Landscape Change Monitoring System (LCMS) Conterminous United States Year of Highest Probability of Slow Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the year LCMS detected Slow Loss with the highest model confidence. See additional information about Slow Loss 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）数据集套件之一。该数据集将所有年度缓慢损失（Slow Loss）汇总为单一图层，展示LCMS检测到该类损失且模型置信度最高的年份。有关缓慢损失的详细信息，请参阅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美国全国景观变化制图与监测系统，其核心目标是依托最新技术与变化检测领域的研究进展，构建一套统一的“最佳可用”景观变化制图方案。由于不存在适用于所有场景的最优算法，LCMS采用模型集成作为预测器，可提升不同生态系统与变化过程下的制图精度（Healey等，2018）。最终产出的LCMS变化、土地覆盖与土地利用数据集套件，完整呈现了美国过去四十年来的景观变化全貌。 LCMS模型的预测因子图层包括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, Gorelick等，2017）进行调取与处理。为生成年度合成影像，研究团队将cFmask（Zhu和Woodcock，2012）、cloudScore、Cloud Score +（Pasquarella等，2023）与TDOM（Chastain等，2019）等云与云阴影掩膜方法，应用于陆地卫星（Landsat）Tier 1及哨兵（Sentinel）2a、2b Level-1C大气顶反射率数据。随后通过计算年度中心点，将每年的影像汇总为单一合成影像。合成影像的时间序列采用LandTrendr算法进行分段（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）。所有无云与云阴影的像素值也通过CCDC算法完成时间分段（Zhu和Woodcock，2014）。LandTrendr、CCDC与地形预测因子可作为独立变量应用于随机森林（random forest, Breiman，2001）模型。LandTrendr预测变量包括拟合值、两两差值、分段时长、变化幅度与变化斜率。CCDC预测变量包括CCDC正弦与余弦系数（前3次谐波）、拟合值，以及来自年度合成影像与LandTrendr所用各像素儒略日（Julian Day）的两两差值。地形预测变量包括高程、坡度、坡向正弦值、坡向余弦值，以及来自美国地质调查局（U.S. Geological Survey）3D高程计划（3DEP, Weiss，2001；U.S. Geological Survey，2019）的地形位置指数。参考数据通过TimeSync工具采集，该工具为基于网页的辅助工具，可帮助分析人员可视化并解译1984年至今的陆地卫星数据序列（Cohen等，2010）。 LCMS的输出产品分为三大类：变化、土地覆盖与土地利用。其基础变化产品可标注扰动区域、植被演替增长区域与稳定景观区域。另有更精细的变化产品，旨在满足针对植被覆盖、水体范围或雪/冰范围的变化成因与类型的监测需求——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。该系统可为时间序列中的每一年生成变化、土地覆盖与土地利用产品，这些产品作为LCMS的核心基础产品支撑后续各类应用。 本数据集记录取自美国农业部（USDA）企业数据清单，该清单会同步至https://data.gov 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。