Concerning the ever-changing wetland environment, the efficient extraction of wetland information holds great significance for the research and management of wetland ecosystems. China’s vast coastal wetlands possess rich and diverse geographical features. This study employs the SegFormer model and Sentinel-2 data to conduct a wetland classification study for coastal wetlands in Yancheng, Jiangsu, China. After preprocessing the Sentinel data, nine classification objects (construction land, Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), Phragmites australis (P. australis), farmland, river system, aquaculture and tidal falt) were identified based on the previous literature and remote sensing images. Moreover, mAcc, mIoU, aAcc, Precision, Recall and F-1 score were chosen as evaluation indicators. This study explores the potential and effectiveness of multiple methods, including data image processing, machine learning and deep learning. The results indicate that SegFormer is the best model for wetland classification, efficiently and accurately extracting small-scale features. With mIoU (0.81), mAcc (0.87), aAcc (0.94), mPrecision (0.901), mRecall (0.876) and mFscore (0.887) higher than other models. In the face of unbalanced wetland categories, combining CrossEntropyLoss and FocalLoss in the loss function can improve several indicators of difficult cases to be segmented, enhancing the classification accuracy and generalization ability of the model. Finally, the category scale pie chart of Yancheng Binhai wetlands was plotted. In conclusion, this study achieves an effective segmentation of Yancheng coastal wetlands based on the semantic segmentation method of deep learning, providing technical support and reference value for subsequent research on wetland values.

Modeling and prediction of forest fire occurrence play a key role in guiding forest fire prevention. From the perspective of the whole world, forest fires are a natural disaster with a great degree of hazard, and many countries have taken mountain fire prediction as an important measure for fire prevention and control, and have conducted corresponding research. In this study, a forest fire prediction model based on LSTNet is proposed to improve the accuracy of forest fire forecasts. The factors that influence forest fires are obtained through remote sensing satellites and GIS, and their correlation is estimated using Pearson correlation analysis and testing for multicollinearity. To account for the spatial aggregation of forest fires, the data set was constructed using oversampling methods and proportional stratified sampling, and the LSTNet forest fire prediction model was established based on eight influential factors. Finally, the predicted data were incorporated into the model and the predicted risk map of forest fires in Chongli, China was drawn. This paper uses metrics such as RMSE to compare with traditional machine learning methods, and the results show that the LSTNet model proposed in this paper has high accuracy (ACC 0.941). This study illustrates that the model can effectively use spatial background information and the periodicity of forest fire factors, and is a novel method for spatial prediction of forest fire susceptibility.