A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data

Figures & data

Table 1. Main service organizations and institutions for Arctic sea ice conditions and meteorological information (the last visit to the websites was on December 22, 2020).

Country	Name	Link	Key function/mission
USA	National Snow and Ice Data Center (NSIDC)	https://nsidc.org	Supports cryospheric research: snow, ice, glaciers, frozen ground, and climate interactions (Tschudi, Meier, & Stewart, 2020).
	National Ice Center (NIC)	https://www.natice.noaa.gov	Provides global to tactical scale ice and snow products, ice forecasting, and other environmental intelligence services for the United States government (Partington et al., 2003).
	International Arctic Buoy Programme (IABP)	https://iabp.apl.uw.edu	Provides meteorological and oceanographic data for real-time operational requirements and research purposes (Rigor & Ortmeyer, 2004).
Russia	Arctic and Antarctic Research Institute (AARI)	http://www.aari.ru	Includes near-real-time observations, weekly/monthly analysis of Arctic sea ice, and long-term statistical analysis (Mahoney et al., 2008).
	Center for High North Logistics Information Office (CHNL IO)	https://arctic-lio.com	Provides information on Arctic sea ice, air pressure, wind, tides, and ocean currents (Wang et al., 2019).
Canada	Canadian Ice Service (CIS)	http://weather.gc.ca/marine	Provides ice information, iceberg maps, and ice model maps in the seas surrounding Canada (Tivy et al., 2011).
	Canadian Coast Guard (CCG)	https://www.marinfo.gc.ca	Provides information on ice conditions in Canadian waters (Mitchell, 2013).
	Ocean and Sea Ice Satellite Application Facility (OSI SAF)	https://osisaf-hl.met.no	Develops, processes and distributes, in near-real-time, products related to key parameters of the ocean-atmosphere interface (Lavergne et al., 2019).
	Year of Polar Prediction (YOPP)	https://yopp.met.no	Enables a significant improvement in environmental prediction capabilities for the polar regions and beyond (Bromwich et al., 2020).
Norway	Norwegian Meteorological Institute (NMI)	http://www.yr.no/hav_og_kyst	Mainly provides weather, ocean and sea ice forecasts.
		https://cryo.met.no	Gives access to the latest products and information about sea ice, snow and permafrost.
Demark	Danish Meteorological Institute (DMI)	http://ocean.dmi.dk	Provides basic data support for marine operational forecasting services in the waters near Denmark (Hestnes, 1985; Balashova et al., 2019).
Iceland	Icelandic Meteorological Office (IMO)	https://en.vedur.is	Provides ice reports during the navigating season (Yang et al., 2020).
Finland	Finnish Meteorological Institute (FMI)	https://en.ilmatieteenlaitos.fi	Produces high-quality observations and research data on the atmosphere and seas (Leviäkangas & Hautala, 2009).

Figure 1. Overall framework of the SNISS.

Table 2. Main technologies of the secondary development engine.

Function	Description of the main technology	Link
User interaction	Vue: Open source front-end framework	https://vuejs.org/index.html
	iView: Vue-based open source user interface (UI) component library	http://iview.talkingdata.com/
	Cesium: Open source 3D JavaScript (JS) library	https://cesium.com/cesiumjs/
	Echarts D3: Open source chart library	http://echarts.apache.org/
Data exchange	Apache Tomcat: Open source implementation of the Java Servlet, JavaServer Pages, Java Expression Language and Java WebSocket technologies	https://tomcat.apache.org/
	Redis: Data buffer container	https://redis.io/
	GeoServer: Image publishing service	http://geoserver.org/

Figure 2. The distribution of Arctic routes.

Table 3. Main data used in this study (the information in this table was updated on August 1, 2021).

No.	Variable	Source	Format	Time	Period	Resolution	Link
1	Sea ice concentration	MASIE-AMSR2	NetCDF	2012 to present	day	4 km	https://nsidc.org/data/G10005/versions/1
		IAP LASG	NetCDF	May 2021 to October 2021	day	1°	http://project.lasg.ac.cn/FGOALS_f2-S2S/index.php
		C3S Global Shipping service	NetCDF	2010 to 2100	month	1°	https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-shipping-arctic?tab=overview
2	Sea ice thickness	PIOMAS model	NetCDF	1978 to present	day	0.1–2.5°	http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid
		IAP LASG	NetCDF	May 2021 to October 2021	day	1°	http://project.lasg.ac.cn/FGOALS_f2-S2S/index.php
		C3S Global Shipping service	NetCDF	2010 to 2100	month	1°	https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-shipping-arctic?tab=overview

Figure 3. Regular grids with different projections (blue represents oceans, and yellow represents land). (a) Regular grid (WGS 1984 World Mercator). (b) Regular grid (North Pole Orthographic).

Table 4. Main three bands of FY-3D/MERSI-II used in this work and its application and physical parameters.

P	Band	R (μm)	L (nm)	S (m)	N (ΔT~K)	M (ρ, K)
Remote sensing of land andcloud boundaries and their characteristics	1	0.470	50	250	100	90%
	2	0.550	50	250	100	90%
	3	0.650	50	250	100	90%

P indicates the primary purpose of each band, Band represents the band number, R represents the centre wavelength, L represents the spectral bandwidth, S represents the spatial resolution, N represents the signal-to-noise ratio or noise equivalent temperature difference, M represents the dynamic range, ρ is the unit of reflectivity, and K is the unit of brightness temperature.

Figure 4. Spatial distribution of the risk index outcome (RIO) for a merchant ship on October 15, 2019.

Figure 5. Optimal route extraction results. The coordinates in the figure represent the row and column numbers of the data; the green grid is the starting point and the red grid is the end point; yellow grids represent lands, light blue grids represent regions with positive RIO and pink ones represents regions with negative RIO.

Figure 6. Optimal route analysis interface (http://arcticroute.tpdc.ac.cn/encesium_index/?type=1&locale=en). Users can select the date for which the distribution of the optimal route is displayed. In figure (a), A: the yellow line on the map is the optimal route calculated by the system for merchant ships sailing in the NEP on July 15, 2020; the map colours represent the RIO for merchant ships on this day. C: visualization of sea ice concentration and thickness along the NEP. D: ten- to fifteen-day temperature forecast along the optimal route. In figure (b), B: the buffer zone where ships can navigate safely.

Figure 7. Automatic extraction process of sea ice image.

Figure 8. Drawing a rectangle to select a region of interest (http://arcticroute.tpdc.ac.cn/encesium_index/?type=2&locale=en).

Figure 9. The online calculation results are automatically displayed in the spherical system.

Table 5. Comparison between SNISS and route planning system using Google Earth.

Name	Main functionalities	Area	3D GIS platform	Factors to be considered for route planning	Calculation method	Other auxiliary functionalities
Route planning system using Google Earth	Route planning for major ports between Asia and Europe	NEP	Google Earth	Distribution of the Arctic floating ice areas, water depth	Manual analysis and processing	Comparing the NEP with the traditional route through the Suez Canal using a dynamic analysis of the cost efficiency
SNISS	Route analyses and automatic sea ice image extraction	NEP	Caesium	Sea ice concentration and thickness, distance	Automatic completion	Changes in sea ice concentration and thickness along the NEP are presented using a 3D chart

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Data availability statement

Data are openly available in a public repository that does not issue DOIs. The sea ice concentration data from 2012 to present are openly available at https://nsidc.org/data/G10005/versions/1, and the sea ice thickness data from 2012 to present are openly available at http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid. In addition, climate projections of the sea ice conditions are openly available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-shipping-arctic?tab=overview.