Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product Article Swipe
George Xian
,
Kelcy Smith
,
Danika Wellington
,
Josephine A. Horton
,
Qiang Zhou
,
Congcong Li
,
Roger F. Auch
,
Jesslyn F. Brown
,
Zhe Zhu
,
Ryan R. Reker
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/essd-14-143-2022
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/essd-14-143-2022
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).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/essd-14-143-2022
- https://essd.copernicus.org/articles/14/143/2022/essd-14-143-2022.pdf
- OA Status
- gold
- Cited By
- 54
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205485607
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205485607Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/essd-14-143-2022Digital Object Identifier
- Title
-
Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change productWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-19Full publication date if available
- Authors
-
George Xian, Kelcy Smith, Danika Wellington, Josephine A. Horton, Qiang Zhou, Congcong Li, Roger F. Auch, Jesslyn F. Brown, Zhe Zhu, Ryan R. RekerList of authors in order
- Landing page
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https://doi.org/10.5194/essd-14-143-2022Publisher landing page
- PDF URL
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https://essd.copernicus.org/articles/14/143/2022/essd-14-143-2022.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://essd.copernicus.org/articles/14/143/2022/essd-14-143-2022.pdfDirect OA link when available
- Concepts
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Land cover, Product (mathematics), Database, Change detection, Remote sensing, Land use, Computer science, Environmental science, Scale (ratio), Geography, Cartography, Mathematics, Engineering, Geometry, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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54Total citation count in OpenAlex
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2025: 17, 2024: 18, 2023: 13, 2022: 6Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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