This data is intended to identify Canadian Alternate Exchange Areas described in https://tc.canada.ca/en/marine-transportation/marine-safety/list-canada-s-designated-alternate-ballast-water-exchange-area-fresh-waters-tp-13617e-2021. The data is not intended for navigation purposes.According to Canada’s Ballast Water Regulations, if your vessel enters waters under Canadian jurisdiction from somewhere other than the U.S. waters within the Great Lakes Basin, and it cannot conduct a ballast water exchange in the areas set out in paragraphs 14(1)(a) and (b) of the regulations, then it will have to conduct a ballast water exchange in one of the areas listed below:-Gulf of St. Lawrence-Atlantic Canada-Western Canada-Canadian Eastern Arctic-Canadian Western Arctic: If you bring your vessel to a Canadian port, offshore terminal or anchorage area in the Western Arctic ballast water must be exchanged in an area as far away from shore as possible, where the water is more than 100 meters deep.Legal Constraints: Users should be aware that the polygons depicting ballast water exchange areas are intended for illustration only and should not be used for navigational or legal purposes.

NHS as officially accepted by the Council of Ministers, mapping by Transport Canada.

The Ontario Radar Digital Surface Model (DSM) has the following features: * source data: 1 arc second spaceborne C-Band Interferometric Synthetic Aperture Radar (IFSAR) data * Ministry of Natural Resources (MNR) Lambert Conformal Conic Projection * vertical datum in both EGM96 and CGVD28, separately * elevation value: floating * local Polynomial Interpolation from vector elevation points * spatial resolution: 30 meter * asurface elevation model This product offers significant advancements in elevation data in the province. [Read the details about these advancements and other technical specifications,](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-radar-digital-surface-model/) including data processing, major spatial characteristics of the Radar DSM, and the steps to generate the Northern Ontario Radar DSM.

