Study on Land Use Classification in Coastal Counties Based on Hierarchical Strategy and Machine Learning- A Case Study of Caofeidian

The spatial distribution pattern of land use holds significant scientific value for regional resource management. Taking Caofeidian District, a coastal county, as the research object, this study conducts classification and pattern analysis of current land use status based on its unique geographical features such as coastal salt pans and wetlands. A hierarchical classification system of “first-level overall classification and second-level detailed classification” is constructed. Using Landsat remote sensing images as the data source, three machine learning algorithms—random forest, support vector machine, and sample-based object- oriented method—are applied for first-level land use extraction. Further, through preprocessing optimization with the OSM sample set, the classification of characteristic second-level land uses such as salt pans and wetlands is realized. The accuracy verification results show that in the first-level land use classification, the sample-based object-oriented extraction method performs the best, with an overall accuracy of 85. 58% (Kappa 0. 79) . The spatial pattern is characterized by “agricultural land and construction land as the dominant types, coexisting with water bodies, unused land, and wetlands”. The second-level land use classification results reveal a pattern of “paddy fields and river surfaces as the main types, with large-scale aggregation of urban, rural, and road land”. Among them, the classification accuracy of characteristic land types such as facility agricultural land is the highest at 93. 22%, and that of salt pans is 85. 63%. The random forest algorithm shows good recognition ability for land types with obvious features but poor performance in identifying land types with similar spectra and shapes (e. g. , ridges around salt pans and roads) . The above research can not only provide data support for land resource allocation in Caofeidian but also provides reference for land use classification research in coastal areas.