Global Land-Cover Map Validation Experiences: Toward the Characterization of Uncertainty

2016 ◽  
pp. 228-245 ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2950
Author(s):  
Yoshie Ishii ◽  
Koki Iwao ◽  
Tsuguki Kinoshita

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.


2010 ◽  
Vol 114 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Mark A. Friedl ◽  
Damien Sulla-Menashe ◽  
Bin Tan ◽  
Annemarie Schneider ◽  
Navin Ramankutty ◽  
...  

2011 ◽  
Vol 03 (02) ◽  
pp. 160-165 ◽  
Author(s):  
Koki Iwao ◽  
Kenlo Nishida Nasahara ◽  
Tsuguki Kinoshita ◽  
Yoshiki Yamagata ◽  
Dave Patton ◽  
...  

2010 ◽  
Vol 114 (8) ◽  
pp. 1805-1816 ◽  
Author(s):  
Sangram Ganguly ◽  
Mark A. Friedl ◽  
Bin Tan ◽  
Xiaoyang Zhang ◽  
Manish Verma

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