Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1481-1487, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1481-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1481-1487, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1481-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

ACCURACY ASSESSMENT OF THE GLOBELAND30 DATASET IN JIANGXI PROVINCE

H. Ren1,2, G. Cai1,2, G. Zhao1,2, and Z. Li1,2 H. Ren et al.
  • 1Department of Survey and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
  • 2Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing 102616, China

Keywords: land cover, GlobeLand30, accuracy assessment, confusion matrix, consistency analysis, Jiangxi Province

Abstract. The Globeland30 dataset is the most highly spatial resolution global land cover mapping product, which developed by the National Geomatics Center of China (NGCC) in 2015. It plays a significant role in environmental monitoring, climate change, and ecosystem assessment, etc. In this study, Jiangxi province was selected as our study area, the 1 : 100000 land use data in 2010 was employed as the reference data. We aim to examine the accuracy of the Globeland30 from three methods, including area error analysis, shape consistency analysis and confusion matrix. The results show as follows: The land cover types in the study area are primarily occupied by the cultivated land and forest, and secondarily by grassland, water bodies and artificial surfaces. The area error of cultivated land, forest and water bodies are all less than 13 %; The general conformance of the shape consistency reaches to 67 %, but the shape consistency of every land type differs to a large degree, the best shape consistency of forests is up to 75 %; The confusion matrix is obtained in two cases of different class boundary with buffer and no buffer area. It is found that the overall accuracy and kappa coefficient of GlobeLand30 are improved with buffer area. The value of overall accuracy is higher than 78 %, the value of kappa coefficient is higher than 0.52.