U.S. Geological Survey 20200213 remote-sensing image Sioux Falls, SD U.S. Geological Survey References: (1) Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159 – 175. (2) Xian, G., Homer, C., Dewitz, J., Fry, J., Hossain, N., and Wickham, J., 2011. The change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogrammetric Engineering and Remote Sensing, Vol. 77(8): 758-762. (3) Coulston, J. W., Moisen, G. G., Wilson, B. T., Finco, M. V., Cohen, W. B., and Brewer, C. K. 2012. Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing 78(7): 715-727. (4) Jin, Suming, Yang, Limin, Zhu, Zhe, Homer, Collin G., A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011: Remote Sensing of Environment, v. 195, p. 44–55, at https://doi.org/10.1016/j.rse.2017.04.021. (5) Selkowitz, D. J., Stehman, S. V., Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska: Remote Sensing of Environment, v. 115, no. 6, p. 1401–1407, at https://doi.org/10.1016/j.rse.2011.01.020. The USGS acknowledges the support of USGS and contractor NLCD 2016 Land Cover Mapping Teams in development of data for this map. https://doi.org/10.5066/P96HHBIE http://www.mrlc.gov The Alaska National Land Cover Database 2016 was created using change detection between the nominal dates of 2011 and 2016 utilizing Google Earth engine composites of Landsat imagery. Traditionally, previous classifications of Alaska used path row data and spectral comparisons between path rows along with ancillary data to derive areas of change. Alaska has many challenges for land cover classification, with the largest of these being a very short “leaf on” imagery season for acceptable phenology. This is compounded by persistent cloud cover during this growing season, and increasing terrain shadow and sun angle problems outside of this season. For this reason, the timeframe of 10 years was needed for the first update to the Alaska land cover in order to have enough acceptable imagery between these 2001 and 2011 nominal dates that met these criteria in order to provide reasonable change detection. For 2016, there was not enough complete path row imagery to do this similar change detection. Google Earth engine was employed to create a composite imagery mosaic that uses much smaller pieces of Landsat imagery to create a complete Landsat imagery snapshot used to create change detection between 2011 and 2016. Although this composite technique is not as scientifically rigorous as utilizing direct Landsat imagery, it was the only method available to us during production that would allow completion of change detection in the desired five year timeframe. The original published 2001 and 2011 Alaska NLCD classifications are generally unchanged, except for slight updates in the northern part of the state to remove perennial ice and snow that were a result of lack of suitable imagery in the 2001 timeframe representing minimum snow and ice extent. See the 2001 and 2011 Land Cover products for the specific metadata process steps associated with the creation of those datasets. The goal of this project is to provide the Nation with complete, current and consistent public domain information on its land use and land cover. Corner Coordinates (projection meters) Upper Left Corner: -2232330(X), 2380110(Y) Lower Right Corner: 1494720(X), 344820(Y) 20140101 20171231 ground condition Complete As needed Urban Alaska -152.4473 -148.0490 63.1013 59.3887 ISO 19115 Topic Category imageryBaseMapsEarthCover NGDA Portfolio Themes NGDA National Geospatial Data Asset Land Use Land Cover Theme None Land Cover GIS U.S. Geological Survey USGS digital spatial data U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of the United States, and associated areas (Federal Information Processing Standard 5-2): Washington, D.C., National Institute of Standards and Technology AK Alaska U.S. Department of Commerce, 1995, Countries, dependencies, areas of special sovereignty, and their principal administrative divisions, Federal Information Processing Standard 10-4,): Washington, D.C., National Institute of Standards and Technology United States U.S. US None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Customer Service Representative U.S. Geological Survey mailing and physical

