Landscape Change Monitoring System (LCMS) Puerto Rico USVI Year of Highest Probability 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 year LCMS detected gain with the highest model confidence. 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.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。该产品将所有年度植被增益汇总为单个图层，该图层展示了LCMS检测到植被增益且模型置信度最高的年份。LCMS是一套基于遥感技术、用于全美景观变化制图与监测的系统，其目标是依托最新技术与变化检测领域的进展，开发统一的方法论，以生成“最优可用”的景观变化地图。由于不存在能在所有场景下均表现最优的算法，LCMS采用集成模型作为预测因子，这一策略提升了跨多种生态系统与变化过程的制图精度（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年度Landsat与Sentinel 2合成影像、LandTrendr和CCDC变化检测算法的输出结果，以及地形信息。所有此类组件均通过Google Earth Engine（Gorelick等，2017）进行获取与处理。为生成年度合成影像，需将cFmask（Zhu与Woodcock，2012）、cloudScore及TDOM（Chastain等，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。随后计算年度中值影像，以将每年的观测数据汇总为单幅合成影像。 合成影像时间序列将通过LandTrendr算法进行时间分段（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）。所有无云与云阴影的影像值也将通过CCDC算法（Zhu与Woodcock，2014）进行时间分段。 原始合成影像值、LandTrendr拟合值、逐对差值、分段时长、变化幅度与斜率，以及CCDC的9月正弦与余弦系数（前3次谐波）、拟合值与逐对差值，再加上来自国家高程数据集（National Elevation Dataset, NED）的高程、坡度、坡向正弦值、坡向余弦值与地形位置指数（Weiss，2001），共同作为随机森林（Random Forest, Breiman，2001）模型的独立预测变量。 参考数据通过TimeSync工具收集，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，2010）。 LCMS的输出分为三类：变化、土地覆盖与土地利用。其中变化特指植被覆盖相关变化，包括缓慢退化、快速退化（后者还涵盖洪水或干涸等水文变化）以及植被增益。上述变量针对Landsat时间序列的每一年进行预测，是LCMS的核心基础产品。