Spatial sampling is a necessary and important method for extracting geospatial data and its methodology directly affects the geo-analysis results. Counter to the deficiency of sepa-rate models of spatial sampling, this article analyzes three crucial elements of spatial sampling (frame, correlation and decision diagram) and induces its general integrated model. The program of Spatial Sampling Integration (SSI) has been developed with Component Object Model (COM) to realize the general integrated model. In two practical applications, i.e. design of the monitoring network of natural disasters and sampling survey of the areas of non-cultivated land, SSI has produced accurate results at less cost, better realizing the cost-effective goal of sampling toward the geo-objects with spatial correlation. The two cases exemplify expanded application and convenient implementation of the general integrated model with inset components in an inte-grated environment, which can also be extended to other modeling of spatial analysis.
In the remote sensing survey of the country land, cost and accuracy are a pair of conflicts, for which spatial sampling is a preferable solution with the aim of an optimal balance between economic input and accuracy of results, or in other words, acquirement of higher ac-curacy at less cost. Counter to drawbacks of previous application models, e.g. lack of compre-hensive and quantitative-comparison, the optimal decision-making model of spatial sampling is proposed. This model first acquires the possible accuracy-cost diagrams of multiple schemes through initial spatial exploration, then regresses them and standardizes them into a unified ref-erence frame, and finally produces the relatively optimal sampling scheme by using the discrete decision-making function (built by this paper) and comparing them in combination with the dia-grams. According to the test result in the survey of the arable land using remotely sensed data, the Sandwich model, while applied in the survey of the thin-feature and cultivated land areas with aerial photos, can better realize the goal of the best balance between investment and accuracy. With this case and other cases, it is shown that the optimal decision-making model of spatial sampling is a good choice in the survey of the farm areas using remote sensing, with its distin-guished benefit of higher precision at less cost or vice versa. In order to extensively apply the model in the surveys of natural resources, including arable farm areas, this paper proposes the prototype of development using the component technology, that could considerably improve the analysis efficiency by insetting program components within the software environment of GIS and RS.