47914 252nd Street

Sioux Falls SD 57198-0001 United States 605/594-6151 605/594-6589 custserv@usgs.gov U.S. Geological Survey Microsoft Windows 7 Version 6.1 (Build 7601: Service Pack 1); ESRI ArcCatalog 9.3.1.4000 (Service Pack 2) A formal accuracy assessment was completed for the original 2001 Land Cover product (citation 5). Change pixels were derived for the 2011 and 2016 Land Cover products based on specific criteria. An accuracy assessment has not been conducted for those change pixels. See the process steps in the metadata of the 2011 and 2016 Land Cover products for more information about the creation of those datasets. This document and the described land cover map are considered "provisional" until a formal accuracy assessment is completed. The U.S. Geological Survey can make no guarantee as to the accuracy or completeness of this information, and it is provided with the understanding that it is not guaranteed to be correct or complete. Conclusions drawn from this information are the responsibility of the user. Manual and automated QA/QC checks on the NLCD 2001 Land Cover Alaska dataset verified the positional accuracy of the pixel grid and confirmed that all thematic attributes belonged to one of the approved NLCD classes. This NLCD product of Alaska 2016 Urban Descriptor is the version dated February 13, 2020. N/A N/A Collin Homer Jon Dewitz Limin Yang 201505 publication https://www.researchgate.net/publication/282254893_Completion_of_the_2011_National_Land_Cover_Database_for_the_Conterminous_United_States_-_Representing_a_Decade_of_Land_Cover_Change_Information Digital and/or Hardcopy 201505 publication date Landsat ETM Landsat Enhanced Thematic Mapper (ETM) Collin Homer Jon Dewitz Limin Yang 201505 publication https://www.researchgate.net/publication/282254893_Completion_of_the_2011_National_Land_Cover_Database_for_the_Conterminous_United_States_-_Representing_a_Decade_of_Land_Cover_Change_Information Digital and/or Hardcopy 201505 publication date USDA United States Department of Agriculture (USDA) USDA Forest Service 20181102 publication What_is_FIA_FS_2018_update.pdf https://www.fia.fs.fed.us/ Digital and/or Hardcopy 20181102 publication date FIA Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service Collin Homer Jon Dewitz Limin Yang 201505 publication https://www.researchgate.net/publication/282254893_Completion_of_the_2011_National_Land_Cover_Database_for_the_Conterminous_United_States_-_Representing_a_Decade_of_Land_Cover_Change_Information Digital and/or Hardcopy 201505 publication date DEM Digital Elevation Model (DEM) Collin Homer Jon Dewitz Limin Yang 201505 publication https://www.researchgate.net/publication/282254893_Completion_of_the_2011_National_Land_Cover_Database_for_the_Conterminous_United_States_-_Representing_a_Decade_of_Land_Cover_Change_Information Digital and/or Hardcopy 201505 publication date USGS/EROS United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Collin Homer Jon Dewitz Limin Yang 201505 publication https://www.researchgate.net/publication/282254893_Completion_of_the_2011_National_Land_Cover_Database_for_the_Conterminous_United_States_-_Representing_a_Decade_of_Land_Cover_Change_Information Digital and/or Hardcopy 201505 publication date USGS NLCD United States Geological Survey (USGS) National Land Cover Database (NLCD) Infrared Imaging Radiometer Suite (VIIRS) Day/Night band Nighttime Lights have been applied by many researchers as a way to study such topics as population density and economic activity. This dataset was used in the generation of training data for the new NLCD 2016 Alaska Impervious Surface. Because so few urban areas exist in Alaska the impervious surface estimates were only created for an area surrounding Anchorage and Homer. The resulting training data generated for this area were used to predict urban areas for the entire state. VIIRS Day/Night band Nighttime Lights (for 2016) was superimposed on NLCD 2011 Impervious Surface data to exclude low density impervious areas outside urban and suburban centers, and ensure that only core urban areas were included in training data development. Two training datasets, one of larger extent, one smaller, were assembled by imposing two separate thresholds of nighttime lights imagery onto the NLCD 2011 Impervious Surface data layer. Each of the two training datasets were used separately to build regression tree models for predicting percent impervious surface using 2011 Landsat imagery as predictive variables. These two sets of regression tree models were the basis of two 2011 initial impervious surface maps. The same two training datasets were used with 2016 Landsat imagery to create two sets of regression tree models and two 2016 initial impervious surface maps. The two pairs of initial impervious surface maps - 2 each for 2011 and 2016 - were compared to remove false estimates caused by high reflectance in non-urban areas, as well as to retain 2011 impervious values unchanged from 2011 to 2016. An updated 2016 impervious surface map was generated. The last step was a clean-up to correct mapping errors with automated processes and some hand editing. For example, false impervious estimates in mines and barren land were removed, and developed areas with low imperviousness, such as city parks and golf courses, were added. Roads for 2011 and 2016 were derived from NAVSTREETS™ and hand edits. For the year 2001, there is no comparable roads source, so the NAVSTREETS™-derived 2011 roads were edited to reflect how the roads should appear for this year. This was done using a combination of impervious change pixels and hand edits for the entire state of Alaska. The impervious descriptor layer and the urban descriptor layer define which impervious layer pixels are roads and provides the best fit description for impervious pixels that are not roads. This should allow users extra flexibility for manipulating and categorizing impervious surface and the ability to more accurately identify new impervious growth for each year. The descriptor information is generated from roads, urban areas, and energy production sites. Landsat ETM USDA FIA DEM USGS/EROS 20200213 USGS NLCD Customer Service Representative U.S. Geological Survey mailing and physical

47914 252nd Street

Sioux Falls SD 57198-0001 United States 605/594-6151 605/594-6589 custserv@usgs.gov Raster Pixel 67844 124236 1 Albers Conical Equal Area 55.000000 65.000000 -154.000000 50.000000 0.000000 0.000000 row and column 30.000000 30.000000 meters World Geodetic System 1984 (WGS 84) WGS_1984 6378137.000000 298.257222 None NLCD Urban Descriptor Layer National Land Cover Database 2016 ObjectID Internal feature number ESRI Sequential unique whole numbers that are automatically generated. ValueUrban Descriptor NLCD 20160Background/No DataNLCD impervious descriptor classes 1 Primary road in urban area NLCD impervious descriptor classes 2 Primary road outside urban area NLCD impervious descriptor classes 3 Secondary road in urban area NLCD impervious descriptor classes 4 Secondary road outside urban area NLCD impervious descriptor classes 5 Tertiary road in urban area NLCD impervious descriptor classes 6 Tertiary road outside urban area NLCD impervious descriptor classes 7 Thinned road in urban area NLCD impervious descriptor classes 8 Thinned road outside urban area NLCD impervious descriptor classes 9 Nonroad imp in urban area NLCD impervious descriptor classes 10 Nonroad imp outside urban area NLCD impervious descriptor classes 11 Energy production site in urban area NLCD impervious descriptor classes 12 Energy production site outside urban area NLCD impervious descriptor classes 127 Background/No Data NLCD impervious descriptor classes Count A nominal integer value that designates the number of pixels that have each value in the file; histogram column in ERDAS Imagine raster attributes table NLCD 2016 Integer Red Red color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2016 0 100 CSS Color Value Percentage 0.1 Green Green color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2016 0 100 CSS Color Value Percentage 0.1 Blue Blue color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2016 0 100 CSS Color Value Percentage 0.1 Opacity A measure of how opaque, or solid, a color is displayed in a layer. NLCD 2016 0 100 percentage 0.1 Imperviousness Attributes Attributes defined by USGS and ESRI U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P96HHBIE None 20200401 Customer Service Representative U.S. Geological Survey mailing and physical

47914 252nd Street

Sioux Falls SD 57198-0001 United States 605/594-6151 605/594-6589 custserv@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999