A comprehensive change detection method for updating the National Land Cover Database to circa 2011

Abstract

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

“…A further subsetting of the reference gages were made as a follow-on to the Hydro-Climatic Data Network (HCDN) 1988 data set (Slack and Landwehr, 1992). These gages, marked HCDN-2009(Lins, 2012, meet the following criteria: (1) have at least 20 years of complete flow data between 1990 and 2009 and were active as of 2009, (2) are a GAGES-II reference gage, (c) have less than 5 % imperviousness as measured by the National Land Cover Database (NLCD-2011;Jin et al, 2013), and (d) passed a manual survey of human impacts in the basin by local Water Science Center evaluators (Falcone et al, 2010). There are 704 gages in the GAGES-II database that are considered HCDN-2009 across the CONUS.…”

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

“…Updating/backdating, an approach that has long been used in visual interpretation (Linke et al, 2009;McDermid et al, 2008;Zhou, Huang, & Cadenasso, 2011), has been increasingly applied in automatic change analysis and classifications (e.g., Xian & Homer, 2010, Jin et al, 2013). An updating/backdating approach can be considered as a synthesis of the post-classification comparison and pre-classification change detection (Xian & Homer, 2010).…”

“…This approach typically started with an existing map, frequently referred as the reference map, based on which the classification and change analysis are conducted. One of the most remarkable examples is the generation of the 2006 National Land Cover Database (NLCD 2006) by updating the NLCD 2001 (Xian & Homer, 2010), which is now used as the reference dataset to create the 2011 NLCD, also using an updating approach (Jin et al, 2013).…”

“…A considerable amount of research have shown that an object-based approach is superior to a pixel-based approach because it can greatly reduce the "salt and pepper" effect, provide an effective way in incorporating spatial, textural, and neighborhood relation in classification and change analysis (Blaschke, 2010;Hansen & Loveland, 2012;. However, previous studies using an automatic updating/ backdating approach, have been largely applying a pixel-based method (e.g., Xian & Homer, 2010, Jin et al, 2013. Relatively few studies have investigated how an object-based updating/backdating approach performs.…”

Developing spatially and thematically detailed backdated maps for land cover studies

“…One such project is the National Land Cover Database for the United States (NLCD) which has developed intermediate-scale (30 m) land cover products for the years 1992years , 2001years , 2006years and 2011years (Vogelmann et al 1998years , 2001Homer et al 2004;Xian, Homer, and Fryc 2009;Jin et al 2012Jin et al , 2013. In this project, the approach to land cover classification was based on unsupervised classifiers for the initial NLCD 1992, and for later periods a decision tree classification method was implemented.…”