Landscape Change Monitoring System (LCMS) Hawaii 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建模生成的逐年土地利用（Land Use）分类结果。有关土地利用的更多细节，请参阅下文的Entity_and_Attribute_Information或Fields章节。LCMS是一套基于遥感技术的全美景观变化制图与监测系统，其核心目标是依托最新技术与变化检测领域的研究进展，构建统一的标准化方法，以生成“最佳可用”的景观变化制图成果。由于不存在适用于所有场景的最优算法，LCMS采用多模型集成方案作为预测器，有效提升了不同生态系统与变化过程下的制图精度（Healey等，2018）。最终产出的LCMS变化、土地覆盖（Land Cover）与土地利用数据集套件，完整呈现了美国过去四十余年的全域景观变化动态。LCMS模型的预测因子层包含LandTrendr与CCDC变化检测算法的输出结果，以及地形信息。所有组件均通过谷歌地球引擎（Google Earth Engine, GEE）完成调取与处理（Gorelick等，2017）。为生成年度合成影像，研究团队将cFmask（Zhu与Woodcock，2012）、cloudScore、Cloud Score +（Pasquarella等，2023）与TDOM（Chastain等，2019）等云与云阴影掩膜方法，应用于Landsat Tier 1以及Sentinel 2a、Sentinel 2b Level-1C级大气顶层反射率数据。随后通过计算年度中心点影像（medoid），将单一年份的所有有效遥感数据汇总为单幅合成影像。合成影像时间序列通过LandTrendr算法实现时间分段（Kennedy等，2010；Kennedy等，2018；Cohen等，2018）。所有无云与云阴影的像素值也通过CCDC算法完成时间分段（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, RF）模型（Breiman，2001）。其中，LandTrendr预测变量包括拟合值、两两差值、分段时长、变化幅度与变化斜率；CCDC预测变量包含前3次谐波的CCDC正弦与余弦系数、拟合值，以及年度合成影像与LandTrendr所用各像素对应儒略日的两两差值。地形预测变量则涵盖美国地质调查局3D高程计划（USGS 3D Elevation Program, 3DEP）提供的高程、坡度、坡向正弦值、坡向余弦值，以及地形位置指数（Weiss，2001）（U.S. Geological Survey，2019）。参考数据采集环节采用TimeSync（一款基于网页的工具）：该工具可帮助分析人员可视化并解读1984年至今的Landsat遥感数据集（Cohen等，2010）。LCMS的输出成果分为三大类：变化产品、土地覆盖产品与土地利用产品。其核心变化产品可识别扰动区域、植被演替生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/冰范围变化的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/或土地利用的类型转型。变化、土地覆盖与土地利用三类产品均针对时间序列中的每一年进行预测，是LCMS的核心基础产品。