terms Moreover,of version update both Microsoft and maintainability and ESRI throughout ArcGIS are themainstream life cycle of software a software packages project. that The provide Arctic good sea continuity is selected in as terms our study of version area (see update Figure and6) maintainability because of two throughout main reasons: the (1) life thecycle availability of a software of digitalproject. sea ice charts in this area; and (2) a sea ice project for the Canadian Ice ServiceThe Archive, Arctic sea namely is selectedCanICE as, hasour developedstudy area a (see sea iceFigure information 6) because database of two andmain web-based reasons: (1) portal the withavailability novel, of interactive digital sea knowledge ice charts in discovery this area; tools and [(2)11 ]a insea this ice area.project Especially, for the Canadian a series Ice of GoogleService Maps-likeArchive, namely sea ice webCanICE application, has developed programming a sea ice interfaces information (APIs) database for JavaScript and web have-based been portal developed with andnovel, an onlineinteractive web sandbox knowledge to facilitate discovery debugging tools [11 and] insharing this area. other Especially, types of ice a web series services of Google have beenMaps provided-like sea [12 ice]. web Although application the implementation programming and interfaces deployment (APIs) of sea for ice JavaScript navigation have service been is targeteddeveloped for and general an online purpose web in anysandbox sea ice to areas, facilitate with thedebugging help of the and open sharing platform other of typesCanICE of, itice would web beservices easier to have test, been integrate provided and further [12]. popularize Although theice navigation implementation service and as part deployment of the CanICE of project. sea ice navigation service is targeted for general purpose in any sea ice areas, with the help of the open platform of CanICE, it would be easier to test, integrate and further popularize ice navigation service as part of the CanICE project.

ISPRS Int. J. Geo-Inf. 2016, 5, 36 7 of 12 ISPRS Int. J. Geo-Inf. 2016, 5, 36 7 of 12

WA EA

WA: Western Arctic Regional EA: Eastern Arctic Regional HB: Hudson Bay Regional EC: Eastern Coast Regional GL: Great Lakes Regional GL

Figure 6. Five regions of interest in the Arctic sea (upper part) where digital sea ice charts are Figure 6. upper part available,Five andregions an example of interest digital in the sea Arctic ice charts sea ( rendered by) where stages digital of development sea ice charts (lower are part available,). and an(Source: example captur digitaled from sea [ ice29] charts, where rendered two figures by are stages adapted of development from without ( lowercopyright part issue). (Source:). captured from [29], where two figures are adapted from without copyright issue). 3.2. Generation of “Road-Network-Like Graph” in Arctic Sea 3.2. Generation of “Road-Network-Like Graph” in Arctic Sea The Arctic sea is divided into five regions of interest (see the upper part in Figure 6), and we Thefocus Arctic on the sea Western is divided Arctic into and fiveEastern regions Arctic of Regions interest because (see the there upper are part many in islands Figure that6), and increase we focus on thethe Western complexity Arctic of sea and ice Easternnavigation. Arctic In the Regions lower part because of Figure there 6, the are visualized many islands sea ice that (colored increase by the its stages of development) together with these islands makes it hard to manually plan route for complexity of sea ice navigation. In the lower part of Figure6, the visualized sea ice (colored by its different types of ships. stages of development) together with these islands makes it hard to manually plan route for different We download the digital sea ice charts on 2 February 2015 and convert it into typesArcGIS of ships.-supported shape file format (the coordinate system is PS100) to carry out the case study in Wethe downloadArctic sea. theBy applying digital sea the ice tw chartso-stage on solution 2 February (see Figure2015 and 1) andconvert using it intothe developed ArcGIS-supported GIS shapepackage file format NavSpace (the, firstly coordinate we calculate system iceis numeral PS100) (INs) to carry for ship out type the caseCAC3 study (refer in to Tablethe Arctic 1 in sea. By applyingSection 2.1). the two-stageThe results solution are visualized (see Figure in Figure1) and 7, usingwhere the the developed red is unnavigable GIS package ice regimesNavSpace ,

