This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Mask out water bodies using a satellite-derived water layer • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing

The data used in this compilation come from the Canadian Gravity Database (CGDB), which is managed by the Canadian Geodetic Survey (CGS), Surveyor General Branch. CGDB includes more than 755 000 observations, including some 232 000 observations on land. The distribution of the land data represents an average of one gravity point per 40 km². The gravity maps, which are gridded to a 2-km interval with a blanking radius of 20 km, currently include data acquired between 1944 and 2015. CGS and partners continue to supplement the CGDB each year. The surveys are conducted using relative gravimeters that measure the gravity difference between two locations. On the landmass, gravity has been measured primarily using static gravimeters. Although measurements at some offshore locations have been collected using static gravimeters on the ocean floor, most are acquired using dynamic gravimeters aboard moving vessels. The relative nature of the gravimeters require that surveys be tied to base (control) stations with known absolute gravity. The base stations are part of the Canadian Gravity Standardization Network (CGSN), which is tied to the International Gravity Standardization Network 1971 (IGSN71). Today, the traditional base stations are being replaced by new base stations that are measured with an absolute gravimeter having an accuracy of 2µGal (2x10-8 m/s²). All relative gravity measurements are integrated into the IGSN71 datum to create a coherent dataset at the global scale. Normal (theoretical) gravity is calculated using the Geodetic Reference System 1980 (GRS80; https://doi.org/10.1007%2FS001900050278). Bouguer anomalies, which include reductions of the elevation and topographical mass to sea level, are calculated using a vertical gravity gradient of ~0.3086 mGal/m (change slightly with latitude and elevation) and a crustal density of 2670 kg/m³.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

The Canadian Gravity Anomaly Data Base consists of approximately 660 000 gravity observations, including 165 000 on land, acquired between 1944 and the present. The data spacing ranges from less than 1 km to over 20 km, with an average spacing between 5 and 10 km. All measurements were reduced to the IGSN71 datum. Theoretical gravity values were calculated from the Geodetic Reference System 1967 (GRS67) gravity formula. Bouguer anomalies were calculated using a vertical gravity gradient of 0.3086 mGal·m-1 and a crustal density of 2 670 kg·m-3.

This metadataset was compiled to document scientific dive operations conducted within the Banc-des-Américains Marine Protected Area (MPA). Its purpose is to enable the tracking of field activities carried out as part of ecological monitoring of the atlantic wolffish (Anarhicas lupus) habitats and other key species.Metadata were collected for each scientific dive, following a standardized protocol implemented since 2014 that includes the date, participating team, GPS coordinates, depth, and dive duration.The released dataset contains only descriptive and contextual information related to each dive operation: dive identifier, site, date, location, conditions of execution, method used, and environmental variables when recorded. It does not include photographs, videos, raw biological observations, or analysis results.Quality control involves standardization of the metadata form, verification of GPS positions and recorded depths, as well as annual internal review of the dive series by the scientific coordination team. Established procedures ensure the completeness, consistency, and traceability of the descriptive data provided.

This data represents the dryness of the land surface based on vegetation conditions. The data is created weekly and uses weekly information on precipitation anomalies (namely the Standardized Precipitation Index or SPI) and satellite vegetation condition derived from Normalized Difference Vegetation Index (NDVI) from the MODIS Satellite. These dynamic data sets along with static data sets on land cover, soil water holding capacity, irrigation, ecozones and land surface elevation are used to model the drought severity, based on the Palmer Drought Severity Index (PDSI). The mapcubist model was trained on historical data and applied in real time to the dynamic inputs to produce drought severity ratings. The model is run at a 1km resolution and was developed by the AAFC, the United States Geological Survey and the United States Drought Monitor at the University of Nebraska Lincoln.