20251104 09021000 1.0 FALSE USFS_EDW_CEC_LandCoverOfNorthAmerica_2010_250m -20037507.067200 20037492.932800 -17284.719500 30083715.280500 1 002 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.6.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 8 FALSE None 1 AMD TRUE continuous unsigned integer 100000 100000 1000 20 2048 20 75 0.01 10 600 300 Basic 0 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC Basic None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D -1 -1 0.8 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC None,Basic,Full NONE <_docversion_ Sync="TRUE">10 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder 250 64000 640000 45965.7877799884 45965.792015463 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape) -1 1 1 0 0 0 0 0 0 FGDC 20251202 11360700 20251202 13074200 150000000 5000 Commission for Environmental Cooperation 1 514 350 4300 1 514 350 4314 393, rue St-Jacques Ouest, Bureau 200 Montréal Québec H2Y 1N9 CA info@cec.org Rasim Latifovic Canada Centre for Remote Sensing 1 613 947 1816 1 613 947 1383 CCRS/NRCan 588 Booth Street Ottawa ON K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca 0900 - 1700 ET, M - F (-5h EST/-4h EDT GMT) INEGI 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque. Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT) 20251202 ArcGIS Metadata 1.0 Commission for Environmental Cooperation 1 514 350 4300 1 514 350 4314 393, rue St-Jacques Ouest, Bureau 200 Montréal Québec H2Y 1N9 CA info@cec.org Canada Centre for Remote Sensing Scientist 1 613 947 1816 1 613 947 1383 CCRS/NRCan 588 Booth Street Ottawa ON K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca 0900 - 1700 ET, M - F (-5h EST/-4h EDT GMT) Instituto Nacional de Estadística y Geografía (INEGI) Director of Natural Resources and Environment Information 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque. Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT) Gratuit-Free IMG http://www.cec.org/naatlas/ http://www.inegi.org.mx Commission for Environmental Cooperation 1 514 350 4300 1 514 350 4314 393, rue St-Jacques Ouest, Bureau 200 Montréal Québec H2Y 1N9 CA info@cec.org Canada Centre for Remote Sensing Scientist 1 613 947 1816 1 613 947 1383 CCRS/NRCan 588 Booth Street Ottawa ON K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca 0900 - 1700 ET, M - F (-5h EST/-4h EDT GMT) Instituto Nacional de Estadística y Geografía (INEGI) Director of Natural Resources and Environment Information 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque. Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT) Gratuit-Free TIFF http://www.cec.org/naatlas/ http://www.inegi.org.mx Commission for Environmental Cooperation 1 514 350 4300 1 514 350 4314 393, rue St-Jacques Ouest, Bureau 200 Montréal Québec H2Y 1N9 CA info@cec.org Canada Centre for Remote Sensing Scientist 1 613 947 1816 1 613 947 1383 CCRS/NRCan 588 Booth Street Ottawa ON K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca 0900 - 1700 ET, M - F (-5h EST/-4h EDT GMT) Instituto Nacional de Estadística y Geografía (INEGI) Director of Natural Resources and Environment Information 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque. Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT) Gratuit-Free MXD http://www.cec.org/naatlas Commission for Environmental Cooperation 1 514 350 4300 1 514 350 4314 393, rue St-Jacques Ouest, Bureau 200 Montréal Québec H2Y 1N9 CA info@cec.org Canada Centre for Remote Sensing Scientist 1 613 947 1816 1 613 947 1383 CCRS/NRCan 588 Booth Street Ottawa ON K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca 0900 - 1700 ET, M - F (-5h EST/-4h EDT GMT) Instituto Nacional de Estadística y Geografía (INEGI) Director of Natural Resources and Environment Information 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque. Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT) Gratuit-Free GeoPDF http://www.cec.org/naatlas http://www.cec.org/naatlas/ USFS_EDW_CEC_LandCoverOfNorthAmerica_2010_250m 2013 1.0 Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector, Natural Resources Canada Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) Comisión Nacional Forestal (CONAFOR) Insituto Nacional de Estadística y Geografía (INEGI) U.S. Geological Survey (USGS) Canada Centre for Remote Sensing (CCRS) Ottawa, Ontario, Canada U.S. Geological Survey Sioux Falls, South Dakota, USA INEGI Mexico City, Mexico Commission for Environmental Cooperation Montréal, Québec, Canada Raster digital data <div style='text-align:Left;'><div><div><p><span>The 2010 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each countryÂs specific requirements. The initial data set of North American Land Cover at 250 meters reflected land cover information for 2005. This 2010 data set was produced by updating the 2005 data to show land cover changes as determined from more recent data. No changes were mapped in Hawaii because newer data were not available. Land cover classification changed between 2005 and 2010 for approximately 1 percent of the continental area. For the continental data sets (including surrounding water fringe) 4150241 pixels (1.03% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 3264779 pixels changed (2.05%); Mexico, 47070 pixels changed (0.15%), and U.S., 836706 pixels changed (0.55%). The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets. The countries worked together to produce a definitive list of land cover classifications for the 2005 data; the same classifications were used for the 2010 data. This document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005).