Landscape Change Monitoring System (LCMS) Puerto Rico USVI Annual Landcover

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010). Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.本产品属于景观变化监测系统（Landscape Change Monitoring System, LCMS）数据集套件的一部分，展示了LCMS逐年建模得到的土地覆盖（Land Cover）类别。有关土地覆盖的更多信息，请参见下文的实体与属性信息（Entity_and_Attribute_Information）或字段（Fields）章节。 LCMS是一套基于遥感技术的系统，用于美国全境的景观变化制图与监测，其目标是依托最新技术与变化检测领域的进展，构建统一的方法体系，以生成“最佳可用”的景观变化制图成果。由于不存在适用于所有场景的最优算法，LCMS采用集成模型作为预测器，从而在多种生态系统与变化过程场景下提升制图精度（Healey等人，2018）。 由此产出的LCMS变化、土地覆盖与土地利用数据集套件，完整呈现了美国全境近四十年来的景观变化全貌。LCMS模型的预测因子图层包含LandTrendr与CCDC变化检测算法的输出结果，以及地形信息。所有组件均通过谷歌地球引擎（Google Earth Engine）进行调取与处理（Gorelick等人，2017）。 为生成年度合成影像，研究团队将cFmask（Zhu与Woodcock，2012）、cloudScore、Cloud Score +（Pasquarella等人，2023）以及TDOM（Chastain等人，2019）等云与云阴影掩膜方法，应用于Landsat Tier 1以及Sentinel 2a、2b Level-1C级大气顶反射率数据。随后通过计算年度中心点（medoid），将每一年度的数据汇总为单幅合成影像。 合成影像时间序列采用LandTrendr（Kennedy等人，2010；Kennedy等人，2018；Cohen等人，2018）进行时序分割。所有无云与云阴影干扰的像素值，也通过CCDC算法（Zhu与Woodcock，2014）完成时序分割。 LandTrendr、CCDC与地形预测因子均可作为独立预测变量，应用于随机森林（Random Forest, Breiman，2001）模型。其中，LandTrendr预测变量包含拟合值、两两差值、片段持续时间、变化幅度与斜率；CCDC预测变量包含CCDC正弦与余弦系数（前3次谐波）、拟合值，以及年度合成影像与LandTrendr所用各像素对应儒略日的两两差值。 地形预测变量包含来自美国地质调查局3D高程计划（USGS 3D Elevation Program, 3DEP）（U.S. Geological Survey，2019）提供的高程、坡度、坡向正弦值、坡向余弦值与地形位置指数（topographic position indices）（Weiss，2001）。 参考数据通过TimeSync收集，这是一款基于网页的工具，可帮助分析人员可视化并解读1984年至今的Landsat数据记录（Cohen等人，2010）。 产品输出分为变化、土地覆盖与土地利用三大类别。其核心变化图层可识别扰动区域、植被演替生长区域与稳定景观区域。此外还提供更详细的变化产品，旨在满足围绕监测植被覆盖、水体范围或雪/冰范围变化的成因与类型的需求，此类变化可能引发或不引发土地覆盖与/或土地利用的转换。变化、土地覆盖与土地利用均针对时间序列中的每一年进行预测，作为LCMS的核心产品。