Zhenjin Li

The spectral similarity of regular and irregular ground objects and the problem of image noise and abnormal pixels in a wide range of ground object extraction have been difficult for accurate wetland classification. This letter proposes a two-order hierarchical classification based on SNIC segmentation (HSNIC), and three new indices are constructed to extract salt marsh vegetation more accurately. Finally, the classification results of Yellow River Delta wetlands from 1990 to 2023 were obtained, and the invasion mechanism of Spartina alterniflora was further analyzed. The results show that: 1) the classification accuracy of the Yellow River Delta by HSNIC reached 88.48%. Compared with RF and object-oriented RF classification, the accuracy is improved; 2) introducing the three new indices significantly enhanced the classification accuracy; 3) the invasion of S. alterniflora influenced the survival environment of local salt marsh vegetation. These research findings provide a basis for decision-making regarding wetland restoration and conservation in the Yellow River Delta.

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.

Long-term industrial activities tend to cause surface subsidence and damage to ground facilities and local ecological environment. Monitoring and analyzing surface subsidence is of great significance to prevent potential disasters. The surface type of the Yellow River Delta in China is complex and there are many industrial activities, so it is necessary to monitor the surface subsidence in this area. Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). Firstly, the CenterNet network is used to automatically detect the subsidence areas from the wide differential interferogram formed by two SAR satellite images and determine the location of the subsidence area. Then, the SBAS-InSAR monitoring is performed on the detected multiple subsidence areas. Finally, the small-scale subsidence results are obtained. In this study, based on 24 Sentinel-1A satellite images acquired from 10 January 2018 to 24 December 2018, nine subsidence areas in Yellow River Delta were detected. Three of them had long-term surface subsidence. They were located in Zhanhua District, Xianhe Town, and Hongguang Village, respectively. This paper focuses on analyzing these three areas. The maximum subsidence rate of Zhanhua District, Xianhe Town, and Hongguang Village were −135.21 mm/a, −330.91 mm/a, and −209.68 mm/a, respectively. In addition, the analysis showed that precipitation in the Zhanhua District could effectively slow down the subsidence rate of the area. The subsidence of Xianhe Town threatened the safety of the Shugang Expressway. The subsidence of Hongguang Village caused the safety risks of buildings. The results of this study prove that CNSBAS-InSAR method is reliable for monitoring subsidence areas and it can provide a reference for local construction and protection of Yellow River Delta.

The excessive exploitation of coal resources has caused serious land subsidence, which seriously threatens the lives of the residents and the ecological environment in coal mining areas. Therefore, it is of great significance to precisely monitor and analyze the land subsidence in the mining area. To automatically detect the subsidence basins in the mining area from the interferometric synthetic aperture radar (InSAR) interferograms with wide swath, a lightweight model for detecting the subsidence basins with an anchor-free and adaptive sample assignment based on the YOLO V5 network, named Light YOLO-Basin model, is proposed in this paper. First, the depth and width scaling of the convolution layers and the depthwise separable convolution are used to make the model lightweight to reduce the memory consumption of the CSPDarknet53 backbone network. Furthermore, the anchor-free detection box encoding method is used to deal with the inapplicability of the anchor box parameters, and an optimal transport assignment (OTA) adaptive sample assignment method is introduced to solve the difficulty of optimizing the model caused by abandoning the anchor box. To verify the accuracy and reliability of the proposed model, we acquired 62 Sentinel-1A images over Jining and Huaibei coalfield (China) for the training model and experimental verification. In contrast with the original YOLO V5 model, the mean average precision (mAP) value of the Light YOLO-Basin model increases from 45.92 to 55.12%. The lightweight modules of the model sped up the calculation with the one billion floating-point operations (GFLOPs) from 32.81 to 10.07 and reduced the parameters from 207.10 to 40.39 MB. The Light YOLO-Basin model proposed in this paper can effectively recognize and detect the subsidence basins in the mining areas from the InSAR interferograms.