</span></p></div></div></div> Information on land cover across North America provided by this data set is valuable for a range of audiences, including international organizations such as the United Nations Environment Programme, nongovernmental conservation organizations, land managers, and scientific researchers. The continental scale land cover data generated under NALCMS can be used to address issues such as climate change, carbon sequestration, biodiversity loss, and changes in ecosystem structure and function, by helping users to better understand the dynamics and continental-scale patterns of North AmericaÂ’s changing environment. The North American Atlas data are intended for geographic display and analysis at the national and continental level. These data should be displayed and analyzed at scales appropriate for 1:10,000,000-scale data. No responsibility is assumed by Natural Resources Canada, Instituto Nacional de Estadística y Geografía, the U.S. Geological Survey, or the Commission for Environmental Cooperation in the use of these data. Rasim Latifovic Canada Centre for Remote Sensing 1 613 947 1816 1 613 947 1383 588 Booth Street Ottawa Ontario K1A 0Y7 CA Rasim.Latifovic@nrcan.gc.ca Instituto Nacional de Estadística y Geografía (INEGI) Director of Natural Resources and Environment Information 52 44 99 10 53 65 Av. Héroe de Nacozari Sur 2301. Fraccionamiento Jardines del Parque Aguascalientes Aguascalientes 20270 MX jose.ornelas@inegi.org.mx 0800 - 1600 (-6h GMT) Collin Homer U.S. Geological Survey 1 605 594 2714 USGS, Earth Resources Observation and Science (EROS) Center Mundt Federal Building 47914 252nd Street Sioux Falls South Dakota 57198-0001 US homer@usgs.gov Mid-latitude Western Hemisphere Northern Hemisphere North America NAFTA North America Free Trade Agreement Canada Mexico United States GCMD Science keywords Land Surface > Land Use/Land Cover > Land Cover Land Surface > Surface Radiative Properties > Reflectance ISO 19115 Topic Category imageryBaseMapsEarthCover Remote sensing Land cover MODIS Reflectance Land Surface Land Use/Land Cover Land Cover Surface Radiative Properties Reflectance imageryBaseMapsEarthCover Remote sensing Land cover MODIS Reflectance Mid-latitude Western Hemisphere Northern Hemisphere North America NAFTA North America Free Trade Agreement Canada Mexico United States US Forest Service USFS IIPP Interdepartmental Imagery Publication Platform Land Use <div style='text-align:Left;'><div><div><p><span>None. Acknowledgement of Natural Resources Canada, Instituto Nacional de Estadística y Geografía, and the U.S. Geological Survey is required in products derived from these data. Acknowledgement of the Commission for Environmental Cooperation would be appreciated in products derived from these data.</span></p><p style='margin:0 0 14 0;'><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</span></p><p style='margin:0 0 14 0;'><span>Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at </span><a href='https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcreativecommons.org%2Fpublic-domain%2Fcc0%2F&amp;data=05%7C02%7Cmark.hammond%40usda.gov%7C8a6ff85e7fde4b57cd8108de15874b67%7Ced5b36e701ee4ebc867ee03cfa0d4697%7C1%7C0%7C638971867987744668%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&amp;sdata=dirAelsY9FS7jvzx9jaCzFeb7ls4yOoKC3jdZ0G%2BWiI%3D&amp;reserved=0' style='text-decoration:underline;'><span style='text-decoration:underline;'>https://creativecommons.org/public-domain/cc0/</span></a><span>). </span></p><p style='margin:0 0 14 0;'><span><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident.</span></span></p><p style='margin:0 0 14 0;'><span><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English.</span></span></p><p style='margin:0 0 14 0;'><span><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at </span></span><a href='https://www.usda.gov/oascr/how-to-file-a-program-discrimination-complaint' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>How to File a Program Discrimination Complaint</span></span></a><span><span> and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: </span></span><a href='mailto:program.intake@usda.gov' style='text-decoration:underline;'><span style='text-decoration:underline;'><span>program.intake@usda.gov</span></span></a><span><span>.</span></span></p><p style='margin:0 0 14 0;'><span><span>USDA is an equal opportunity provider, employer, and lender.</span></span></p></div></div></div> Although these data have been processed successfully on computer systems at the CCRS, INEGI, USGS, and the Commission for Environmental Cooperation, no warranty expressed or implied is made by these agencies regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. Use of trademarks or formats does not constitute endorsement by these agencies. No responsibility is assumed by the CCRS, INEGI, USGS, or the CEC in the use of these data. None. true 170 -50 84 14 Ground condition 2010-01 2010-12 The standard distribution formats for this data set are listed below. Users interested in receiving the data as a GRID or BIN file may contact the CEC. Land cover monitoring, the goal of the NALCMS project, requires multiple land cover maps, ideally at annual intervals. It has been shown that post-classification change detection, i.e. the comparison of two land cover maps, does not yield satisfying results because it highly overestimates changes. Therefore, the NALCMS builds upon a two-step approach that 1) estimates the change between two sample years, also called maximum