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A New Method for Detecting Plastic-Mulched Land Using GF-2 Imagery
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Researchers developed a new remote sensing method for detecting plastic-mulched agricultural land using GF-2 satellite imagery by introducing a K-T transform component specifically enhanced for plastic identification. The method was combined with texture metrics and spectral bands in an object-oriented classification approach. The study demonstrates improved accuracy in mapping plastic mulch coverage, which is important for monitoring agricultural plastic waste that contributes to soil microplastic pollution.
Plastic mulch residues threaten soil fertility and contribute to microplastic pollution, creating an urgent need for accurate, rapid mapping of plastic-mulched land (PML). This study presents a novel method for detecting PML from GF-2 imagery by introducing the second component of the K-T transform as a PML-enhancement feature to compensate for the sensor’s limited spectral bands. The K-T component was fused with selected texture metrics and the original spectral bands, and an object-oriented classification framework was applied to delineate PML. Validation shows that the proposed method achieves high identification accuracy for PML and good transferability, with accuracies exceeding 90% across the four selected study areas. Moreover, the method demonstrates strong temporal stability: classification accuracies exceeded 90% for two different time periods within the same study area. Compared with methods reported in previous studies, our approach attains comparable accuracy while offering higher classification efficiency. Overall, the proposed method enables accurate PML identification from GF-2 imagery and provides a valuable reference for agricultural planning and ecological protection.
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