IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

The Grassland Inventory provides a standardized, high-resolution land cover classification for the grassland ecosystems in Canada. Developed using a random forest classification on multi-temporal Sentinel-1 SAR and Sentinel-2 Optical imagery, the series differentiates intact native grasslands from high-disturbance, tame perennial forage systems; two classes that are spectrally and phenologically similar, yet critical to differentiate and quantify accurately for carbon and biodiversity modelling. Each release in the series includes a categorical 10 m land cover raster and a companion continuous likelihood layer representing model confidence in the native grassland class. As new classifications are added (semi-decadal) and geographic extent increased, the series will enable consistent temporal comparisons to track grassland dynamics and land cover change to support operational and research applications within AAFC and stakeholders.

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions.This product consists of FCOVER indicator during peak-season (June-July-August) at 100m resolution covering Canada's land mass.

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.

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

Since 1988, the governments of Canada and Quebec have been working together to conserve, restore, protect and develop the St. Lawrence River under the St. Lawrence Action Plan (SLAP). One of the projects identified under the theme of biodiversity conservation is the development of an integrated plan for the conservation of the natural environments and biodiversity of the St. Lawrence River. The identification of priority sites for conservation has been the first step of this planning exercise. Conservation planning of natural environments requires a reliable, accurate and up-to-date image of the spatial distribution of ecosystems in the study area. In order to produce an Atlas of Priority Sites for Conservation in the St. Lawrence Lowlands, an updated cartography of the land cover of this vast territory was undertaken. This project required obtaining reliable information on the natural environments of the St. Lawrence Lowlands. Although several land cover mapping projects have been conducted for specific types of habitats, it was particularly important to obtain a homogeneous product that would cover the entire territory and that would provide the most detailed information on its various thematic components: agricultural, aquatic, human-modified and forest environments, wetlands as well as old fields and bare ground. The methodology used to produce the land cover mapping of the St. Lawrence Lowlands thus relied mainly on combining and enhancing the best existing products for each theme. This project was made in collaboration with MDDELCC as part of the St. Lawrence Action Plan (SLAP).

This land cover data set was derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor operating on board the United States National Oceanic and Atmospheric Administration (NOAA) satellites. Information on the NOAA series of satellites can be found at www.noaa.gov/satellites.html The vegetation and land cover information set has been classified into twelve categories. Information on the classification of the vegetation and land cover, raster to vector conversion, generalization for cartographic presentations is included in the paper "The Canada Vegetation and Land Cover: A Raster and Vector Data Set for GIS Applications - Uses in Agriculture" (https://geogratis.cgdi.gc.ca/download/landcover/scale/gis95ppr.pdf). A soil quality evaluation was obtained by cross-referencing the AVHRR information with Census of Agriculture records and biophysical (Soil Landscapes of Canada) data and is also included in the above paper. AVHRR Land Cover Data approximates a 1:2M scale and was done originally for Agriculture Canada. The projection used is Lambert Conformal Conic (LCC) 49/77 with origin at 49N 95W.