potential change, and 2) updates the classification of only those pixels that were identified as potential change. This process ensures that changes in classification will only focus on areas where a previous algorithm detected the potential for change and the remainder of the map remains without modifications. For more information on NALCMS, see http://www.cec.org/Page.asp?PageID=122&ContentID=25501. Natural Resources Canada has North American Land Cover information available online at http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/optical/2208#_North_American_Land. The U.S. Geological Survey has North American Land Cover information available online at http://landcover.usgs.gov/nalcms.php. For more information on MODIS, see http://modis.gsfc.nasa.gov/ The following table describes the display of land cover classification in the .img file: >Value Class RGB values >---------------------------------------------------------------------------- >1 Temperate or sub-polar needleleaf forest 0 0.24 0 >2 Sub-polar taiga needleleaf forest 0.58 0.61 0.44 >3 Tropical or sub-tropical broadleaf > evergreen forest 0 0.39 0 >4 Tropical or sub-tropical broadleaf > deciduous forest 0.12 0.67 0.02 >5 Temperate or sub-polar broadleaf > deciduous forest 0.08 0.55 0.24 >6 Mixed forest 0.36 0.46 0.17 >7 Tropical or sub-tropical shrubland 0.7 0.62 0.18 >8 Temperate or sub-polar shrubland 0.7 0.54 0.2 >9 Tropical or sub-tropical grassland 0.91 0.86 0.37 >10 Temperate or sub-polar grassland 0.88 0.81 0.54 >11 Sub-polar or polar shrubland-lichen-moss 0.61 0.46 0.33 >12 Sub-polar or polar grassland-lichen-moss 0.73 0.83 0.56 >13 Sub-polar or polar barren-lichen-moss 0.25 0.54 0.45 >14 Wetland 0.42 0.64 0.54 >15 Cropland 0.9 0.68 0.4 >16 Barren lands 0.66 0.67 0.68 >17 Urban 0.86 0.13 0.15 >18 Water 0.3 0.44 0.64 >19 Snow and Ice 1 0.98 1 > The Commission for Environmental Cooperation (CEC) is an international organization created by Canada, Mexico, and the United States of America under the North American Agreement on Environmental Cooperation (NAAEC). The CEC was established to address regional environmental concerns, help prevent potential trade and environmental conflicts, and to promote the effective enforcement of environmental law. The Agreement complements the environmental provisions of the North American Free Trade Agreement (NAFTA). Further information on the CEC is available from http://www.cec.org/ or from >Commission for Environmental Cooperation >393, rue St-Jacques Ouest >Bureau 200 >Montréal (Québec) >H2Y 1N9 Canada > >Telephone: 1 514 350 4300 >Facsimile: 1 514 350 4314 >Electronic mail: info@cec.org > The following references provide additional information on processing methodologies and may be referenced in the process steps, below. Guindon et al. (2011). An improved method for the annual mapping of forest disturbance across Canada based on MODIS 250m data and decision/regression trees. 32nd Canadian Symposium on Remote Sensing, June 13-16, Sherbrooke, Quebec. Timoney, K.P., La Roi, G.H., Zoltai, S.C., and Robinson, A.G. (1992). The high subarctic forest-tundra of northwestern Canada: Position, width, and vegetation gradients in relation to climate. Arctic, 45, 1-9. Pavlic, G., and Latifovic, R. (2007). Canada-wide 250-m Water Fraction Coverage derived from National Topographic Data Base. NRCan, ESS Program: Understanding Canada from Space, Project: Land Surface Characterization. Pouliot, D., Latifovic, R., Zabcic, N., Guindon, L., and Olthof, I. (2013). Development and assessment of a 250m spatial resolution MODIS annual land cover time series (2000-2011) for Canada derived from change based updating. In review. Olthof, I., Pouliot, D., Fernandes, R., and Latifovic, R, (April 2005). Landsat-7 ETM+ radiometric normalization comparison for northern mapping applications. Remote Sensing of Environment, Volume 95, no. 3:388–398. Beaubien, J., Cihlar, J., Simard, G., and Latifovic, R. (1999). Land cover from multiple thematic mapper scenes using a new enhancement-classification methodology. Journal of Geophysical Research, 104:27909 – 27920. Cihlar, J., Xiao, Q., Beaubien, J., Fung, K., & Latifovic, R. (1998) Classification by progressive generalization: a new automated methodology for remote sensing multichannel data. International Journal of Remote Sensing 19:2685-2704. Khlopenkov, K., & Trichtchenko, A. (2008). Implementation and evaluation of concurrent gradient search method for reprojection of MODIS level 1B imagery. IEEE Transaction on Geoscience and Remote Sensing, 46:2016–2027. Latifovic, R., and Olthof, I. 2004. Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90:153-165 Latifovic, R., Zhu, Z., Cihlar, J., Giri, C., & Olthof, I. (2004). Land cover mapping of North and Central America - Global Land Cover 2000. Remote Sensing of Environment, 89:116-127. Luo, Y., Trishchenko, A., & Khlopenkov, K. (2008). Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sensing of Environment, Volume 112:4167-4185. Trichtchenko, A., Luo Y., & Khlopenkov, K. (2006). A method for downscaling MODIS land channels to 250m spatial resolution using adaptive regression and normalization, Proceedings of SPIE – The International Society for Optical Engineering v.6366 (2006) art. no. 636607. 