Overall Methodology Design for the United States National Land Cover Database 2016 Products

Suming Jin,Danny Howard,Collin Homer,Zhe Zhu,Jon Dewitz,Limin Yang,Patrick Danielson,Congcong Li,George Xian

doi:10.3390/rs11242971

Abstract

The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.

Highlights

Information from land cover and land use (LCLU) change products is essential for climate and environmental change studies, proper land management and land-use decision making, and regional and global sustainable development [1–4]
We focus on describing the new methodology for the National Land Cover Database (NLCD) 2016 land cover and land cover change product, and demonstrate the process with results from four representative Landsat path/rows
We developed multi-date change detection models to create a Forest Disturbance Year map of 2001 to 2016 based on a 7-date time series (i.e., 2001, 2004, 2006, 2008, 2011, 2013, and 2016) of Normalized Spectral Distance (NSD), which was derived like a z-score by using an individual Landsat image and persistent forest areas as reference

Summary

Introduction

Information from land cover and land use (LCLU) change products is essential for climate and environmental change studies, proper land management and land-use decision making, and regional and global sustainable development [1–4]. The process, method, legend, and database approach were subsequently revised and improved to produce the modern era of NLCD with products produced in 2001, 2006, and 2011 [17–19]. These new NLCD products were no longer compatible with NLCD 1992. NLCD 2011 products, which were released in 2013, represented a decade of consistently produced land cover change and impervious surface change information for the Conterminous United States (CONUS) across three periods at five-year intervals: 2001, 2006, and 2011 [19]. NLCD products have become a cornerstone in U.S land cover applications and are widely used in such areas as climate modeling, hydrology, land management, environmental planning, urban development, wildlife habitat, and ecosystem assessment and education [20–27]

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Conclusion