This paper presents a comprehensive knowledge-based similarity classifier that uses remote sensing images and other auxiliary data to map spatial heterogeneity, for stratifying sample units distributed at the geographical landscape in order to improve the precision of the estimate of interest. Our method emphasises the decrease of bias so as to produce the high-quality stratifying frame. For this purpose, the method takes some necessary measures such as use of auxiliary variables including spectral bands, physical and socioeconomic data to help cluster analysis, correlation analysis between auxiliary variables and the goal variable to remove irrelevant data and consideration of spatial correlation in cluster analysis through the density-based unsupervised learning etc. Furthermore, considering the time-consuming characteristic of clustering huge spatiotemporal datasets, the method uses non-parametric supervised learning to induce rules for clustered classes. The rules could be efficiently used to group pixels into different classes of similarity. Then in the method, the pixel-level similarity image was vectorised into polygons with different group labels, thus producing the vector map of geospatial heterogeneity as an easy-to-use stratification frame. Last, to have an accurate estimation of the goal variable, our method re-divided sample units while the units covered by different strata and considered the effect of the sample size in the estimation algorithm. In the survey case of the cultivated land area, the proposed method achieves higher accuracy and a better coefficient of relative efficiency (RE) of stratification with its estimate closer to the observed value in comparison with other stratification strategies, e.g., k-means, SOM and those similar to eco-regions. Our method has potential practical merits as a good stratification strategy can increase the precision and considerably save the cost of sampling for many large regions, such as those in China, to be surveyed.
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Document Type: Research Article
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China,Department of Computing, The Hong Kong Polytechnic University, Hong Kong
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China
Department of Computing, The Hong Kong Polytechnic University, Hong Kong
January 1, 2009
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