8 pp. Esri ArcGIS 13.6.0.59527 1 -171.281222 -45.562459 83.510235 7.763233 USDA - GEO; USDA - USFS – GTAC SM.FS.data@usda.gov Washington DC US USFS Chief Information Office, Enterprise Data Warehouse The Quantitative Attribute Accuracy reports listed below reflect the accuracy of the 2005 data used as a source. The 2010 data show a change in classification for approximately 1 percent of the land surface, so attribute accuracy is assumed to be essentially the same as for the 2005 data set. The above listed value is the overall accuracy obtained for the Canada land cover data using the reference data set. 70 The Quantitative Attribute Accuracy reports listed below reflect the accuracy of the 2005 data used as a source. The 2010 data show a change in classification for approximately 1 percent of the land surface, so attribute accuracy is assumed to be essentially the same as for the 2005 data set. The above listed value is the overall accuracy obtained for the United States land cover data using a cross-validation estimate from the decision tree model. 69 The Quantitative Attribute Accuracy reports listed below reflect the accuracy of the 2005 data used as a source. The 2010 data show a change in classification for approximately 1 percent of the land surface, so attribute accuracy is assumed to be essentially the same as for the 2005 data set. The above listed value is the overall accuracy obtained for the Mexican land cover data using a subset of 20 percent of the training samples from the decision tree model. 85 No tests for logical consistency have been performed on this data set. Data completeness reflects the content of the original MODIS data. A series of monthly MODIS composites were produced prior to the compilation of land cover data for each country. Input data used in land cover mapping were generated from the following original Level 1B MODIS data (collection 5): a) MOD02QKM - Level 1B 250m swath data, 5-min Granules; b) MOD02HKM - Level 1B 500m swath data, 5-min granules; c) MOD03 – Level 1 Geolocation information, 1km swath data, 5-min granules. All input data are available from the NASA Level 1 and Atmosphere Archive and Distribution System (LAADS), http://ladsweb.nascom.nasa.gov/ The MODIS 500m land channels (B3-B7) were downscaled to 250m spatial resolution as described by Trishchenko et al. (2006) and then re-projected into Lambert Azimuthal Equal Area projection using software developed at the CCRS (Khlopenkov et al., 2008). Two original 250m MODIS channels and five downscaled 250m MODIS land channels were assembled into North-America-wide 10-day composites following the methods developed in Luo et al., (2008). MODIS 10-day composites were further processed to reduce data noise due to different viewing conditions and remaining cloud and atmosphere contamination. A temporal rank filter was applied, calculated as the maximum of minimum values for two overlapping moving windows. For this implementation, a window size of three ten-day composites was used with one pixel overlap between windows. After rank filtering, a procedure to detect residual spikes in the data was applied based on a neighbourhood comparison. Within a moving window of 3 composites, if the neighbouring pixels were both above or below the centre pixel by 25%, the centre pixel was linearly interpolated from its neighbours. The filtered 10-day composites were averaged to generate monthly composites. The processed data set contained 12 monthly composites of channels 1 (B1, VIS) and 2 (B2, near-infrared NIR) at 250m spatial resolution and five channels designed for land applications (bands B3 to B7) resampled to 250m spatial resolution. 2009 MOD02QKM MOD02HKM MOD03 NA Monthly composites 2005 MODIS monthly composites for 2010 were produced using the same process as for the 2005 data, but with updated source material. 2013 MOD02QKM 2012 MOD02HKM 2012 MOD03 2012 NA Monthly composites 2010 Version 2 of the 2005 Land Cover was used as the starting point for all three countries. Areas of potential change were identified and then reclassified as necessary. Country-specific processes are described in this process step and the ones below. Please see the points-of-contact if additional processing information is needed. For Canada, the 2010 land cover was derived from a time series of land cover, developed for Canada from 2000 to 2010 at an annual time step, using MODIS data as input (Pouliot et al., 2013). The method used to update land cover for each year in the time series followed a change- and evidence-based classification approach. It was applied separately for six mapping zones. Mapping zones were selected to have similar spectral and land cover properties that could be optimized within the zone so as to reduce class confusion. Change detection was a critical step, as the basic premise of the method required changes to be detected with high confidence. Three methods were used to capture different types of change and at different temporal scales. Annual negative change results were generated in previous research by Guindon et al. (2011), which mostly captured forest-stand-replacing disturbances such as harvesting and fire. New methods were developed to capture annual positive change in disturbed areas and to account for subtle multi-year change of either a negative or positive nature. Negative change refers to vegetation loss and positive change to vegetation establishment and growth. In the context of multi-year change, examples of the negative case include recurrent insect defoliation, gradual urbanization, and persistent climatic or hydrological alteration. For the positive case, examples include gradual regeneration of disturbed areas and climate-related vegetation change. To predict the new land cover label for a change area, several sources of evidence were combined including the class membership output