ﬁrstly we calculate ice numeral (INs) for ship type CAC3 (refer to Table1 in Section 2.1). The results are visualized in Figure7, where the red is unnavigable ice regimes ( i.e., with negative IN values) while the blue is navigable ice regimes (i.e., with positive IN values). Obviously, sometimes the islands are geographically connected with unnavigable regimes, which are spatially merged together to obtain ISPRSISPRS Int.Int. J.J. GeoGeo--Inf.Inf. 20120166,, 55,, 3366 88 ofof 1212

((i.i.e.e.,, withwith negativenegative ININ values)values) whilewhile thethe blueblue isis navigablenavigable iceice regimesregimes ((i.e.i.e.,, withwith positivepositive ININ values).values). ISPRS Int. J. Geo-Inf. 2016, 5, 36 8 of 12 Obviously,Obviously, sometimessometimes thethe islandsislands areare geographicallygeographically connectedconnected withwith unnavigableunnavigable regimes,regimes, whichwhich areare spatially spatially merged merged together together to to obtain obtain obstacles obstacles when when gener generatingating Voronoi Voronoi Diagram Diagramss.. The The white white linesobstacleslines inin FigureFigure when 77 generating areare thethe generatedgenerated Voronoi Diagrams.skeletonskeleton lines,lines, The whitewhichwhich lines areare usedused in Figure toto buildbuild7 are thethe the “road generated“road--networknetwork skeleton--likelike graph”lines,graph” which inin thethe are seasea used forfor automaticautomatic to build theshipship “road-network-like routeroute planning.planning. OnOn graph” thethe rightright in isis the thethe sea detaileddetailed for automatic viewview ofof shipanan areaarea route ofof interestplanning.interest inin Onthethe theblackblack right rectanglerectangle is the detailed onon thethe left.left. view of an area of interest in the black rectangle on the left.

FigureFigure 7. 7. 7. NavigableNavigableNavigable (or (or (or positive) positive) positive) regimes regimes regimes in blue, in in blue, blue, unnavigable unnavigable unnavigable (or negative) (or (or negative) negative) regimes regimes regimes in red and in in red redskeleton and and skeletonlinesskeleton in white lineslines in onin whitewhite 2 February onon 22 FebruaryFebruary 2015 for ship20152015 type forfor shipship CAC3. typetype CAC3.CAC3.

FigureFigure 8. 8. NorthwestNorthwest Passage: Passage: red red lines lines are are possible possible shipship routes routes ( (ssource:ource: [[3030]],, where where this this figure figure is is Figure 8. Northwest Passage: red lines are possible ship routes (source: [30], where this ﬁgure is directlydirectly citedcited withoutwithout copyrightcopyright issueissue).). directly cited without copyright issue).

AccordingAccording toto thethe methodologymethodology describeddescribed inin SectionSection 2.2,2.2, wewe cancan builbuildd upup aa “road“road--networknetwork--likelike graph”graph”According basedbased onon to thethe the generatedgenerated methodology skeletonsskeletons described (white(white in lineslines Section inin FigureFigure2.2, we 7).7). can ThenThen build wewe up cancan a “road-network-likeapplyapply anyany shortestshortest graph” based on the generated skeletons (white lines in Figure7). Then we can apply any shortest path algorithms to the graph to calculate the shortest and safest routes for ship navigation. It means ISPRS Int. J. Geo-Inf. 2016, 5, 36 9 of 12

