This data depicts site suitability for the establishment of area-based Short Rotation Woody Crops (SRWC) of hybrid poplar on lands eligible (i.e. non-forested) for afforestation across Canada. Determining the feasibility of a large-scale afforestation program is one approach being investigated by the Government of Canada to increase Canada's potential to sequester carbon from the atmosphere and/or produce bioproducts and bioenergy. Large-scale afforestation, however, requires knowledge of where it is suitable to establish and grow trees. Spatial models based on Boolean logic and/or statistical models within a geographic information system may be used for this purpose, but empirical environmental data are often lacking, and the association of these data to land suitability is most often a subjective process. As a solution to this problem, a fuzzy-logic modeling approach to assess site suitability for afforestation of hybrid poplar (Populus spp.) and willow (Salix spp.) in Canada was developed. Expert knowledge regarding the selection and magnitudes of environmental variables were integrated into fuzzy rule sets from which estimates of site suitability were generated and spatially presented. The environmental variables selected included growing season precipitation, climate moisture index, growing degree days, the Canada Land Inventory capability for agriculture and elevation. Site suitability is generally defined as the fitness of a given type of land for a particular use. For this assessment, site suitability was defined as the fitness of edaphic, climatic and topographic conditions to establish and grow SRWC species at rates 8 times those of native species. Suitability index values range from 1-100, with higher values corresponding to higher suitability. Approximately 246,000 km2, or 38% of the eligible land base within Canada was found to be suitable for afforestation using Short Rotation Woody Crops (SRWC) of hybrid poplar and/or willow.

This data depicts site suitability for the establishment of area-based Short Rotation Woody Crops (SRWC) of willow on lands eligible (i.e. non-forested) for afforestation across Canada. Determining the feasibility of a large-scale afforestation program is one approach being investigated by the Government of Canada to increase Canada's potential to sequester carbon from the atmosphere and/or produce bioproducts and bioenergy. Large-scale afforestation, however, requires knowledge of where it is suitable to establish and grow trees. Spatial models based on Boolean logic and/or statistical models within a geographic information system may be used for this purpose, but empirical environmental data are often lacking, and the association of these data to land suitability is most often a subjective process. As a solution to this problem, a fuzzy-logic modeling approach to assess site suitability for afforestation of hybrid poplar (Populus spp.) and willow (Salix spp.) in Canada was developed. Expert knowledge regarding the selection and magnitudes of environmental variables were integrated into fuzzy rule sets from which estimates of site suitability were generated and spatially presented. The environmental variables selected included growing season precipitation, climate moisture index, growing degree days, the Canada Land Inventory capability for agriculture and elevation. Site suitability is generally defined as the fitness of a given type of land for a particular use. For this assessment, site suitability was defined as the fitness of edaphic, climatic and topographic conditions to establish and grow SRWC species at rates 8 times those of native species. Suitability index values range from 1-100, with higher values corresponding to higher suitability. Approximately 246,000 km2, or 38% of the eligible land base within Canada was found to be suitable for afforestation using Short Rotation Woody Crops (SRWC) of hybrid poplar and/or willow.

The mosaic of the AAFC colour orthophotos is a mosaic dataset of the commissioned georeferenced color digital orthophotos commissioned by Agriculture and Agri-Food Cananda. The imagery was delivered in GeoTIF and ECW formats.

The Crop Stress Index is the ratio of actual evapotranspiration (AET) to potential evapotranspiration (PET) express as: CSI = 1-(AET/PET) AET and PET are calculated within the Versatile Soil Moisture Budget (VSMB) model using temperature and precipitation data and a crop-specific biometeorological time scale model to estimate growth stage (Robertson, 1968), with crop specific phenological and crop water extraction coefficients taken from Chipanshi et al 2013. The WDI ranges between 0 and 1, with a value closer to 1 indicating higher stress Crop Stress Index is modelled for each climate station using measured precipitation and temperature

Crop development stage in a numerical scale. All living organisms move from one stage of development to the next over time. For annual crops, it life cycle (growing season) completed within a year. Crop water use differs from one stage to another mostly due to the differences in the amount of green leaves, thus crop stage is closely related to its water consumption and water stress condition. Crop stages are mostly controlled by growing season heat accumulation and regulated by day-length crop some crops. The crop stages provided here are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) , and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

