Spatially Balanced Sampling for Validation of GlobeLand30 Using Landscape Pattern-Based Inclusion Probability

Huan Xie, Fang Wang, Yali Gong, Xiaohua Tong, Yanmin Jin, Ang Zhao, Chao Wei, Xinyi Zhang and Shicheng Liao
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Huan Xie: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Fang Wang: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Yali Gong: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Xiaohua Tong: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Yanmin Jin: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Ang Zhao: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Chao Wei: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Xinyi Zhang: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Shicheng Liao: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China

Sustainability, 2022, vol. 14, issue 5, 1-19

Abstract: Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.

Keywords: land-cover product; spatially balanced sampling; landscape index (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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