Main Article Content
Based on the scientific identification and evaluation of scenic resources, the formulation of planning and management is of great significance to the sustainable development of tourism in national parks. This paper uses GF-2 high-resolution remote sensing images as the data source, and the Yesanpo National Park as the research area. It uses pixel-based MLC, NN, and SVM three classification methods to identify scenic resources, and adopts the system sampling method evaluation 3 methods to identify the classification accuracy of scenic resources, effectively improving the objectivity and accuracy of classification accuracy evaluation. The results show that the three classification methods used in GF-2 images meet the accuracy requirements of scenic resource identification, which is an effective method to identify scenic resources. Due to different geomorphic features, the erosion Zhanggu landform-Baili Xia scenic areas uses MLC classification, and granite fracture structure The canyon landform-LongmenTianguan scenic areas uses NN classification, and the karst cave spring landform-Yugu Dong scenic areas uses SVM classification with the highest overall accuracy. The results show that the proposed method can be applied to the identification of large-scale, high-resolution scenic resources, and provide more refined data support for scenic resource analysis and sustainable development of local tourism.