Landscape Change Monitoring System (LCMS) Hawaii Year of Highest Probability of Fast Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Fast Loss into a single layer showing the year LCMS detected Fast Loss with the highest model confidence. See additional information about Fast 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.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快速损失（Fast Loss）汇总为单一图层，展示LCMS检测到快速损失且模型置信度最高的年份。有关Fast Loss的详细信息，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。其研发目标是依托最新技术与变化检测领域的进展，构建统一方法以生成「最佳可用」的景观变化地图。由于不存在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器，这一策略提升了各类生态系统与变化过程中的地图精度（Healey等，2018）。最终生成的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与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）进行调取与处理。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以及Sentinel 2a、2b Level-1C大气顶反射率数据。随后计算年度中值合成影像，以将每一年的数据汇总为单幅合成影像。合成影像时间序列将通过LandTrendr（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）进行时间分段。所有无云与云阴影的像素值也将通过CCDC算法（Zhu与Woodcock，2014）进行时间分段。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。LandTrendr预测变量包括拟合值、成对差异、分段时长、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，以及来自年度合成影像与LandTrendr所用各像素的儒略日的成对差异。地形预测变量则包括来自美国地质调查局3D高程计划（3D Elevation Program, 3DEP，U.S. Geological Survey，2019）的高程、坡度、坡向正弦值、坡向余弦值以及地形位置指数（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，2010）。LCMS的输出分为三类：变化、土地覆盖与土地利用。其基础变化图层会标注受干扰区域、植被演替增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——这类变化可能会或不会引发土地覆盖与/或土地利用的转换。变化、土地覆盖与土地利用产品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。