Optimizing remote sensing methods for forest stand density estimation in mountainous areas: a UAV-sentinel-2 synergy

Stand density is a key parameter for assessing forest structure and ecological function, and its remote sensing-based estimation is critically important for monitoring forest carbon stocks. As the primary component of forest resources in southern China, plantations are typically distributed across mountainous regions with complex terrain. The estimation of stand density using remote sensing in these areas faces numerous challenges due to factors such as topographic variation and interference from understory vegetation. Taking Shaoguan City in Guangdong Province as a case study, this research focuses on typical plantation areas dominated by Eucalyptus, Cunninghamia lanceolata, and Pinus massoniana, and proposes an optimized estimation method that integrates multi-source remote sensing data. Several improvements were made upon traditional approaches, including: (1) the Enhanced Vegetation Index (EVI) was utilized to reduce interference from understory vegetation and improve the accuracy of canopy cover estimation for standing trees; (2) the Modified Green-Red Vegetation Index (MGRVI) was introduced to improve the accuracy of individual tree canopy cover estimation; (3) the SCS+C topographic correction method was employed to mitigate the effects of terrain factors-specifically slope and aspect-on the accuracy of surface reflectance derived from remote sensing data; (4) a comparative experiment across spatial resolutions of 10 m, 30 m, 60 m, and 90 m was conducted, and 30 m was identified as the optimal scale for stand density estimation, offering a balance between accuracy and regional adaptability. The study demonstrates that tree species classification using the Random Forest algorithm achieved an accuracy of 93.22 %. Stand density estimation attained the highest performance at a 30 m × 30 m spatial resolution, with an R² of 0.85 and an RMSE of fewer than 40 trees per hectare. These results highlight the effectiveness of the proposed method for accurately and efficiently estimating stand density in mountainous plantation forests, offering practical support for regional forest resource inventories and ecological assessments.