Forest ecosystems play an important role in maintaining the stability of the biosphere and improving the ecological environment. The valuation of forest ecosystem services provides data to support the implementation of forest ecosystem conservation and the development of ecological-compensation standards. We used multiple sources of data, such as remote-sensing and ground data, and we employed the methods of substitute market, shadow project, and contingent valuation. We valued the forest ecosystem services of Pudacuo National Park in Shangri-La, China, which consisted of six functions: soil conservation, forest nutrient retention, water conservation, carbon fixation and oxygen released, forest health care, and atmospheric environmental purification. The results showed that: the value of forest ecological services in Pudacuo National Park was 4.49 × 10⁹ yuan·a⁻¹, with higher values of carbon fixation and oxygen released, water conservation, and forest health care, in the following order: carbon fixation and oxygen released (3.85 × 10⁹ yuan·a⁻¹), water conservation (3.40 × 10⁸ yuan·a⁻¹), forest health care (1.44 × 10⁸ yuan·a⁻¹), soil conservation (1.15 × 10⁸ yuan·a⁻¹), forest nutrient retention (3.29 × 10⁷ yuan·a⁻¹), and atmosphere environmental purification (1.17 × 10⁷ yuan·a⁻¹). In addition, the value of services per stand and unit area is discussed, and the results of the study will inform the government’s ecological-compensation criteria in high-quality environmental areas.

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R² of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning.