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We have found 85 datasets for the keyword " topographie". You can continue exploring the search results in the list below.
Datasets: 90,973
Contributors: 41
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85 Datasets, Page 1 of 9
Bathymetric Gridded Data Overview
CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.
Reconnaissance Karst Potential Mapping
An interpretation of bedrock geology, topography and other sources of information that shows the potential for karst formations. This is a reconnaissance level map for all of British Columbia
Nova Scotia Hydrographic Network
The Nova Scotia Hydrographic Network is an enhanced version of the Nova Scotia Topographic Database's Water Features theme. This dataset includes network spines for connectivity of water flow and various attribution for flow direction, priority of water flow and toponymic objects where applicable.
Landsat Circa 2010 Top of Atmosphere Reflectance Mosaic of Canada
Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.
Visual Landscape Inventory - Viewing Direction (Lines)
A direction one looks from a viewpoint towards a visual landscape. When a view is panoramic, it is to the middle of that panoramic view
Physiographic Regions of Canada
Canada’s landmass is very diversified and comprises 7 distinctive areas called physiographic regions, each of which has its own unique topography and geology. Physiographic regions are large areas that share similar relief and landforms shaped by common geomorphic processes and geological history. Physiographic regions are often used to describe Canada’s geography to show regional differences in climate, vegetation, population and the economy.This dataset collection contains three interrelated datasets mapping the location of Canada’s 7 different physiographic regions, their 21 subregions and many divisions (landforms).
14 Class - Canadian Ecological Domain Classification from Satellite Data
14 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).
100 Class - Canadian Ecological Domain Classification from Satellite Data
100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).
40 Class - Canadian Ecological Domain Classification from Satellite Data
40 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).
TOPEX
TOPEX (acronym which means “topographical exposure”) is an index of topographic exposure. It reflects the influence of local topography on the degree of exposure to wind and on its behavior (Ruel et al. 2002). TOPEX data are presented in the form of a matrix file (raster) whose spatial resolution is 50 m with a projection in Nad 1983 Quebec Lambert. This raster covers the entire territory of Quebec located approximately south of 52°40' and west of 61°10' and extends beyond the borders of Quebec by 75 to 125 m (in order to have values on the entire Quebec territory covered). TOPEX values were calculated using the Digital Terrain Model (DTM) from the NASA Shuttle Radar Topography Mission (SRTM). This MNT is provided in a WGS 84 projection (EPSG: 4326) with a resolution of one arcsecond (+/- 30 m). A mosaic of the SRTM tiles was created to then project the MNT into Lambert and resample it at 50 m. TOPEX can be used as an input in a windfall vulnerability assessment system. However, it should be borne in mind that this index does not take into account the wind channeling effect that may occur in some places and that can influence windfall. In addition, other factors must be taken into consideration in order to carry out a complete analysis of the risk of windfall. RUEL, J.-C., S. J. MITCHELL and M. DORNIER, 2002. A GIS based approach to map wind exposure for windthrow hazard rating. Northern Journal of Applied Forestry, 19 (4): 183-187.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**
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