Journal Article

Assessing the Accuracy of Multi-Temporal GlobeLand30 Products in China Using a Spatiotemporal Stratified Sampling Method

Liao, Shicheng, Xie, Huan, Gong, Yali, Lu, Yao, Tong, Xiaohua, Jin, Yanmin, Wei, Chao

2023

Overview

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the “change” strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes.

• Publisher: MDPI AG
• Subject: Accuracy / accuracy assessment / area estimation / Artificial satellites in remote sensing / Binary codes / China / Classification / climate / Climate change / Datasets / Deforestation / ecosystems / Electronic data processing / Empirical analysis / Global climate / GlobeLand30 / Land cover / Land use / Methods / Remote sensing / sample size / Sampling / Sampling methods / spatiotemporal stratified random sampling / Stratification