from a decision tree classifier, class temporal transition likelihood, and regional and neighbourhood class proportions. The class with the maximum combined evidence was taken as the updated land cover label. A base land cover map for 2005 was used for the update and served as the classification training data source. To capture class temporal variability, training samples were taken for pixels that had not changed within plus or minus 1 year of the base map. Temporal filtering of the time series was the final step applied to remove highly improbable land cover transitions once all maps in the time series were processed. Several sources of ancillary data were incorporated in the classification to develop or refine the initial base map. These included a 250-meter spatial resolution water fraction map assembled from the National Topographic Data Base at 1:2500000 scale (Palvic and Latifovic, 2007), digital elevation data at 1:50000 scale, Canada road network data at 1:250000 scale converted to road density within a 250 meter grid cell, and an indicator of the Taiga class likelihood based on proximity to the northern treeline (Timoney et al., 1992) and conifer class density measured within a 50x50 pixel window of the 2005 land cover base map. Exponentially decreasing membership functions were defined for the distance and density layers and multiplied to calculate the final Taiga likelihood estimate. 2013 LandCover2005_v2 NA Monthly Composites 2005 NA Monthly Composites 2010 Canada ancillary data CCRS_NLCD 2010 In Mexico, potential change estimation built on the monthly MODIS image composites of all seven spectral bands. Image data from 2010 were normalized to the corresponding band and month from 2005 using Theil-Sen regression (Olthof et al. 2005). Next, the normalized difference vegetation index (NDVI) was calculated. Based on the NDVI, textural Laplace and Sobel filters with 3x3 kernel size were applied to enhance change detection along land cover edges. In total a set of 120 data layers were used: data for 7 spectral bands, NDVI, Sobel, and Laplace filters (10 data layers), for 12 months of the year. This set of data layers was produced for both 2005 and 2010, and compared between the two subject years. A small difference in values between the 2005 and 2010 data indicates areas that have not changed, whereas extreme negative and positive values indicate changes. For areas of change, the most appropriate lower and upper cut-off thresholds in the difference image histogram (lower and upper quantile) must be determined, and also the frequency, i.e. number of times, out of the 120 data layers used for comparison, that a pixel should be flagged as changed. The appropriate threshold values for the lower quantile, upper quantile, and frequency were defined by training an algorithm using pairs of Landsat images from April (the end of the dry season) and October (the end of the rainy season) for 2005 and 2010, from which change was estimated by visual interpretation. The optimal thresholds were defined as: lower quantile, 1 percent; upper quantile, 99 percent; and frequency (the number of data layers indicating change), 30. The resulting change mask identified areas of potential change; errors remaining in the change mask were removed by manual editing. The mask was assessed with 10m SPOT 5 HRG images for nine sites; the overall accuracy was 80 percent, with a commission error rate of 20 percent and a omission error rate of 50 percent. Map updating employs the above-described change mask, the baseline version 2 land cover map from 2005, and spectral data from the year 2010. Updating builds on the assumption that all pixels that were not identified as potential change will indicate no change between 2005 and 2010. For each class, 1,500 samples were selected from unchanged areas within 1-2 km of potential areas of change; those samples were used to train the decision tree model which was then applied to classify areas of change within the potential change mask area for the year 2010. 2013 LandCover2005_v2 NA Monthly Composites 2005 NA Monthly Composites 2010 Landsat 2005 Landsat 2010 SPOT 5 HRG 2005 SPOT 5 HRG 2010 INEGI_NLCD 2010 In the United States, land cover for 2010 was mapped by first running a spectral change analysis model to identify areas that potentially had changed between 2005 and 2010. Included in the model were burn areas from MTBS, with an effort made to model burn severity and potential recovery. Only fires from 2010 and earlier were considered in the modeling. Areas of potential change were classified through a process similar to that used to classify the 2005 data, using a combination of MODIS composite imagery and ancillary data. Adjustments to the model were made based on experience gained in producing the 2005 Version 2 data set. Classification was achieved by use of a classification and decision tree method (DT). The specific DT program employed is called SEE5, which implements a gain ratio criterion in tree development and pruning (Quinlan, 1993). SEE5 also implemented several advanced features that can aid and improve land cover classification, including boosting and cross-validation. Boosting is a technique for improving classification accuracy, while cross-validation can provide a certain level of estimation regarding the land cover classification quality. In addition, SEE5 can generate a confidence estimate for each classified pixel and record the associated classification logic in a text file that can be readily interpreted and incorporated into a metadata system. To conduct the land cover classification using DT, a large quantity of training data was required. The change pixels represent a conservative estimate of change based on both spectral analysis and thematic change between land cover modeled for both 2005 and 2010. Much of the change is in fire perimeter areas. Burned areas were reclassified as necessary, based on the length of time since the fire occurred. Alaska was processed separately from the conterminous United States, using DEM, SRTM, and CADEM. The conterminous United States was processed using DEM elevation data. Hawaii was not updated because newer data were not available. The resulting data set was checked against Landsat MRLC for quality assurance. 