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 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m 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 NS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NS 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.
Highlights
Land cover provides basic geospatial information for applications in the fields of global environmental change, natural resources management, carbon and nitrogen cycle, and ecological monitoring [1,2,3]
The sample size NS decreases with the increase of sample constraints (C), Shannon’s evenness index (SHEI), and area-weighted mean patch fractal dimension (AWMPFD), whereas it increases with an increase of contagion index (CONTAG) and patch richness density (PRD)
We found that the regression coefficients of the independent variables are more significant when area-weighted mean shape index (AWMSI), AWMPFD, and PRD are removed
Summary
Land cover provides basic geospatial information for applications in the fields of global environmental change, natural resources management, carbon and nitrogen cycle, and ecological monitoring [1,2,3]. This study derived a sample model was developed by considering the scale effect and surface spatial heterogeneity, with size estimation using a stratified sampling approach. A watershed unit withheterogeneity, ecological and geographical model was on developed by considering the scale effect and surface spatial with emphasis significance was introduced in this study as the basic spatial unit for performing the accuracy on two aspects of these issues. As the characteristics theheterogeneity land cover dataset, this studyaffect computed severalsize major indicators and ofvalidating the spatial would inevitably the sample usedlandscape for validating the land assessed their impacts on the surface heterogeneity in watershed units, thereby reaching the goal cover dataset, this study computed several major landscape indicators and assessed their impactsof this (to heterogeneity develop a reasonable model units, to estimate thereaching sample size).
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