Study Of Sampling And Optimizing Methods For Monitoring The Quality Of Cultivated Land In County Area

Monitoring points in county area are the foundation to reflect changes of cultivated land quality, which directly affect the result and accuracy of farmland grading. Considering the different applicable conditions, this paper presents a pre-processing method to determine the number of sampling points, a sampling and optimizing method based on spatial simulated annealing and a sampling method based on Kriging estimation error. We choose Beijing Daxing district as the study area to conduct a series of experiments and comparative analysis. The main research contents and experimental results are as follows:(1) Study of pre-processing method and spatial analysis, including preprocessing the data of cultivated land quality before sampling, exploring the spatial correlation and spatial distribution pattern of cultivated land quality, setting up six sampling points for abnormal farmland, and determining the appropriate variation function parameters and Kriging interpolation model by variation analysis.(2) Study the method to determine the number of sampling points. The appropriate number of sampling points could be found by analyzing the change trend of sampling number and sampling precision. Experimental results show that at least forty one monitoring points should be set up in Daxing district and the prediction accuracy of population mean stays in an accurate level with the relative error of1%.(3) Study of sampling and optimizing method based on spatial simulated annealing. The mean of Kriging standard error is chosen to be the optimization criterion. Under the guidance of optimization criterion, sampling points are set up through spatial simulated annealing algorithm and adjusted by potential change areas, rivers, roads and expert knowledge. Experimental results show that the Kriging standard error of this method is smaller than simple random sampling, stratified sampling and traditional grids when the numbers of samples are49. Thus this method can effectively predict the quality of cultivated land and monitor the changes.