Designing a Multi-Objective, Multi-Support Accuracy Assessment of the 2001 National Land Cover Data (NLCD 2001) of the Conterminous United States

2008 ◽  
Vol 74 (12) ◽  
pp. 1561-1571 ◽  
Author(s):  
Stephen V. Stehman ◽  
James D. Wickham ◽  
Timothy G. Wade ◽  
Jonathan H. Smith
Keyword(s):  
United States ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Multi Objective ◽  
Land Cover Data

Thematic accuracy of the 1992 National Land-Cover Data for the western United States

2004 ◽  
Vol 91 (3-4) ◽  
pp. 452-468 ◽  
Author(s):  
J.D Wickham ◽  
S.V Stehman ◽  
J.H Smith ◽  
L Yang
Keyword(s):  
United States ◽  
Land Cover ◽  
Western United States ◽  
Land Cover Data ◽  
Thematic Accuracy

scholarly journals Accuracy assessment of Globeland30: a case study of Ontario,Canada

2021 ◽  
Author(s):  
Rishita Rangarh
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Landsat Imagery ◽  
Global Scale ◽  
Data Set ◽  
Error Matrix ◽  
Successful Model ◽  
Land Cover Data ◽  
Made In

GlobeLand30 is the world’s first 30m high resolution land cover data set (Chen et al. 2014) and has been a successful model of Big-Data mining from a host of Landsat imagery, thereby contributing to and enhancing the existing global geospatial knowledge base (GlobeLand30 2014). As there is a lot of uncertainty and errors in the global land cover data, therefore it becomes very difficult to validate land cover on a global scale. Efforts on validating Globeland30 data have been made in various parts of the world in the past and will continue to be done. The objective of this project is to validate GlobeLand30 data set by carrying out a case study in Ontario, Canada. The adopted methodology for doing validation is by using cell-to-cell benchmarking (Maria et al. 2015), thereby deriving Error Matrix, and its derivatives, which includes overall accuracy, user accuracy, producer accuracy and kappa coefficient. The results show that an overall accuracy of 84.14% is obtained for GlobeLand30 data with consideration of shadows, which is relatively a high percentage number indicating that the GlobeLand30 data classification is highly accurate for Ontario, Canada. Keywords: land cover; GlobeLand30; accuracy assessment; Ontario

Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States

2021 ◽  
Vol 257 ◽  
pp. 112357
Author(s):  
James Wickham ◽  
Stephen V. Stehman ◽  
Daniel G. Sorenson ◽  
Leila Gass ◽  
Jon A. Dewitz
Keyword(s):  
United States ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Thematic Accuracy
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