Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

Abstract

Abstract. The increasing availability of high-quality remote sensing data and advanced technologies has spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at a 30 m resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover products and five products related to land surface change. The LCMAP land cover products were validated using a collection of ∼25 000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and shared Mesos cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land change datasets that include land cover, land cover change, and spectral change spanning from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future. The LCMAP products introduced in this paper are freely available at https://doi.org/10.5066/P9W1TO6E (LCMAP, 2021).

Assessment of public and private land cover change in the United States from 1985–2018

“…Regional analyses of four megaregions were based on Omernik ecoregions (Omernik and Griffith 2014) using boundaries generally defined in previous USGS regional studies (Sleeter et Published freely available LCMAP Reference data v1.2 (Pengra et al 2020c) is selected for this study because it allows for statistically rigorous area estimates of land cover composition and change on public and private land across CONUS when combined with the PAD-US data. Pengra et al (2020b) report that all LCMAP reference data are generated by trained interpreters who assign (1) land use, (2) land cover, and (3) change processes for every year between 1985 and 2018 to each reference sample plot using the TimeSync (Cohen et al 2010) Landsat time series visualization and data collection tool (Pengra et al 2020c, Xian et al 2022. After TimeSync interpretation, the information was translated to the appropriate LCMAP land cover class, providing a single land cover reference label for each sample plot (Pengra et al 2020b, Pengra et al 2020c.…”

SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm

“…The initial input for the synthetic ETa was land cover data from the Land Change Monitoring Assessment and Projection (LCMAP) project, which were derived from the CCDC algorithm. LCMAP utilizes ARD [36] after cloud-contaminated pixels have been removed using the Fmask algorithm [37,38]. To generate LCMAP data, CCDC fits a harmonic model to each individual band in the Landsat ARD to generate per-pixel estimates [19].…”

A global time series dataset to facilitate forest greenhouse gas reporting

“…This experiment used expert time series photointerpretations covering 25 000 CONUS-based plots from the LCMAP & LCMS projects (Pengra et al 2020, Housman et al 2022, Xian et al 2022 as reference data. This dataset has both land cover and land use interpretations spanning 1986-2020, and this experiment used the dataset's six land use classes: Forest, Developed, Agriculture, Wetland, Rangeland, and Other.…”

“…This feature vector contains per-band quartile means, two half means, min, max, mean, and median, and the magnitude differences between the min and max, half means, and the first and fourth quartiles. Additionally, we included feature vectors generated from the global CCDC dataset, influenced by the methodology of the USGS LCMAP project (Xian et al 2022). Since CCDC parameters remain the same across years until the algorithm finds a breakpoint, there is no chance of predicting an intersegment land use class change if the only predictor variables are the segments' harmonic coefficients.…”