UNCERTAINTY ASSESSMENT OF GLOBELAND30 LAND COVER DATA SET OVER CENTRAL ASIA
Keywords: GlobeLand30, land cover, remote sensing, accuracy, Central Asia
Abstract. GlobeLand30, the world’s first 30m-resolution global land cover data set, has recently been issued for research on global change at a fine resolution. Given the accuracy of GlobeLand30 data may show significant variation in different parts of the world and data quality at continental scale has not been validated yet, this study aims to evaluate the uncertainty of the data over Central Asia. Since it is difficult to get long-term historical ground references, GlobeLand30 data at the most recent epoch (i.e., GlobeLand30-2010) was assessed. In the test, a large sample size was adopted, and more than 25 thousand samples were selected by a random sampling scheme and interpreted manually as ground references based on higher resolution imagery at the same epoch, such as images from ZY-3 (China Resources Series) satellite and Google earth. Cross validation of image interpretation by three well-trained interpreters was adopted to make the references more reliable. Error matrix and Kappa coefficient were utilized to quantify data accuracies in terms of classification accuracy. Results show that the GlobeLand30-2010 data presents an overall accuracy of 46% in the study area. As for specific land cover types, bare land illustrates a high user’s accuracy but a lower producer’s accuracy. At the same time, the accuracies of grassland and forest are significantly lower than other types. The majority of misclassification types come from bare land. It implies a difficulty of distinguishing grassland or forest from bare land in the study area. In addition, the confusion between shrub land and grassland also results in the misclassification. The results serve as a useful reference of data accuracy for further analysis of land cover change in Central Asia as well as the applications of GlobeLand30 data at a regional or continental scale.