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.

As well as very high resolution (VHR) remote sensing technology and deep learning, methods for detecting changes in buildings have made great progress. Despite this, there are still some problems with the incomplete detection of change regions and rough edges. To this end, a change detection network for building VHR remote sensing images based on Siamese EfficientNet B4-MANet (Siam-EMNet) is proposed. First, a bi-branches pretrained EfficientNet B4 encoder structure is constructed to enhance the performance of feature extraction and the rich shallow and deep information is obtained; then, the semantic information of the building is input into the MANet decoder integrated by the dual attention mechanism through the skip connection. The position-wise attention block (PAB) and multi-scale fusion attention block (MFAB) capture spatial relationships between pixels in the global view and channel relationships between layers. The integration of dual attention mechanisms ensures that the building contour is fully detected. The proposed method was evaluated on the LEVIR-CD dataset, and its precision, recall, accuracy, and F1-score were 92.00%, 88.51%, 95.71%, and 90.21%, respectively, which represented the best overall performance compared to the BIT, CDNet, DSIFN, L-Unet, P2V-CD, and SNUNet methods. Verification of the efficacy of the suggested approach was then conducted.