A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies

Abstract The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.

[1]  Hugo Carrão,et al.  Combining per-pixel and object-based classifications for mapping land cover over large areas , 2014 .

[2]  Limin Yang,et al.  Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data , 2003 .

[3]  Russell G. Congalton,et al.  Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data , 2017, Int. J. Digit. Earth.

[4]  Geoff Smith Hybrid Pixel- and Object-Based Approach to Habitat Condition Monitoring , 2013 .

[5]  C. Homer,et al.  An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD , 2013 .

[6]  Bonnie Ruefenacht,et al.  Comparison of Three Landsat TM Compositing Methods: A Case Study Using Modeled Tree Canopy Cover , 2016 .

[7]  J. Townshend,et al.  Global, Landsat-based forest-cover change from 1990 to 2000 , 2014 .

[8]  Warren B. Cohen,et al.  Modeling Percent Tree Canopy Cover: A Pilot Study , 2012 .

[9]  J. Wickham,et al.  Accuracy assessment of NLCD 2006 land cover and impervious surface , 2013 .

[10]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[11]  J. Townshend,et al.  Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover , 2016 .

[12]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[13]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[14]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[15]  Joanne C. White,et al.  Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data , 2015 .

[16]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[17]  Joanne C. White,et al.  Land cover 2.0 , 2018 .

[18]  Thomas R. Loveland,et al.  USING MULTISOURCE DATA IN GLOBAL LAND-COVER CHARACTERIZATION: CONCEPTS, REQUIREMENTS, AND METHODS , 1993 .

[19]  Conghe Song,et al.  Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record , 2013 .

[20]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[21]  Suming Jin,et al.  Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .

[22]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[23]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[24]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[25]  Glenn Shafer,et al.  Implementing Dempster's Rule for Hierarchical Evidence , 1987, Artif. Intell..

[26]  A. Belward,et al.  The IGBP-DIS global 1km land cover data set, DISCover: First results , 1997 .

[27]  Limin Yang,et al.  An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery , 2003 .

[28]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[29]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[30]  J. Wickham,et al.  Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results , 2003 .

[31]  Rasim Latifovic,et al.  Multitemporal land cover mapping for Canada: methodology and products , 2005 .

[32]  G. Moisen,et al.  Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance , 2016 .

[33]  Warren B. Cohen,et al.  Trajectory-based change detection for automated characterization of forest disturbance dynamics , 2007 .

[34]  Suming Jin,et al.  A comprehensive change detection method for updating the National Land Cover Database to circa 2011 , 2013 .

[35]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[36]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[37]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[38]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[39]  Guoqing Sun,et al.  Hierarchical mapping of Northern Eurasian land cover using MODIS data , 2011 .

[40]  John A. Richards,et al.  Knowledge-based techniques for multi-source classification , 1990 .

[41]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[42]  C. Homer,et al.  Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods , 2010 .

[43]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[44]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[45]  Collin G. Homer,et al.  Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[46]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[47]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[48]  Min Feng,et al.  A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm , 2016, Int. J. Digit. Earth.

[49]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[50]  L. Gass,et al.  Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes , 2017 .

[51]  Rick Mueller,et al.  The Multi-Resolution Land Characteristics (MRLC) Consortium - 20 Years of Development and Integration of USA National Land Cover Data , 2014, Remote. Sens..