Landscape Change Monitoring System Conterminous United States Year of Highest Probability of Gain (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information 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 annual Landsat and Sentinel 2 composites, 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, 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). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. 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. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat 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（LCMS）数据集系列。该系列展示了LCMS针对每年所建模的变化类别。关于变化的详细信息请参阅下方的实体与属性信息部分。LCMS是一种基于遥感技术的系统，旨在对美国的景观变化进行映射和监测。其目标是通过运用最新的技术和变化检测的进步，采用一种一致的方法，以生成景观变化的“最佳可用”地图。鉴于没有算法能在所有情况下都表现出最佳性能，LCMS采用模型集成的方法作为预测因子，从而提高了在不同生态系统和变化过程中的地图准确性（Healey等人，2018年）。LCMS变化、土地覆盖和土地利用地图系列为过去四十年美国景观变化的全面描绘提供了依据。LCMS模型的预测层包括年度Landsat和Sentinel-2复合数据、LandTrendr和CCDC变化检测算法的输出，以及地形信息。这些组件均通过Google Earth Engine（Gorelick等人，2017年）进行访问和处理。为了生成年度复合数据，对Landsat Tier 1和Sentinel-2a及2b Level-1C大气顶反射数据应用了cFmask（Zhu和Woodcock，2012年）、cloudScore和TDOM（Chastain等人，2019年）的云和云阴影掩膜方法。然后计算每年的中位数以将每年的数据总结为单一复合数据。复合时间序列使用LandTrendr（Kennedy等人，2010年；Kennedy等人，2018年；Cohen等人，2018年）进行时间分段。所有无云和无云阴影的值也使用CCDC算法（Zhu和Woodcock，2014年）进行时间分段。原始复合值、LandTrendr拟合值、成对差异、分段持续时间、变化幅度和斜率，以及CCDC 9月1日的正弦和余弦系数（前三个谐波）、拟合值和成对差异，以及来自国家高程数据集（NED）的高程、坡度、方位角正弦、方位角余弦和地形位置指数（Weiss，2001年）均作为随机森林（Breiman，2001年）模型中的独立预测变量。参考数据通过TimeSync收集，这是一个基于网络的工具，有助于分析师可视化并解读1984年至今的Landsat数据记录（Cohen等人，2010年）。输出数据分为三类：变化、土地覆盖和土地利用。变化具体涉及植被覆盖，包括缓慢损失、快速损失（包括水文变化，如淹没或干燥）和增长。这些值针对Landsat时间序列的每年进行预测，并作为LCMS的基础产品。此记录来自向https://data.gov目录提供数据的美国农业部企业数据目录。该记录的数据包括以下资源：ISO-19139元数据、ArcGIS Hub数据集、ArcGIS GeoService。欲获取完整信息，请访问https://data.gov。