2013 LandCover2005_v2 NA Monthly composites 2005 NA Monthly composites 2010 MTBS DEM CADEM SRTM Landsat MRLC USGS_NLCD 2010 To produce the seamless land cover map of North America, the three national land cover products (Canada, Mexico, USA) were edge matched along the borders using an object-oriented segmentation technique. Land cover regions that crossed borders were reclassified using the majority class segment. 2013 CCRS_NLCD 2010 USGS_NLCD 2010 INEGI_NLCD 2010 Spatial and attribute information 2005 Land Cover of North America at 250 meters LandCover2005_v2 2013 2.0 Canada Centre for Remote Sensing (CCRS), Earth Sciences Sector, Natural Resources Canada Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) Comisión Nacional Forestal (CONAFOR) Insituto Nacional de Estadística y Geografía (INEGI) U.S. Geological Survey (USGS) Canada Centre for Remote Sensing (CCRS) Ottawa, Ontario, Canada U.S. Geological Survey Sioux Falls, South Dakota, USA INEGI Mexico City, Mexico Commission for Environmental Cooperation Montréal, Québec, Canada Raster digital data http://www.cec.org/naatlas/ Ground condition 2005 Spatial and attribute information MODIS Level 1B 250m swath data, 5-min granules MOD02QKM 2008 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Level 1B 500m swath data, 5-min granules MOD02HKM 2008 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Level 1 Geolocation information, 1km swath data, 5-min granules MOD03 2008 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Monthly composites 2005 NA Monthly Composites 2005 2009 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada Raster digital data Processing date 2009 Spatial and attribute information MODIS Level 1B 250m swath data, 5-min granules MOD02QKM 2012 2012 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Level 1B 500m swath data, 5-min granules MOD02HKM 2012 2012 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Level 1 Geolocation information, 1km swath data, 5-min granules MOD03 2012 2012 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada NASA Ottawa, Ontario, Canada Raster digital data http://ladsweb.nascom.nasa.gov/ Publication date 2008 Spatial and attribute information MODIS Monthly composites 2010 NA Monthly Composites 2010 2013 Canada Centre for Remote Sensing, Earth Sciences Sector, Natural Resources Canada Raster digital data Processing date 2013 Attribute information Ancillary data Canada ancillary data Various Various Various The following data sets were used to aid cluster labelling: >Data set Source >----------------------------------------------------------------------------------- >Water Fraction Map of Canada http://www.geobase.ca > assembled from the National > Topographic Data Base (NTDB) > >Canada Digital Elevation Data, http://www.geobase.ca > Level 1 > >National Road Network, Canada http://www.geobase.ca > >Satellite database for the land CCRS > cover of Canada is a sample > of LANDSAT TM/ETM+ scenes > representing the distribution > of land cover across Canada. > >Earth Observation for Sustainable CFS > Development (EOSD) of Forests > Land Cover Classification > >National Land and Water http://www.agr.gc.ca/eng/?id=1343071073307#a9 > Information System (NLWIS) > Land Cover for agricultural > regions of Canada, circa 2000 > >Circa-2000 Northern Land Cover http://geogratis.cgdi.gc.ca > of Canada > >Classification of Agricultural Agricultural Financial Services > Lands in Alberta, Saskatchewan Corporation Saskatchewan Crop > and the Peace Region of Insurance Corporation > British Columbia British Columbia Ministry of > Agriculture and Lands (Business > Risk management Division) > >Northern Treeline (Timoney http://data.arcticatlas.org > et al., 1992) > >North American Regional http://nomads.ncdc.noaa.gov > Reanalysis, Daily Dataset > Degree days > >Fire data base CCRS > >Ground truth data CCRS Date of acquisition 2009 Spatial and attribute information Landsat Thematic Mapper, LandsatLook Images with Geographic Reference Landsat 2005 NASA NASA Washington, DC Raster digital data Mexico used Landsat data for 76 locations; for each location there was an image for April 2005, and in most cases also for October 2005. The following row/path locations were used: 26/40, 26/41, 26/42, 26/43, 26/44, 27/42, 27/43, 28/40, 28/41, 28/42, 28/43, 28/44, 29/39, 29/40, 29/41, 29/42, 29/43, 29/44, 29/45, 30/39, 30/40, 30/41, 30/42, 30/43, 30/44, 31/39, 31/40, 31/41, 31/42, 31/43, 31/44, 31/45, 32/38, 32/39, 32/40, 32/41, 32/42, 32/43, 32/44, 33/38, 33/39, 33/40, 33/41, 33/42, 33/43, 33/44, 34/38, 34/39, 34/40, 34/41, 34/42, 34/43, 35/38, 35/39, 35/40, 35/41, 35/42, 35/43, 36/38, 36/39, 36/40, 36/41, 36/42, 37/38, 37/39, 37/40, 37/41, 38/38, 38/39, 38/40, 38/41, 39/37, 39/38, 39/39, 40/37, 40/38. Detailed information on Landsat source IDs, which indicate the specific date of