Landscape Change Monitoring System (LCMS) Southeast Alaska Most Recent Year Of Fast Loss (Image Service)

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 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逐年建模得到的土地利用分类结果。有关土地利用的详细信息，请参阅下文的实体与属性信息（Entity_and_Attribute_Information）小节。 LCMS是一套基于遥感技术、用于美国全境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研究进展，开发一套统一的方法，生成“可用最优”的景观变化制图成果。由于没有任何一种算法可在所有场景下表现最优，LCMS采用集成模型作为预测器，以此提升不同生态系统与变化过程下的制图精度（Healey等，2018）。LCMS生成的变化、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余年的景观变化全貌。 LCMS模型的预测因子图层包括年度Landsat与Sentinel 2合成数据、LandTrendr和CCDC变化检测算法的输出结果，以及地形信息。所有数据均通过Google Earth Engine（Gorelick等，2017）调取并处理。为生成年度合成数据，研究团队将cFmask（Zhu与Woodcock，2012）、cloudScore与TDOM（Chastain等，2019）的云与云阴影掩膜方法，应用于Landsat Tier 1以及Sentinel 2a、2b Level-1C级的大气层顶反射率数据。随后计算年度中值（medoid），将单一年份的观测数据汇总为单幅合成影像。 合成影像的时间序列通过LandTrendr算法完成时间分段（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）。所有无云与无云阴影的观测值也通过CCDC算法（Zhu与Woodcock，2014）完成时间分段。原始合成影像数值、LandTrendr拟合值、逐对差值、分段时长、变化幅度与变化斜率，以及CCDC算法得到的9月正弦与余弦系数（前3次谐波）、拟合值与逐对差值，再加上来自美国国家高程数据集（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工具采集，这是一款基于网页的工具，可帮助分析人员可视化并解译1984年至今的Landsat观测序列（Cohen等，2010）。 该数据集的输出成果分为三类：变化产品、土地覆盖产品与土地利用产品。其中变化产品专门针对植被覆盖，包括缓慢退化、快速退化（还涵盖洪涝或干涸等水文变化）以及覆盖增益三类。上述成果针对Landsat时间序列的每一个年份进行预测，是LCMS的基础产品。 本数据集记录源自美国农业部（USDA）企业数据清单，该清单为https://data.gov目录提供数据支持。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。