ISPRS Int. J. Geo-Inf. 2016, 5, 36 9 of 12 path algorithms to the graph to calculate the shortest and safest routes for ship navigation. It means that the ship route will navigate along the skeletons. One question may arise here: Are there any real seathat routes the ship that route also will follow navigate the skeleton along the method? skeletons. In Figure One question 8, the Northwest may arise here:Passage Are sea there route any that real connectssea routes the that Atlantic also follow and the Pacific skeleton Oceans method? through In Figure the Arctic8, the O Northwestcean is shown Passage as sea the route red lines. that Althoughconnects the the Atlantic Northwest and PaciﬁcPassage Oceans has been through served the as Arctic the recommended Ocean is shown route as the for red all lines. types Although of ships withoutthe Northwest considering Passage the hasvariation been servedof sea ice as over the recommendedtime, visually we route can for see all that types it follows of ships the without sea ice navigationconsidering principle: the variation keep of as sea far ice as over possible time, visuallyaway from we canunnavigable see that it regimes. follows the Because sea ice of navigation this, the principle:Northwest keep Passage as far is as consistent possible with away the from proposed unnavigable method regimes. in this Because study. More of this, importantly, the Northwest the proposedPassage is method consistent has with the theadvantages proposed in method dealing in with this sea study. ice Morevariation importantly, over time the with proposed the support method of GIShas thetechniqu advantageses. in dealing with sea ice variation over time with the support of GIS techniques.

3.3. Automatic S Shiphip R Routeoute P Planninglanning An origin and destinationdestination pairpair OO andand DD (two (two white white triangles triangles in in Figure Figure9), 9) respectively,, respectively, is setis set up up in inthe the local local area area of interest.of interest. By applyingBy applying the Dijkstrathe Dijkstra shortest shortest path path algorithm algorithm integrated integrated in NavSpace in NavSpaceto the “road-network-liketo the “road-network graph”-like graph” in the sea, in the we cansea, getwethe can shortest get the yetshortest safest yet path safest (shown path as (shown the bold as black the boldline inblack Figure line9a), in ofFigure which 9a), the of total which length the istotal 84.4 length kilometers. is 84.4 Askilometers. mentioned As previously, mentioned the previously, shortest paththe shortest is also thepath safest is also because the safest such because path also such follows path thealso safest follows navigation the safest regulation, navigationi.e. regulation,, to keep as i.e. far, toas keep possible as far away as possible from unnavigable away from regimes unnavigable when regimes navigating when in thenavigating sea. in the sea.

(a) (b)

Figure 9. The shortest and safest path (a) in black and the weighted shortest and safest path (b) in Figure 9. The shortest and safest path (a) in black and the weighted shortest and safest path (b) in black between Origin and Destination pair for ship type CAC3.

Noticeably, according to [27], it is also important that the ships should navigate along the coastlinesNoticeably, as much according as possible to [27 in], itthe is alsosea. importantTo solve this that problem, the ships a should weighted navigate shortest along path the algorithm coastlines isas developedmuch as possible in NavSpace in the sea.. In To weighted solve this shortest problem, path a weighted algorithm, shortest rather path than algorithm using the is developed geometric NavSpace lengthin of each. In skeleton, weighted we shortest use weight path of algorithm,each skeleton rather line thanto calculate using the shortest geometric route. length The weight of each is calculatedskeleton, we in usethe weightfollowing of each way: skeleton we simply line calculate to calculate the shortest closest route.distance The between weight iseach calculated skeleton in line the segmentfollowing and way: coast we simplyline, and calculate the weight the closest to each distance skeleton between line is each just skeletonin inverse line proportion segment and to coastsuch normalizedline, and the distance. weight toIt means each skeleton that the line closer is justthe inskeleton inverse line proportion is to coast to lines, such the normalized higher the distance. weight ofIt means the skeleton that the line closer segment the skeleton has, and line vice is toversa coast. After lines, that the, higher when we the calculate weight of the the shortest skeleton path line vice versa betweensegment has, OD and pairs, we also. After obtain that, the when weight we calculate of each the possible shortest path. path To between satisfy OD the pairs, above we twoalso navigationobtain the weight safety regulations,of each possible we canpath. just To selectsatisfy such the above a path two that navigation has the highest safety regulations, weight and we its

