Water retention is one of the important services provided by ecosystems. Water retention is also the basis for multiple other services, such as hydropower development, river continuity, and biodiversity. However, there are clear tradeoffs among these services. Tradeoffs are already a hot topic in ecosystem services research, but the tradeoff between hydropower and river visual landscape services (RVLS) has not yet been investigated. In this study, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) hydropower model for spatial mapping of watershed services. The proportion of the inflow of power stations to annual flow was proposed as the indicator for RVLS. Finally, based on an evaluation of historical hydropower development, different flow recovery scenarios were set up, and the tradeoff relationship between hydropower and landscape services was analyzed. The results showed that the tradeoff between the hydropower service and RVLS in mainstream displayed obvious spatial and temporal changes. With the development of hydropower, the increase of hydropower services caused a rapid decline in RVLS. The difference of two service scores fell from 1.0 in 1958 to 0.52 in 2015. The tradeoff intensity showed a turbulent decline downstream, which was closely related to the cascades’ development. The tradeoff was reversible. Through the flow scheduling of the reservoir group, the RVLS of each river section can be basically restored, while the hydropower service decline was only 29%.

Obtaining surface spatio-temporal data rapidly, automatically and accurately is an important issue in agriculture informationization and intellectualization. Samples obtained by conventional manual visual interpretation are difficult to adapt the demands of land resources information extraction. Low altitude remote sensing technology as a kind of emerging technology for earth observation in recent years. Based on this, spatio-temporal data mining technology was introduced, and knowledge transfer learning mechanism was used, a novel landuse information classification method based on knowledge transfer learning (KTLC) was proposed. Firstly, new image was segmented by improved mean shift algorithm to obtain image objects. Secondly, the vector boundary of the objects and former historical landuse thematic map were matched and nested, invariant objects were obtained through overlay analysis, and purification of invariant object was finished by spectral and spatial information threshold filtering. The historical features category knowledge of thematic map was transferred to the new image objects. Finally, current images classification mapping was completed based on decision tree, and landuse classification mapping results were completed by the KTLC and eCognition for landuse information mapping classification (EC). The experimental results showed that KTLC could obtain accuracies equivalent to EC, and also outperforms EC in terms of efficiency.