Landscape Change Monitoring System (LCMS) Hawaii Annual QA Bits

<div style='text-align:Left;'><div><div><p><span>This QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA in the Entity_and_Attribute_Information or Fields section below.</span></p><p><span /><span>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.</span></p><p><span>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).</span></p><p><span /><span>Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.</span></p></div></div></div>

Tags: basemaps, change-detection, continuous, digital-spatial-data, earthcover, environment, gis, imagery, land-cover, land-cover-change, land-use, land-use-change, land-use-land-cover-theme, national-geospatial-data-asset, ngda, remote-sensing
Organization: Department of Agriculture