Background China has made great progress in ecological restoration. However, there have been no analyses on ecological restoration for specific terrestrial ecosystems. This study identified the important knowledge gaps and advances related to terrestrial ecological restoration in China. Results 7973 papers published between 1978 and 2020 were investigated and about 962 articles were used in this analysis after manually screening. Since the first large national ecological restoration project in 1978, the most frequently studied ecosystem has shifted from farmland ecosystems in 1978–2000 to forest ecosystems after 2000. Forests were the most common ecosystem type investigated, while less attention was paid to wetlands and riparian systems. Meanwhile, the most common ecological issue shifted from environmental pollution in 1978–2000 to the declining resource-carrying capacity of ecosystems after 2000. Studies of ecoregions on the Loess Plateau catchment accounted for more than 40% of papers reviewed in this study, with predominant emphasis on soil and water conservation functionality. Besides, revegetation and afforestation characterized most ecological restoration projects in China, but the natural restoration was relatively less adopted. Additionally, the important tool of reference ecosystem was only used in four studies. Conclusions Ecological restoration has made significant progress in China. We investigated how the ecological restoration can be implemented more effectively. More projects should be implemented for restorative work in wetlands and riparian systems in future. The tradeoff between restorative activities, water resources, and carbon sink needs further research efforts. More emphasis on biodiversity conservation is warranted. Newly developed theory (e.g., stepwise ecological restoration) and the recently issued Chinese National Guidelines for Ecological Restoration Projects should be more effectively implemented in future restorative works. This study provides essential information for future restorative work in China. It also provides insights into the development of policy relevant to restoration and adaptive management during the U.N. restoration decade.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.