数字农科院2.0

Selecting Appropriate Spatial Scale For M.apping Plastic-Mulched Farmland With S atellite Remote Sensing Imagery

文献类型： 外文期刊

作者： Hasituya; Chen, ZX; Wang, LM; Liu, J

作者机构：

关键词： plastic-mulched farmland (PMF); mapping; appropriate spatial scale; GF-1 satellite imagery; local variance function; supervised classifier

期刊名称： REMOTE SENSING

ISSN： 2072-4292

年卷期： 2017 年 9 卷 3 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： In recent years, the area of plastic-mulched farmland (PMF) has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV) function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1) satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM) algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

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