the image, is available from Mexico. http://earthexplorer.usgs.gov/ Ground condition 2005 Spatial and attribute information Landsat Thematic Mapper, LandsatLook Images with Geographic Reference Landsat 2010 NASA NASA Washington, DC Raster digital data Mexico used Landsat data for 76 locations; for each location there was an image for April 2010, and for all but one location there was also an image for October 2010. The following row/path locations were used: 26/40, 26/41, 26/42, 26/43, 26/44, 27/42, 27/43, 28/40, 28/41, 28/42, 28/43, 28/44, 29/39, 29/40, 29/41, 29/42, 29/43, 29/44, 29/45, 30/39, 30/40, 30/41, 30/42, 30/43, 30/44, 31/39, 31/40, 31/41, 31/42, 31/43, 31/44, 31/45, 32/38, 32/39, 32/40, 32/41, 32/42, 32/43, 32/44, 33/38, 33/39, 33/40, 33/41, 33/42, 33/43, 33/44, 34/38, 34/39, 34/40, 34/41, 34/42, 34/43, 35/38, 35/39, 35/40, 35/41, 35/42, 35/43, 36/38, 36/39, 36/40, 36/41, 36/42, 37/38, 37/39, 37/40, 37/41, 38/38, 38/39, 38/40, 38/41, 39/37, 39/38, 39/39, 40/37, 40/38. Detailed information on Landsat source IDs, which indicate the specific date of the image, is available from Mexico. http://earthexplorer.usgs.gov/ Ground condition 2010 Spatial and attribute information SPOT 5 High Resolution Geometric data SPOT 5 HRG 2005 Centre National d’Études Spatiales (CNES) CNES Paris, France Raster digital data SPOT data were used for quality assurance. There were eight SPOT images taken on various dates in 2005 and one in 2006. Detailed information on SPOT source IDs, which indicate the specific date of the image, row and path, and processing level, is available from Mexico. http://www.astrium-geo.com/en/143-spot-satellite-imagery Ground condition 2005-04-04 2006-02-26 Spatial and attribute information SPOT 5 High Resolution Geometric data SPOT 5 HRG 2010 Centre National d’Études Spatiales (CNES) CNES Paris, France Raster digital data SPOT data were used for quality assurance. There was one SPOT image taken in 2009, and eight SPOT images taken on various dates in 2010. Detailed information on SPOT source IDs, which indicate the specific date of the image, row and path, and processing level, is available from Mexico. http://www.astrium-geo.com/en/143-spot-satellite-imagery Ground condition 2009-11-15 2010-11-07 Spatial and attribute information National Elevation Dataset DEM 2008 U.S. Geological Survey U.S. Geological Survey Reston, VA Raster digital data http://ned.usgs.gov/ Ground condition 2005 Spatial and attribute information Cross-border DEM CADEM Natural Resources Canada Raster digital data Data acquisition date 2013 Spatial and attribute information Shuttle Radar Topography Mission SRTM 2009 National Geospatial-Intelligence Agency (NGA) National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory, NASA Pasadena, CA Raster digital data http://www2.jpl.nasa.gov/srtm/ Ground condition 2000 Spatial and attribute information. Multi-Temporal Burn Severity Fire Perimeters MTBS 2012-10-03 U.S. Geological Survey, Center for Earth Resources Observation and Science (EROS) U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center (RSAC) withheld Publication date 2012-10-03 Spatial and attribute information Landsat MRLC Landsat MRLC 2001 NASA NASA Washington, DC Raster digital data http://earthexplorer.usgs.gov/ Publication date 2001 Spatial and attribute information 2010 Land Cover Map of North America at 250m: Canada CCRS_NLCD 2010 Canada Centre for Remote Sensing Canada Centre for Remote Sensing Ottawa, Ontario, Canada Raster digital data Ground condition 2010 Spatial and attribute information Land Cover Map of Mexico for 2010 at 250m INEGI_NLCD 2010 Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) Comisión Nacional Forestal (CONAFOR) Insituto Nacional de Estadística y Geografía (INEGI) Insituto Nacional de Estadística y Geografía Mexico City, Mexico Raster digital data Ground condition 2010 Spatial and attribute information 2010 North American Land Cover Database for the United States USGS_NLCD 2010 U.S. Geological Survey U.S. Geological Survey Sioux Falls, South Dakota, USA Raster digital data Ground condition 2010 2 35000 250.000000 37000 250.000000 1 0 -20037507.067200 -17284.719500 -20037507.067200 30083715.280500 20037492.932800 30083715.280500 20037492.932800 -17284.719500 -7.067200 15033215.280500 Land cover classification grid cell Any of the data elements in the 2010 North American land cover file North American Atlas GIS Group Land cover classification grid cell value The value is an indication of the land cover class North American Atlas GIS Group 1 Temperate or sub-polar needleleaf forest. Forests generally taller than three meters and more than 20 percent of total vegetation cover. This type occurs across the northern United States, Canada and mountainous zones of Mexico. The tree crown cover contains at least 75 percent of needleleaved species. Land Cover Classification System (LCCS) code: 20134. North American Atlas GIS Group 2 Sub-polar taiga needleleaf forest. Forest and woodlands with trees generally taller than three meters and more than 5 percent of total vegetation cover with shrubs and lichens commonly present in the understory. The tree crown cover contains at least 75 percent of needleleaved species. This type occurs across Alaska and northern Canada and may consist of treed muskeg or wetlands. Forest canopies are variable and often sparse, with generally greater tree cover in the southern latitude parts of the zone than the north. LCCS code: 20229. North American Atlas GIS Group 3 Tropical or sub-tropical broadleaf evergreen forest. Forests