Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

Huiqun Ren; Guoyin Cai; Mingyi Du

doi:10.3390/s19204430

Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

Sensors (Basel). 2019 Oct 12;19(20):4430. doi: 10.3390/s19204430.

Authors

Huiqun Ren¹, Guoyin Cai^{2

3}, Mingyi Du^{4

5}

Affiliations

¹ School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China. 2108521517043@stu.bucea.edu.cn.
² School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China. cgyin@bucea.edu.cn.
³ Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China. cgyin@bucea.edu.cn.
⁴ School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China. dumingyi@bucea.edu.cn.
⁵ Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China. dumingyi@bucea.edu.cn.

Abstract

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth's subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 meter spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 meter land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R² is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NsN_S obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NsNsN_S is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.

Keywords: Jiangxi Province; land cover products; sample size estimation; satellite images; surface heterogeneity.