This data publication contains an optimized mosaic of PALSAR-2 L-band dual-polarized radar backscatter summer composite for the year 2020 across Canada (excluding the Arctic Archipelago). Its primary purpose is to offer the best possible L-band radar summer-like composite mosaic mostly tailored for i) classifying natural treed or shrubby vegetation covers, and ii) estimating their structural attributes, such as height and biomass. ## Methodology:This product is based on the freely available and open dataset of yearly JAXA Global PALSAR-2/PALSAR Mosaics ver. 1 (hereafter JAXA GPM v1). They were generated by the Japanese space agency (JAXA) using PALSAR L-band synthetic aperture radar sensors aboard the Advanced Land Observing Satellites (ALOS): ALOS-2 PALSAR-2 (2015 to 2020) and ALOS PALSAR (2007 to 2010). JAXA GPM v1 provide yearly mosaics orthorectified and slope-corrected L-band HH- and HV-polarized gamma naught (γ°) backscatter amplitude with 25-m pixel size and scaled as 16-bit data (Shimada et al. 2014). JAXA GPM v1 are accessible as a Google Earth Engine image collection at https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR.The yearly 2007 to 2020 JAXA GPM v1 dataset across Canada underwent a post-processing and compositing methodology implemented in Google Earth Engine, as detailed in Pontone et al. 2024 and summarized in a pdf “Readme” file provided with the dataset. In summary, the method involves these three steps: 1. Post-processing of yearly γ° HH and HV datasets: handling data gaps, filtering speckle noise, and generating two radar vegetation indices, the HV/HH ratio (HVHH) and the radar forest degradation index (RFDI).2. Temporal compositing from 2015 to 2020 of post-processed yearly γ° PALSAR-2 HH, HV, HVHH, and RFDI backscatter data aimed to i) address data gaps and ii) mitigate detrimental backscatter fluctuations across ALOS-2 orbits resulting from numerous out-of-summer acquisitions.3. Generating the final PALSAR-2 L-band γ° radar backscatter summer composite circa 2020 raster files. ## Performance et limitations:The resulting Canada-wide, excluding the Arctic Archipelago, gap-free and radiometrically optimized mosaic of circa 2020 PALSAR-2 L-band backscatter summer composite was found significantly improved compared to the single-year 2020 JAXA GPM v1 mosaic, particularly in northern boreal Canada (Pontone et al. 2024). However, this product should be considered as a summer-like composite and users should be mindful of the following known limitations: • In northwestern Canada, there were often minimal to no summer PALSAR-2 acquisitions, resulting in residual backscatter fluctuations across ALOS-2 orbits.• The composite may exhibit patchy radiometric noise in areas that experienced major disturbances (fires, harvesting) between 2015 and 2020 despite they were accounted for in our compositing methodology.• This product is deemed less performant, or possibly not suitable, for i) characterizing highly dynamic land cover types such as grasslands, croplands, and water bodies, or for ii) estimating soil and/or vegetation moisture content for the year 2020.As a final note, JAXA released an improved GPM ver. 2 that was not available at the time of this study. A preliminary analysis shows that the circa 2020 PALSAR-2 composite product still seems to outperform the 2020 JAXA GPM v2 in northern Canada. ## Additional Information on Dataset: This dataset comprises four raster geotiff files of circa 2020 L-band PALSAR-2 summer temporal composites as mosaics of orthorectified and radiometrically slope corrected dual-polarized HH and HV gamma naught (γ°) backscatter amplitude, along with two radar vegetation indices (HVHH, RFDI), all scaled as 16-bit Digital Number (DN) values with 30-m pixel size in Lambert conformal conic projection. An additional 8-bit RGB quick-view file is also provided. A companion pdf ”Readme” file provides further details about these geotiff files and equations to convert DN values to γ° absolute intensity values. ## Dataset Citation: Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209In addition, please provide credits to the Japanese space agency JAXA with the mention “Original Global PALSAR-2/PALSAR Mosaics v1 provided by JAXA (©JAXA)” ## Publication Reference for Product Development and Use in Wetland Mapping: Pontone, N., Millard, K., Thompson, D., Guindon, L., Beaudoin, A. (2024). A hierarchical, Multi-Sensor Framework for Peatland Sub-Class and Vegetation Mapping Throughout the Canadian Boreal Forest. Remote Sensing for Ecology and Conservation (accepted for publication).## Cited reference: Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Tomohiro, S., Thapa, T., Lucas, R. (2014). New Global Forest/Non-Forest Maps from ALOS PALSAR Data (2007-2010). Remote Sensing of Environment, 155, pp. 13-31. https://doi.org/ 10.1016/j.rse.2014.04.014

High-Resolution Digital Elevation Model (HRDEM) generated from LiDAR. This data collection includes a Digital Terrain Model (DTM) and a Digital Surface Model (DSM). The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013). Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The data in this collection have been reprojected from the source reference system to the Canada Atlas Lambert projection (EPSG:3979). **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

All files and resources that allow you to connect to planned and real-time data from the Société de transport de Trois-Rivières. GTFS 2024**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

High-Resolution Digital Elevation Model (HRDEM) generated from LiDAR. This data collection includes a Digital Terrain Model (DTM) and a Digital Surface Model (DSM). The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013). Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The data in this collection have been reprojected from the source reference system to the Canada Atlas Lambert projection (EPSG:3979). **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

Operability for the Revelstoke TSA (topr_rv)

The Sentinel mirror is maintained by the Government of Canada through the Copernicus collaborative ground segment program as well as EUMETSAT. Data is made available as quickly as possible based on Canada coverage availability at the source. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The NRN product is distributed in the form of thirteen provincial or territorial datasets and consists of two linear entities (Road Segment and Ferry Connection Segment) and three punctual entities (Junction, Blocked Passage, Toll Point) with which is associated a series of descriptive attributes such as, among others: First House Number, Last House Number, Street Name Body, Place Name, Functional Road Class, Pavement Status, Number Of Lanes, Structure Type, Route Number, Route Name, Exit Number. The development of the NRN was realized by means of individual meetings and national workshops with interested data providers from the federal, provincial, territorial and municipal governments. In 2005, the NRN edition 2.0 was alternately adopted by members from the Inter-Agency Committee on Geomatics (IACG) and the Canadian Council on Geomatics (CCOG). The NRN content largely conforms to the ISO 14825 from ISO/TC 204.