generally taller than five meters and more than 20 percent of total vegetation cover. These occur in the southern United States and Mexico. These forests have greater than 75 percent of tree crown cover represented by evergreen species. LCCS code: 20090. North American Atlas GIS Group 4 Tropical or sub-tropical broadleaf deciduous forest. Forests generally taller than five meters and more than 20 percent of total vegetation cover. These occur in the southern United States and Mexico. These forests have greater than 75 percent of tree crown cover represented by deciduous species. LCCS code: 20132. North American Atlas GIS Group 5 Temperate or sub-polar broadleaf deciduous forest. Forests generally taller than three meters and more than 20 percent of total vegetation cover. These occur in the northern United States, Canada and mountainous zones of Mexico. These forests have greater than 75 percent of tree crown cover represented by deciduous species. LCCS code: 20227. North American Atlas GIS Group 6 Mixed forest. Forests generally taller than three meters and more than 20 percent of total vegetation cover. Neither needleleaf nor broadleaf tree species occupy more than 75 percent of total tree cover, but are co-dominant. LCCS codes: 20092, 20090, 20134, 20132, 20229, 20227. North American Atlas GIS Group 7 Tropical or sub-tropical shrubland. Areas dominated by woody perennial plants with persistent woody stems less than five meters tall and typically greater than 20 percent of total vegetation. This class occurs across the southern United States and Mexico. LCCS code: 21450-13476. North American Atlas GIS Group 8 Temperate or sub-polar shrubland. Areas dominated by woody perennial plants with persistent woody stems less than three meters tall and typically greater than 20 percent of total vegetation. This class occurs across the northern United States, Canada and highlands of Mexico. LCCS code: 21450-12050. North American Atlas GIS Group 9 Tropical or sub-tropical grassland. Areas dominated by graminoid or herbaceous vegetation generally accounting for greater than 80 percent of total vegetation cover. These areas are not subject to intensive management such as tilling, but can be utilized for grazing. This class occurs across the southern United States and Mexico. LCCS code: 21669. North American Atlas GIS Group 10 Temperate or sub-polar grassland. Areas dominated by graminoid or herbaceous vegetation, generally accounting for greater than 80 percent of total vegetation cover. These areas are not subject to intensive management such as tilling, but can be utilized for grazing. This class occurs across Canada, United States and highlands of Mexico. LCCS code: 21537-12212. North American Atlas GIS Group 11 Sub-polar or polar shrubland-lichen-moss. Areas dominated by dwarf shrubs with lichen and moss typically accounting for at least 20 percent of total vegetation cover. This class occurs across northern Canada and Alaska. LCCS codes: 20022-12050, 21454-12212, 21439-3012. North American Atlas GIS Group 12 Sub-polar or polar grassland-lichen-moss. Areas dominated by grassland with lichen and moss typically accounting for at least 20 percent of total vegetation cover. This class occurs across northern Canada and Alaska. LCCS codes: 21454-12212, 20022-12050, 21439-3012. North American Atlas GIS Group 13 Sub-polar or polar barren-lichen-moss. Areas dominated by a mixture of bare areas with lichen and moss that typically account for at least 20 percent of total vegetation cover. This class occurs across northern Canada and Alaska. LCCS codes: 21468, 21454-12212, 20022-12050. North American Atlas GIS Group 14 Wetland. Areas dominated by perennial herbaceous and woody wetland vegetation which is influenced by the water table at or near surface over extensive periods of time. This includes marshes, swamps, bogs, mangroves, etc., either coastal or inland where water is present for a substantial period annually. LCCS codes: 42349, 41809. North American Atlas GIS Group 15 Cropland. Areas dominated by intensively managed crops. These areas typically require human activities for their maintenance. This includes areas used for the production of annual crops, such as corn, soybeans, wheat, maize, vegetables, tobacco, cotton, etc.; perennial grasses for grazing; and woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class does not represent natural grasslands used for light to moderate grazing. LCCS codes: 10037, 10025, 21441, 21453. North American Atlas GIS Group 16 Barren lands. Areas characterized by bare rock, gravel, sand, silt, clay, or other earthen material, with little or no "green" vegetation present regardless of its inherent ability to support life. Generally, vegetation accounts for less than 10 percent of total cover. LCCS codes: 6001, 6004. North American Atlas GIS Group 17 Urban and built-up. Areas that contain at least 30 percent or greater urban constructed materials for human activities (cities, towns, transportation etc.) LCCS code: 5003. North American Atlas GIS Group 18 Water. Areas of open water, generally with less than 25 percent cover of non-water cover types. This class refers to areas that are consistently covered by water. LCCS codes: 8001, 7001. North American Atlas GIS Group 19 Snow and ice. Areas characterized by a perennial cover of ice and/or snow, generally greater than 25 percent of total cover. LCCS codes: 8005, 8008. North American Atlas GIS Group EPSG 6.18.3(9.3.1.2) Band_1 18.964844 0.037109 8 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