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

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL
James Wickham, Stephen V. Stehman, Daniel G. Sorenson, Leila Gass, Jon A. Dewitz
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引用次数: 108

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.
NLCD 2019年美国周边土地覆盖专题精度评估
国家土地覆盖数据库(NLCD)是一个通过多分辨率土地特征(MRLC)联盟生产的产品套件,是一个可操作的土地覆盖监测项目。NLCD以2001年为基准年,每2 - 3年发布一次土地覆盖数据库。最近发布的NLCD2019将数据库延长至18年。采用分层随机抽样的方法,对NLCD2019数据库2016年和2019年的土地覆盖参考数据进行二级和一级分类。对于这两个日期,当一致性定义为地图标签与主要参考标签之间的匹配时,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备用参考标签之间的匹配时,OA增加到87.1%±0.7%。在第一级分类层次,当一致性定义为地图标签与主要参考标签之间的匹配时,2016年和2019年的土地覆盖OA均为83.1%±0.9%,当一致性还包括备用参考标签时,OA增加到90.3%±0.7%。当一致性仅定义为地图标签与主要参考标签之间的匹配时,NLCD2019数据库中2016年土地覆盖的二级和一级OA比NLCD2016数据库中2016年土地覆盖成分高5%。当协议还包括替代参考标签时,NLCD2019数据库没有实现改进。当协议包含主要或替代标签时,森林损失和草收益的用户准确度(UA)为0.70%,草损失、草收益和水收益的用户准确度(UA)通常为70%,而其他变化主题的用户准确度(UA)通常<50%。我们对地图与参考资料的一致性进行了分析后的回顾,以确定不一致的模式,并在对地图和参考数据收集程序进行潜在调整的背景下讨论了这些发现,这些调整可能会导致未来地图准确性的提高。
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来源期刊
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍: GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
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