Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China.

Human activities has increasingly threatened the biodiversity of the world. Biodiversity science is a discipline that depends on scale, and research questions are often affected by the ecological process of multi-temporal scales. The traditional survey methods of biodiversity are often limited by human and material resources. It is therefore urgent to integrate different data sources in the biodiversity sciences. The remote sensing technique has developed from optical remote sensing to the multi-source remote sensing including different platforms combined with various sensors, and further to integrate the hyperspectral and hyper spatial resolution and light detection and ranging (LiDAR). The large coverage, the accessibility to remote areas, and the long-term repeatability of the remote sensing technique provide new and better solutions for studying ecological and scientific issues at different temporal and spatial scales. In this paper, we review the opportunity and challenges in the application of remote sensing in biodiversity sciences and conservation practices. Specifically, we focus on the applications of remote sensing in the issues related to the population dynamics, species interaction and community diversity, functional traits and functional diversity and biodiversity management. We suggest that the satellite and airborne remotes that employed multi-band or hyperspectral, high spatial resolution and LiDAR provide biodiversity information from different scopes, and •综述• © 808 生 物 多 样 性 Biodiversity Science 第 26 卷 遥感专题 will play essential roles in the investigation of biodiversity in large-scale and remote areas. In the near future, species discrimination technique based on spectral characteristics and structure detection based on LiDAR will improve our understanding of the biodiversity sciences and management. We suggest to strengthen the communication between remote-sensing scientists and biodiversity researchers to promote the application of remote sensing technologies in biodiversity research and at different temporal and spatial scales.