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Park staff visually evaluate tree health within 20m x 20m forest plots in August each year. Plots are rotated every 5 years (6 plots/year) and monitored according to term Ecological Monitoring and Assessment Network (EMAN) Protocols and Standards. Crown condition, diameter at breast height, and stem defects are used as indicators of tree health to help identify the symptoms of tree and forest decline.

TINP evaluates seedling and sapling density within 5 subplots of the 20m x 20m EMAN forest plots in August each year. Plots are rotated every 5 years (6 plots/year) and are monitored according to term Ecological Monitoring and Assessment Network (EMAN) Protocols and Standards.

The “Land Cover for Agricultural Regions of Canada, circa 2000” is a thematic land cover classification representative of Circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer.

The Ontario Railway Network (ORWN) is seven data classes that use the federal GEOBASE standard for the National Railway Network (NRWN) geospatial data. The seven data classes include: * track * crossing * junctions * marker posts * structure lines * structure points * stations Although the ORWN is mainly used as base data for cartographic products, users will also get railway-associated attributes.

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013).

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013).

Statistics on commercial fishing licences issued by Fisheries and Oceans Canada in the Pacific Region. You will also find information collected through the registration of commercial fishing vessels. Data are available from 1985 onward.

The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

The categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.

The ‘Circa 1995 Landcover of the Prairies’dataset is a geospatial raster data layer portraying the rudimentaryland cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer.

The roads and linear features in this data set were compiled and interpreted in association with the Vegetation Inventory (1995-2006). It is a representation of various linear features on the land base. The linear features were classified by the interpreters based on the NTDB classification system for roads and trails, at that time.When used in conjunction with other road or linear transportation networks, there may be duplication of lines and information.