ISPRS Int. J. Geo-Inf. 2016, 5, 36 10 of 12

ISPRS Int. J. Geo-Inf. 2016, 5, 36 10 of 12 can just select such a path that has the highest weight and its length is also close to the shortest path. lengthIt is a kind is also of close balance to the between shortest travel path. distance It is a kind and safety.of balance For between example, travel on the distance right side and of safety. Figure For9b, example,the length on of the the right path side (black of boldFigure line) 9b, between the length the of OD the pair path is (black 93.5 kilometers. bold line) between It is a bit the longer OD thanpair isthe 93.5 one kilometers. in Figure9 a,It butis a hasbit longer the higher thanweight, the one whichin Figure means 9a, but that has it best the ﬁtshigher the twoweight, navigation which means rules: thatcloser it tobest cost fits lines the two and navigation having higher rules: weight. closer to cost lines and having higher weight.

33.4..4. Isovist: A Automaticutomatic I Identificationdentification of N Navigableavigable A Areasreas In SectionsSections 3.23.2 andand 3.3 3.3, we we demonstrated demonstrated how how to to build build a “road-network-like a “road-network-like graph” graph” in the in seathe andsea andfinally finally apply apply the Dijkstra the Dijkstra shortest shortest path and path weighted and weighted shortest shorte path algorithmsst path algorithms to the graph to the to graph calculate to calculatethe shortest the and shortest safest and route. safest However, route. However, in some situations, in some situations, navigators navigators need be aware need ofbe the aware navigable of the navigableareas from areas current from location current of location the ship of to the guide ship ship to guide navigation, ship navigation, e.g., fishing e.g., ship fishing needs ship to randomly needs to randomlynavigate in naviga somete sea in areas. some Figuresea areas. 10 is Figure the example 10 is the of example visual analysis of visual in analysis the Arctic in sea.the Arctic sea.

Figure 10. The navigable/visible area area in in green green generated generated using using IsovistIsovist forfor Type Type CAC3 CAC3 ship, ship, where where the yellow point is the current position.

The yellowyellow pointpoint in in Figure Figure 10 10 is theis the current current ship ship location, location, and theand green the green polygon polygon area is area the visibleis the visibleor navigable or navigable area, where area, wewhere can we apply can Isovistapply asIsovist described as described in Section in Section 2.3. We 2.3. implement We implement the Isovist the Isovistalgorithm algorithm as a spatial as a analysis spatial analysis tool and tool integrate and integrate it as part it of asNavSpace part of toNavSpace support to sea support ice navigation. sea ice navigation.It should be It noted should that, be thoughnoted that, the Isovist thoughtool the is Isovist developed tool is to developed identify the to navigableidentify the or navigable visible areas or whenvisible navigating areas when in navigating the sea, based in the on sea, the based boundary on the of boundary the visibility of the area, visibility we are area, able towe know are able more to knowinformation more information of the reachable of the area, reachable e.g., whether area, ite.g., is near whether the coastline it is near or the if there coastline is any or kind if there of ice. is Thisany kindtool canof ice. help This navigator tool can make help quicknavigator decisions make inquick some decision emergencys in some situations. emergency situations. It should also be noted that, though the generation of shortest and safest path as well as the IsovisIsovistt analysis is automatic and in real time, the data processing is semi-automaticsemi-automatic and not in real time. For For example, example, the the generation generation of of skeleton skeleton lines lines takes takes around around ten minutes using a mainstream desktop computer with typical configuration conﬁguration for each type of shipship using weekly ice charts. Once the data processing processing is is done, done, the the initialization initialization of the of “road-network-like the “road-network graph”-like graph” takes around takes twoaround minutes. two Theminutes. Canadian The Canadian Ice Service Ice updates Service theupdates ice data the onice adata daily on or a weeklydaily or basis,weekly therefore, basis, therefore, there is enoughthere is enoughtime to processtime to iceprocess data toice support data to sea support ice navigation. sea ice navigation. In the future, In the in the future, case thatin the sea case ice datathat updatesea ice datain real-time, update a in dynamic real-time, Voronoi a dynamic Diagram Voronoi method Diagram should method be developed should to be generate developed real-time to generate sea ice realnavigation-time sea skeletons, ice navigation which means skeletons, instead whichof updating means the instead whole Voronoi of updating Diagrams, the only whole the changedVoronoi Diagramsea ice areass, only will the be changed reconstructed. sea ice areas will be reconstructed.

