Landscape Change Monitoring System (LCMS) Hawaii Annual Landcover

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover 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.

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（Landscape Change Monitoring System, LCMS）数据集套件的组成部分，展示了LCMS逐年建模得到的土地覆盖（Land Cover）类别。有关土地覆盖的更多详情，请参阅下文的「实体与属性信息」或「字段」部分。

LCMS是一套基于遥感（remote sensing）技术的系统，用于绘制并监测美国全境的景观变化。其研发目标是依托最新技术与变化检测（change detection）领域的进展，形成一套统一方法，以生成「可获得的最优」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采用多模型集成（ensemble of models）作为预测器，这一设计提升了其在多种生态系统与变化过程中的制图精度（Healey等人，2018）。最终产出的LCMS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等人，2017）进行调取与处理。为生成年度合成影像，研究团队将cFmask（Zhu与Woodcock，2012）、cloudScore、Cloud Score +（Pasquarella等人，2023）与TDOM（Chastain等人，2019）等云与云阴影掩膜方法应用于Landsat Tier 1以及Sentinel 2a、2b Level-1C的大气顶反射率数据。随后通过计算年度中值（medoid），将单一年份的遥感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等人，2010；Kennedy等人，2018；Cohen等人，2018）。所有无云与云阴影的像元值也将通过CCDC算法进行时间分段（Zhu与Woodcock，2014）。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预测变量包含拟合值、两两差值、分段时长、变化幅度与斜率；CCDC预测变量包含CCDC的正弦与余弦系数（前3次谐波）、拟合值，以及年度合成影像与LandTrendr所用各像素的儒略日（Julian Day）差值。地形预测变量则包含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、坡向余弦值与地形位置指数（topographic position indices, 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工具，可辅助分析人员解读1984年至今的Landsat数据记录（Cohen等人，2010）。

本数据集的输出分为三类：变化、土地覆盖与土地利用。其基础变化产品会标注受干扰区、植被演替生长区与稳定景观区域。此外还有更详细的变化产品，旨在满足针对植被覆盖、水体范围或冰雪范围的变化成因与类型开展监测的需求——这类变化可能引发也可能不引发土地覆盖与/或土地利用的转换。变化、土地覆盖与土地利用数据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。

本数据集记录源自美国农业部企业数据目录（USDA Enterprise Data Inventory），该目录同步至https://data.gov 公开目录。本记录包含以下资源：ISO-19139元数据、ArcGIS Hub Dataset、ArcGIS GeoService。如需获取完整信息，请访问https://data.gov。