A Synchronous Acquisition Method for Dominant Tree Species and Forest Age in Complex Mountainous Terrain Through Growth Characteristics Matching

Accurate acquisition of tree species and forest age information is crucial for forest ecosystem protection and sustainable development. Although such information can be obtained through remote sensing technology, accurate extraction of tree species and forest age still faces significant challenges due to the remote sensing data characteristics and the complex and variable mountainous natural geographical environment. This article uses the Landsat-NDVI long-term series data from 1991 to 2021 to address this problem. A growth characteristic change model is established for the main dominant tree species in Shangri-La city, including Pinus yunnanensis, Pinus densata Mast, Picea & Abies, and Quercus acutissima, throughout the growth cycle in vertical zones at different altitudes. Besides, the study compared the normalized difference vegetation index (NDVI) time-series data of the area to be identified with the growth characteristic model to match the appropriate growth range automatically, and their similarity is determined to complete tree species classification. The forest age is then obtained based on the optimal matching point of the NDVI time-series data in the growth model. This method can simultaneously obtain tree species and forest age information and acquire forest age when the image duration exceeds the remote sensing image record. Ultimately, the overall accuracy of tree species classification reached 83.42%, and the forest age fitting results also showed a high degree of correlation, with a coefficient of determination ( $R^{2}$ ) of 0.92. The root mean square error (RMSE) of forest age in Shangri-La, dominantly covered by mature forests/overmature forests, has dropped to 10.59. This study significantly improves remote sensing technology’s accuracy and application efficiency in multiparameter inversion of forest resources while providing a new technical means for forest resource management and ecological protection.