ISPRS Int. J. Geo-Inf. 2016, 5, 36 11 of 12

4. Conclusions In this study, we make several important new contributions to the literature on automatic ice navigation support service in a time-geographic context. From a methodological perspective, we formalize algorithms for automatic ship routing and visual analysis based on ice numeral, and develop an analytical GIS package. It is available as an open source tool (link available for downloading at [31]) for validation and extension. This easy-to-use ArcGIS tool is also useful for interested readers in relevant research, for example, indoor navigation study. The methodology is demonstrated using the Canadian Ice Service Archive data. A number of future studies can be conducted. We have demonstrated the capacity to automatically navigate in the sea in real time and implemented it in an open source GIS package. An online version with a real-time dashboard is to be designed for sea ice navigation agency by integrating live data feeds and other types of real-time sea events for managing operations, which will offer researchers new insights in real-time sea ice navigation support. As for the validation of the methods and results, especially using real ship routes, it will be our future work as long as we have access to real navigation data.

Acknowledgments: This research has been funded in part by the Beaufort Regional Environmental Assessment (BREA) Initiative, which was funded by the Aboriginal Affairs and Northern Development Canada, and by the National Science and Engineering Research Council of Canada (Grant #RGPIN/250346-2011). The authors also wish to thank the Canadian Ice Service Archive for the ice data used in this study and its staff for their comments received during CanICE project execution. Author Contributions: Xintao Liu, Shahram Sattar and Songnian Li conceived of and designed the study. Xintao Liu and Shahram Sattar analyzed the data and performed the experiments. Xintao Liu, Shahram Sattar and Songnian Li wrote and revised the paper together. All authors have read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Snider, D. Ice navigation in the Northwest Passage. In Proceedings of the Ocean Innovation 2005, Rimouski, QC, Canada; Quebec, QC, Canada, 23 October 2005. 2. Transport Canada. Arctic Ice Regime Shipping System (AIRSS): User Assistance Package for the Implementation of Canada’s Arctic Ice Regime Shipping System (AIRSS); Transport Canada: Ottawa, ON, Canada, 1998. 3. Bell, T.; Briggs, R.; Bachmayer, R.; Li, S. Augmenting Inuit knowledge for safe sea-ice travel—The SmartICE information system. In Oceans-St. John's; IEEE: New York, NY, USA, 2014; pp. 1–9. 4. Transportation Safety Board of Canada. Available online: http://www.tsb.gc.ca/eng/stats/marine/2012/ ss12.asp (accessed on 17 January 2016). 5. Barry, R. The sea ice database. In The Geophysics of Sea Ice; Untersteiner, N., Ed.; Plenum Press: New York, NY, USA, 1986; pp. 1099–1134. 6. Drinkwater, K.F. Climatic Data for the Northwest Atlantic: A Sea Ice Database for the Gulf of St. Lawrence and the Scotian Shelf ; Fisheries & Oceans Canada, Maritimes Region, Ocean Sciences Division, Bedford Institute of Oceanography: Halifax, NS, Canada, 1999. 7. Knight, R.W. Introduction to a new sea-ice database. Ann. Glaciol. 1984, 5, 81–84. 8. Lenormand, F.; Duguay, C.R.; Gauthier, R. Development of a historical ice database for the study of climate change in Canada. Hydrol. Process. 2002, 16, 3707–3722. [CrossRef] 9. Liu, X.; Li, S. Towards a spatiotemporal database model for Canadian ice service portal. In Proceedings of Canadian Institute of Geomatics Annual Conference and the 2013 International Conference on Earth Observation for Global Changes (EOGC’2013), Toronto, ON, Canada, 5–7 June 2013. 10. Eicken, H.; Lovecraft, A.L.; Druckenmiller, M.L. Sea-ice system services: a framework to help identify and meet information needs relevant for Arctic observing networks. Arctic 2009, 62, 119–136. [CrossRef] 11. Li, S.; Xiong, C.; Ou, Z. A Web GIS for sea ice information and an ice service archive. Trans. GIS 2011, 15, 189–211. [CrossRef] 12. Liu, X.; Li, S.; Huang, W.; Gong, J. Designing sea ice web APIs for ice information services. Earth Sci. Inform. 2015, 8, 483–497. [CrossRef] ISPRS Int. J. Geo-Inf. 2016, 5, 36 12 of 12

