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美国本土2019年国家土地覆盖数据库(NLCD)的专题精度评估

Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States.

作者信息

Wickham James, Stehman Stephen V, Sorenson Daniel G, Gass Leila, Dewitz Jon A

机构信息

Office of Research and Development, Environmental Protection Agency, Research Triangle Park, NC, USA.

College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA.

出版信息

GIsci Remote Sens. 2023 Mar 1;60(1):2181143. doi: 10.1080/15481603.2023.2181143.

Abstract

The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a base year of 2001, NLCD releases a land cover database every 2-3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User's accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer's accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.

摘要

国家土地覆盖数据库(NLCD)是通过多分辨率土地特征(MRLC)联盟生成的一组产品,是一项实用的土地覆盖监测计划。从2001年基准年开始,NLCD每2至3年发布一次土地覆盖数据库。最近发布的NLCD2019将该数据库扩展到了18年。我们实施了分层随机抽样,以收集NLCD2019数据库2016年和2019年部分在分类层次结构二级和一级的土地覆盖参考数据。对于这两个日期,当一致性仅定义为地图标签与主要参考标签匹配时,二级土地覆盖总体精度(OA)为77.5%±1%(±值为标准误差),当一致性定义为地图标签与主要或替代参考标签匹配时,精度提高到87.1%±0.7%。在分类层次结构的一级,当一致性仅定义为地图标签与主要参考标签匹配时,2016年和2019年的土地覆盖OA均为83.1%±0.9%,当一致性还包括替代参考标签时,精度提高到90.3%±0.7%。当一致性仅定义为地图标签与主要参考标签匹配时,NLCD2019数据库中2016年土地覆盖的二级和一级OA比NLCD2016数据库的2016年土地覆盖部分高5%。当一致性还包括替代参考标签时,NLCD2019数据库没有实现精度提升。当一致性包括主要或替代标签时,森林损失和草地增加的用户精度(UA)>70%,而所有其他变化主题的UA通常<50%。草地损失和增加以及水域增加的生产者精度(PA)>70%,而其他变化主题的PA通常<50%。我们对地图-参考一致性进行了事后分析审查,以确定不一致模式,并在可能导致未来地图精度提高的地图绘制和参考数据收集程序的潜在调整背景下讨论了这些发现。

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