Landscape Change Monitoring System (LCMS) Alaska Annual Landuse

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use 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.本产品隶属于景观变化监测系统（Landscape Change Monitoring System, LCMS）数据集套件，展示了LCMS建模得到的逐年土地利用分类结果。有关土地利用的详细信息，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景观变化制图与监测系统，覆盖美国全境。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器，这一设计有效提升了不同生态系统与变化过程下的地图精度（Healey等，2018）。 由此生成的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余年间全美范围内的景观变化全貌。 LCMS模型的预测图层包含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Google Earth Engine（Gorelick等，2017）进行访问与处理。 为生成年度合成影像，研究人员将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级大气顶反射率数据。随后通过计算年度中心点（medoid），将每一年的多源影像汇总为单幅合成影像。 合成影像的时间序列将通过LandTrendr（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）进行分段处理。所有无云与云阴影遮挡的像素值也将通过CCDC算法（Zhu与Woodcock，2014）完成时间分段。 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、两两差值、片段时长、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，以及年度合成影像与LandTrendr所用各像素儒略日的两两差值。地形预测变量则包括来自美国地质调查局3D高程计划（USGS 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工具采集，该工具为基于网页的可视化平台，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别：变化、土地覆盖与土地利用。其基础变化图层可识别受干扰区域、植被演替增长区域与稳定景观区域。此外还提供更细分的变化产品，旨在满足针对植被覆盖、水体范围或雪/冰范围的变化原因与类型的监测需求——这类变化可能引发或不引发土地覆盖与/或土地利用的转换。 所有时间序列年份的变化、土地覆盖与土地利用数据均为预测所得，是LCMS的核心基础产品。