The Green Plastic Cover (GPC) around power transmission towers is one of the main external hazards to power transmission lines, and conducting remote sensing identification of GPC is of great significance for the management of external hazards to power transmission lines. Existing remote sensing identification studies mainly focus on plastic greenhouses and plastic mulches, with relatively few studies on GPC, especially in areas around power transmission towers. Consequently, this research selected four areas along transmission corridors in Jiangsu Province as a case study to boost GPC mapping performance through integrating the focal loss function into the TransUNet model using Sentinel-1 and Sentinel-2 data, and subsequently conducting with the U-Net model and Deeplabv3+ model for comparative experiment to validate the superiority of the model selected in this study. The experimental results demonstrated that the TransUNet model performs well in extracting GPC, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. It is feasible and effective to utilize the model in this paper to identify GPC along transmission corridors, which can provide a decision-making basis for the comprehensive management of the risk of external damage.
Guizhou Province, situated in the southwest of China, boasts diverse and complex geographical environments and abundant forest resources. However, it faces threats from natural disasters like forest fires. Accurate estimation of combustible fuel load in Guizhou is crucial for assessing fire risks and implementing effective fire management strategies. This paper employs remote sensing technology, utilizing various remote sensing datasets including satellite imagery and ground observation data, combined with geostatistical methods to retrieve combustible fuel load in Guizhou. Furthermore, the application value of the retrieval results in fire prevention management and forest resource protection is discussed.
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