DOI: 10.5066/p9jz5yyb
title: LCMAP Land Cover and Land Change Sample Data

description: The Land Change Monitoring Assessment and Projection (LCMAP) sample raster dataset is a suite of five spectral change and five land cover (and land cover derivative) products. The LCMAP approach is the foundation for an integrated land change science framework led by the U.S. Geological Survey (USGS). The sample data represent prototype products that are examples of a new generation of integrated land change and land cover data on an annual time step. The sample data were calculated using the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014) and are derived from a time series of satellite imagery consisting of all available cloud- and shadow-free pixels in the USGS Landsat Analysis Ready Data (ARD) archive (Dwyer and others, 2018). The CCDC methodology supports the continuous tracking and characterization of changes in land cover, and condition; in the future, it will enable assessments of current, historical, and future processes of change. Landsat ARD, as the source data for LCMAP, are standardized Landsat data that are pre-processed to ensure the data meet a minimum set of requirements and are organized into a form that allows immediate analysis with a minimum of additional user effort. ARD data are provided as tiled, georegistered, surface reflectance products defined in a common equal area projection and tiled into a common grid. ARD observations must be transformed into time-series vectors before further calculations using the CCDC methodology. The CCDC methodology, initially developed at Boston University (Zhu and Woodcock, 2014), has been adopted and modified by USGS for LCMAP. CCDC involves harmonic modeling that characterizes the seasonality, trend, and breaks based on the time-series spectral reflectance data from multiple Landsat bands (i.e., green, red, near-infrared, short-wave infrared). The CCDC approach involves two major components: change detection and classification. The change detection component utilizes the available high-quality surface reflectance data in a pixel-based time series to calculate a mathematical model for the spectral response of each pixel and to estimate the dates at which the spectral time-series data diverge from past responses or patterns. The basis of change detection is the comparison of clear satellite observations with model predictions. 'Divergence' (referred to as a model 'break') often is identified as the result of an abrupt change (e.g. wildfire, logging, mining, and urban development) but may also result from a gradual shift (e.g., forest regrowth, insect infestation, disease) in the spectral signal over time. Breaks are detected by CCDC by applying a criterion based on the root mean square error. Time periods with established models are referred to as 'model segments.' After a break is identified in the time series, a new model can be established following the break provided there are enough clear observations going forward in time. The classification component of CCDC involves using the coefficients of time series models as the inputs for land cover classification. The CCDC method has the capability to generate land cover for any time in the time series; the USGS has selected an annual time step for land cover classification. The suite of land cove and change products are nominally identified at a central point in the year, July 1. Classification is performed using a boosted decision tree method based on training data developed from 2001 NLCD land cover classes (Homer and others, 2007). The land cover legend for the Primary and Secondary Land Cover products is comparable to an Anderson level 1.