LCMAP Land Cover and Land Change Sample Data

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.土地变化监测评估与投影（Land Change Monitoring Assessment and Projection, LCMAP）样本栅格数据集是一套涵盖5种光谱变化产品与5种土地覆盖（及土地覆盖衍生产品）的数据集。LCMAP方法是由美国地质调查局（U.S. Geological Survey, USGS）牵头构建的综合土地变化科学框架的核心基础。本样本数据属于原型产品，代表了新一代按年度时间步长生成的综合土地变化与土地覆盖数据。 本样本数据采用朱与伍德科克（Zhu and Woodcock, 2014）提出的持续变化检测与分类（Continuous Change Detection and Classification, CCDC）算法计算生成，其数据源为美国地质调查局陆地卫星分析就绪数据（Landsat Analysis Ready Data, ARD）档案中所有可用的无云无阴影像素构成的卫星影像时间序列（Dwyer等, 2018）。 CCDC方法可实现对土地覆盖及其状态变化的持续追踪与特征刻画；未来还将支持对当前、历史及未来变化过程的评估。作为LCMAP数据源的陆地卫星分析就绪数据（Landsat ARD）是经过标准化预处理的陆地卫星数据，可确保数据满足最低标准要求，且其组织形式可让用户无需额外大量操作即可直接开展分析。ARD数据以分幅地理配准的地表反射率产品形式提供，采用统一的等面积投影坐标系，并被分幅至统一网格中。在使用CCDC方法开展进一步计算前，需将ARD观测数据转换为时间序列向量。 CCDC方法最初由波士顿大学提出（Zhu and Woodcock, 2014），后经美国地质调查局针对LCMAP进行适配与改进。CCDC采用谐波建模方法，基于多波段陆地卫星光谱反射率时间序列数据（即绿光、红光、近红外、短波红外波段）刻画季节特征、变化趋势与突变点。CCDC方法包含两大核心模块：变化检测与分类。 变化检测模块利用基于像元的时间序列高质量地表反射率数据，为每个像元的光谱响应构建数学模型，并估算光谱时间序列数据偏离过往响应或模式的日期。变化检测的核心是将清晰的卫星观测数据与模型预测结果进行对比。“偏离”（亦称模型“突变”）通常由突发变化（如野火、采伐、采矿与城市开发）导致，但也可能源于光谱信号随时间发生的渐进式转变（如森林恢复、虫害侵袭、病害蔓延）。CCDC通过基于均方根误差的判别准则检测突变点。已构建模型的时间段被称为“模型段”。若在时间序列中识别出突变点，且突变后仍有足够多的清晰观测数据，则可在突变后构建新的模型。 CCDC的分类模块以时间序列模型的系数作为土地覆盖分类的输入特征。CCDC方法可生成时间序列中任意时间点的土地覆盖数据，美国地质调查局选择按年度时间步长开展土地覆盖分类。该套土地覆盖与变化产品的标称时间节点定为每年的年中时刻——7月1日。分类采用基于2001年全国土地覆盖数据库（National Land Cover Database, NLCD）土地覆盖类别生成的训练数据训练的提升决策树方法（Homer等, 2007）。一级与二级土地覆盖产品的土地覆盖分类体系可与安德森一级分类体系（Anderson Level 1）对标。