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.

The AAFC Infrastructure Flood Mapping in Saskatchewan 20 centimeter colour orthophotos is a collection of georeferenced color digital orthophotos with 20 cm pixel size. The imagery was delivered in GeoTIF and ECW formats. The TIF and ECW mosaics were delivered in the same 1 km x 1 km tiles as the LiDAR data, and complete mosaics for each area in MrSID format were also provided. The digital photos were orthorectified using the ground model created from the DTM Key Points. With orthorectification, only features on the surface of the ground are correctly positioned in the orthophotos. Objects above the surface of the ground, such as building rooftops and trees, may contain horizontal displacement due to image parallax experienced when the photos were captured. This is sometimes apparent along the cut lines between photos. For positioning of above-ground structures it is recommended to use the LiDAR point clouds for accurate horizontal placement.

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

Index Grid for NTS 1:250,000 scale maps

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

Our Imagery Base Maps and Mosaics of a number of Raster Datasets.  This includes the ASTER DEM, CDED and Shaded Relief Datasets.  As well as a number of mosaics, including SPOT, RapidEye, Landsat, and MVI Landcover data.

BTM ( Baseline Thematic Mapping) Landsat Image Catalogue Acquisition Dates. This polygon coverage contains the date of capture of the Landsat images making up the seamless BC Landsat image catalogue. This is not a multipart feature

This dataset includes the extent of the boreal forest as well as the extent of managed boreal forest worldwide. The extent of boreal forest was produced from Brandt et al. (2013) and a modified version of Goudilin (1987). Managed forest was defined as suggested by IPCC (2003) using data from FAFS (2009), Gauthier et al. (2014), See et al. (2015) and AICC maps. The extent of managed forest mostly includes areas managed for wood production, areas protected from large-scale disturbances as well as formal protected areas. Both boreal forest extent and managed boreal forest extent are available in raster and vector data. Please cite this data product as: Boucher, D., D.G. Schepaschenko, S. Gauthier, P. Bernier, T. Kuuluvainen, A. Z. Shvidenko. 2024. World boreal forest and managed boreal forest extent. DOI: 10.23687/88d70716-2600-4995-8d5f-86f96e383abf These data were presented in the following article: Gauthier, S., P. Bernier, T. Kuuluvainen, A. Z. Shvidenko, D. G. Schepaschenko. 2015. Boreal forest health and global change. Science 349:819-822. DOI: 10.1126/science.aaa9092 References: J. P. Brandt, M. D. Flannigan, D. G. Maynard, I. D. Thompson, W. J. A. Volney, Environ. Rev. 21, 207–226 (2013) I. S. Goudilin, Landscape map of the USSR. Legend to the landscape map of the USSR. Scale 1:2 500 000. Moscow, Ministry of Geology of the USSR (1987) [in Russian]. Inter-governmental panel on climate change (IPCC). J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, et al., Eds., Good practice guidance for land use, land-use change and forestry (IPCC/NGGIP/IGES, Kanawaga, 2003) Federal Agency of Forest Service (FAFS), Forest Fund of the Russian Federation (state by 1 January 2009) (Federal Agency of Forest Service, Moscow, 2009) [in Russian] S. Gauthier et al., Environ. Rev. 22, 256–285 (2014). See et al., Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change. (2015). doi:10.1016/j.techfore.2015.03.002 Alaska Interagency Coordination Center (AICC). Fire Information. https://fire.ak.blm.gov/content/maps/aicc/Large%20Maps/Alaska_Fire_Management_Options.pdf (the version of 2014 was used)

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

Data represents surface water occurrence frequency (percentage), which describes the frequency for each grid appeared as water in the 30 years time period of 1991 to 2020. The data covers Canada’s entire landmass including all transboundary watersheds, and is at 30-meter spatial resolution. The surface water occurrence frequency is derived using the surface water model of Wang et al. (2023) from all-available monthly water data observed by the Landsat satellites (Pekel et al., 2016). Here, permanent waters are represented by 100%, and permanent land surfaces by 0%, of water occurrence for a 30-meter by 30-meter grid. References: Pekel, J.-F., A. Cottam, N. Gorelick, A.S. Belward, 2016, High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418-422. Wang, S., J. Li, and H. A. J. Russell, 2023, Methods for Estimating Surface Water Storage Changes and Their Evaluations. Journal of Hydrometeorology, DOI: https://doi.org/10.1175/JHM-D-22-0098.1.