13. Ou, Z. An integrated spatial information system for ice service. In Proceedings of the 20th ISPRS Congress, Istanbul, Turkey, 12–23 July 2004; pp. 12–23. 14. Arctic and Antarctic Research Institute (AARI). Available online: http://www.aari.nw.ru/index_en.html (accessed on 17 January 2016). 15. Canadian Ice Service (CIS), Environment Canada. Available online: https://www.ec.gc.ca/glaces-ice (accessed on 17 January 2016). 16. Finnish Meteorological Institute (FMI). Available online: http://en.ilmatieteenlaitos.ﬁ/ (accessed on 17 January 2016). 17. NASA. An Overview of EOSDIS. Available online: https://earthdata.nasa.gov/about-eosdis (accessed on 17 January 2016). 18. National Ice Center Institute (NIC). Available online: http://www.natice.noaa.gov (accessed on 17 January 2016). 19. Polar Data Catalogue (PDC). Available online: https://www.polardata.ca/ (accessed on 17 January 2016). 20. Chen, X.; Liu, X.; Li, S.; Chow, A. A comparative study on three EOF analysis techniques using decades of Arctic sea-ice concentration data. J. Cent. South Univ. 2015, 22, 2681–2690. [CrossRef] 21. Rayner, N.A.; Parker, D.E.; Horton, E.B.; Folland, C.K.; Alexander, L.V.; Rowell, D.P.; Kent, E.C.; Kaplan, A. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res.: Atmos. 2003, 108.[CrossRef] 22. Rothrock, D.A.; Thomas, D.R.; Thorndke, A.S. Principal component analysis of satellite passive microwave data over sea ice. J. Geophys. Res.: Oceans 1988, 93, 2321–2332. [CrossRef] 23. Howell, S.E.; Yackel, J.J. A vessel transit assessment of sea ice variability in the Western Arctic, 1969–2002: Implications for ship navigation. Can. J. Remote. Sens. 2004, 30, 205–215. [CrossRef] 24. Khon, V.C.; Mokhov, I. Arctic climate changes and possible conditions of arctic navigation in the 21st century. Izvestiya. Atmos. Ocean. Phys. 2010, 46, 14–20. [CrossRef] 25. MaxSea, Marine Navigation Software. Available online: http://www.maxsea.com (accessed on 17 January 2016). 26. Canadian Ice Service (CIS). Manual of Standard Procedures for Observing and Reporting Ice Condition; Canadian Ice Service, Environment Canada: Ottawa, ON, Canada, 2005. 27. Canadian Coast Guard (2012). Ice Navigation in Canadian Waters; Icebreaking Program, Maritime Services, Canadian Coast Guard, Fisheries and Oceans Canada: Ottawa, ON, Canada, 2012. 28. Turner, A.; Penn, A. Making isovists syntactic: Isovist integration analysis. In Proceeding of the 2nd International Symposium on Space Syntax, Brasilia, Brazil, 29 March 1999. 29. CanICE. Available online: http://geocolab.ryerson.ca/ice (accessed on 10 March 2016). 30. Geology. Available online: http://geology.com (accessed on 10 March 2016). 31. NavSpace. Available online: https://github.com/xintao/NavSpace (accessed on 10 March 2016).

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).