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

Röð uppréttra loftmynda úr loftmyndasafni Náttúrufræðistofnunar sem unnar voru á árunum 2013 til 2018 hjá Jarðvísindastofnun HÍ, sem partur af tveimur verkefnum: 1 - Mælingar á jöklabreytingum úr sögulegum loftmyndum. Þetta verkefni var unnið af Joaquín M.C. Belart í M.Sc. og Ph.D. hjá Jarðvísindastofnun. Útvaldar loftmyndir frá 1945 til 1994 voru skannaðar hjá Landmælingum Íslands sérstaklega fyrir þetta verkefni. Vinnsla þessara loftmynda fór fram með því að nota "Ground Control Points" (GCP) sem teknir voru úr lidarmælingum á íslenskum jöklum. Úrvinnsla gagna úr Drangajökli fór fram með ERDAS hugbúnaðinum. Nánari upplýsingar um vinnsluna er að finna í Magnússon o.fl., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). Úrvinnsla gagna frá öðrum jöklum var unnin með MicMac hugbúnaðinum, einnig með GCP teknir af lidar. Nánari upplýsingar um vinnsluna eru fáanlegar í Belart o.fl., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap -for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) og Belart o.fl., 2020 (https://www.frontiersin.org/articles/10.31630/feart/full.316390/feart. 2 - Mælingar á hraunmagni Heklugosanna á 20. öld. Þetta verkefni var unnið af Gro B.M. Pedersen sem hluti af verkefni þar sem unnið var að umhverfiskortlagningu og vöktun Íslands með fjarkönnun "Environmental Mapping and Monitoring of Iceland by Remote Sensing" (EMMIRS, fjármagnað af Rannís) á árunum 2015-2018. Loftmyndirnar af Heklu frá 1945 til 1992 voru skannaðar af Landmælingum Íslands. Vinnsla þessara mynda var gerð með ERDAS hugbúnaðinum og nánari upplýsingar um vinnsluna er hægt að nálgast í Pedersen o.fl., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) --------------------------------------------------------------------------------------------------------------- A series of orthomosaics using the archives of aerial photographs from Náttúrufræðistofnun (Loftmyndasafn) created between 2013 and 2018 at the Institute of Earth Sciences, as part of two projects: 1 - Measurements of glacier changes from historical aerial photographs. This project was conducted by Joaquín M.C. Belart during his M.Sc. and his Ph.D. at the Institute of Earth Sciences. A selection of aerial photographs from 1945 to 1994 were scanned at Náttúrufræðistofnun specifically for this project. The processing of these aerial photographs was done using Ground Control Points (GCPs) extracted from lidar surveys of Icelandic glaciers. The processing of the data from Drangajökull ice cap was done using the ERDAS software. Further details on the processing are available in Magnússon et al., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). The processing of the data from other glaciers was done using the MicMac software, also with GCPs extracted from lidar. Further details of the processing are available in Belart et al., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap-for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) and Belart et al., 2020 (https://www.frontiersin.org/articles/10.3389/feart.2020.00163/full) 2 - Measurements of the lava volumes of the Hekla eruptions in the 20th century. This project was conducted by Gro B.M. Pedersen as part of the Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS, financed by Rannís) project between 2015-2018. The aerial photographs of Hekla from 1945 to 1992 were scanned by Náttúrufræðistofnun. The processing of these photographs was done using the ERDAS software, and further details of the processing are available in Pedersen et al., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) References: Belart J.M.C., Magnússon E., Berthier E., Pálsson, F., Aðalgeirsdóttir, G., & Jóhannesson, T. (2019). The geodetic mass balance of Eyjafjallajökull ice cap for 1945–2014: Processing guidelines and relation to climate. Journal of Glaciology, 65(251), 395-409. doi:10.1017/jog.2019.16 Belart J.M.C., Magnússon E., Berthier E., Gunnlaugsson Á.Þ., Pálsson F., Aðalgeirsdóttir G., Jóhannesson T, Thorsteinsson T and Björnsson H (2020) Mass Balance of 14 Icelandic Glaciers, 1945–2017: Spatial Variations and Links With Climate. Front. Earth Sci. 8:163. doi: 10.3389/feart.2020.00163 Magnússon, E., Belart, J.M.C., Pálsson, F., Ágústsson, H., and Crochet, P.: Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland, The Cryosphere, 10, 159–177, https://doi.org/10.5194/tc-10-159-2016, 2016. Pedersen, G. B. M., Belart, J. M. C., Magnússon, E., Vilmundardóttir, O. K., Kizel, F., Sigurmundsson, F. S., et al. (2018). Hekla volcano, Iceland, in the 20th century: Lava volumes, production rates, and effusion rates. Geophysical Research Letters, 45, 1805–1813. https://doi.org/10.1002/2017GL076887