Landscape Change Monitoring System (LCMS) Puerto Rico USVI Most Recent Year of Gain (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Gain into a single layer showing the most recent year LCMS detected Gain. See additional information about Gain 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. This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.本产品属于景观变化监测系统（Landscape Change Monitoring System, LCMS）数据集套件的组成部分。该数据集将所有年度增益（Gain）汇总为单个图层，展示LCMS检测到的最新年度增益。有关增益的更多详细信息，请参阅下文的“实体与属性信息”或“字段”章节。 LCMS是一套基于遥感技术的系统，用于绘制并监测全美范围内的景观变化。其核心目标是依托最新技术与变化检测领域的研究进展，开发统一的标准化方法，以生成“最佳可用”的景观变化地图。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因子，这一策略有效提升了不同生态系统与变化过程下的地图制图精度（Healey等，2018）。最终产出的LCMS变化、土地覆盖与土地利用地图套件，完整呈现了过去四十年来全美范围内的景观变化全貌。 LCMS模型的预测因子图层包括LandTrendr和CCDC变化检测算法的输出结果，以及地形信息。所有这些组件均通过Google Earth Engine（Gorelick等，2017）进行访问与处理。为生成年度合成影像，需将cFmask（Zhu与Woodcock，2012）、cloudScore、Cloud Score +（Pasquarella等，2023）以及TDOM（Chastain等，2019）等云与云阴影掩膜方法，应用于Landsat Tier 1及Sentinel 2a、2b Level-1C大气层顶反射率数据。随后计算年度中值（medoid），将每一年的遥感数据汇总为单张合成影像。合成时间序列将通过LandTrendr算法进行时间分割（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）。所有无云及无云阴影的像素值也将通过CCDC算法进行时间分割（Zhu与Woodcock，2014）。 LandTrendr、CCDC与地形预测因子均可作为独立变量应用于随机森林（random forest，Breiman，2001）模型。其中，LandTrendr预测变量包括拟合值、成对差值、片段持续时间、变化幅度与斜率；CCDC预测变量包括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所用各像素的儒略日对应的成对差值；地形预测变量则包括来自美国地质调查局3D高程项目（3DEP，U.S. Geological Survey，2019）的高程、坡度、坡向正弦值、坡向余弦值与地形位置指数（Weiss，2001）。 参考数据通过TimeSync工具采集，这是一款基于网页的可视化工具，可帮助分析人员解读1984年至今的Landsat数据记录（Cohen等，2010）。 LCMS的输出成果分为三大类：变化、土地覆盖与土地利用。其基础变化图层会标注扰动区、植被演替生长区与稳定景观区。此外还有更详细的变化产品，旨在满足针对植被覆盖、水体范围或雪/冰范围变化的成因与类型进行监测的需求——此类变化可能引发或不引发土地覆盖与/或土地利用的转型。变化、土地覆盖与土地利用数据会针对时间序列中的每一年进行预测，作为LCMS的核心基础产品。 本数据集记录取自美国农业部企业数据清单（USDA Enterprise Data Inventory），该清单会将数据同步至https://data.gov 官方目录。本记录包含以下资源：ISO-19139元数据、ArcGIS Hub数据集、ArcGIS地理服务。如需获取完整信息，请访问https://data.gov。