The conservation and utilization of cultural heritage resources play a crucial role in the sustainable development of regional tourism in mountainous multi-ethnic areas. Nevertheless, these heritage resources tend to be numerous and geographically dispersed, poorly accessible, and difficult to preserve and utilize, which makes utilizing them holistically at a regional level challenging. Furthermore, each cultural heritage has a specific capacity for cultural dissemination, and establishing the links between heritage sites can enhance the overall cultural influence of a region and promote the development of regional tourism. Consequently, this study aims to promote the conservation and utilization of cultural heritage by constructing a cultural landscape tourism corridor network in the region. A potential cultural landscape tourism corridor network was first constructed based on the least-cost path analysis. Then, the layout and hierarchy of the potential network were optimized based on the existing traffic layout in the area. Finally, for mountainous multi-ethnic areas with poor access to transportation, the tourism corridor network may support the holistic conservation and use of the region’s cultural heritage and the development of regional cultural tourism.

Accurate quantification of forest biomass (FB) is the key to assessing the carbon budget of terrestrial ecosystems. Using remote sensing to apply inversion techniques to the estimation of FBs has recently become a research trend. However, the limitations of vertical scale analysis methods and the nonlinear distribution of forest biomass stratification have led to significant uncertainties in FB estimation. In this study, the biomass characteristics of forest vertical stratification were considered, and based on the integration of random forest and least squares (RF-LS) models, the FB prediction potential improved. The results indicated that compared with traditional biomass estimation methods, the overall R² of FB retrieval increased by 12.01%, and the root mean square error (RMSE) decreased by 7.50 Mg·hm⁻². The RF-LS model we established exhibited better performance in FB inversion and simulation assessments. The indicators of forest canopy height, soil organic matter content, and red-edge chlorophyll vegetation index had greater impacts on FB estimation. These indexes could be the focus of consideration in FB estimation using the integrated RF-LS model. Overall, this study provided an optimization method to map and evaluate FB by fine stratification of above-ground forest and reveals important indicators for FB inversion and the applicability of the RF-LS model. The results could be used as a reference for the accurate inversion of subtropical forest biomass parameters and estimation of carbon storage.