Íslenska: Frá 2015 hefur verið opið aðgengi að hæðargögnum af Norðurheimskautinu (norður af 60°N, þar með talið af Íslandi). Gögnin hafa gengið undir nafninu ArticDEM og eru frá Polar Geospatial Center sem er staðsett í University of Minnesota (https://www.pgc.umn.edu/data/arcticdem/). Gögnin urðu til við vinnslu mikils magns af landhæðarlíkönum, flest frá 2012 en elstu gögnin eru frá 2008. Landhæðarlíkönin eru unnin úr steríópörum af gervitunglamyndum frá WorldView 1-3 og GeoEye-1. Notast var við SETSM sem er opinn hugbúnaður fyrir stafrænar myndmælingar á Bluewaters ofurtölvu University of Illinois. Hvert landhæðarlíkan hefur 2x2 m upplausn og dekkar um 18X100 km stórt svæði á jörðu. Samstarf Landmælinga Íslands, Veðurstofunnar og Polar Geospatial Center leiddi til þess að eftirfarandi aðferðir voru þróaðar til þess að vinna með gífurlegt magn gagna. Aðferðirnar eru: 1- Samræma staðsetningu allra landhæðarlíkana 2-Búa til samsett landhæðarlíkan úr öllum líkönunum með því að búa til þekju sem geymir tíma gagnanna. Hver pixill í samsetta líkaninu sem er unnið úr ArcticDEM er miðgildi allra líkana sem fyrirfinnast á svæðinu. Þar að auki: 3-Löguðum vötn >2km2 4-Bættum við lidar líkönum af jöklum úr Icelandic glaciers (Jóhanesson and others, 2013) 5-Bættum við gögnum sem safnað var með dróna úr Surtsey (Óskarsson and others, 2020) 6-Breyting úr geoíðu- í sporvöluhæði English: Since 2015, elevation data from the Arctic (north of 60°N, including Iceland) started to be openly available through the ArcticDEM project, led by the Polar Geospatial Center, University of Minnesota (https://www.pgc.umn.edu/data/arcticdem/). This data consists of a large amount of Digital Elevation Models (DEMs) repeatedly acquired (multitemporal), typically from 2012-present, and the oldest data reaching back to 2008. The DEMs are derived from satellite sub-meter stereo imagery, particularly from WorldView 1-3 and GeoEye-1. The processing of the DEMs was done using SETSM, an open-source digital photogrammetric software, in the Bluewaters supercomputer (University of Ilinois). Each DEM has 2x2m resolution and a footprint of ~18x100km. In a collaborative effort between the National Land Survey of Iceland, the Icelandic Meteorological Office and the Polar Geospatial Center, we developed methods to handle and process a large amount of data available for Iceland. The methods developed consisted of: 1-Spatial adjustment of all the available DEMs, for homogeneity and consistency in the location of each individual DEM. 2-Robust mosaicking into one single DEM of Iceland, by taking advantage of the multi-temporal coverage of DEMs. Each pixel of the mosaic corresponds to a median elevation value from the possible elevations available from the ArcticDEM. In addition, we did the following corrections of the resulting mosaic: 3-Fixing of lakes >2km2 4-Combination with lidar DEMs available for Icelandic glaciers (Jóhanesson and others, 2013) 5-Combination with a drone-based survey of Surtsey (Óskarsson and others, 2020) 6-Conversion from ellipsoidal to orthometric elevations

HRL, 6 háupplausnargagnalög: yfirborðsgegndræpi, skógar (trjákrónuþéttleiki), skógar (barrtré/lauftré), graslendi, votlendi, vötn. Rastagögn, 20 m myndpunktsstærð, upprunaleg og endurbætt gagnalög og skýrslur, ISN2004. Hægt er að sækja gögnin á niðurhalssíðu Landmælinga Íslands. Nánari upplýsingar um hvert lag fylgja gögnunum. HRL, 6 High Resolution Layers: imperviousness, tree cover density, forest type, grasslands, wetlands, permanent water bodies. Raster data, 20 m pixel size, intermediate and enhanced results, data and verification/enhancement reports, ISN2004. The datasets can be downloaded from the National Land Survey of Iceland Download Site where more details information about each layer are included.

The 1 cm resolution digital surface model (DSM) was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The DSM covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008

The 0.34 cm resolution orthomosaic was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The orthomosaic covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008.

Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression. The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C. Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Multiple layers are provided. First, the fire season length is shown across Canada for a reference period (1981-2010). Difference in projected fire season length compared to reference period is shown for the short- (2011-2040), medium- (2041-2070), and long-term (2071-2100) under the RCP 8.5 (continued emissions increases) and, for the long-term (2071-2100), under RCP 2.6 (rapid emissions reductions).