Antarctica has been significantly influenced by global climate change. Owing to the spatiotemporal limitations of existing datasets, budgetary constraints, logistical challenges, and adverse temperature and climatic conditions of Antarctica, researchers face great challenges. Unmanned aerial vehicles (UAVs) have helped to solve this issue because they can collect high-resolution spatiotemporal data and conduct operations in inaccessible locations at a low cost and with ease compared with in situ observation and conventional spaceborne and airborne remote sensing. The development and testing of UAVs for use in polar environments mainly focus on enhancing UAV performance in extreme Antarctic conditions by improving their endurance, wind resistance, and aerial photography stability. The equipped multisensors, flexible data collection and operation window, and high-spatiotemporal resolution all contribute to making UAVs the most powerful platform for cryospheric research. In recent years, a series of UAV-related studies on the cryosphere have been published. However, a thorough review that explicitly details the scientific progress and possibilities of using UAVs in Antarctic polar research is lacking. In this era of rapid global and regional climate change, it is becoming increasingly necessary to employ UAVs to investigate the finer changes in the Antarctic ice sheet (AIS) and ice shelves. This work investigates the use of UAVs in the monitoring of the glacial microtopography (including rifts and crevasses, surface subsidence, and melting ponds), ice surface landforms, atmosphere, flora and fauna, sea ice, subglacial environment, and other aspects of Antarctic glaciology investigation, and it speculates on their future use in multidisciplinary research.

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented.