基于遥感识别误差校正面积的农作物种植面积抽样高效分层指标研究——以冬小麦为例

doi:10.3864/j.issn.0578-1752.2018.04.007

Abstract

Abstract: 【Objective】Remote sensing has a strong superiority in crop planting area acquisition. It is critical to design an efficient hierarchical indicator in a stratified sampling investigation. Traditional method neglects the classifying error of the classification results, which reduces the sampling efficiency. Therefore, a new hierarchical indicator (S_correct) that can correct the error area is presented in this paper, which is in favor of raising the efficiency of the sampling survey. 【Method】The yield of winter wheat in Tongzhou and Daxing district of Beijing was as a study object. The data source was GF-1 imagery (resolution: 16 m, phase: 2015-04-04) obtained in winter wheat growing season. This paper designed the S_correct which was based on an algorithm involved the error of omission and the error of commission at pixel scale. This indicator summed all pixels’ correct scale in a sampling unit to correct the area scale indicator (S). A certain plan was devised for Multiple sampling by using S_correct and the area scale indicator (S). This plan constructed regular square grids (size: 90-300 m) as the sampling frames, set the number of layers, determined the hierarchical boundary method and the allocation method for sample capacity, etc. In order to testify the validity of S_correct, this paper analyzed the correlation between this indicator and true value, estimated the total winter wheat area by multiple sampling, after that, compared S_correct with S in sampling variance, average relative error ( ), CV value, etc. The typical corrected regions for pixel with error were selected to compare and analyze for verifying the effectiveness at pixel scale. 【Result】 (1) By comparing and analyzing the typical corrected regions, the error distribution could accord with the actual circumstance for both the error of omission and the error of commission. This indicator could correct error pixel. With the sizes of the sampling unit, S_correct was better than S in the correlation, and the value was greater than 0.7, which can ensure a high and stable correlation with the truth value. The validity of S_correct had been verified by experiments. (2) S_correct was better than S in the stability, accuracy and CV value. Its total variance was 1.70×10¹³-2.41×10¹³, and the total variance obtained by using S was 2.05×10¹³-3.11×10¹³. Its was 4.21%-5.00%. The obtained by using S was 4.87%-5.98%. Therefore, nearly 1% could be enhanced stability in accuracy. Besides, S_correct could reduce CV value nearly 0.8%. 【Conclusion】The effectiveness and advantages of S_correct had been proved by the experimental results. The indicator can improve the accuracy of sampling and the stability of inference in a certain extent.

Key words: winter wheat, acreage estimate, spatial stratified sampling, hierarchical indicator, classification error, area scale

YANG JunWen, ZHANG JinShui, PAN YaoZhong, SUN PeiJun, ZHU Shuang. An Efficient Hierarchical Indicator Based on the Correction Area of Remote Sensing Identification Error for Planting Acreage Sampling——A Case Study of Winter Wheat[J].Scientia Agricultura Sinica, 2018, 51(4): 675-687.

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An Efficient Hierarchical Indicator Based on the Correction Area of Remote Sensing Identification Error for Planting Acreage Sampling——A Case Study of Winter Wheat

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