This data is a snapshot of Ontario’s forests using the latest available data. Summaries include: * area and volume of forest types * common tree species * other land information such as area by forest region, ecoregions and other land classes Visual display: * The forest information presented in this data is also available in interactive maps and charts * Maps and charts allow you to view the data in finer detail and allows for comparisons between forest types and regions You can also view: * [Forest resources of Ontario 2021](https://www.ontario.ca/document/forest-resources-ontario-2021)

Spatial location of all harvesting identified in the first 10 years of 2012 Forest Management Plans for Crown timber licenses. Blocks are identified by broad treatment category and by the period (2012-2016 and 2017-2022) they are available for harvest.

The spatial representation for a Natural Resource (NR) Region, that is an administrative area established by the Ministry, within NR Areas. These boundaries are designated by the Lieutenant Governor in council and published as regulations which establishes the Ministry's management areas. This dataset supersedes WHSE_ADMIN_BOUNDARIES.FADM_REGION

The spatial representation for a Natural Resource (NR) District, that is an administrative area established by the Ministry, within NR Regions.

The spatial representation for a Natural Resource Area. An NR AREA is an amalgamation of all NR Regions within the supplied boundaries and acts as an administrative area to these regions as established by the Ministry. This dataset supersedes WHSE_ADMIN_BOUNDARIES.NRO_ADM_AREAS_SP

A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

The digital atlas contains distribution information of dominant forest plants, species assemblages (vegetation types), and some habitat and structural characteristics for the eco-district 6e10 and Greater Park Ecosystem (GPE) — St. Lawrence Islands National Park. We used statistical modeling and prediction to make the distributional information for the entire study area. We extrapolated information from geo-referenced vegetation data collected during summers from 2005 to 2007 with other spatial layers, such as digital elevation and remote sensing derivatives. The maps are in raster (grid) format at a 10m resolution. You can navigate them by following the documents and readme files in the individual folders or in the main document folder called ‘6e10_documents’. In southern Ontario, there is a lack of current ‘wall-to wall’, fine-scale, vegetation class and species vegetation mapping to support diverse applications and initiatives related to natural resource management, conservation and landscape and land-use planning. These maps could serve as baseline information for: * natural heritage design and planning * Species at Risk (SAR) recovery planning * state of biodiversity reporting * forest management and planning * invasive species management * ecological goods and services estimates * wildlife habitat modeling and mapping * additional applications and research techniques * requiring mapped vegetation information __How to reference this dataset__ This product should be referenced as: Puric-Mladenovic, Danijela, Julia Buck; David Bradley, Robert Arends, Silvia Strobl, and Nayna Khalatkar (2008). _Digital atlas of predicted species distributions, vegetation assemblages and habitat characteristics for the eco-district 6e10 and GPE — St. Lawrence Islands National Park’_, version 1.0.. Information Management and Spatial Analysis Unit, Southern Science and Information Section, OMNR, Peterborough, Ontario. __Additional time period information__ Field sampling was collected during the summer months from 2005 to 2007. The following activities took place during 2008: * spectral and environmental data assembly * predictive modelling and mapping Alternate title: Plant atlas for St. Lawrence Islands National Park

Forest Management in Canada 2020 Vector Tile Layer This vector tile layer is used in the Story Map of Forest Management in Canada, 2020.

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-d3a27829e209 In 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

Canadian forest management has multiple goals and varies in intensity. Governments, forest companies, Indigenous peoples, communities, and many other stakeholders are all involved in the forest management planning process. Management goals and the plans developed by professional foresters to achieve these goals differ from place to place. Canadian forests are often grouped into two categories: managed forest and unmanaged forest. This type of classification is sometimes useful, but the reality is much more complex and interesting. This interactive story map provides information on designations, ownership, forest tenures, and land protection statuses, and provides a comprehensive picture of the geography of Canada's managed forests. It has been updated from an earlier version to show land designations in 2020. **This third party metadata element was translated using an automated translation tool (Amazon Translate).**